Proceedings doi: 10.3390/proceedings2024097121
Authors: Rajendra P. Shukla Johan G. Bomer Daniel Wijnperle Naveen Kumar Janwa El Maiss Divya Balakrishanan Aruna Chandra Singh Vihar P. Georgiev Cesar Pascual Garcia Sivashankar Krishnamoorthy Sergii Pud
In the ElectroMed project, we are interested in screening certain peptide sequences for their ability to selectively interact with antibodies or MHC proteins. This poses a combinatorial challenge that requires a highly multiplexed setup of label-free immunosensors. Label-free FET-based immunosensors are good candidates due to their high multiplexing capability and fast response time. Nanowire-based FET sensors have shown high sensitivity but are unreliable for clinical applications due to drift and gate stability issues. To address this, a label-free immuno-FET architecture based on planar junctionless FET devices is proposed. This geometry can improve the signal-to-noise ratio due to its larger planar structure, which is less prone to defects that cause noise and is better suited to the functionalization of different receptor molecules.
]]>Applied Sciences doi: 10.3390/app14072878
Authors: Jure Penga Tino Vidović Gojmir Radica Željko Penga
As marine traffic is contributing to pollution, and most vessels have predictable routes with repetitive load profiles, to reduce their impact on environment, hybrid systems with proton exchange membrane fuel cells (PEMFC-s) and battery pack are a promising replacement. For this purpose, the new approach takes into consideration an alternative to diesel propulsion with the additional benefit of carbon neutrality and increase of system efficiency. Additionally, in the developed numerical model, control of the PEMFC–battery hybrid energy system with balance of plant is incorporated with repowering existing vessels that have two diesel engines with 300 kWe. The goal of this paper is to develop a numerical model that analyzes and determines an equivalent hybrid ship propulsion system for a known traveling route. The developed numerical model consists of an interconnected system with the PEMFC stack and a battery pack as power sources. The numerical model was developed and optimized to meet the minimal required power demand for a successful route, which has variable loads and sees ships sail daily six times along the same route—in total 54 nautical miles. The results showed that the equivalent hybrid power system consists of a 300 kWe PEMFC stack and battery pack with 424 kWh battery and state of charge varying between 20 and 87%. To power this new hybrid power system, a hydrogen tank of 7200 L holding 284.7 kg at pressure of 700 bar is required, compared to previous system that consumed 1524 kg of diesel and generated 4886 kg of CO2.
]]>Machine Learning and Knowledge Extraction doi: 10.3390/make6020034
Authors: Lucileide M. D. da Silva Sérgio N. Silva Luísa C. de Souza Karolayne S. de Azevedo Luiz Affonso Guedes Marcelo A. C. Fernandes
The literature on ECG delineation algorithms has seen significant growth in recent decades. However, several challenges still need to be addressed. This work aims to propose a lightweight R-peak-detection algorithm that does not require pre-setting and performs classification on a sample-by-sample basis. The novelty of the proposed approach lies in the utilization of the typicality eccentricity detection anomaly (TEDA) algorithm for R-peak detection. The proposed method for R-peak detection consists of three phases. Firstly, the ECG signal is preprocessed by calculating the signal’s slope and applying filtering techniques. Next, the preprocessed signal is inputted into the TEDA algorithm for R-peak estimation. Finally, in the third and last step, the R-peak identification is carried out. To evaluate the effectiveness of the proposed technique, experiments were conducted on the MIT-BIH arrhythmia database (MIT-AD) for R-peak detection and validation. The results of the study demonstrated that the proposed evolutive algorithm achieved a sensitivity (Se in %), positive predictivity (+P in %), and accuracy (ACC in %) of 95.45%, 99.61%, and 95.09%, respectively, with a tolerance (TOL) of 100 milliseconds. One key advantage of the proposed technique is its low computational complexity, as it is based on a statistical framework calculated recursively. It employs the concepts of typicity and eccentricity to determine whether a given sample is normal or abnormal within the dataset. Unlike most traditional methods, it does not require signal buffering or windowing. Furthermore, the proposed technique employs simple decision rules rather than heuristic approaches, further contributing to its computational efficiency.
]]>Applied Sciences doi: 10.3390/app14072877
Authors: Grace Russell Alexander Nenov John T. Hancock
Molecular hydrogen (H2) is a low-molecular-weight, non-polar and electrochemically neutral substance that acts as an effective antioxidant and cytoprotective agent, with research into the effects of H2 incorporation into the food chain, at various stages, rapidly gaining momentum. H2 can be delivered throughout the food growth, production, delivery and storage systems in numerous ways, including as a gas, as hydrogen-rich water (HRW), or with hydrogen-donating food supplements such as calcium (Ca) or magnesium (Mg). In plants, H2 can be exploited as a seed-priming agent, during seed germination and planting, during the latter stages of plant development and reproduction, as a post-harvest treatment and as a food additive. Adding H2 during plant growth and developmental stages is noted to improve the yield and quality of plant produce, through modulating antioxidant pathways and stimulating tolerance to such environmental stress factors as drought stress, enhanced tolerance to herbicides (paraquat), and increased salinity and metal toxicity. The benefits of pre- and post-harvest application of H2 include reductions in natural senescence and microbial spoilage, which contribute to extending the shelf-life of animal products, fruits, grains and vegetables. This review collates empirical findings pertaining to the use of H2 in the agri-food industry and evaluates the potential impact of this emerging technology.
]]>Proceedings doi: 10.3390/proceedings2024097122
Authors: Asia Kalinichenko Benjamin Junker Udo Weimar Nicolae Bârsan
A simple, direct method for the determination residual hexane content in refined oils was developed, which makes use of commercial Semiconducting Metal Oxides (SMOX) sensors and is proposed as an alternative to the currently used standards (ISO 9832:2002, ISO 2719:2016). The main advantages are related to the direct measurement of the headspace of oil samples. The measurements are performed at an oil sample temperature of 30 °C and by spiking the samples with hexane in the 8–132 mg·kg−1 range, which is in line with the requirements of current standard for the maximum residue limit set by European Union regulation. Using separate measurements performed with the help of a computer-controlled gas mixing system it is possible to determine the relationship between the concentration of hexane in oil and in the headspace.
]]>Applied Sciences doi: 10.3390/app14072876
Authors: Anna Ndiaye Alassane Traore Papa Sam Gueye Zachary Senwo Momar Ndiaye Abdoulaye Diop
Identifying heavy metal and pesticide contaminants is an essential step in assessing the health indicators of rice cultivation and consumption in Africa. Information on the contaminant levels of the imported and cultivated rice consumed in Senegal seems lacking. In this study, we assessed heavy metals, pesticides, ash, and protein in rice using rice samples from India, Thailand, South America, Vietnam, and China. Arsenic, Pb, Cd, Ni, Cu, Mo, Co, Cr, and Al are usually found in the soils used for rice cultivation in northern Senegal. While the heavy metal levels measured in soils were above the threshold limit, only Pb, Cd, and Al were found in cultivated rice. In all the analyzed rice samples from each country, there were certain amounts of Pb, As Al, and Cd. The concentration ranges in the six countries were as follows: 0.635–1.165 mg kg−1 for Pb, 0.047–0.438 mg kg−1 for As, 2.22–95.54 mg kg−1 for Al, and 0.002–0.082 mg kg−1 for Cd. The protein content in cultivated rice in Senegal was 7.31 mg kg−1, while the average from the imported rice ranged between 6.42% and 7.32%. The humidity levels in imported rice ranged between 11.12% and 12.95%. The fat content for the rice from six countries ranged between 0.22% and 0.67%, and the ash content ranged between 0.23% and 0.48%. These results allowed for the determination of the carbohydrate concentration, which varied between 79.18% and 80.82%. Indeed, freshly harvested rice grains typically contain around 80% carbohydrates. We noticed the presence of pesticides in all rice samples. The levels of three pesticides (total Pyrethrin, Bensulfuron-methyl, Propanyl, and 2,4D) were found to be beyond their maximum residue limits (MRLs) from the Codex Alimentarius, whereas deltamethrin was found to be below its MRL. This study indicates the presence of heavy metals carcinogenic to humans (Al, As, Cd, and Pb). Additionally, this study reveals the presence of deltamethrin, which is classified as probably carcinogenic to humans (Group 1), and 2,4-dichlorophenoxyacetic acid, which is classified as possibly carcinogenic to humans (Group 2B).
]]>Information doi: 10.3390/info15040185
Authors: Eduardo Morales-Vargas Hayde Peregrina-Barreto Rita Q. Fuentes-Aguilar Juan Pablo Padilla-Martinez Wendy Argelia Garcia-Suastegui Julio C. Ramirez-San-Juan
Microvasculature analysis is an important task in the medical field due to its various applications. It has been used for the diagnosis and threat of diseases in fields such as ophthalmology, dermatology, and neurology by measuring relative blood flow or blood vessel morphological properties. However, light scattering at the periphery of the blood vessel causes a decrease in contrast around the vessel borders and an increase in the noise of the image, making the localization of blood vessels a challenging task. Therefore, this work proposes integrating known information from the experimental setup into a deep learning architecture with multiple inputs to improve the generalization of a computational model for the segmentation of blood vessels and depth estimation in a single inference step. The proposed R-UNET + ET + LA obtained an intersection over union of 0.944 ± 0.065 and 0.812 ± 0.080 in the classification task for validation (in vitro) and test sets (in vivo), respectively, and a root mean squared error of 0.0085 ± 0.0275 μm in the depth estimation. This approach improves the generalization of current solutions by pre-training with in vitro data and adding information from the experimental setup. Additionally, the method can infer the depth of a blood vessel pixel by pixel instead of in regions as the current state of the art does.
]]>Applied Sciences doi: 10.3390/app14072875
Authors: Lixia Li Shanhe Jiang Jin Bai Kun Su Haiteng Hu Lei Zhang
In this paper, a novel single-phase double-leaf multi-stage acoustic black hole (SDM-ABH) is proposed. Compared with the traditional double-leaf ABH metamaterials, the unit cell consists of multiple sub-ABH structures arranged in a gradient periodically along the length direction. The energy band structure of the SDM-ABH metamaterial is calculated by the finite element method, and it is found that its weight decreases with the increase in the number of stages, but the bandgap ratio and attenuation both increase. By analysing the vibration modes at special points and the vibration displacement response of finite construction, it is revealed that strong attenuation at a low-frequency broadband is caused by the increase in the number of sub-ABHs that appear to have ABH effects due to the increase in the number of stages. In addition, the effect of structural parameters on the bandgap is investigated, and it is found that SDM-ABH metamaterials can be modulated at low frequencies by changing the truncation thickness and the power exponent of the sub-acoustic black hole, in which the increase in the truncation thickness t leads to the gradual weakening of the ABH effect of the sub-ABH until it disappears. The strong low-frequency attenuation properties of SDM-ABH metamaterials provide a method for a lightweight vibration damping design of metamaterials.
]]>Brain Sciences doi: 10.3390/brainsci14040331
Authors: Assunta Virtuoso Giuseppa D’Amico Federica Scalia Ciro De Luca Michele Papa Grazia Maugeri Velia D’Agata Celeste Caruso Bavisotto Agata Grazia D’Amico
Glioblastoma multiforme (GBM) stands out as the most tremendous brain tumor, constituting 60% of primary brain cancers, accompanied by dismal survival rates. Despite advancements in research, therapeutic options remain limited to chemotherapy and surgery. GBM molecular heterogeneity, the intricate interaction with the tumor microenvironment (TME), and non-selective treatments contribute to the neoplastic relapse. Diagnostic challenges arise from GBM advanced-stage detection, necessitating the exploration of novel biomarkers for early diagnosis. Using data from the literature and a bioinformatic tool, the current manuscript delineates the molecular interplay between human GBM, astrocytes, and myeloid cells, underscoring selected protein pathways belonging to astroglia and myeloid lineage, which can be considered for targeted therapies. Moreover, the pivotal role of extracellular vesicles (EVs) in orchestrating a favorable microenvironment for cancer progression is highlighted, suggesting their utility in identifying biomarkers for GBM early diagnosis.
]]>Proceedings doi: 10.3390/proceedings2024097119
Authors: Marco Cen-Puc Tim de Rijk Dirk Lehmhus Walter Lang
This work presents the effect of thermal treatment on the electrical insulation of strain sensors on aluminum substrates. The sensors are meant to be embedded into cast aluminum parts, which are heat-treated for strengthening via precipitation hardening. For sensor manufacturing, thick film materials are used for the electrical insulation and its connection tracks, whereas sensing platinum structures are produced by sputtering. The effectiveness of different insulation thicknesses was tested for a treatment regime of 7 h at 535 °C, which matches solution heat treatment conditions as the most demanding part of the precipitation hardening process. The results showed that insulation is partially lost after treatment, and six consecutive insulating layers are required to produce an insulation capable of withstanding an extended heat treatment.
]]>Brain Sciences doi: 10.3390/brainsci14040330
Authors: Shannon Bosshard Emma Rodero Isabel Rodríguez-de-Dios Jamie Brickner
Whilst radio, podcasts, and music streaming are considered unique audio formats that offer brands different opportunities, limited research has explored this notion. This current study analyses how the brain responds to these formats and suggests that they offer different branding opportunities. Participants’ engagement, attitude, attention, memory, and physiological arousal were measured while each audio format was consumed. The results revealed that music streaming elicited more positive attitudes, higher attention, greater levels of memory encoding, and increased physiological arousal compared to either radio or podcasts. This study emphasises the importance for brands of utilising diverse audio channels for unique branding and marketing opportunities.
]]>Brain Sciences doi: 10.3390/brainsci14040329
Authors: Letícia Silvestri Paludetto Luiza Larrubia Alvares Florence Julio Torales Antonio Ventriglio João Maurício Castaldelli-Maia
Craving is one of the most important symptoms of cocaine use disorder (CUD) since it contributes to the relapse and persistence of such disorder. This systematic review aimed to investigate which brain regions are modulated during cocaine craving. The articles were obtained through searches in the Google Scholar, Regional BVS Portal, PubMed, and Scielo databases. Overall, there was a selection of 36 studies with 1574 individuals, the majority being participants with CUD, whereby about 61.56% were individuals with CUD and 38.44% were controls (mean age = 40.4 years). Besides the methodological points, the neurobiological investigations comprised fMRI (58.34%) and PET (38.89%). The induction of cocaine craving was studied using different methods: exposure to cocaine cues (69.45%), stressful stimuli, food cues, and methylphenidate. Brain activations demonstrated widespread activity across the frontal, parietal, temporal, and occipital lobes, basal ganglia, diencephalon, brainstem, and the limbic system. In addition to abnormalities in prefrontal cortex activity, abnormalities in various other brain regions’ activity contribute to the elucidation of the neurobiology of cocaine craving. Abnormalities in brain activity are justified not only by the dysfunction of dopaminergic pathways but also of the glutamatergic and noradrenergic pathways, and distinct ways of inducing craving demonstrated the involvement of distinct brain circuits and regions.
]]>Applied Sciences doi: 10.3390/app14072874
Authors: Ali Alzahrani Nigel Thomas
Mobile ad hoc networks (MANETs) are wireless multi-hop networks that do not rely on any fixed infrastructure, unlike traditional networks. Nodes in MANETs are formed dynamically and are free to move in any direction at variable speeds. The special characteristics of MANETs make them vulnerable to flooding attacks, which can have a negative impact on their performance. Moreover, due to their nature, employing solutions designed for traditional networks is not feasible. One potential solution to enhance the performance of MANETs in the face of network attacks is to implement trust management. This paper evaluates the performance of Ad hoc On-Demand Distance Vector (AODV) Routing in the presence of a flooding attack. We propose a direct trust management scheme to detect and isolate malicious nodes and implement this scheme on AODV. We name the modified protocol Trusted AODV (TAODV) and, finally, compare the performance of AODV and TAODV when both are under a flooding attack to measure the improvement achieved by our suggested scheme.
]]>Big Data and Cognitive Computing doi: 10.3390/bdcc8040037
Authors: Kenneth David Strang
A critical worldwide problem is that ransomware cyberattacks can be costly to organizations. Moreover, accidental employee cybercrime risk can be challenging to prevent, even by leveraging advanced computer science techniques. This exploratory project used a novel cognitive computing design with detailed explanations of the action-research case-study methodology and customized machine learning (ML) techniques, supplemented by a workflow diagram. The ML techniques included language preprocessing, normalization, tokenization, keyword association analytics, learning tree analysis, credibility/reliability/validity checks, heatmaps, and scatter plots. The author analyzed over 8 GB of employee behavior big data from a multinational Fintech company global intranet. The five-factor personality theory (FFPT) from the psychology discipline was integrated into semi-supervised ML to classify retrospective employee behavior and then identify cybercrime risk. Higher levels of employee neuroticism were associated with a greater organizational cybercrime risk, corroborating the findings in empirical publications. In stark contrast to the literature, an openness to new experiences was inversely related to cybercrime risk. The other FFPT factors, conscientiousness, agreeableness, and extroversion, had no informative association with cybercrime risk. This study introduced an interdisciplinary paradigm shift for big data cognitive computing by illustrating how to integrate a proven scientific construct into ML—personality theory from the psychology discipline—to analyze human behavior using a retrospective big data collection approach that was asserted to be more efficient, reliable, and valid as compared to traditional methods like surveys or interviews.
]]>Journal of Imaging doi: 10.3390/jimaging10040083
Authors: Teresa P. Pham Thomas Sanocki
In today’s fast paced, attention-demanding society, executive functions and attentional resources are often taxed. Individuals need ways to sustain and restore these resources. We first review the concepts of attention and restoration, as instantiated in Attention Restoration Theory (ART). ART emphasizes the role of nature in restoring attention. We then discuss the essentials of experiments on the causal influences of nature. Next, we expand the concept of ART to include modern, designed environments. We outline a wider perspective termed attentional ecology, in which attention behavior is viewed within a larger system involving the human and their interactions with environmental demands over time. When the ecology is optimal, mental functioning can be a positive “flow” that is productive, sustainable for the individual, and sometimes creative.
]]>Proceedings doi: 10.3390/proceedings2024094064
Authors: George Bellis Paris Papaggelos Evangeli Vlachogianni Ilias Laleas Stefanos Moustos Thanos Patas Sokratis Poulios Nikos Tzioumakis Giannis Giakas Giorgos Tsiogkas Christos Kokkotis Dimitrios Tsaopoulos
Lameness is a crucial welfare issue in the modern dairy cattle industry, that if not identified and treated early causes losses in milk production and leads to early culling of animals. At present, the most common methods used for lameness detection and assessment are various visual locomotion scoring systems, which are labour-intensive, and the results may be subjective. The purpose of this project is to develop an integrated system for early detection of lameness in cattle, using force plate gait analysis and pattern recognition techniques to identify changes in gait which indicate the onset of lameness. The system will be tested on the natural onset of lameness in an organised farm environment.
]]>Economies doi: 10.3390/economies12040079
Authors: Joseph Antwi Baafi
This study examines the intricate relationship between natural resource abundance, with a specific focus on oil production, and its impact on economic growth in Ghana. Through the application of the robust Fully Modified OLS methodology and using data spanned from 1960–2021 the research underscores the essential inclusion of oil as a significant variable in comprehending economic growth dynamics. Contrary to traditional resource curse theories, the study unveils a positive nexus between oil production and economic growth, particularly within a comprehensive variable framework. This finding challenges simplistic resource curse notions and underscores the need for a holistic economic perspective. Overall, the results show that the impact of oil production on economic growth is sensitive to the inclusion or exclusion of other variables in the model. In Model 1, where all variables are included, oil production has a significant positive (0.0112**) impact on growth. Ghana’s success in avoiding the resource curse is attributed to a multifaceted strategy encompassing diversified economic approaches, transparent governance, and responsible oil revenue management. Importantly, the inclusion of oil as a pivotal variable is well-justified by its tangible contributions to economic growth. The observed positive impacts emphasize the benefits of harnessing oil resources while maintaining a holistic view of the broader economic context. Looking ahead, the insights inform policymakers in resource-rich nations, illustrating how strategic resource management—illustrated by oil—can drive resilient and comprehensive economic growth. Ghana’s experience serves as a compelling template for informed policy decisions, offering valuable lessons for achieving sustainable prosperity.
]]>Proceedings doi: 10.3390/proceedings2024097118
Authors: Eva Melnik Steffen Kurzhals Valerio Beni Giorgio C. Mutinati Rainer Hainberger
In this study, poly(ethylene glycol) dimethacrylate (PEG-DMA)-based hydrogels were investigated with respect to the diffusion properties of methylene blue (MB) and MB conjugated proteins (MB-BSA and MB-IgG). Electrochemical sensors were used to monitor the diffusion process via the redox-active MB-label. All tested molecules showed good mobility in the hydrogel. Also, the release of MB-BSA could be demonstrated after drying the hydrogel containing MB-BSA, which is a promising result for the development of hydrogel-based reagent reservoirs for biosensing.
]]>Processes doi: 10.3390/pr12040688
Authors: Fakhar Mustafa Rehan Ahmad Khan Sherwani Muhammad Ali Raza Jumanah Ahmed Darwish
Many researchers employed Poisson distribution-based control charts to monitor count data. Nevertheless, these charts can handle count data that deviate from the Poisson assumption of equal mean and variance. This paper suggests a new control chart (CC) that uses the generalized Conway–Maxwell–Poisson (GCOMP) distribution, which can deal with count data that have different levels of dispersion and zero-inflation (ZI). The proposed chart is designed considering the total number of counts. The main advantage of this study is that it pays attention to the tails of the count data when monitoring the process. The performance is measured by the average run length using L control limits at different sample sizes and parametric settings. The findings demonstrate that, for count data with varying tail behaviors, the proposed chart performs better compared to existing CCs. ZI count data can also be monitored with the proposed chart. The proposed chart can be applied in a variety of fields, as verified by the examples provided in this paper.
]]>Fractal and Fractional doi: 10.3390/fractalfract8040197
Authors: Samuel Ogunjo Holger Kantz
There is an increasing interest in determining if there exist observable patterns or structures within the digits of irrational numbers. We extend this search by investigating the interval in position between two consecutive occurrences of the same digit, a kind of waiting time statistics. We characterise these by the burstiness measure which distinguishes if the inter-event times are periodic, bursty, or Poisson processes. Furthermore, the complexity–entropy plane was used to determine if the intervals are stochastic or chaotic. We analyse sequences of the first 1 million digits of the numbers π, e, 2, and ϕ. We find that the intervals between single, double, and triple digits are Poisson processes with a burstiness measure in the range −0.05≤B≤0.05 for the four numbers studied. This result is supported by a complexity–entropy plane analysis, which shows that the time intervals have the same characteristics as Gaussian noise. The four irrational numbers have identical degrees of complexity and burstiness in their inter-event analysis.
]]>Stats doi: 10.3390/stats7020021
Authors: Scott J. Richter Melinda H. McCann
Nonparametric combinations of permutation tests for pairwise comparison of scale parameters, based on deviances, are examined. Permutation tests for comparing two or more groups based on the ratio of deviances have been investigated, and a procedure based on Higgins’ RMD statistic was found to perform well, but two other tests were sometimes more powerful. Thus, combinations of these tests are investigated. A simulation study shows a combined test can be more powerful than any single test.
]]>Mathematics doi: 10.3390/math12071023
Authors: Markus Neumayer Thomas Suppan Thomas Bretterklieber Hannes Wegleiter Colin Fox
The reconstruction of the spatial complex conductivity σ+jωε0εr from complex valued impedance measurements forms the inverse problem of complex electrical impedance tomography or complex electrical capacitance tomography. Regularized Gauß-Newton schemes have been proposed for their solution. However, the necessary computation of the Jacobian is known to be computationally expensive, as standard techniques such as adjoint field methods require additional simulations. In this work, we show a more efficient way to computationally access the Jacobian matrix. In particular, the presented techniques do not require additional simulations, making the use of the Jacobian, free of additional computational costs.
]]>Mathematics doi: 10.3390/math12071022
Authors: Jie Liu Jian-Ping Sun
In this paper, the problem of clustering component synchronization of nonlinearly coupled complex networks with nonidentical nodes and asymmetric couplings is investigated. A pinning control strategy is designed to achieve the clustering component synchronization with respect to the specified components. Based on matrix analysis and stability theory, clustering component synchronization criteria are established. Two numerical simulations are also provided to show the effectiveness of the theoretical results.
]]>Electronics doi: 10.3390/electronics13071267
Authors: Dahui Yoo MiJin Kim Inho Kang Ho-Jun Lee
Power cycling tests (PCTs) assess the reliability of power devices by closely simulating their operating conditions. A PCT was performed on commercially available 1.2 kV 4H-SiC power metal–oxide–semiconductor field-effect transistors to observe its impact on the 4H-SiC/SiO2 interface. High-resolution transmission electron microscopy and electron energy loss spectroscopy measurements showed variations in the length of the 4H-SiC/SiO2 transition layer, depending on whether the device was power cycled. Moreover, the total resistance at Vg >> Vt in Rtot − (Vg-Vt)−1 graph increased to 16.5%, while it changed more radically to 47.3% at Vg ≈ Vt. The threshold voltage shifted negatively. These variations cannot be expected solely through the wearout of the package.
]]>Processes doi: 10.3390/pr12040687
Authors: Yannan Xiang Siyi Tian Xinyu Luo Chenggang Cai Yaowen Du Hailong Yang Haiyan Gao
The content of differentially abundant metabolites in the fermentation broth of grapefruit peels fermented by Cordyceps militaris at different fermentation times was analyzed via LC‒MS/MS. Small molecule metabolites and differential metabolic pathways were analyzed via multivariate analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment. A total of 423 metabolites were identified at 0, 2, 6, and 10 days after fermentation. Among them, 169 metabolites showed differential abundance, with significant differences observed between the fermentation liquids of every two experimental groups, and the metabolite composition in the fermentation liquid changed over the fermentation time. In summary, the upregulation and downregulation of metabolites in cancer metabolic pathways collectively promote the remodeling of cancer cell metabolism, facilitating increased glycolysis, alterations in TCA cycle flux, and enhanced biosynthesis of the macromolecules required for rapid proliferation and survival. This study provides new perspectives on the development of high-value-added agricultural and forestry byproducts and the development and research of functional foods.
]]>Processes doi: 10.3390/pr12040686
Authors: Guang Zhang Runhua Hu Dapeng Yin Desheng Chen Haolin Zhou Zhe Lin
Butterfly valves are widely used in the pipeline transportation industry due to their safety and reliability, as well as their low manufacturing and operation costs. Cavitation is a common phenomenon in the butterfly valve that can lead to serious damage to a valve’s components. Therefore, it is important to investigate the generation and evolution of cavitation in butterfly valves. In this study, LES and the Zwart model were used as the turbulence and cavitation models, respectively, to simulate cavitation through a butterfly valve. The influence of the valve opening degree and inlet flow velocity on dynamic cavitation through the butterfly valve were studied. Furthermore, the cavitated flow field was examined, along with the performance coefficients of the butterfly valve. With the increase in the incoming flow velocity, the high-speed jet zone over a large-range and low-pressure zone appeared inside the downstream of butterfly valve, which affected its stability and the cavitation generation through the valve. Furthermore, the flow coefficient decreased with the increase in vapor volume. In addition, the results indicated that cavitation was more easily induced for smaller valve opening degrees, and the interaction between cavitation and solid walls was stronger. Due to the existence of cavitation, the flow characteristics of butterfly valves are seriously affected.
]]>Algorithms doi: 10.3390/a17040142
Authors: Shubhendu Kshitij Fuladi Chang-Soo Kim
In the real world of manufacturing systems, production planning is crucial for organizing and optimizing various manufacturing process components. The objective of this paper is to present a methodology for both static scheduling and dynamic scheduling. In the proposed method, a hybrid algorithm is utilized to optimize the static flexible job-shop scheduling problem (FJSP) and dynamic flexible job-shop scheduling problem (DFJSP). This algorithm integrates the genetic algorithm (GA) as a global optimization technique with a simulated annealing (SA) algorithm serving as a local search optimization approach to accelerate convergence and prevent getting stuck in local minima. Additionally, variable neighborhood search (VNS) is utilized for efficient neighborhood search within this hybrid algorithm framework. For the FJSP, the proposed hybrid algorithm is simulated on a 40-benchmark dataset to evaluate its performance. Comparisons among the proposed hybrid algorithm and other algorithms are provided to show the effectiveness of the proposed algorithm, ensuring that the proposed hybrid algorithm can efficiently solve the FJSP, with 38 out of 40 instances demonstrating better results. The primary objective of this study is to perform dynamic scheduling on two datasets, including both single-purpose machine and multi-purpose machine datasets, using the proposed hybrid algorithm with a rescheduling strategy. By observing the results of the DFJSP, dynamic events such as a single machine breakdown, a single job arrival, multiple machine breakdowns, and multiple job arrivals demonstrate that the proposed hybrid algorithm with the rescheduling strategy achieves significant improvement and the proposed method obtains the best new solution, resulting in a significant decrease in makespan.
]]>Electronics doi: 10.3390/electronics13071266
Authors: Luqiao Yin Wenqing Gao Jingjing Liu
To address susceptibility to noise interference in Micro-LED displays, a deep convolutional dictionary learning denoising method based on distributed image patches is proposed in this paper. In the preprocessing stage, the entire image is partitioned into locally consistent image patches, and a dictionary is learned based on the non-local self-similar sparse representation of distributed image patches. Subsequently, a convolutional dictionary learning method is employed for global self-similarity matching. Local constraints and global constraints are combined for effective denoising, and the final denoising optimization algorithm is obtained based on the confidence-weighted fusion technique. The experimental results demonstrate that compared with traditional denoising methods, the proposed denoising method effectively restores fine-edge details and contour information in images. Moreover, it exhibits superior performance in terms of PSNR and SSIM. Particularly noteworthy is its performance on the grayscale dataset Set12. When evaluated with Gaussian noise σ=50, it outperforms DCDicL by 3.87 dB in the PSNR and 0.0012 in SSIM.
]]>Buildings doi: 10.3390/buildings14040939
Authors: Alexander Koutamanis
Planning regulations determine a substantial part of buildings, but their constraints are usually not included in the setup of a BIM model or used explicitly for design guidance, but only tested in compliance checks once a model has been made. This is symptomatic of wider tendencies and ingrained biases that emphasize tacit knowledge and assume that information in a project starts from scratch—an assumption that runs contrary to predesign information ordering practices, as well as to the findings of creativity studies. In terms of process control, it negates important possibilities for feedforward. The paper proposes that BIM and design computerization, in general, should avoid the generate-and-test view of design, the view of design knowledge as tacit, and the adherence to analogue workflows, but develop, instead, approaches and workflows that keep information explicit and utilize it to frame design problems. To demonstrate this, we describe an exercise in which the expectation that the geometric representation of planning regulations returns permissible building envelopes was tested on the basis of a large number of cases produced by students who each collected planning regulations for a particular plot of land in the Netherlands and modelled their constraints in BIM, using a workflow that can be accommodated within the scope of predesign information gathering in any project. The results confirm that, for a large part of Dutch housing, the representation of planning regulations in BIM returns the permissible building envelope, and, so, forms a clear frame for subsequent design actions. They also suggest that including such information in the setup of a model is constructive and feasible, even for novices, and produces a bandwidth view of project information that integrates pre-existing information in a BIM workflow through feedforward. By extension, they also indicate a potential for a closer relation between analysis and synthesis in BIM, characterized by transparency and simultaneity, as well as the thorough understanding of problem constraints required for both efficiency and creativity.
]]>Applied Sciences doi: 10.3390/app14072864
Authors: Andres Hernandez-Matamoros Hiroaki Kikuchi
In the rapidly evolving landscape of healthcare technology, the critical need for robust privacy safeguards is undeniable. Local Differential Privacy (LDP) offers a potential solution to address privacy concerns in data-rich industries. However, challenges such as the curse of dimensionality arise when dealing with multidimensional data. This is particularly pronounced in k-way joint probability estimation, where higher values of k lead to decreased accuracy. To overcome these challenges, we propose the integration of Bayesian Ridge Regression (BRR), known for its effectiveness in handling multicollinearity. Our approach demonstrates robustness, manifesting a noteworthy reduction in average variant distance when compared to baseline algorithms such as LOPUB and LOCOP. Additionally, we leverage the R-squared metric to highlight BRR’s advantages, illustrating its performance relative to LASSO, as LOPUB and LOCOP are based on it. This paper addresses a relevant concern related to datasets exhibiting high correlation between attributes, potentially allowing the extraction of information from one attribute to another. We convincingly show the superior performance of BRR over LOPUB and LOCOP across 15 datasets with varying average correlation attributes. Healthcare takes center stage in this collection of datasets. Moreover, the datasets explore diverse fields such as finance, travel, and social science. In summary, our proposed approach consistently outperforms the LOPUB and LOCOP algorithms, particularly when operating under smaller privacy budgets and with datasets characterized by lower average correlation attributes. This signifies the efficacy of Bayesian Ridge Regression in enhancing privacy safeguards in healthcare technology.
]]>Applied Sciences doi: 10.3390/app14072873
Authors: Taotao Xie Jiawei Zhang Dawei Xiao Qing Ji
To analyze the induced electric field characteristics generated by the rotation and shaking of underwater metal vehicles, a mathematical model of the induced electric field generated by the underwater metal vehicles was derived using Faraday’s electromagnetic induction law. A mathematical model of the induced electric field on the electrode pairs of metal vehicles shaking in different coordinate system planes was established through in-depth analysis. Based on this, a three-component output model of the induced electric field output by the three-axis sensor was obtained when the measurement system was shaking at all three angles. At a constant speed, the induced electric field interference output by the measurement system is a static signal. The value of the static electric field is proportional to the vehicle’s speed and the value of the geomagnetic field, and the value of each component is related to the direction of movement and the value of the geomagnetic field component. The simulation results show that when the navigation body is moving at a constant speed, the induced electric field is a static electric field with a magnitude of mV/m. In a stable state, the induced electric field noise generated by changes in pitch, roll, and heading sway is at the nV/m level and does not have a significant impact on detection. The correctness of the theoretical model has been verified through experiments on offshore speedboat platforms, and it is feasible to use metal navigation bodies for ship electric field detection.
]]>Processes doi: 10.3390/pr12040685
Authors: Agus Purwanto Muhammad Nur Ikhsanudin Putri Putih Puspa Asri Afifah Salma Giasari Miftakhul Hakam Cornelius Satria Yudha Hendri Widiyandari Endah Retno Dyartanti Arif Jumari Adrian Nur
Lithium-ion batteries (LIBs) remain the cornerstone of EV technology due to their exceptional energy density. The selection of cathode materials is a decisive factor in LIB technology, profoundly influencing performance, energy density, and lifespan. Among these materials, nickel-rich NCM cathodes have gained significant attention due to their high specific capacity and cost-effectiveness, making them a preferred choice for EV energy storage. However, the transition from the laboratory-scale to industrial-scale production of NMC-811 cathode material presents challenges, particularly in optimizing the oxidation process of Ni2+ ions. This paper addresses the challenges of transitioning NMC-811 cathode material production from a lab scale to a pilot scale, with its high nickel content requiring specialized oxidation processes. The important point emphasized in this transition process is how to produce cathode materials on a pilot scale, but show results equivalent to the laboratory scale. Several optimization variations are carried out, namely, the optimization of the heating rate and the calcination and sintering temperatures, as well as oxygen variations. These two aspects are important for large-scale production. This paper discusses strategies for successful pilot-scale production, laying the foundation for industrial-scale manufacturing. Additionally, NMC-811 cathodes are incorporated into 18650 cylindrical cells, advancing the adoption of high-performance cathode materials.
]]>Processes doi: 10.3390/pr12040684
Authors: Maria Elena Del Giudice Mahnaz Sharafkhani Mario Di Nardo Teresa Murino Maria Chiara Leva
A machine is described as an assembly that has a drive system installed or is planned to have a drive system installed and that is constituted of linked elements or components, at least one of which moves, that are connected for a particular application (ISO12100). Different types of risks are present in machines, and exposure to them can cause harm or even death. When risk has been adequately reduced, machinery safety considers a machine’s ability to complete its intended duty throughout its life cycle. A literature review was carried out using “safety of machinery” as a keyword, which produced an analysis of 29 papers published from 2008 to 2024. The papers were examined through bibliometric analysis of the year of publication, country, citation statistics, and study of the keywords. These studies were classified into accident analysis papers, papers focused on the normative, papers that addressed risk assessment tools, and papers that conducted quantitative research. In addition, a more in-depth analysis of the articles associated with the keywords with the highest number of occurrences was carried out. Lastly, studies with quantitative analyses were analysed to identify new possible aspects that it is necessary to investigate.
]]>Mathematics doi: 10.3390/math12071021
Authors: Mao Luo Huigang Qin Xinyun Wu Caiquan Xiong Dahai Xia Yuanzhi Ke
This paper presents an algorithm for effectively maintaining the minimum spanning tree in dynamic weighted undirected graphs. The algorithm efficiently updates the minimum spanning tree when the underlying graph structure changes. By identifying the portion of the original tree that can be preserved in the updated tree, our algorithm avoids recalculating the minimum spanning tree from scratch. We provide proof of correctness for the proposed algorithm and analyze its time complexity. In general scenarios, the time complexity of our algorithm is comparable to that of Kruskal’s algorithm. However, the experimental results demonstrate that our algorithm outperforms the approach of recomputing the minimum spanning tree by using Kruskal’s algorithm, especially in medium- and large-scale dynamic graphs where the graph undergoes iterative changes.
]]>Electronics doi: 10.3390/electronics13071265
Authors: Li Gu Guo Zou
A nonlinear capacitance compensation technique is presented in this paper to enhance the linearity of a power amplifier (PA) in the GaN process. The method involves placing an MSM varactor device alongside the GaN HEMT device, which works as the amplifying unit such that the overall capacitance observed at the amplifier input is constant, thus improving linearity. This approach is a reliable and straightforward way to improve PA linearity in the GaN process. The proof-of-concept prototype in this study involves the fabrication of a PA device using a standard GaN HEMT process, which successfully integrates the proposed compensation technique and demonstrates excellent compatibility with existing processes. The prototype has a saturation output power of 18 dBm, a peak power-added efficiency of 51.8%, and a small signal gain of 15.5 dB at 1 GHz. The measured AM–PM distortion at the 5 dB compression point is reduced by more than 50% compared to that of an uncompensated device. Furthermore, the results of third-order intermodulation distortion demonstrate the effectiveness of the linearity enhancement concept, with values improved by more than 5 dB in the linear region compared to those of the uncompensated device. All of the results demonstrate the potential utility of this design approach for wireless communication applications.
]]>Mathematics doi: 10.3390/math12071020
Authors: Aleksandar Kemiveš Lidija Barjaktarović Milan Ranđelović Milan Čabarkapa Dragan Ranđelović
Many methods exist for solving the problem of evaluating efficiency in different processes. They are divided into two basic groups, parametric and non-parametric methods, which can have significant differences in the results. In this study, the authors consider the process of assessing the business climate depending on realized foreign investments. Due to the expected difference in efficiency assessment using different approaches, the goal of this paper is to create an optimization model of an ensemble for efficiency assessment that uses both types of methods with the aim of creating a symmetrical approach that achieves better results than each type of method individually. The proposed solution simultaneously analyzes the impact of different factors on foreign investments in order to determine the most important factors and thus enable each local government to ensure the best possible efficiency in this process. The innovative idea of this study is in the inclusion of classification and feature selection methods of machine learning to fulfill the set goal. Our research, focused on a specific case study in various cities across the Republic of Serbia, evaluated the effectiveness of that process. This study extends previous research and confirms the published results, highlighting the advantages of the newly proposed model.
]]>Applied Sciences doi: 10.3390/app14072872
Authors: Denis A. Vrazhnov Daria A. Ovchinnikova Tatiana V. Kabanova Andrey G. Paulish Yury V. Kistenev Nazar A. Nikolaev Olga P. Cherkasova
The possibility of the differentiation of glioblastoma from traumatic brain injury through blood serum analysis by terahertz time-domain spectroscopy and machine learning was studied using a small animal model. Samples of a culture medium and a U87 human glioblastoma cell suspension in the culture medium were injected into the subcortical brain structures of groups of mice referred to as the culture medium injection groups and glioblastoma groups, accordingly. Blood serum samples were collected in the first, second, and third weeks after the injection, and their terahertz transmission spectra were measured. The injection caused acute inflammation in the brain during the first week, so the culture medium injection group in the first week of the experiment corresponded to a traumatic brain injury state. In the third week of the experiment, acute inflammation practically disappeared in the culture medium injection groups. At the same time, the glioblastoma group subjected to a U87 human glioblastoma cell injection had the largest tumor size. The THz spectra were analyzed using two dimensionality reduction algorithms (principal component analysis and t-distributed Stochastic Neighbor Embedding) and three classification algorithms (Support Vector Machine, Random Forest, and Extreme Gradient Boosting Machine). Constructed prediction data models were verified using 10-fold cross-validation, the receiver operational characteristic curve, and a corresponding area under the curve analysis. The proposed machine learning pipeline allowed for distinguishing the traumatic brain injury group from the glioblastoma group with 95% sensitivity, 100% specificity, and 97% accuracy with the Extreme Gradient Boosting Machine. The most informative features for these groups’ differentiation were 0.37, 0.40, 0.55, 0.60, 0.70, and 0.90 THz. Thus, an analysis of mouse blood serum using terahertz time-domain spectroscopy and machine learning makes it possible to differentiate glioblastoma from traumatic brain injury.
]]>Processes doi: 10.3390/pr12040683
Authors: Tianzhi Wang Ci Yang Peizhe Sun Mingna Wang Fawei Lin Manuel Fiallos Soon-Thiam Khu
Micro–nanobubbles (MNBs) can generate ·OH in situ, which provides a new idea for the safe and efficient removal of pollutants in water supply systems. However, due to the difficulty in obtaining stable MNBs, the generation efficiency of ·OH is low, and the removal efficiency of pollutants cannot be guaranteed. This paper reviews the application research of MNB technology in water security from three aspects: the generation process of MNBs in water, the generation rule of ·OH during MNB collapse, and the control mechanisms of MNBs on pollutants and biofilms. We found that MNB generation methods are divided into chemical and mechanical (about 10 kinds) categories, and the instability of the bubble size restricts the application of MNB technology. The generation of ·OH by MNBs is affected by the pH, gas source, bubble size, temperature, and external stimulation. And the pH and external stimulus have more influence on ·OH generation in situ than the other factors. Adjusting the pH to alkaline or acidic conditions and selecting ozone or oxygen as the gas source can promote ·OH generation. MNB collapse also releases a large amount of energy, during which the temperature and pressure can reach 3000 K and 5 Gpa, respectively, making it efficient to remove ≈90% of pollutants (i.e., trichloroethylene, benzene, and chlorobenzene). The biofilm can also be removed by physical, chemical, and thermal effects. MNB technology also has great application potential in drinking water, which can be applied to improve water quality, optimize household water purifiers, and enhance the taste of bottled water. Under the premise of safety, after letting people of different ages taste water samples, we found that compared with ordinary drinking water, 85.7% of people think MNB water is softer, and 73.3% of people think MNB water is sweeter. This further proves that MNB water has a great prospect in drinking water applications. This review provides innovative theoretical support for solving the problem of drinking water safety.
]]>Entropy doi: 10.3390/e26040300
Authors: Hiroki Murakami Norimasa Yamada
Human movements are governed by a tradeoff between speed and accuracy. Previous studies that have investigated the tradeoff relationship in sports movements involving whole-body movements have been limited to examining the relationship from the perspective of competition-specific movements, and the findings on whether the relationship is valid have not been unified. Therefore, this study incorporated a vertical jump task with the introduction of a condition in which landing position control was added to evaluate the essence of a sports movement that requires both speed and accuracy. Accuracy was examined using a method that quantifies the coordinates of the landing and takeoff positions using entropy. The mechanism of that tradeoff was then examined by confirming the phenomenon and analyzing the 3D vector trajectories. An increase in accuracy and a decrease in speed were observed when the landing position was the control target, even in the vertical jumping task normally performed at maximum effort, and the 3D velocity vector was characterized by the following: a reduced scalar and a more vertical direction. While the entropy from the takeoff to the landing position seemed to decrease when the accuracy of the landing position improved, the following noteworthy results were obtained given the characteristics of the vertical jump. Unlike traditional feedback control in the entropy reduction in hand movements, the trajectory is predetermined in a feedforward-like manner by controlling the initial velocity vector at takeoff, which allows the landing point to be adjusted.
]]>Applied Sciences doi: 10.3390/app14072871
Authors: Attilio Matera Giuseppe Altieri Francesco Genovese Luciano Scarano Giuseppe Genovese Paola Pinto Mahdi Rashvand Hazem S. Elshafie Antonio Ippolito Annamaria Mincuzzi Giovanni Carlo Di Renzo
The marketing value of table grapes is contingent upon several quality requirements, mostly related to microbial decay, sugar/acidity ratio, and colour. This research explores the impact of combining organic-cultured compatible techniques to delay disorders along with organic grape distribution in post-harvest. Aurebasidum pullulans in-field application on grape bunches at three growing stages as a biocontrol agent against grey mould growth coupled with massive modified atmosphere packaging (MMAP; 20% CO2, 10% O2) equipped with a breathable valve was tested. The in-field treatment had a significant impact on the colour and sugar content of the grapes at harvest and the mould count evolution during storage, whilst the trend of the other parameters was mainly affected by the interaction of the variables tested. The untreated batch experienced the worst behaviour and the packaging was paramount in preserving the moisture content and appearance of the bunches. The findings of this study may contribute to developing novel practices for setting a smart distribution of organic table grapes and reducing food waste.
]]>Journal of Imaging doi: 10.3390/jimaging10040082
Authors: Bilal Ahmad Pål Anders Floor Ivar Farup Casper Find Andersen
A wireless capsule endoscope (WCE) is a medical device designed for the examination of the human gastrointestinal (GI) tract. Three-dimensional models based on WCE images can assist in diagnostics by effectively detecting pathology. These 3D models provide gastroenterologists with improved visualization, particularly in areas of specific interest. However, the constraints of WCE, such as lack of controllability, and requiring expensive equipment for operation, which is often unavailable, pose significant challenges when it comes to conducting comprehensive experiments aimed at evaluating the quality of 3D reconstruction from WCE images. In this paper, we employ a single-image-based 3D reconstruction method on an artificial colon captured with an endoscope that behaves like WCE. The shape from shading (SFS) algorithm can reconstruct the 3D shape using a single image. Therefore, it has been employed to reconstruct the 3D shapes of the colon images. The camera of the endoscope has also been subjected to comprehensive geometric and radiometric calibration. Experiments are conducted on well-defined primitive objects to assess the method’s robustness and accuracy. This evaluation involves comparing the reconstructed 3D shapes of primitives with ground truth data, quantified through measurements of root-mean-square error and maximum error. Afterward, the same methodology is applied to recover the geometry of the colon. The results demonstrate that our approach is capable of reconstructing the geometry of the colon captured with a camera with an unknown imaging pipeline and significant noise in the images. The same procedure is applied on WCE images for the purpose of 3D reconstruction. Preliminary results are subsequently generated to illustrate the applicability of our method for reconstructing 3D models from WCE images.
]]>Buildings doi: 10.3390/buildings14040938
Authors: Lijun Ma Meng Sun Yunlong Zhang
In order to facilitate waste glass recycling and enable the monitoring of concrete structures, this study prepares a new type of self-sensing engineered cementitious composite (ECC) via the use of glass sand instead of silica sand. The health monitoring of a concrete structure is achieved through the addition of polypropylene (PP) fibers to enhance the flexural toughness of concrete, and adding carbon fibers (CFs) to make the concrete self aware, enabling it to sense the load changes and structural damage. The fiber dosage of ECC is optimized to analyze the effects of different fiber types and dosages on the mechanical and self-sensing properties of concrete. The results show that the hybrid fibers produce a good synergistic effect on mechanical properties, and the presence of excess fibers causes the mechanical properties of concrete to deteriorate. The critical fiber volume fraction required for the strain hardening of PP ranges from 0.75% vol to 1% vol. At different PP dosages, the CF dosage shows a positive correlation with the initial crack strength. By analyzing the effect of varied curing times and CF doping on the initial resistivity, it is found that the threshold value of CF conductivity is 0.7% vol. The role of CFs in the flexural sensitivity and pressure sensitivity tests is explained from the perspective of fiber distribution, and the fiber distribution theory is verified with scanning electron microscopy (SEM). The optimal level of CF doping for flexural sensitivity and pressure sensitivity is determined to be 1.1% vol and 0.7% vol via the use of self-sensing performance tests, respectively. An increase in PP fiber doping leads to a decrease in the initial resistivity and self-sensing properties of the material. The results of this research provide guidance regarding how to determine the optimal fiber dosage flexibly for different engineering works.
]]>Algorithms doi: 10.3390/a17040141
Authors: Armin Soltan Peter Washington
Breast cancer is the most common cancer affecting women globally. Despite the significant impact of deep learning models on breast cancer diagnosis and treatment, achieving fairness or equitable outcomes across diverse populations remains a challenge when some demographic groups are underrepresented in the training data. We quantified the bias of models trained to predict breast cancer stage from a dataset consisting of 1000 biopsies from 842 patients provided by AIM-Ahead (Artificial Intelligence/Machine Learning Consortium to Advance Health Equity and Researcher Diversity). Notably, the majority of data (over 70%) were from White patients. We found that prior to post-processing adjustments, all deep learning models we trained consistently performed better for White patients than for non-White patients. After model calibration, we observed mixed results, with only some models demonstrating improved performance. This work provides a case study of bias in breast cancer medical imaging models and highlights the challenges in using post-processing to attempt to achieve fairness.
]]>Electronics doi: 10.3390/electronics13071264
Authors: Luis Oyarzún Encarnación Castillo Luis Parrilla Uwe Meyer-Baese Antonio García
Non-invasive fetal electrocardiography (NI-ECG) is based on the acquisition of signals from electrodes on the mother’s abdominal surface. This abdominal ECG (aECG) signal consists of the maternal ECG (mECG) along with the fetal ECG (fECG) and other noises and artifacts. These records allow the acquisition of valuable and reliable information that helps ensure fetal well-being during pregnancy. This paper proposes a procedure based on principal component analysis (PCA) to obtain a single-channel master abdominal ECG record that can be used as input to fetal heart rate extraction techniques. The new procedure requires three main processing stages: PCA-based analysis for fECG-component extraction, polarity test, and curve fitting. To show the advantages of the proposal, this PCA-based method has been used as the feeding stage to a previously developed clustering-based method for single-channel aECG fetal heart rate monitoring. The results obtained for a set of real abdominal ECG recordings from annotated public aECG databases, the Abdominal and Direct Fetal ECG Database and the Challenge 2013 Training Set A, show improved efficiency in fetal heart rate extraction and illustrate the benefits derived from the use of such a master abdominal ECG channel. This allows us to achieve proper fetal heart rate monitoring without the need for manual inspection and selection of channels to be processed, while also allowing us to analyze records that would have been discarded otherwise.
]]>Applied Sciences doi: 10.3390/app14072867
Authors: Yanhua Zhang Lei Yang Yuehua Cheng Kaixin Ying
The study of satellite performance evaluation can reveal the ability of satellite systems to fulfil corresponding tasks in the space environment, and provide information support for the resource allocation and mission scheduling of in-orbit satellites. In this paper, we took the satellite attitude control system in attitude tracking mode as the research object. In accordance with the system’s mission requirements, the control performance evaluation indicator set, characterized by a generalized grey number, is constructed to tackle the uncertainty and inadequacy of information contained in flight status data resulting from the complex space operating environment and sensor measurement noise. An improved principal component analysis method based on generalized grey number is proposed to solve the weight amplification caused by the correlation between performance indicators and realize the weight allocation of the indicators. Finally, the grey-target decision model is established to determine the weights of the performance indicators, and the performance evaluation model is established under the tracking mode. The feasibility of the grey-target decision-evaluation model based on the improved principal component is confirmed through comparative experiments.
]]>Buildings doi: 10.3390/buildings14040937
Authors: Sebastian Pech Maximilian Autengruber Markus Lukacevic Roman Lackner Josef Füssl
In recent years, the use of timber as a building material in larger construction applications such as multi-story buildings and bridges has increased. This requires a better understanding of the material to realize such constructions and design them more economically. However, accurate computational simulations of timber structures are challenging due to the complexity and inhomogeneity of this naturally grown material. It exhibits growth inhomogeneities such as knots and fiber deviations, orthotropic material behavior and moisture dependence of almost all physical parameters. Describing the creep response of wood under real climate conditions is particularly difficult. Changes in moisture content, plasticity and viscoelasticity affect moisture-induced stresses and potentially lead to cracks and structural damage. In this paper, we apply a material model that combines time and moisture-dependent behavior with multisurface plasticity to simulate cross-sections of different dimensions over a 14-month climate period. Our findings indicate that considering this long-term behavior has a minor impact on moisture-induced stresses during the drying period. However, during the wetting period, neglecting the time- and moisture-dependent material behavior of wood leads to a significant overestimation of tensile stresses within the cross-section, resulting in unrealistic predictions of wetting-induced fracture. Therefore, simulations during wetting periods require a sophisticated rheological model to properly reproduce the stress field.
]]>Mathematics doi: 10.3390/math12071019
Authors: Yunlong Qiu Haiyang Wu Yuntong Dai Kai Li
Self-oscillatory systems have great utility in energy harvesting, engines, and actuators due to their ability to convert ambient energy directly into mechanical work. This characteristic makes their design and implementation highly valuable. Due to the complexity of the motion process and the simultaneous influence of multiple parameters, computing self-oscillatory systems proves to be challenging, especially when conducting inverse parameter design. To simplify the computational process, a combined approach o0f Random Forest (RF) and Backpropagation Neural Network (BPNN) algorithms is employed. The example used is a self-rotating skipping rope made of liquid crystal elastomer (LCE) fiber and a mass block under illumination. Numerically solving the governing equations yields precise solutions for the rotation frequency of the LCE skipping rope under various system parameters. A database containing 138,240 sets of parameter conditions and their corresponding rotation frequencies is constructed to train the RF and BPNN models. The training outcomes indicate that RF and BPNN can accurately predict the self-rotating skipping rope frequency under various parameters, demonstrating high stability and computational efficiency. This approach allows us to discover the influences of distinct parameters on the rotation frequency as well. Moreover, it is capable of inverse design, meaning it can derive the corresponding desired parameter combination from a given rotation frequency. Through this study, a deeper understanding of the dynamic behavior of self-oscillatory systems is achieved, offering a new approach and theoretical foundation for their implementation and construction.
]]>Electronics doi: 10.3390/electronics13071261
Authors: Min-Wook Hwang Young-Min Kwon Kwang-Cheol Ko
Magnetic resonance wireless power transmission consists of a source coil and relay coil (transmission coil (Tx-coil), receiving coil (Rx-coil)). The relay coil is designed with windings and a series capacitor, which are resonant with the input voltage frequency. Magnetic resonant wireless power transmission by a relay coil enables the transmission of power from a few centimeters to several meters. Recently, research has been conducted on the shape and material of each coil to increase the transmission distance. However, limitations remain with respect to increasing the transmission distance. Specifically, the optimization of the electrical characteristics of the relay coil is necessary to increase the transmission distance and improve efficiency. In this study, we configured the inductance of the relay coil to be approximately 95 μH, 270 μH, and 630 μH. Accordingly, we designed the series capacitors to have the same resonant frequency and analyzed the transmission characteristics of each relay coil. We confirmed that as the inductance increased, the transmission efficiency increased by up to 10%. The relay coil was designed to have an inductance of approximately three to six times that of the source coil (load coil). Thus, the optimal design of the relay coil is believed to be the most efficient and economical coil design.
]]>Electronics doi: 10.3390/electronics13071263
Authors: Junting Gao Chunrong Peng Tsutomu Yoshinaga Guorong Han Siri Guleng Celimuge Wu
The digital twin (DT) paradigm represents a groundbreaking shift in the Internet of Vehicles (IoV) landscape, acting as an instantaneous digital replica of physical entities. This synthesis not only refines vehicular design but also substantially augments driver support systems and streamlines traffic governance. Diverging from the prevalent research which predominantly examines DT’s technical assimilation within IoV infrastructures, this review focuses on the specific deployments and goals of DT within the IoV sphere. Through an extensive review of scholarly works from the past 5 years, this paper provides a fresh and detailed perspective on the significance of DT in the realm of IoV. The applications are methodically categorized across four pivotal sectors: industrial manufacturing, driver assistance technology, intelligent transportation networks, and resource administration. This classification sheds light on DT’s diverse capabilities to confront and adapt to the intricate challenges in contemporary vehicular networks. The intent of this comprehensive overview is to catalyze innovation within IoV by providing an essential reference for researchers who aspire to swiftly grasp the complex dynamics of this evolving domain.
]]>Information doi: 10.3390/info15040184
Authors: Jingwen Yang Ruohua Zhou
Whisper speaker recognition (WSR) has received extensive attention from researchers in recent years, and it plays an important role in medical, judicial, and other fields. Among them, the establishment of a whisper dataset is very important for the study of WSR. However, the existing whisper dataset suffers from the problems of a small number of speakers, short speech duration, and lack of neutral speech with the same-text as the whispered speech in the same dataset. To address this issue, we present Whisper40, a multi-person Chinese WSR dataset containing same-text neutral speech spanning around 655.90 min sourced from volunteers. In addition, we use the current state-of-the-art speaker recognition model to build a WSR baseline system and combine the idea of transfer learning for pre-training the speaker recognition model using neutral speech datasets and transfer the empirical knowledge of specific network layers to the WSR system. The Whisper40 and CHAINs datasets are then used to fine-tune the model with transferred specific layers. The experimental results show that the Whisper40 dataset is practical, and the time delay neural network (TDNN) model performs well in both the same/cross-scene experiments. The equal error rate (EER) of Chinese WSR after transfer learning is reduced by 27.62% in comparison.
]]>Electronics doi: 10.3390/electronics13071262
Authors: Yingnan Zhang Zhizhong Kang Zhen Cao
In the geological research of the Moon and other celestial bodies, the identification and analysis of impact craters are crucial for understanding the geological history of these bodies. With the rapid increase in the volume of high-resolution imagery data returned from exploration missions, traditional image retrieval methods face dual challenges of efficiency and accuracy when processing lunar complex crater image data. Deep learning techniques offer a potential solution. This paper proposes an image retrieval model for lunar complex craters that integrates visual and depth features (LC2R-Net) to overcome these difficulties. For depth feature extraction, we employ the Swin Transformer as the core architecture for feature extraction and enhance the recognition capability for key crater features by integrating the Convolutional Block Attention Module with Effective Channel Attention (CBAMwithECA). Furthermore, a triplet loss function is introduced to generate highly discriminative image embeddings, further optimizing the embedding space for similarity retrieval. In terms of visual feature extraction, we utilize Local Binary Patterns (LBP) and Hu moments to extract the texture and shape features of crater images. By performing a weighted fusion of these features and utilizing Principal Component Analysis (PCA) for dimensionality reduction, we effectively combine visual and depth features and optimize retrieval efficiency. Finally, cosine similarity is used to calculate the similarity between query images and images in the database, returning the most similar images as retrieval results. Validation experiments conducted on the lunar complex impact crater dataset constructed in this article demonstrate that LC2R-Net achieves a retrieval precision of 83.75%, showcasing superior efficiency. These experimental results confirm the advantages of LC2R-Net in handling the task of lunar complex impact crater image retrieval.
]]>Applied Sciences doi: 10.3390/app14072870
Authors: Huankun Wang Man Xu Zijian Cao
The valve-controlled cylinder drive system is the most common type among hydraulic applications. Nonlinear behaviour in such systems is inevitable when the valve spool is around its null position. We utilised the component linking method to investigate the nonlinearities in a Moog valve-controlled asymmetric cylinder drive system by simulation in Fortran, in which a generalised concept is introduced and validated by comparing to the experimental results. An X factor is proposed in the generalised concept to describe the asymmetric cylinder state, which is a constant when the cylinder is extending or retracting, but numerically calculated when the valve spool is in the underlap region. This analytical solution is approximately 200 times more computationally efficient than the numerical solution method. This paper utilises the component linking method to simulate the Moog valve-controlled asymmetric cylinder drive system in Matlab Simulink, and proposes an analytical solution for the X factor when the valve spool is in the underlap region. This analytical solution is approximately 200 times more computationally efficient than the numerical solution method.
]]>Applied Sciences doi: 10.3390/app14072869
Authors: Wang Cao Bai Wei Hu Shan
This study endeavors to explore the intricate interplay between the fundamental skills of basketball—defensive slide, crossover dribbling, and full approach jump—and the shoe outsole friction coefficient, with the overarching goal of advancing our comprehension regarding the pivotal role of footwear in athlete performance. Employing a comprehensive methodology that integrates 3D motion capture, force platform dynamometry, and biomechanical modeling, the study seeks to quantify the inherent motor control intricacies associated with these fundamental skills. Data collection involved 12 varsity players, and the research systematically assesses the influence of the shoe friction coefficient on both skill quality and injury risk, utilizing a set of 13 parameters for evaluation. The findings unveil that, with an increased friction coefficient, the following changes occur: for the defensive slide, we observed decreased contact time (p < 0.05), boosted medio–lateral impulse (p < 0.05), and lowered ankle torque (p < 0.01); for crossover dribbling, we observed increased anterior–posterior impulse (p < 0.05) and ankle torque (p < 0.05); for the full approach jump, we observed decreased contact time (p < 0.05) and increased jump height (p < 0.05). Generally, the equal increment in the shoe outsole friction coefficient did not result in equal changes in the selected parameters of motor skill control, indicating a non-linear relationship between the performance quality of essential basketball skills and the shoe friction coefficient. The results suggest the potential existence of an optimal value for skill execution. Notably, the study identifies that, while an augmentation in the friction coefficient enhances specific skill aspects, there is a discernible saturation point, signifying diminishing returns. This investigation makes a substantial contribution to our understanding of the precise impacts of shoe friction coefficients on basketball skills, thereby prompting considerations for the judicious selection of optimal friction coefficients and advocating for possible personalized footwear recommendations based on individual biomechanical profiles.
]]>Applied Sciences doi: 10.3390/app14072868
Authors: Sijie Wang Min Gong Haojun Wu Xiaodong Wu Xiangyu Liu
In tunnel smooth blasting, optimizing the water interval charging structure of peripheral holes is of great significance in improving the effect of smooth blasting and reducing the unit consumption of explosives. Addressing the issue of a single traditional evaluation standard, this paper proposes a composite index evaluation method for rock blasting damage in different zones, and the best charging structure is optimized according to the evaluation results. Taking Liyue Road Tunnel Light Smooth Blasting Project in Chongqing as the Research Background, the numeric models were established with ten kinds of charge structures, the charge structures and explosive quantity were optimized according to the evaluation results, and then the field tests were conducted. The results show that when the length of the water medium at the bottom of the hole is 20 cm, the damage range of the retained rock mass can be controlled while ensuring rock fragmentation. If the length of the water medium at the orifice and in the center of the hole is more than 30 cm, it will affect the superposition effect of the blast stress wave, resulting in under-excavation; in the preferred charge structure, the ratio of the length of the upper and lower explosives reaches 1:3, and the ratio of the length of the water medium is 2:2:1, which achieves a better rock-breaking effect in the field test.
]]>Future Internet doi: 10.3390/fi16040114
Authors: Kalgaonkar El-Sharkawy
Accurate perception is crucial for autonomous vehicles (AVs) to navigate safely, especially in adverse weather and lighting conditions where single-sensor networks (e.g., cameras or radar) struggle with reduced maneuverability and unrecognizable targets. Deep camera–radar fusion neural networks offer a promising solution for reliable AV perception under any weather and lighting conditions. Cameras provide rich semantic information, while radars act like an X-ray vision, piercing through fog and darkness. This work proposes a novel, efficient camera–radar fusion network called NeXtFusion for robust AV perception with an improvement in object detection accuracy and tracking. Our proposed approach of utilizing an attention module enhances crucial feature representation for object detection while minimizing information loss from multi-modal data. Extensive experiments on the challenging nuScenes dataset demonstrate NeXtFusion’s superior performance in detecting small and distant objects compared to other methods. Notably, NeXtFusion achieves the highest mAP score (0.473) on the nuScenes validation set, outperforming competitors like OFT (35.1% improvement) and MonoDIS (9.5% improvement). Additionally, NeXtFusion demonstrates strong performance in other metrics like mATE (0.449) and mAOE (0.534), highlighting its overall effectiveness in 3D object detection. Furthermore, visualizations of nuScenes data processed by NeXtFusion further demonstrate its capability to handle diverse real-world scenarios. These results suggest that NeXtFusion is a promising deep fusion network for improving AV perception and safety for autonomous driving.
]]>Symmetry doi: 10.3390/sym16040398
Authors: Huixu Dong Yuanzheng Ge Rui Zhou Hongyan Wang
Recently, a signal sorting algorithm based on the congruence transform has been proposed, which is effective in dealing with the staggered Pulse Repetition Interval (PRI) signals. It can effectively sort the staggered PRI signals and obtain the sub-PRI sequence directly without sub-PRI ranking, and it is less affected by interfered pulses and pulse loss. Nevertheless, we find that the algorithm causes pseudo-peaks in the remainder histogram when sorting signals such as sliding PRI, sinusoidal PRI, etc. (collectively referred to as periodic PRI signal in this paper) and pseudo-peaks will cause errors in signal sorting. To solve the issue of pseudo-peaks when sorting periodic PRI signals, an improved sorting algorithm based on congruence transform is proposed. According to the analysis of the congruence characteristics of the periodic PRI signal, a novel method is proposed to identify pseudo-peaks based on the histogram peak amplitude and symmetric difference set. The signal sorting algorithm based on congruence transform is improved to achieve a good sorting effect on periodic PRI signals. Simulation experiments demonstrate that the novel algorithm can effectively sort periodic PRI signals and improve Precall, Pd, and Pf by 6.9%, 5.1%, and 3.2%, respectively, compared to the typical similar algorithms.
]]>Applied Sciences doi: 10.3390/app14072866
Authors: Keivan Kaboutari Abdelghafour Abraray Stanislav Maslovski
Conventional beamforming methods for reconfigurable reflector antennas assume full control over the amplitude and phase of the reflected field. Here, we develop a novel beamforming methodology for reflecting Programmable Metasurfaces (PMS) with capacitive memory. Although utilizing such fully reactive PMS simplifies antenna design and reduces energy consumption, the PMS reflection magnitude is unity and thus a global optimization of the reflection phases over the PMS unit cells is required in each beamforming scenario. We propose an implementation of such an optimization method rooted in the traditional Fourier transform-based beamforming and evaluate its performance. Additionally, we show that a pair of trained feed-forward neural networks (FFNN) with one input, one hidden, and one output layer can replace time-consuming global optimizations in the case of a PMS comprising 3×10 unit cells. We train the FFNNs on a dataset obtained for typical single- and dual-beam beamforming scenarios. After training, the FFNNs perform requested beamforming tasks within a fraction of second and with about the same accuracy as the original optimization algorithm. The proposed methodology may find applications in future mobile telecommunication systems that require real-time beamforming on low-end hardware. The same beamforming methodology can be also employed in short-range wireless power transfer systems.
]]>Mathematics doi: 10.3390/math12071018
Authors: Bernardo G. Rodrigues Francesco G. Russo
We describe the nonabelian exterior square G∧^G of a pro-p-group G (with p arbitrary prime) in terms of quotients of free pro-p-groups, providing a new method of construction of G∧^G and new structural results for G∧^G. Then, we investigate a generalization of the probability that two randomly chosen elements of G commute: this notion is known as the “ complete exterior degree” of a pro-p-group and we will use it to characterize procyclic groups. Among other things, we present a new formula, which simplifies the numerical aspects which are connected with the evaluation of the complete exterior degree.
]]>ISPRS International Journal of Geo-Information doi: 10.3390/ijgi13040115
Authors: Sevim Sezi Karayazi Gamze Dane Theo Arentze
Understanding visitors’ spatial choice behavior is important in developing effective policies to counteract overcrowdedness in attractive urban heritage areas. This research presents a comprehensive analysis of visitor location choice behavior, aiming to address two primary objectives. First, this paper investigates the relationship between visitor segments and the choice of particular Points of Interest (POIs). Second, this paper explores the impacts of visitors’ experiences and visitor segments on their revisit intentions. We used a sample of 320 visitors who had been to Amsterdam within the last five years to collect data about their location choice behavior and intention to revisit after a recent visit to the city. Combining the revealed choices and intentions of pre-defined visitor segments obtained from a stated choice experiment, association rules are extracted to reveal differences in the patterns of behaviors related to the segment. The findings identify associations between various POIs, including museums such as the Rijksmuseum and Madame Tussauds, and visitor classes, which include “cultural attraction seekers”, “selective sightseers”, and “city-life lovers”. Furthermore, binary logistic regression analysis reveals that affective experiences, such as feelings of comfort, happiness, and annoyance, have a significant influence on visitors’ intentions to revisit the destination in the future. This research found that “cultural attraction seekers” and “selective sightseers” display a higher likelihood of considering a return visit to the city.
]]>Processes doi: 10.3390/pr12040682
Authors: Jinlin Zhu Zhong Liu Xuyang Lou Furong Gao Zheng Zhang
This paper studies the use of varying threshold in the statistical process control (SPC) of batch processes. The motivation is driven by how when multiple phases are implicated in each repetition, the distributions of the features behind vary with phases or even the time; thus, it is inconsistent to uniformly bound them by an invariant threshold. In this paper, we paved a new path for learning and monitoring batch processes based on an efficient framework integrating a model termed conditional dynamic variational auto-encoder (CDVAE). Phase indicators are first used to split the data and are then separated, serving as an extra input for the model in order to alleviate the learning complexity. Dissimilar to the routine using features across all timescales, only features relevant to local timestamps are aggregated for threshold calculation, producing a varying threshold that is more specific for the process variations occurring among the timeline. Leveraged upon this idea, a fault detection panel is devised, and a deep reconstruction-based contribution diagram is illustrated for locating the faulty variables. Finally, the comparative results from two case studies highlight the superiority in both detection accuracy and diagnostic performance.
]]>Electronics doi: 10.3390/electronics13071260
Authors: Ci He Yasheng Zhang Jia Ke Mingwu Yao Chen Chen
Digital twin technology provides a reliable paradigm to address the high trial-and-error costs and limited perception capabilities in satellite networking. However, the dynamic constellation topology and real-time twin applications remain significant challenges in satellite network design. This paper proposes a network topology simulation approach that dynamically analyzes the inter-satellite topology based on pre-calculated ephemeris and orbital information. Furthermore, the paper introduces a digital twin algorithm based on network virtualization, cloud platform management, and software-defined networking to validate and analyze the twin requirements at different stages. Finally, a low Earth orbit (LEO) constellation twin validation environment is constructed to verify the networking protocols at various stages. The experimental results demonstrate the performance of the proposed twin systems at different stages.
]]>Mathematics doi: 10.3390/math12071017
Authors: Liang Li Yiqiu Mao
The current article focuses on the examination of nonlinear instability and dynamic transitions in a double-diffusive rotating couple-stress fluid layer. The analysis was based on the newly developed dynamic transition theory by T. Ma and S. Wang. Through a comprehensive linear spectrum analysis and investigation of the principle of exchange of stability (PES) as the thermal Rayleigh number crosses a threshold, the nonlinear orbital changes during the transition were rigorously elucidated utilizing reduction methods. For both single real and complex eigenvalue crossings, local pitch-fork and Hopf bifurcations were discovered, and directions of these bifurcations were identified along with transition types. Furthermore, nondimensional transition numbers that signify crucial factors during the transition were calculated and the orbital structures were illustrated. Numerical studies were performed to validate the theoretical results, revealing the relations between key parameters in the system and the types of transition. The findings indicated that the presence of couple stress and a slow diffusion rate of solvent and temperature led to smoother nonlinear transitions during convection.
]]>Inventions doi: 10.3390/inventions9020036
Authors: Cristian A. Hernández-Salazar Camilo E. Chamorro Octavio A. González-Estrada
The study of pig bones, due to their similarity with human tissues, has facilitated the development of technological tools that help in the diagnosis of diseases and injuries affecting the skeletal system. Radiomic techniques involving medical image segmentation, along with finite element analysis, enable the detailed study of bone damage, loss of density, and mechanical functionality, which is a significant advancement in personalized medicine. This study involves conducting experimental tests on L3–L6 pig vertebrae under axial loading conditions. The mechanical properties of these vertebrae are analyzed, and the maximum loads they can sustain within the elastic range are determined. Additionally, three-dimensional models are generated by segmenting computerized axial tomography (CAT) scans of the vertebrae. Digital shadows of the vertebrae are constructed by assigning an anisotropic material model to the segmented geometries. Then, finite element analysis is performed to evaluate the elastic characteristics, stress, and displacement. The findings from the experimental data are then compared to the numerical model, revealing a strong correlation with differences of less than 0.8% in elastic modulus and 1.53% in displacement. The proposed methodology offers valuable support in achieving more accurate medical outcomes, employing models that serve as a diagnostic reference. Moreover, accurate bone modeling using finite element analysis provides valuable information to understand how implants interact with the surrounding bone tissue. This information is useful in guiding the design and optimization of implants, enabling the creation of safer, more durable, and biocompatible medical devices that promote optimal osseointegration and healing in the patient.
]]>Applied Sciences doi: 10.3390/app14072865
Authors: Chenyang Wang Guangyuan Zheng Hongtao Shan
The detection of image similarity is critical to trademark (TM) legal registration and court judgment on infringement cases. Meanwhile, there are great challenges regarding the annotation of similar pairs and model generalization on rapidly growing data when deep learning is introduced into the task. The research idea of metric learning is naturally suited for the task where similarity of input is given instead of classification, but current methods are not targeted at the task and should be upgraded. To address these issues, loss-driven model training is introduced, and a hybrid-margin softmax (HMS) is proposed exactly based on the peculiarity of TM images. Two additive penalty margins are attached to the softmax to expand the decision boundary and develop greater tolerance for slight differences between similar TM images. With the HMS, a Siamese neural network (SNN) as the feature extractor is further penalized and the discrimination ability is improved. Experiments demonstrate that the detection model trained on HMS can make full use of small numbers of training data and has great discrimination ability on bigger quantities of test data. Meanwhile, the model can reach high performance with less depth of SNN. Extensive experiments indicate that the HMS-driven model trained completely on TM data generalized well on the face recognition (FR) task, which involves another type of image data.
]]>Mathematics doi: 10.3390/math12071016
Authors: Omar Mutab Alsalami Efat Yousefpoor Mehdi Hosseinzadeh Jan Lansky
A flying ad hoc network (FANET) is formed from a swarm of drones also known as unmanned aerial vehicles (UAVs) and is currently a popular research subject because of its ability to carry out complicated missions. However, the specific features of UAVs such as mobility, restricted energy, and dynamic topology have led to vital challenges for making reliable communications between drones, especially when designing routing methods. In this paper, a novel optimized link-state routing scheme with a greedy and perimeter forwarding capability called OLSR+GPSR is proposed in flying ad hoc networks. In OLSR+GPSR, optimized link-state routing (OLSR) and greedy perimeter stateless routing (GPSR) are merged together. The proposed method employs a fuzzy system to regulate the broadcast period of hello messages based on two inputs, namely the velocity of UAVs and position prediction error so that high-speed UAVs have a shorter hello broadcast period than low-speed UAVs. In OLSR+GPSR, unlike OLSR, MPR nodes are determined based on several metrics, especially neighbor degree, node stability (based on velocity, direction, and distance), the occupied buffer capacity, and residual energy. In the last step, the proposed method deletes two phases in OLSR, i.e., the TC message dissemination and the calculation of all routing paths to reduce routing overhead. Finally, OLSR+GPSR is run on an NS3 simulator, and its performance is evaluated in terms of delay, packet delivery ratio, throughput, and overhead in comparison with Gangopadhyay et al., P-OLSR, and OLSR-ETX. This evaluation shows the superiority of OLSR+GPSR.
]]>Computers doi: 10.3390/computers13040090
Authors: Ihar Volkau Sergei Krasovskii Abdul Mujeeb Helen Balinsky
We propose a monitoring system for detecting illicit and copyrighted objects in digital manufacturing (DM). Our system is based on extracting and analyzing high-dimensional data from blueprints of three-dimensional (3D) objects. We aim to protect the legal interests of DM service providers, who may receive requests for 3D printing from external sources, such as emails or uploads. Such requests may contain blueprints of objects that are illegal, restricted, or otherwise controlled in the country of operation or protected by copyright. Without a reliable way to identify such objects, the service provider may unknowingly violate the laws and regulations and face legal consequences. Therefore, we propose a multi-layer system that automatically detects and flags such objects before the 3D printing process begins. We present efficient computer vision algorithms for object analysis and scalable system architecture for data storage and processing and explain the rationale behind the suggested system architecture.
]]>Mathematics doi: 10.3390/math12071015
Authors: Cecilia Leal-Ramírez Héctor Alonso Echavarría-Heras
Background: The evaluation of the development of a student’s abilities and skills through a learning activity is a topic strongly questioned by the education system in Mexico. Several instruments have been developed to achieve said evaluation. However, these involve both qualitative and subjective assessment, thereby avoiding the possibility of unambiguously verifying the development of a student’s aptitudes. Methods: We developed a new instrument composed of an integrated instruction and a dynamic fuzzy inference system. Integrated instruction is a table that contains a set of instructions and a set of indicators that make it possible to evaluate knowledge, procedure, and attitude without establishing qualitative or subjective criteria to rank them. The dynamic fuzzy inference system assesses indicators under a criterion to demonstrate the development of a student’s abilities and skills. Results: The method was applied to three different learning activities, where the assessment was precise and transparent for the student, contributing to an extraordinary identification of the acquainted knowledge, procedure, and attitude that the student displayed to develop the activity. Conclusions: Our instrument evaluates the development of abilities and skills without ambiguity or subjectivity, making efficient feedback possible and allowing it to be perfected without difficulties for future adaptations.
]]>Applied Sciences doi: 10.3390/app14072863
Authors: Mathioudakis Aretakis Alexiou
Data from the steady-state operation of gas turbine engines are used in gas path diagnostic procedures. A method to identify steady-state operation is thus required. This paper initially explains and demonstrates the factors that cause a deviation in engine health when transient data are used for diagnosis and shows that there is a threshold in the slope of time traces, below which the variation in engine health parameters is acceptable. A methodology for deriving a criterion for steady-state operation based on actual flight data is then presented. The slope of the exhaust gas temperature variation with time and the size of its time-series window, from which this slope is determined, are the required parameters that must be specified when applying this criterion. It is found that the values of these parameters must be selected so that a sufficient number of steady-state points are available without compromising the accuracy of the diagnostic procedure.
]]>Processes doi: 10.3390/pr12040681
Authors: Stefan Kuzevic Marcela Tausova Katarina Culkova Lucia Domaracka Danylo Shyp
Sustainable energy presently represents the energy of the future, which should be based on the application respecting the importance of energy priorities, increasing regional self-sufficiency, regional control of energy, and regulation of resource use. In the area of energy supply, the use of RES has been increasingly popular, mainly due to the instability in the energy market and the political situation worldwide. Paper’s ambition is to evaluate the efficiency of the selected RES use in the specific conditions of Slovakia, with the aim to achieve the EU targets. This is important due to the increasing use of RES in Slovakia. The objective of this paper is achieved through an analysis of the energy profit of the RES system, comparing the costs of the proposed solutions. The evaluation is carried out by calculating the energy and economic efficiency of three possible buildings used in the research. Using the data obtained, the results show the most suitable alternative for each building. The resulting findings provide a valuable insight for governments in identifying the best projects for RES use. The result will be methodology creation as a base for local administration and communities to elaborate plans with a goal to extend RES use.
]]>Buildings doi: 10.3390/buildings14040936
Authors: Sevda Aliparast Sermin Onaygil
In this field study, we examined the impact of human-centered lighting on an open-plan office environment, involving the participation of sixty office workers. The objective was to investigate the effects of the Circadian Stimulus (CS) and Equivalent Melanopic Lux (EML) metrics. This study took place at Istanbul Technical University in Istanbul, Turkey. The office was equipped with single Correlated Color Temperature (CCT) light emitting diode (LED) sources, featuring two different light beam distributions: Direct Suspended Linear (L1) and Direct and Indirect Suspended Linear (L2). To minimize energy consumption, we proposed simulations for a suspended individual lighting system. The office workers were invited to complete visual cognitive performance tests, proofreading tasks, and the Karolinska Sleepiness Scale (KSS) test to measure alertness. Additionally, participants were asked to provide feedback on the comfort criteria associated with the designed human-centered lighting concept. The preliminary findings from part 1 of this field study shed light on the potential of office lighting modifications in enhancing energy efficiency and meeting the standards set by WELL v2 2023 Q4 and UL Design Guideline 24480 (2019). Part 2 of this study will further optimize the proposed lighting quality concept to determine the most suitable individual lighting solution for office workers.
]]>Applied Sciences doi: 10.3390/app14072862
Authors: Kinga Kazimierska Anna Erkiert-Polguj Urszula Kalinowska-Lis
Colostrum, the first milk produced by mammals, is rich in various bioactive components that provide numerous health benefits to newborns, such as growth factors, hormones, immunoglobulins, cytokines, and enzymes. Topical application of bovine or equine colostrum has been found to improve regeneration, accelerate cutaneous wound healing, and have moisturizing, protective, and anti-aging properties. The aim of this study was to examine the effect of a cosmetic preparation containing sheep colostrum on skin with signs of aging in mature women. Fifty-two women, aged 40–70, were randomized into two groups to receive either colostrum or placebo cream. The participants applied the cream for eight weeks. Skin hydration, TEWL, sebum, erythema, and tone were measured using a standardized Courage + Khazaka electronic GmbH Multi Probe Adapter; skin elasticity was measured with a cutometer, and images were taken by FotoMedicus. The treatment increased skin moisture, reduced TEWL, and improved skin firmness. These findings were confirmed by the subjective survey. The participants reported, inter alia, improved skin softness and less redness and hypersensitivity. Sheep colostrum cream was more effective at improving skin conditions than placebo cream. Colostrum creams can improve certain aspects of skin quality, especially the hydrolipid barrier, and overall rejuvenation.
]]>Symmetry doi: 10.3390/sym16040397
Authors: Predrag Jovanović Vesna Borka Jovanović Duško Borka Alexander F. Zakharov
In this paper we use a modification of the Newtonian gravitational potential with a non-linear Yukawa-like correction, as it was proposed by C. Will earlier to obtain new bounds on graviton mass from the observed orbits of S-stars around the Galactic Center (GC). This phenomenological potential differs from the gravitational potential obtained in the weak field limit of Yukawa gravity, which we used in our previous studies. We also assumed that the orbital precession of S-stars is close to the prediction of General Relativity (GR) for Schwarzschild precession, but with a possible small discrepancy from it. This assumption is motivated by the fact that the GRAVITY Collaboration in 2020 and in 2022 detected Schwarzschild precession in the S2 star orbit around the Supermassive Black Hole (SMBH) at the GC. Using this approach, we were able to constrain parameter λ of the potential and, assuming that it represents the graviton Compton wavelength, we also found the corresponding upper bound of graviton mass. The obtained results were then compared with our previous estimates, as well as with the estimates of other authors.
]]>Mathematics doi: 10.3390/math12071014
Authors: Kazeem Babatunde Akande Samuel Tosin Akinyemi Nneka O. Iheonu Alogla Monday Audu Folashade Mistura Jimoh Atede Anne Ojoma Victoria Iyabode Okeowo Abdulrahaman Lawal Suleiman Kayode Oshinubi
Anthrax, a zoonotic disease with serious public health consequences, has been the subject of rigorous mathematical and statistical modeling to better understand its dynamics and to devise effective control techniques. In this study, we propose a novel mathematical risk-structured model for anthrax disease spread that includes both qualitative and quantitative evaluations. Our research focuses on the complex interplay between host–anthrax interactions and zoonotic transmission. Our mathematical approach incorporates bifurcation analysis and stability considerations. We investigate the dynamic behavior of the proposed model under various settings, shedding light on the important parameters that determine anthrax transmission and persistence. The normalized forward sensitivity analysis method is used to determine the parameters that are relevant to reducing Rc and, by extension, disease spread. Through scenario simulation of our model, we identify intervention techniques, such as enlightenment of the populace, that will effectively minimize disease transmission. Our findings provide insights into anthrax epidemiology and emphasize the importance of effective disease management. Bifurcation investigations reveal the existence and stability of numerous equilibria, allowing for a better understanding of the behavior of the system under various scenarios. This study adds to the field of anthrax modeling by providing a foundation for informed decision-making regarding public health measures. The use of a mathematical modeling approach improves our ability to anticipate and control anthrax epidemics, ultimately helping to protect both human and animal populations.
]]>Buildings doi: 10.3390/buildings14040935
Authors: Ferhat Karaca Aidana Tleuken Rocío Pineda-Martos Sara Ros Cardoso Daniil Orel Rand Askar Akmaral Agibayeva Elena Goicolea Güemez Adriana Salles Huseyin Atakan Varol Luis Braganca
Due to its intricate production processes, complex supply chains, and industry-specific characteristics, the construction industry faces unique challenges in adopting circular economy (CE) principles that promote resource equity. To address this issue, this study aims to delve into identifying stakeholders’ opinions and perceptions regarding key CE strategies across different stages of the building life cycle (BLC). Both European and non-European stakeholders within the “CircularB” COST Action network and beyond participated in this research. Three methods were employed to assess stakeholders’ opinions: an online survey, a structured survey with a semi-guided workshop, and creative thinking round table discussions. Natural language processing (NLP), specifically topic modelling and sentiment analysis, was used to analyse the data collected from the online survey, which gathered text-based opinions from 209 participants on the cost-benefit aspects of circularity strategies. The structured survey, which collected data from 43 workshop participants, evaluated the perceived importance of CE strategies across various BLC phases and assessed the adoption of selected CE strategies in current or past projects. Finally, the Six Thinking Hats® activity, employed in the round table discussions, generated ideas from 25 professionals regarding the broader implementation challenges and opportunities of CE in construction. The research findings highlight the need to bridge the gap between theory and practice by fostering active industry stakeholder involvement in the transition to a CE model. The analyses of the collected stakeholder opinions through the three activities contribute to proactive and collaborative efforts aimed at advancing resource equity in the construction sector and promoting just and inclusive resource use. In summary, this research offers a comprehensive understanding of stakeholders’ opinions on CE strategies and provides guidance for the development of targeted policies and strategies to accelerate the integration of CE principles in the construction industry.
]]>Applied Sciences doi: 10.3390/app14072861
Authors: Moisés Falces-Prieto Luis Manuel Martínez-Aranda Javier Iglesias-García Samuel López-Mariscal Javier Raya-González
The pre-season plays a crucial role in the preparation of professional football players, as it allows for an extensive focus on training sessions compared to the more congested schedules during the in-season period, especially in professional football leagues. This study aimed to describe the workload during a 6-week pre-season in Belgian professional football players and to analyse and compare the workloads for players in each microcycle according to several variables of external workload (e.g., distance covered at some velocities). Seventeen male Belgian professional football players competing in the second division of the Belgian league system participated in the study. Throughout the 6 weeks, the players were closely monitored during both training sessions and friendly matches using Global Positioning System (GPS) devices. Several parameters, including total distance covered and distance at different velocities, were recorded. Accelerating and decelerating distances, as well as the number of sprints, were also captured. Statistical analysis was based on a repeated measures ANOVA, percentage dynamics, and effect size calculations. The results obtained showed a progressive increase in the distance travelled at different intensities from week 1 (i.e., lower values) to week 3 (i.e., higher values), with reductions in these values in week 6, prior to the start of the official competition. Similarly, the peak of accelerations and decelerations were observed in week 2 and week 3, with decrements at the end of the pre-season period. This comprehensive investigation attempts to shed light on the effects and dynamic changes in external workload during the crucial pre-season, contributing valuable insights for coaches and practitioners in football conditioning and training programs, especially concerning optimal preparation for the beginning of the league’s season.
]]>Processes doi: 10.3390/pr12040680
Authors: Zhang Ma Feng Zhang Li Wang Zhang Li Chen
The 1515 mining face in Yongming Coal Mine was upward mined across half of the goaf along the panel direction. In this paper, the methods of field measurement, theoretical analysis, and numerical simulation were used to study the overlying rock fracture structure, support load characteristics, and the mechanism of mine pressure behavior across half of the goaf. The results indicate that the support load of the 1515 upward mining face across half of the goaf along the panel direction exhibits distinct zoning characteristics. The maximum support load is 1.37 times the minimum support load. The development height of the roof separation in the up-mining area is 1.74 times that in the entity coal area, at 9.1 m and 5.22 m respectively. The height of separation and hanging roof length increase and decrease, respectively, along the initial rock fracture area, tensile fracture area, structural fracture area, and compacted fracture area. Based on the definition of the variation coefficient “m” for immediate roof height and hanging roof coefficient “n”, a partitioned method for calculating support loads in the upward mining face across half of the goaf was proposed. Finally, the key parameter values for support loads in each zoning were provided and validated.
]]>Applied Sciences doi: 10.3390/app14072860
Authors: Ercan Işık Fatih Avcil Rabia İzol Aydın Büyüksaraç Hüseyin Bilgin Ehsan Harirchian Enes Arkan
The 6th February 2023 Pazarcık and Elbistan earthquakes (Mw = 7.7 and Mw = 7.6) caused great destruction in many cities and were the disaster of the century for Türkiye. The greatest destruction was caused in the provinces of Hatay, Kahramanmaraş, and Adıyaman during these earthquakes, which were independent of each other and occurred on the same day. Information about earthquakes and strong ground motion records is given within the scope of this study. Reinforced concrete (RC) structures which constitute a large part of the urban building stock in the earthquake region were exposed to structural damage at different levels. The structural damage in the RC structures in the city center, Gölbaşı, and Kahta districts of the province of Adıyaman was evaluated within the scope of earthquake and civil engineering after field investigations. Insufficient RC, low-strength concrete reinforcement problems, RC frame failure, heavy overhang, short columns, soft story, and pounding effect are the main causes of the earthquake damage. The presence of these factors that reduce the earthquake resistance of RC structures increased the damage level. In addition, the fact that the earthquakes occurred nine hours apart and the continuation of aftershocks during that period negatively affected the damage levels. It has been observed that structures that receive the necessary engineering services during the construction and project phases ensure the safety of life and property, even if the structure is slightly damaged. In this study, we also tried to reveal whether the target displacements were satisfactorily represented by numerical analysis for a sample RC structure.
]]>Electronics doi: 10.3390/electronics13071259
Authors: Gwendolin Rohner Jonas Huber Spasoje Mirić Johann W. Kolar
This article presents a comprehensive comparative evaluation of a three-phase Three-Level (3L) Flying Capacitor Converter (FCC) and a spbi, specifically a converter system formed by two Series-Stacked Two-Level three-phase Converters (2L-SSC), for the realization of a 7.5 kW Integrated Motor Drive (IMD) with a high short-term overload capability. The 2L-SSC requires a motor with two three-phase windings and a split DC-link, but uses standard six-switch, two-level transistor configurations. In contrast, the bridge legs of the 3lfcc feature flying capacitors whose voltages must be actively balanced. Despite the 800 V DC-link voltage, both topologies employ the same set of 650 V GaN power transistors, i.e., the same total chip area, and if operated at the same switching frequency, show identical semiconductor losses. edm damage of the motor bearings is a relevant issue caused by the common-mode (CM) voltages of the inverter stage. The high effective switching frequency of the 3lfcc and the possibility of CM voltage canceling in the 2L-SSC facilitate mitigation of edm by means of CM chokes, whereby a substantially smaller CM choke with lower losses suffices for the 2L-SSC; based on exemplary designs, the 2L-SSC features only about 75% of the total volume and 85% of the nominal losses of the 3lfcc. If, alternatively, motor-friendliness is maximized by including DC-referenced sine-wave output filters, the 3lfcc’s higher effective switching frequency and the 2L-SSC’s need for two sets of filters due to the dual-winding-set motor change the outcome. In this case, the 3lfcc features only about 60% of the volume and only about 55% of the 2L-SSC’s nominal losses.
]]>Mathematics doi: 10.3390/math12071013
Authors: Jie Yang Byung Gook Lee
The distributed leader-follower control of multi-agent systems is discussed. Each agent is expressed in a discrete-time and non-linear dynamic model with an unknown parameter and can be affected by its neighbors’ history information. For each agent, to identify the parameter, one switching set of the parameter estimates is constructed and the optimal parameter estimate is chosen based on the index switching function. Using the given desired reference signal, the leader agent’s control law is designed, and relying on the neighbors’ history information, each follower agent’s local control law is designed. With the designed distributed tracking adaptive control laws, the whole system tracks the given desired reference signal, and in the face of strong couplings the closed-loop system ultimately reaches an agreement. Finally, by comparing simulations of the control strategy with a normal projection algorithm, the results indicate that the adaptive control method with a switching set of the parameter estimates is effective in improving the control performance.
]]>Buildings doi: 10.3390/buildings14040934
Authors: Mazen A. Al-Sinan Abdulaziz A. Bubshait Zainab Aljaroudi
Recent advancements in machine learning (ML) applications have set the stage for the development of autonomous construction project scheduling systems. This study presents a blueprint to demonstrate how construction project schedules can be generated automatically by employing machine learning (ML) and building information modeling (BIM). The proposed solution should utilize building information modeling (BIM) international foundation class (IFC) 3D files of previous projects to train the ML model. The training schedules (the dependent variable) are intended to be prepared by an experienced scheduler, and the 3D BIM files should be used as the source of the scheduled activities. Using the ML model can enhance the generalization of model application to different construction projects. Furthermore, the cost and required resources for each activity could be generated. Accordingly, unlike other solutions, the proposed solution could sequence activities based on an ML model instead of manually developed constraint matrices. The proposed solution is intended to generate the duration, cost, and required resources for each activity.
]]>Applied Sciences doi: 10.3390/app14072857
Authors: Yakui Chen Yongquan Yu Xiaoyu Fang Yinhuan Zhou Diannan Lu
Cadmium (Cd) has been widely used in industry applications, leading to water and soil contamination. This study investigated the potential ability of Pseudomonas nitroreducens (11830) to perform the biosorption of cadmium from aqueous solution and soil. The biosorption characteristics were described using equilibrium isotherm and kinetic studies. The Langmuir adsorption isotherm indicated a better fit with the experimental data (R2 = 0.980), with a maximum capacity of 160.51 mg/g at 30 °C in an initial aqueous solution of 300 mg/L Cd2+. The experiments followed a pseudo-second-order kinetics model (R2 > 0.99), especially at a low initial concentration. The biosorption mechanisms involved were determined through scanning electron microscopy (SEM), transmission electron microscopy (TEM), energy-dispersive spectroscopy (EDS) and protein analysis. The SEM and TEM figures showed that the morphology of cells changed before and after the adsorption of Cd, and the EDS confirmed that Cd was absorbed on the surface of the cell. The analysis of proteins indicated that the protein species increased after the stimulation of Cd, which further confirmed the biosorption mechanism. A pot experiment confirmed that 11830 could passivate the cadmium in soil and reduce its uptake and utilization by Houttuynia cordata Thunb (H. cordata). This work demonstrates the potential application of microorganisms in inhibiting the accumulation of Cd in crops.
]]>Applied Sciences doi: 10.3390/app14072859
Authors: Young-Jun Park Chang-Yong Yi
In this study, we delve into a novel approach by employing a sensor-based pattern recognition model to address the automation of construction equipment activity analysis. The model integrates time of flight (ToF) sensors with deep convolutional neural networks (DCNNs) to accurately classify the operational activities of construction equipment, focusing on piston movements. The research utilized a one-twelfth-scale excavator model, processing the displacement ratios of its pistons into a unified dataset for analysis. Methodologically, the study outlines the setup of the sensor modules and their integration with a controller, emphasizing the precision in capturing equipment dynamics. The DCNN model, characterized by its four-layered convolutional blocks, was meticulously tuned within the MATLAB environment, demonstrating the model’s learning capabilities through hyperparameter optimization. An analysis of 2070 samples representing six distinct excavator activities yielded an impressive average precision of 95.51% and a recall of 95.31%, with an overall model accuracy of 95.19%. When compared against other vision-based and accelerometer-based methods, the proposed model showcases enhanced performance and reliability under controlled experimental conditions. This substantiates its potential for practical application in real-world construction scenarios, marking a significant advancement in the field of construction equipment monitoring.
]]>Economies doi: 10.3390/economies12040078
Authors: Paraskevi Boufounou Nikolaos Eriotis Theodoros Kounadeas Panagiotis Argyropoulos John Poulopoulos
Corruption poses a significant challenge to economic development and governance worldwide, with its detrimental effects permeating various levels of society. In the context of Greece, where corruption has been a longstanding issue, the role of internal audit mechanisms within local government organizations (LGOs) emerges as paramount. This paper presents a comprehensive analysis of the internal control landscape within LGO revenue departments, focusing on factors influencing its effectiveness and proposing strategies for improvement. Drawing upon survey data and regression analyses, this study highlights the crucial role of robust internal control mechanisms in combating corruption and fostering economic development. The findings underscore the importance of competent personnel, legislative compliance, interdepartmental collaboration, and technology utilization in enhancing internal control practices. Despite existing legislation, gaps in internal control implementation persist, including understaffing, inadequate procedures, and limited access to information. This study emphasizes the transformative potential of effective internal audit measures in mitigating corruption at the local level, thereby contributing to broader economic growth and societal well-being. Recommendations for strengthening the internal control structures within LGOs include the formal establishment of internal audit functions, adherence to professional standards, and the promotion of information system utilization. By addressing the corruption and inefficiencies within LGOs, this research underscores the pivotal role of institutional effectiveness in promoting transparency, accountability, and sustainable economic progress.
]]>Electronics doi: 10.3390/electronics13071258
Authors: Alejandro Clemente Paula Arias Levon Gevorkov Lluís Trilla Sergi Obrador Rey Xavier Sanchez Roger José Luis Domínguez-García Àlber Filbà Martínez
The implementation of energy storage system (ESS) technology with an appropriate control system can enhance the resilience and economic performance of power systems. However, none of the storage options available today can perform at their best in every situation. As a matter of fact, an isolated storage solution’s energy and power density, lifespan, cost, and response time are its primary performance constraints. Batteries are the essential energy storage component used in electric mobility, industries, and household applications nowadays. In general, the battery energy storage systems (BESS) currently available on the market are based on a homogeneous type of electrochemical battery. However, a hybrid energy storage system (HESS) based on a mixture of various types of electrochemical batteries can potentially provide a better option for high-performance electric cars, heavy-duty electric vehicles, industries, and residential purposes. A hybrid energy storage system combines two or more electrochemical energy storage systems to provide a more reliable and efficient energy storage solution. At the same time, the integration of multiple energy storage systems in an HESS requires advanced control strategies to ensure optimal performance and longevity of the system. This review paper aims to provide a comprehensive overview of the control systems used in HESSs for a wide range of applications. An overview of the various control strategies used in HESSs is offered, including traditional control methods such as proportional–integral–derivative (PID) control, and advanced control methods such as model predictive control (MPC), droop control (DC), sliding mode control (SMC), rule-based control (RBC), fuzzy logic control (FLC), and artificial neural network (ANN) control are discussed. The paper also highlights the recent developments in HESS control systems, including the use of machine learning techniques such as deep reinforcement learning (DRL) and genetic algorithms (GA). The paper provides not only a description and classification of various control approaches but also a comparison between control strategies from the evaluation of performance point of view. The review concludes by summarizing the key findings and future research directions for HESS control systems, which is directly linked to the research on machine learning and the mix of different control type strategies.
]]>Buildings doi: 10.3390/buildings14040933
Authors: Haneul Lee Seokheon Yun
Accurately predicting construction costs during the initial planning stages is crucial for the successful completion of construction projects. Recent advancements have introduced various machine learning-based methods to enhance cost estimation precision. However, the accumulation of authentic construction cost data is not straightforward, and existing datasets frequently exhibit a notable presence of missing values, posing challenges to precise cost predictions. This study aims to analyze diverse substitution methods for addressing missing values in construction cost data. Additionally, it seeks to evaluate the performance of machine learning models in cost prediction through the removal of conditional outliers. The primary goal is to identify and propose optimal strategies for handling missing value in construction cost records, ultimately improving the reliability of cost predictions. According to the analysis results, among single imputation methods, median imputation emerges as the most suitable, while among multiple imputation methods, lasso regression imputation produces the most superior outcomes. This research contributes to enhancing the trustworthiness of construction cost predictions by presenting a pragmatic approach to managing missing data in construction cost performance records, thereby facilitating more precise project planning and execution.
]]>Buildings doi: 10.3390/buildings14040932
Authors: Zhang Gao Wang Liu
More complex geological conditions could be encountered with the construction of urban subway projects. At present, many subway tunnels have been built in composite strata with upper soft and lower hard layers, but the presence of a cavity in the strata increases the risk of collapse during construction. In this paper, a series of model experiments and discrete element methods were conducted to investigate the failure behavior of composite strata with a cavity caused by tunnel excavation disturbance. The influence of the distance between the cavity and vault (hd) and the distance between the soil–rock interface and vault (hr) on the collapse of the composite strata are analyzed. The research results indicate that tunnel collapse exhibits progressive failure because of the forming of a collapsed arch in the strata. If the hd is greater than the tunnel span (D), the arch can be stabilized without other disturbances. Additionally, the thickness of the tunnel rock layer affects the height of the collapsed arch significantly, as it is difficult to form a stable arch when the hr is less than 2/3 D. Finally, reasonable construction safety distances are proposed based on the possibility of forming a stable arch collapse in the tunnel and determining the range of the collapse.
]]>Applied Sciences doi: 10.3390/app14072846
Authors: Pingping Cao Qiang Niu Yanping Zhu Tao Li
A novel zero-reference low-light image-enhancement approach based on noise estimation (ZLEN) is proposed to mitigate noise interference in image-enhancement processes, while the tenets of zero-reference and lightweight network architecture are maintained. ZLEN improves the high-order curve expression governing the mapping of low-light images to their enhanced counterparts, addressing image noise through a meticulously designed noise-estimation module and a zero-reference noise loss function. First, the higher-order curve expression with a noise term is defined, and then the noise map undergoes feature extraction through the semantic-aware attention module; following this, the resulting features are integrated with the low-light image. Ultimately, a lightweight convolutional neural network is adjusted to estimate higher-order curve parameters that link the low-light image to its enhanced version. Notably, ZLEN achieves luminance enhancement and noise reduction without paired or unpaired training data. Rigorous qualitative and quantitative evaluations were conducted on diverse benchmark datasets, demonstrating that ZLEN attained state-of-the-art (SOAT) status among existing zero-reference and unpaired-reference image-enhancement methodologies, while it exhibited comparable performance to full-reference image-enhancement methods. To confirm the practicality and robustness of ZLEN, the luminance enhancement was applied to mine images, which yielded satisfactory results.
]]>Journal of Sensor and Actuator Networks doi: 10.3390/jsan13020024
Authors: Auwalu Muhammad Abdullahi Ado Haruna Ronnapee Chaichaowarat
Physiotherapy is the treatment to recover a patient’s mobility and limb function after an injury, illness, or disability. Rehabilitation robots can be used to replace human physiotherapists. To ensure safety during robot physical therapy, the patient’s limb needs to be controlled to track a desired joint trajectory, and the torque due to interaction force/torque needs to be measured and regulated. Therefore, hybrid impedance and admittance with position control (HIPC) is required to track the trajectory and simultaneously regulate the contact torque. The literature describes two structures of HIPC: (1) a switched framework between admittance and impedance control operating in parallel (HIPCSW); and (2) a series connection between admittance and impedance control without switching. In this study, a hybrid adaptive impedance and position-based admittance control (HAIPC) in series is developed, which consists of a proportional derivative-based admittance position controller with gravitational torque compensation and an adaptive impedance controller. An extended state observer is used to estimate the interaction joint torque due to human stiff contact with the exoskeleton without the use of force/torque sensor, which is then used in the adaptive algorithm to update the stiffness and damping gains of the adaptive impedance controller. Simulation results obtained using MATLAB show that the proposed HAIPC significantly reduces the mean absolute values of the actuation torques (control inputs) required for the shoulder and elbow joints in comparison with HIPC and HIPCSW.
]]>Fractal and Fractional doi: 10.3390/fractalfract8040196
Authors: Miglena N. Koleva Lubin G. Vulkov
This paper is concerned with solving the problem of identifying the control vector problem for a fractional multi-order system of nonlinear ordinary differential equations (ODEs). We describe a quasilinearization approach, based on minimization of a quadratic functional, to compute the values of the unknown parameter vector. Numerical algorithm combining the method with appropriate fractional derivative approximation on graded mesh is applied to SIS and SEIR problems to illustrate the efficiency and accuracy. Tikhonov regularization is implemented to improve the convergence. Results from computations, both with noisy-free and noisy data, are provided and discussed. Simulations with real data are also performed.
]]>Applied Sciences doi: 10.3390/app14072858
Authors: Alejandra Sánchez-Guzmán Héctor Iván Bedolla-Rivera Eloy Conde-Barajas María de la Luz Xochilt Negrete-Rodríguez Marcos Alfonso Lastiri-Hernández Francisco Paúl Gámez-Vázquez Dioselina Álvarez-Bernal
Agriculture is a sector of great importance for Mexico’s economy, generating employment and contributing significantly to the country’s gross domestic product. The Bajio stands out as one of the most productive agricultural regions in Mexico. However, intensive agricultural practices in this area have caused a progressive deterioration and loss of soil fertility. This study focused on evaluating the quality of soils used for agriculture in the Bajio region of the State of Guanajuato, Mexico. This evaluation, utilised soil quality indexes (SQIs) based on a total of 27 physicochemical, biological and enzymatic indicators. These indicators were selected by means of a principal component analysis (PCA), which allowed for the identification of a minimum set of data. The SQIs developed in this study categorised soils into different quality levels, ranging from low to high, mainly based on the values observed in the biological indicators (SMR and qCO2), which comprised the established SQIs. The inclusion of these biological indicators provides the developed SQIs with greater sensitivity to detect minor disturbances in agricultural soils due to human activity, compared with SQIs consisting only of physicochemical indicators. The developed SQIs can be used to ensure high-quality food production in soils used for corn cultivation under similar conditions, both nationally and internationally.
]]>Applied Sciences doi: 10.3390/app14072856
Authors: Zhang Gai Sha
Thelephora ganbajun Zang, a rare wild macrofungus, has significant culinary and medicinal value. However, it also has a high cost attributed to its inability to achieve artificial cultivation and its strict environmental requirements. To reveal the intricacies of its development, we conducted a comprehensive analysis of the proteome and metabolome in three pivotal developmental stages: the mycelium, the primordium, and the fruiting body. In our investigation, genes exhibiting various expression levels across multi-omics analyses were identified as potential candidates implicated in growth, development, or metabolic regulation. The aim of this study was to provide a clearer direction for understanding the fundamental metabolic activities and growth stages of this species. Label-free proteomic sequencing revealed a critical juncture in ectomycorrhiza formation, particularly during the transition from the mycelium to the primordium. Secreted proteins, signaling proteins, membrane proteins, and proteins with unidentified functions were rapidly synthesized, with certain amino acids contributing to the synthesis of proteins involved in signaling pathways or hormone precursor substances. In the metabolomics analysis, the classification of secondary metabolites revealed a noteworthy increase in lipid substances and organic acids, contributing to cell activity. The early mycelial development stage exhibited vigorous cell metabolism, contrasting with a decline in cell division activity during fruiting body formation. In our findings, the integration of metabolomic and transcriptomic data highlighted the potential key role of folate biosynthesis in regulating early ectomycorrhiza development. Notably, the expression of alkaline phosphatase and dihydrofolate synthase genes within this pathway was significantly up-regulated in the mycelium and fruiting body stages but down-regulated in the primordium stage. This regulation primarily influences dihydrofolate reductase activity and B vitamin synthesis.
]]>Economies doi: 10.3390/economies12040077
Authors: Marek Nagy Katarina Valaskova Erika Kovalova Marcel Macura
The financial markets, shaped by dynamic forces, including macroeconomic trends and technological advancements, are influenced by a multitude of factors impacting the S&P 500 stock index, a pivotal indicator in the US equity markets. This paper highlights the significance of understanding the exogenous variables affecting the index’s profitability for academics, portfolio managers, and investment professionals. Amid the global ramifications of the S&P 500, particularly in combating the eroding purchasing power caused by inflation, investing in stock indexes emerges as a means to safeguard wealth. The study employs various statistical techniques, emphasizing a methodical approach to uncover influential variables, and using static regression and autoregressive models for immediate and time-lagged effects. In conclusion, the findings have broad practical implications beyond investment strategy, extending to portfolio construction and risk management. Acknowledging inherent uncertainties in financial market forecasts, future research endeavors should target long-term trends, specific influences, and the impact of exchange rate fluctuations on index evolution. Collaboration across regulatory bodies, academia, and the financial industry is underscored, holding the potential for effective risk monitoring and bolstering overall economic and financial market stability. This research serves as a foundational step towards enhancing market understanding and facilitating more efficient investment decision-making approaches.
]]>Applied Sciences doi: 10.3390/app14072852
Authors: Zhao Ning Jia Chai Su Wang
Mobile laser scanning (MLS) systems have become an important technology for collecting and measuring road information for highway maintenance and reconstruction services. However, the efficient and accurate extraction of unstructured road surfaces from MLS point cloud data collected on highways is challenging. Specifically, the complex and unstructured characteristics of road surveying point cloud data lead to traditional 3D point cloud segmentation. When traditional 3D point cloud algorithms extract unstructured road surfaces, over-segmentation and under-segmentation often occur, which affects efficiency and accuracy. To solve these problems, this study introduces an enhanced road extraction method that integrates supervoxel and trajectory information into a traditional region growing algorithm. The method involves two main steps: first, a supervoxel data structure is applied to reconstruct the original MLS point cloud data, which diminishes the calculation time of the point cloud feature vector and accelerates the merging speed of a similar region; second, the trajectory information of the vehicle is used to optimize the seed selection strategy of the regio growing algorithm, which improves the accuracy of road surface extraction. Finally, two typical highway section tests (flat road and slope road) were conducted to validate the positioning performance of the proposed algorithm in an MLS point cloud. The results show that, compared with three kinds of traditional road surface segmentation algorithms, our method achieves an average extraction recall and precision of 99.1% and 96.0%, and by calculating the recall and precision, an F1 score of 97.5% can be obtained to evaluate the performance of the proposed method, for both datasets. Additionally, our method exhibits an average road surface extraction time that is 45.0%, 50.3%, and 55.8% faster than those of the other three automated segmentation algorithms.
]]>Applied Sciences doi: 10.3390/app14072854
Authors: Wang Mitrani Wipat Moreland Haystead Zhang Robertson
The employment of Microbially Induced Calcium Carbonate Precipitation (MICP) is of increasing interest as a technique for environmentally sustainable soil stabilisation. Recent advancements in synthetic biology have allowed for the conception of a pressure-responsive MICP process, wherein bacteria are engineered to sense environmental loads, thereby offering the potential to stabilise specific soil regions selectively. In this study, a 2D smart bio-geotechnical model is proposed based on a pressure-responsive MICP system. Experimentally obtained pressure-responsive genes and hypothetical genes with different pressure responses were applied in the model and two soil profiles were evaluated. The resulting model bridges scales from gene expression within bacteria cells to geotechnical simulations. The results show that both strata and gene expression–pressure relationships have a significant influence on the distribution pattern of calcium carbonate precipitation within the soil matrix. Among the evaluated experimental genes, Gene A demonstrates the best performance in both of the two soil profiles due to the effective stabilisation in the centre area beneath the load, while Genes B and C are more effective in reinforcing peripheral regions. Furthermore, when the hypothetical genes are utilised, there is an increasing stabilisation area with a decreased threshold value. The results show that the technique can be used for soil reinforcement in specific areas.
]]>Applied Sciences doi: 10.3390/app14072855
Authors: Mentari Putri Jati Muhammad Irfan Luthfi Cheng-Kai Yao Amare Mulatie Dehnaw Yibeltal Chanie Manie Peng-Chun Peng
This study proposes the utilization of an optical fiber vibration sensor for detecting the superposition of extremely close frequencies in vibration signals. Integration of deep neural networks (DNN) proves to be meaningful and efficient, eliminating the need for signal analysis methods involving complex mathematical calculations and longer computation times. Simulation results of the proposed model demonstrate the remarkable capability to accurately distinguish frequencies below 1 Hz. This underscores the effectiveness of the proposed image-based vibration signal recognition system embedded in DNN as a streamlined yet highly accurate method for vibration signal detection, applicable across various vibration sensors. Both simulation and experimental evaluations substantiate the practical applicability of this integrated approach, thereby enhancing electric motor vibration monitoring techniques.
]]>Buildings doi: 10.3390/buildings14040931
Authors: Changren Ke Yihui Fan Junling Jiang
In order to study the effect of the support mode of a staggered truss system on the continuous collapse resistance performance of a steel structure, four finite element models were established based on the bracing arrangement of a five-story steel frame structure. The situations of different columns on the first floor removed were classified into eight scenarios, and five models of each scenario were analyzed with nonlinear dynamic analyses. Finally, a computational metric based on energy robustness was proposed to evaluate the robustness of the structure. The results of nonlinear dynamic analyses indicated that the staggered truss system significantly improved the resistance to progressive collapse of steel frame structures and effectively increased the redundancy of steel frame structures. All four bracing methods effectively reduced the vertical displacement at the point of failure, with the peak displacement at the point of failure reduced by a maximum of 84 percent and a minimum of 41 percent compared to a pure frame structure. Moreover, the staggered truss system can reduce some axial force peaks in the adjacent columns adjacent to the failed columns. The structural robustness coefficients of Model A, Scheme 1, Scheme 2, Scheme 3, and Scheme 4 are 1.144, 1.339, 1.306, 1.584, and 1.176, respectively, according to the proposed robustness calculation method, which shows that the braced steel frame structure has improved robustness over the original structure. The staggered truss system improves the robustness of the steel frame structure so that the steel frame structure has better resistance to progressive collapse.
]]>Symmetry doi: 10.3390/sym16040396
Authors: Ze Cui Lang Kou Zenghao Chen Peng Bao Donghai Qian Lang Xie Yue Tang
Although robots have been widely used in a variety of fields, the idea of enabling them to perform multiple tasks in the same way that humans do remains a difficulty. To solve this, we investigate the learning from demonstration (LFD) system with our independently designed symmetrical humanoid dual-arm robot. We present a novel action feature matching algorithm. This algorithm accurately transforms human demonstration data into task models that robots can directly execute, considerably improving LFD’s generalization capabilities. In our studies, we used motion capture cameras to capture human demonstration actions, which included combinations of simple actions (the action layer) and a succession of complicated operational tasks (the task layer). For the action layer data, we employed Gaussian mixture models (GMM) for processing and constructing an action primitive library. As for the task layer data, we created a “keyframe” segmentation method to transform this data into a series of action primitives and build another action primitive library. Guided by our algorithm, the robot successfully imitated complex human tasks. Results show its excellent task learning and execution, providing an effective solution for robots to learn from human demonstrations and significantly advancing robot technology.
]]>Journal of Imaging doi: 10.3390/jimaging10040081
Authors: Heidi Lindroth Keivan Nalaie Roshini Raghu Ivan N. Ayala Charles Busch Anirban Bhattacharyya Pablo Moreno Franco Daniel A. Diedrich Brian W. Pickering Vitaly Herasevich
Computer vision (CV), a type of artificial intelligence (AI) that uses digital videos or a sequence of images to recognize content, has been used extensively across industries in recent years. However, in the healthcare industry, its applications are limited by factors like privacy, safety, and ethical concerns. Despite this, CV has the potential to improve patient monitoring, and system efficiencies, while reducing workload. In contrast to previous reviews, we focus on the end-user applications of CV. First, we briefly review and categorize CV applications in other industries (job enhancement, surveillance and monitoring, automation, and augmented reality). We then review the developments of CV in the hospital setting, outpatient, and community settings. The recent advances in monitoring delirium, pain and sedation, patient deterioration, mechanical ventilation, mobility, patient safety, surgical applications, quantification of workload in the hospital, and monitoring for patient events outside the hospital are highlighted. To identify opportunities for future applications, we also completed journey mapping at different system levels. Lastly, we discuss the privacy, safety, and ethical considerations associated with CV and outline processes in algorithm development and testing that limit CV expansion in healthcare. This comprehensive review highlights CV applications and ideas for its expanded use in healthcare.
]]>Processes doi: 10.3390/pr12040679
Authors: Ting Sun Zhiliang Wen Jin Yang Kaidie Yang Zengcheng Han Jiayuan He
Natural gas hydrate reservoirs, with shallow burial, poor cementation, and low strength, are prone to submarine landslides triggered by hydrate decomposition during extraction. Prior studies have inadequately considered factors such as the dynamic decomposition of hydrates during depressurization, and its impacts on the reservoir’s geomechanical properties. In this paper, a coupled thermal–hydraulic–mechanical–chemical mathematical model of hydrate decomposition is proposed, and the dynamic geomechanical response and the effect of hydrate decomposition on seafloor settlement and slope destabilization during the process of depressurization mining are analyzed by combining the strength discount method with the example of a hydrate-bearing seafloor slope in the Shenhu area. Furthermore, the study employs an orthogonal experimental design along with range and variance analysis to gauge the impact of critical factors (degree of hydrate decomposition, seawater depth, hydrate reservoir burial depth, hydrate reservoir thickness, and slope angle) on slope stability. The findings suggest that hydrate decomposition is non-uniform and is influenced by stratigraphic temperature gradients and gravity. In the region where hydrate decomposition occurs, the decrease of pore pressure leads to the increase of effective stress. Additionally, the decomposition of hydrates decreases the shear modulus of sediments, leading to deformation and reduced permeability in the affected area. Over a three-year period of depressurization mining, the significantly reduced safety factor increases the risk of landslides. Various factors play a role in the control of submarine slope stability, with slope inclination being the primary factor, followed by the degree of hydrate decomposition, reservoir thickness, burial depth, and seawater depth. Among these factors, hydrate burial depth and seawater depth have a positive correlation with submarine slope stability, while increases in other factors generally decrease stability. These research findings have important implications for the safe exploitation of slopes that contain hydrates.
]]>Applied Sciences doi: 10.3390/app14072853
Authors: Kuei-Hsiang Chao Thi Bao-Ngoc Nguyen
The main purpose of this study was to research and develop maximum power point tracking (MPPT) of a photovoltaic module array (PVMA) with partial module shading and sudden changes in solar irradiance. Modified cat swarm optimization (MCSO) was adopted to track the global maximum power point (GMPP) of the PVMA. Upon a sudden changes in solar irradiance or when certain modules in the PVMA were shaded, the maximum power point (MPP) of the PVMA will change accordingly, and multiple peak values may appear on the power–voltage (P-V) characteristic curve. Therefore, if the tracking pace is constant, the time required to track the MPP might extend, and under certain circumstances, the GMPP might not be tracked, as only the local maximum power point (LMPP) can be tracked. To prevent this problem, a maximum power point tracker based on MCSO is proposed in this paper in order to adjust the tracking pace along with the slope of the P-V characteristic curve and the inertia weight of the iteration formula. The initial voltage for tracking commencement was set to 0.8 times the voltage at the maximum power point of the PVMA under standard test conditions. Firstly, MATLAB 2022a was used to construct the four-series, three-parallel PVMA model under zero shading and partial shading. The feedback of PVMA voltage and current was obtained, where the GMPP was tracked with MCSO. From the simulation results, it was proven that, under different shading percentages and sudden changes in solar irradiance for partial modules in the PVMA, the MCSO proposed in this paper provided better tracking speed, dynamic response, and steady performance compared to the conventional CSO.
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