Applied Sciences

23 pages, 9394 KiB

Open AccessArticle

Small-Sample Bearings Fault Diagnosis Based on ResNet18 with Pre-Trained and Fine-Tuned Method

by Junlin Niu, Jiafang Pan, Zhaohui Qin, Faguo Huang and Haihua Qin

Appl. Sci. 2024, 14(12), 5360; https://doi.org/10.3390/app14125360 - 20 Jun 2024

Viewed by 560

In actual production, bearings are usually in a normal working state, which results in a lack of data for fault diagnosis (FD). Yet, the majority of existing studies on FD of rolling bearings focus on scenarios with ample fault data, while research on [...] Read more.

In actual production, bearings are usually in a normal working state, which results in a lack of data for fault diagnosis (FD). Yet, the majority of existing studies on FD of rolling bearings focus on scenarios with ample fault data, while research on diagnosing small-sample bearings remains scarce. Therefore, this study presents an FD method for small-sample bearings, employing variational-mode decomposition and Symmetric Dot Pattern, combined with a pre-trained and fine-tuned Residual Network18 (VSDP-TLResNet18). The approach utilizes variational-mode decomposition (VMD) to break down the signal, determining the k value and the best Intrinsic-Mode Function (IMF) component based on center frequency and kurtosis criteria. Following this, the chosen IMF component is converted into a two-dimensional image using the Symmetric Dot Pattern (SDP) transform. In order to maximize the discrimination between two-dimensional fault images, Pearson correlation analysis is carried out on the parameters of SDP to select the optimal parameters. Finally, we use the pre-trained and fine-tuned method combined with ResNet18 for small-sample FD to improve the diagnosis accuracy of the model. Relative to alternative approaches, the suggested method demonstrates strong performance when dealing with small-sample FD. Full article

(This article belongs to the Collection Bearing Fault Detection and Diagnosis)

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24 pages, 5596 KiB

Open AccessArticle

Fault-Tolerant Model Predictive Control Applied to a Sewer Network

by Antonio Cembellín, María J. Fuente, Pastora Vega and Mario Francisco

Appl. Sci. 2024, 14(12), 5359; https://doi.org/10.3390/app14125359 - 20 Jun 2024

Viewed by 315

Abstract

This paper presents a Fault-Tolerant Model Predictive Control (FTMPC) algorithm applied to a simulation model for sewer networks. The aim of this work is to preserve the operation of the predictive controller as much as possible, in accordance with its operational objectives, when [...] Read more.

This paper presents a Fault-Tolerant Model Predictive Control (FTMPC) algorithm applied to a simulation model for sewer networks. The aim of this work is to preserve the operation of the predictive controller as much as possible, in accordance with its operational objectives, when there may be anomalies affecting the elements of the control system, mainly sensors and actuators. For this purpose, a fault detection and diagnosis system (FDD) based on a moving window principal component analysis technique (MWPCA) will be developed to provide an online fault monitoring solution for large-scale complex processes (e.g., sewer systems) with dynamically changing characteristics, and a reconfiguration algorithm for the MPC controller taking advantage of its own features such as constraint handling. Comparing the results obtained considering various types of faults, with situations of normal controlled operation and with the behavior of the sewer network when no control is applied, will allow some conclusions to be drawn at the end. Full article

(This article belongs to the Special Issue Advances in Intelligent Control and Engineering Applications)

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14 pages, 3526 KiB

Open AccessArticle

Kaolin–Fly Ash Composite for Pb²⁺ and AsO₄³⁻ Adsorption from Aqueous System

by Barbora Doušová, Eva Bedrnová, Kateřina Maxová, David Koloušek, Miloslav Lhotka, Lukáš Pilař and Milan Angelis

Appl. Sci. 2024, 14(12), 5358; https://doi.org/10.3390/app14125358 - 20 Jun 2024

Viewed by 276

Abstract

The expected benefit of composite adsorbents generally consists in their growing applicability, thanks to the combination of the adsorption properties of individual components. Composite adsorbents were prepared as mixtures of kaolin from a Czech deposit (kaolin Sedlec, SK) and two fly ashes (FAs) [...] Read more.

The expected benefit of composite adsorbents generally consists in their growing applicability, thanks to the combination of the adsorption properties of individual components. Composite adsorbents were prepared as mixtures of kaolin from a Czech deposit (kaolin Sedlec, SK) and two fly ashes (FAs) from a fluidised bed boiler in Czech operations differing in fuel type. The mixtures of SK with FA in a ratio of 50:50% wt. were prepared at 20 °C, 65 °C, and 110 °C in an autoclave. The source materials and composite adsorbents were tested for the adsorption of lead as Pb²⁺, and arsenic as AsO₄³⁻ from model solutions in laboratory conditions. The adsorption of Pb²⁺ proceeded quantitatively on the source materials except SK, and on both the composites, with an adsorption yield of >97% and a low adsorbent consumption (~2 g.L⁻¹). The AsO₄³⁻ adsorption proceeded selectively only on both FAs, with an adsorption yield of >97% again. The adsorption of AsO₄³⁻ on the composite adsorbents achieved a worse yield (˂80%), with about ten times more adsorbent consumption (~20 g.L⁻¹). An increased preparation temperature did not affect the Pb²⁺ adsorption at all, but it reduced the efficiency of AsO₄³⁻ adsorption by up to 30%. The SK–FA composites proved to have promising properties, mostly as cation-active adsorbents. Full article

(This article belongs to the Special Issue Novel Ceramic Materials: Processes, Properties and Applications)

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15 pages, 329 KiB

Open AccessArticle

The Influence of Game-Related Statistics on the Final Results in FIBA Global and Continental Competitions

by Jasmin Komić, Slobodan Simović, Denis Čaušević, Dan Iulian Alexe, Michal Wilk, Babina Rani and Cristina Ioana Alexe

Appl. Sci. 2024, 14(12), 5357; https://doi.org/10.3390/app14125357 - 20 Jun 2024

Viewed by 524

Abstract

Sport, particularly in the realm of professional competition, is a domain of human endeavor that is increasingly dependent on the use of analytical statistical information. Consequently, mathematics and statistics are becoming increasingly crucial elements in sports. Although experts recognize the importance of analytics [...] Read more.

Sport, particularly in the realm of professional competition, is a domain of human endeavor that is increasingly dependent on the use of analytical statistical information. Consequently, mathematics and statistics are becoming increasingly crucial elements in sports. Although experts recognize the importance of analytics in women’s basketball, the literature addressing this subject remains limited. The objective of this study is to employ quantitative methodologies to discover prevailing patterns in global women’s basketball representation. The entities examined in this article were the games contested during the 2021 Olympic Games, the 2022 World Cup, and the 2023 continental championships. Two regression models were created for the research, using thirteen standard variables observed in the game. The evaluation of the regression model was conducted using the stepwise regression method, incorporating dimensionality reduction based on the outcomes of factor analysis. Among the 14 models that were observed, 13 of them exhibited strong and moderate linkages, while only 1 displayed weak connections and lacked statistical significance. The primary factors that account for the disparity between winning and losing teams in games are primarily associated with shooting accuracy toward the basket. When examining individual championships, the percentage surpassed 50% in all cases except for AfroBasket. However, when considering the overall results, the significance of shooting rose to 86%. The variable representing offensive rebound efficiency had a significant influence on the outcome, being present in all individual competitions, whereas defensive rebound efficiency was only considered in the overall results. Full article

(This article belongs to the Special Issue Advances in Sports Science and Movement Analysis)

18 pages, 13275 KiB

Open AccessArticle

A Multidisciplinary Approach to the Evaluation of Air Quality and Thermo-Hygrometric Conditions for the Conservation of Heritage Manuscripts and Printed Materials in Historic Buildings: A Case Study of the Sala del Dottorato of the University of Perugia as a Model for Heritage Preservation and Occupants’ Comfort

by Elisa Moretti, Fabio Sciurpi, Maria Giulia Proietti and Monica Fiore

Appl. Sci. 2024, 14(12), 5356; https://doi.org/10.3390/app14125356 - 20 Jun 2024

Viewed by 273

Abstract

The Sala del Dottorato (Hall of Graduates) is a magnificent library in the University of Perugia which plays the double role of providing optimal conservation of valuable books and manuscripts while also hosting important events. This double role is closely connected to contrasting [...] Read more.

The Sala del Dottorato (Hall of Graduates) is a magnificent library in the University of Perugia which plays the double role of providing optimal conservation of valuable books and manuscripts while also hosting important events. This double role is closely connected to contrasting indoor microclimatic conditions. This paper presents the results of a multidisciplinary study, begun in 2019, which investigates optimal conditions for the conservation of volumes by monitoring thermo-hygrometric and air quality parameters. The study describes the current conditions of the Hall (in terms of air temperature, relative humidity and concentration of CO₂), highlighting critical aspects, defining strategies for their mitigation and control, and outlining future developments. Improvement measures relate to the installation of a permanent monitoring system with alarm settings and data storage, technical interventions on the windows, and the restoration of several volumes. The paper shows the importance of monitoring as an instrument of control in real time and provides guidelines for management to be implemented according to indoor microclimatic conditions. Full article

(This article belongs to the Special Issue Hygrothermal Behaviour of Cultural Heritage and Climate Changes)

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13 pages, 4389 KiB

Open AccessArticle

Experimental Study on Rock Deformation Localization Using Digital Image Correlation and Acoustic Emission

by Tongzhen Xing, Haibin Zhu and Yimin Song

Appl. Sci. 2024, 14(12), 5355; https://doi.org/10.3390/app14125355 - 20 Jun 2024

Viewed by 218

Abstract

In this study, the digital image correlation (DIC) method and acoustic emission (AE) technology were combined to study the evolution of rock deformation localization in detail. The second-order spatial–temporal subset DIC (STS-DIC) algorithm was proposed and used for measuring strongly heterogeneous deformation fields [...] Read more.

In this study, the digital image correlation (DIC) method and acoustic emission (AE) technology were combined to study the evolution of rock deformation localization in detail. The second-order spatial–temporal subset DIC (STS-DIC) algorithm was proposed and used for measuring strongly heterogeneous deformation fields of red sandstone specimens under uniaxial compression. The evolution of the deformation field was analyzed with a focus on the deformation localization stage. The length and width of the deformation localization band (DLB) were measured, and the relationships between the relative sliding rate of the DLB, the relative opening rate of the DLB, and the AE counts were identified. Deformation localization was found to result from the rapid evolution of the strain concentration before the peak stress. The complete development of the DLB is an inducing factor for catastrophic rock failure, and the failure modes of the rock specimens were consistent with the final state of the DLB. A good correlation was identified between the AE counts and the relative displacement rate of the DLB, and the sliding rate was found to have a significant influence on the AE counts. Full article

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13 pages, 828 KiB

Open AccessArticle

Secure IoT Communication: Implementing a One-Time Pad Protocol with True Random Numbers and Secure Multiparty Sums

by Julio Fenner, Patricio Galeas, Francisco Escobar and Rail Neira

Appl. Sci. 2024, 14(12), 5354; https://doi.org/10.3390/app14125354 - 20 Jun 2024

Viewed by 239

Abstract

We introduce an innovative approach for secure communication in the Internet of Things (IoT) environment using a one-time pad (OTP) protocol. This protocol is augmented by incorporating a secure multiparty sum protocol to produce OTP keys from genuine random numbers obtained from the [...] Read more.

We introduce an innovative approach for secure communication in the Internet of Things (IoT) environment using a one-time pad (OTP) protocol. This protocol is augmented by incorporating a secure multiparty sum protocol to produce OTP keys from genuine random numbers obtained from the physical phenomena observed in each device. We have implemented our method using ZeroC-Ice v.3.7, dependable middleware for distributed computing, demonstrating its practicality in various hybrid IoT scenarios, particularly in devices with limited processing capabilities. The security features of our protocol are evaluated under the Dolev–Yao threat model, providing a thorough assessment of its defense against potential cyber threats. Full article

(This article belongs to the Special Issue Advances in Internet of Things (IoT) Technologies and Cybersecurity)

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16 pages, 5023 KiB

Open AccessArticle

Research on Intelligent Recognition for Plant Pests and Diseases Based on Improved YOLOv8 Model

by Yuchun Wang, Cancan Yi, Tao Huang and Jun Liu

Appl. Sci. 2024, 14(12), 5353; https://doi.org/10.3390/app14125353 - 20 Jun 2024

Viewed by 264

Abstract

Plant pests and diseases are important parts of insect disease control and the high-quality development of agriculture. Traditional methods for identifying plant diseases and pests suffer from low accuracy and slow speed, while the existing machine learning methods are constrained by environmental and [...] Read more.

Plant pests and diseases are important parts of insect disease control and the high-quality development of agriculture. Traditional methods for identifying plant diseases and pests suffer from low accuracy and slow speed, while the existing machine learning methods are constrained by environmental and technological factors, leading to low recognition efficiency. To address the issue of the above problems, this paper has proposed an intelligent recognition algorithm based on the improved YOLOv8 model, which has high recognition accuracy and speed. Firstly, in the Backbone network, the Global Attention Mechanism (GAM) is adopted to weigh the important feature information, thereby improving the accuracy of the model. Secondly, in the mixed feature part of the Neck network, the Receptive-Field Attention Convolutional (RFA Conv) operation is used instead of standard convolution operations to enhance the processing ability for feature information and to reduce computational complexity and costs, thus improving the network performance. After verifying the rice and cotton datasets, the accuracy indicator mean average precision (mAP) reaches 71.27% and 82.91%, respectively, in the two different datasets. Comparing these indices with those of the Faster R-CNN, YOLOv7, and the original YOLOv8 model, the results fully demonstrate the effectiveness and superiority of the improved model in terms of detection accuracy. Full article

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20 pages, 37178 KiB

Open AccessArticle

Modern Design of Carrier for Overhead Conveyor

by Lukáš Hruzík, Jiří Struž, Miroslav Trochta, Lukáš Klapetek and Daniel Pišťáček

Appl. Sci. 2024, 14(12), 5352; https://doi.org/10.3390/app14125352 - 20 Jun 2024

Viewed by 214

Abstract

Modern industry should apply modern design in the construction of production facilities. This is typically the case with belt production, where parts are moved towards the worker, or when moving parts from the factory to the warehouse and shipping area. There is a [...] Read more.

Modern industry should apply modern design in the construction of production facilities. This is typically the case with belt production, where parts are moved towards the worker, or when moving parts from the factory to the warehouse and shipping area. There is a relatively high energy consumption associated with moving these parts. The size of the consumption is mainly determined not only by the size of the transported components and the transport technology, but also by the design of the hoppers used for transport. One way to reduce material handling costs is to make the equipment used for moving parts more efficient. A more efficient carrier should, above all, be lighter. Topological optimization can serve very well for this weight reduction. Of course, the reduction in weight not only has an effect on lower power consumption, but also on the wear of other components. Hence, later in this article, we try to quantify these impacts and assess how much benefit the use of a modern designed carrier can bring. It is also important to consider the cost of producing new carriers versus modifying existing ones. The paper describes the application of the modern designed carrier and compares it with the existing carriers as well as modified existing carriers. Full article

(This article belongs to the Section Mechanical Engineering)

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16 pages, 4594 KiB

Open AccessArticle

Lithium Concentrations in Saline Brines of the Shu–Sarysu Depression

by Refat T. Baratov, Eleonora Y. Seitmuratova, Ermek Z. Murtazin, Diyas O. Dautbekov, Vyacheslav N. Kelyukhov, Nurgali S. Shadiyev and Moldir A. Mashrapova

Appl. Sci. 2024, 14(12), 5351; https://doi.org/10.3390/app14125351 - 20 Jun 2024

Viewed by 219

Abstract

This article presents the results of a study on lithium mineralization in salt flats and underground aquifers of the Shu–Sarysu depression. Analysis of brine samples collected from 2022 to 2023, utilizing spectroscopy and X-ray diffraction, reveals elevated concentrations that hold commercial promise. These [...] Read more.

This article presents the results of a study on lithium mineralization in salt flats and underground aquifers of the Shu–Sarysu depression. Analysis of brine samples collected from 2022 to 2023, utilizing spectroscopy and X-ray diffraction, reveals elevated concentrations that hold commercial promise. These findings may have significant implications for exploration efforts and estimation regarding the lithium resource potential, which is currently in high demand. This article examines data regarding lithium brine deposits worldwide, focusing on their geology. The research methodology involves delineating regions of salt flat distribution through remote sensing data interpretation, fieldwork, and laboratory analysis, particularly for surface brines. Underground lithium-bearing brines are detected within oil and gas structures. The article presents findings from analytical studies conducted on saline and co-produced formation brines collected during the 2022 field season, with a specific focus on the epiplatform regions of Kazakhstan, encompassing the Shu–Sarysu depression. Full article

(This article belongs to the Section Earth Sciences)

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15 pages, 667 KiB

Open AccessArticle

Investigation of Structural Seismic Vulnerability Using Machine Learning on Rapid Visual Screening

by Ioannis Karampinis, Lazaros Iliadis and Athanasios Karabinis

Appl. Sci. 2024, 14(12), 5350; https://doi.org/10.3390/app14125350 - 20 Jun 2024

Viewed by 208

Abstract

Seismic vulnerability assessment is one of the most impactful engineering challenges faced by modern societies. Thus, authorities require a reliable tool that has the potential to rank given structures according to their seismic vulnerability. Various countries and organizations over the past decades have [...] Read more.

Seismic vulnerability assessment is one of the most impactful engineering challenges faced by modern societies. Thus, authorities require a reliable tool that has the potential to rank given structures according to their seismic vulnerability. Various countries and organizations over the past decades have developed Rapid Visual Screening (RVS) tools aiming to efficiently estimate vulnerability indices. In general, RVS tools employ a set of structural features and their associated weights to obtain a vulnerability index, which can be used for ranking. In this paper, Machine Learning (ML) models are implemented within this framework. The proposed formulation is used to train binary classifiers in conjunction with ad hoc rules, employing the features of various Codes (e.g., the Federal Emergency Management Agency, New Zealand, and Canada). The efficiency of this modeling effort is evaluated for each Code separately and it is clearly demonstrated that ML-based models are capable of outperforming currently established engineering practices. Furthermore, in the spirit of the aforementioned Codes, a linearization of the fully trained ML model is proposed. ML feature attribution techniques, namely SHapley Additive exPlanations (SHAP) are employed to introduce weights similar to engineering practices. The promising results motivate the potential applicability of this methodology towards the recalibration of the RVS procedures for various types of cases. Full article

(This article belongs to the Collection Geoinformatics and Data Mining in Earth Sciences)

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42 pages, 7686 KiB

Open AccessArticle

Parallel GPU-Acceleration of Metaphorless Optimization Algorithms: Application for Solving Large-Scale Nonlinear Equation Systems

by Bruno Silva, Luiz Guerreiro Lopes and Fábio Mendonça

Appl. Sci. 2024, 14(12), 5349; https://doi.org/10.3390/app14125349 - 20 Jun 2024

Viewed by 245

Abstract

Traditional population-based metaheuristic algorithms are effective in solving complex real-world problems but require careful strategy selection and parameter tuning. Metaphorless population-based optimization algorithms have gained importance due to their simplicity and efficiency. However, research on their applicability for solving large systems of nonlinear [...] Read more.

Traditional population-based metaheuristic algorithms are effective in solving complex real-world problems but require careful strategy selection and parameter tuning. Metaphorless population-based optimization algorithms have gained importance due to their simplicity and efficiency. However, research on their applicability for solving large systems of nonlinear equations is still incipient. This paper presents a review and detailed description of the main metaphorless optimization algorithms, including the Jaya and enhanced Jaya (EJAYA) algorithms, the three Rao algorithms, the best-worst-play (BWP) algorithm, and the new max–min greedy interaction (MaGI) algorithm. This article presents improved GPU-based massively parallel versions of these algorithms using a more efficient parallelization strategy. In particular, a novel GPU-accelerated implementation of the MaGI algorithm is proposed. The GPU-accelerated versions of the metaphorless algorithms developed were implemented using the Julia programming language. Both high-end professional-grade GPUs and a powerful consumer-oriented GPU were used for testing, along with a set of hard, large-scale nonlinear equation system problems to gauge the speedup gains from the parallelizations. The computational experiments produced substantial speedup gains, ranging from 33.9× to 561.8×, depending on the test parameters and the GPU used for testing. This highlights the efficiency of the proposed GPU-accelerated versions of the metaphorless algorithms considered. Full article

(This article belongs to the Special Issue Parallel, Distributed and Cloud Computing: Status, Prospects and Future)

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15 pages, 1336 KiB

Open AccessArticle

Analysis of Kinematic Variables According to Menstrual Cycle Phase and Running Intensity: Implications for Training Female Athletes

by Carolina Domínguez-Muñoz, Juan del Campo, Alberto García, José Guzmán, Rafael Martínez-Gallego and Jesús Ramón-Llin

Appl. Sci. 2024, 14(12), 5348; https://doi.org/10.3390/app14125348 - 20 Jun 2024

Viewed by 345

Abstract

Depending on the phase of the menstrual cycle, different values of running kinematic variables can be obtained. The aim of this study is to analyze whether there are changes in the kinematic variables in running throughout the menstrual cycle and to relate them [...] Read more.

Depending on the phase of the menstrual cycle, different values of running kinematic variables can be obtained. The aim of this study is to analyze whether there are changes in the kinematic variables in running throughout the menstrual cycle and to relate them to running performance and injury prevention. Eight regular female runners and triathletes performed a maximal treadmill test, as well as a submaximal test (6′ stages at 50%, 60% and 80% of maximal aerobic speed) in each of the phases of the menstrual cycle: menstruation phase (day 2.4 ± 0.7), follicular phase (day 10.4 ± 2.2) and luteal phase (day 21.8 ± 2.1). Running dynamics were measured using RunScribe. For parametric data, a general linear model of repeated measures was applied, with two intrasubject independent variables, menstrual cycle phases (with three levels: Menstruation, Follicular, and Luteal) and running intensity (with four levels relative to the maximum speed reached in the test: 100%, 80%, 60%, and 50%). For variables with non-normal distributions, Friedman tests were performed with Wilcoxon post-tests adjusted for significance according to Bonferroni. The maximum stance velocity from foot strike to the point of maximum pronation (°/s) was higher in the menstruation phase than in the follicular and luteal phases (p = 0.008), the step rate (s/min) was higher in the follicular phase than in the menstruation and luteal phases (p = 0.049), the vertical velocity (m/s) was lower in the follicular phase than in the menstruation (p = 0.004) and luteal phases (p = 0.003), and the contact time (ms) was lower in the luteal phase than in the menstruation and follicular phases. These results suggest that training at high intensities could be a factor in greater risk of injury in female athletes, especially in the menstruation phase, finding in the luteal phase and at an intensity of 80% a greater efficiency in the running. Full article

(This article belongs to the Special Issue Applied Biomechanics: Sport Performance and Injury Prevention III)

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20 pages, 12382 KiB

Open AccessArticle

High Strain Rate Response of Sandstones with Different Porosity under Dynamic Loading Using Split Hopkinson Pressure Bar (SHPB)

by Grzegorz Stopka, Roman Gieleta, Robert Panowicz, Daniel Wałach and Grzegorz Piotr Kaczmarczyk

Appl. Sci. 2024, 14(12), 5347; https://doi.org/10.3390/app14125347 - 20 Jun 2024

Viewed by 236

Abstract

This article presents the results of dynamic tests of sandstone samples differing in strength parameters and porosity, which were carried out with the use of the split Hopkinson pressure bar (SHPB). For this study, three types of sandstones were considered: two from the [...] Read more.

This article presents the results of dynamic tests of sandstone samples differing in strength parameters and porosity, which were carried out with the use of the split Hopkinson pressure bar (SHPB). For this study, three types of sandstones were considered: two from the region of India (Kandla Grey and Apricot Pink) and one from Central Europe (Barwald). The strength parameters of the samples were identified in static tests (UCS, BTS tests), whereas the porosity was measured using computed tomography. The performed scanning allowed the volume of the pores and their distribution in the samples to be identified. Dynamic tests involved loading the cylindrical samples with a diameter of 23 m in the range of high strain rates, i.e.,

\dot{ε}

= 10² ÷ 10³/s, using the SHPB (split Hopkinson pressure bar) method. Samples with three different values of slenderness were used for testing (L/D = 1, 0.75 and 0.5). Based on the dynamic characteristics of the samples, the maximum dynamic stresses, Dynamic Increase Factor (DIF) and the amount of energy absorbed by the samples were determined. The conducted research indicates a significant impact of material porosity on the amount of dissipated energy under conditions of high strain rates. The research indicates that the values of this parameter for Apricot Pink and Kandla Grey sandstones (slenderness L/D = ¾ and L/D = ½) are similar, although the uniaxial compressive strength (UCS) of Kandla Grey sandstone is approximately 60% higher than that of Apricot Pink sandstone. As a result of the sample destruction process, various forms of sample destruction were obtained. The performed grain analysis indicates a significant increase in the smallest fraction (<0.5 mm) in the case of the sandstone with the highest porosity (Apricot Pink—55% of mass outcome) in comparison to the sandstone with the lowest porosity (Kandla Grey—12% of mass outcome). Full article

(This article belongs to the Section Materials Science and Engineering)

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23 pages, 6854 KiB

Open AccessArticle

Predictive Model for Long-Term Lane Occupancy Rate Based on CT-Transformer and Variational Mode Decomposition

by Gaoxiang Liu, Xin Yu and Danyang Liu

Appl. Sci. 2024, 14(12), 5346; https://doi.org/10.3390/app14125346 - 20 Jun 2024

Viewed by 219

Abstract

Lane occupancy is a crucial indicator of traffic flow and is significant for traffic management and planning. However, predicting lane occupancy is challenging due to numerous influencing factors, such as weather, holidays, and events, which render the data nonsmooth. To enhance lane occupancy [...] Read more.

Lane occupancy is a crucial indicator of traffic flow and is significant for traffic management and planning. However, predicting lane occupancy is challenging due to numerous influencing factors, such as weather, holidays, and events, which render the data nonsmooth. To enhance lane occupancy prediction accuracy, this study introduces a fusion model that combines the CT-Transformer (CSPNet-Attention and Two-stage Transformer framework) with the Temporal Convolutional Neural Network-Long Short-Term Memory (TCN-LSTM) models alongside the Variational Mode. This includes a long-term lane occupancy prediction model utilizing the Variational Mode Decomposition (VMD) technique. Initially, the Variational Mode Decomposition decomposes the original traffic flow data into multiple smooth subsequences. Subsequently, each subsequence’s autocorrelation and partial correlation coefficients ascertain the presence of seasonal characteristics. Based on these characteristics, the CT-Transformer and TCN-LSTM models process each subsequence for long-term lane occupancy rate prediction, respectively. Finally, predictions from both models are integrated using variable modes to derive the ultimate lane occupancy predictions. The core CT-Transformer model, an enhancement of the GBT (Two-stage Transformer) model, comprises two phases: autoregressive and prediction. The autoregressive phase leverages historical data for initial predictions inputted into the prediction phase. Here, the novel CSPNet-Attention mechanism replaces the conventional attention mechanism in the Encoder, reducing memory usage and computational resource loss, thereby enhancing the model’s accuracy and robustness. Experiments on the PeMS public dataset demonstrate that the proposed model surpasses existing methods in predicting long-term lane occupancy, offering decent reliability and generalizability. Full article

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21 pages, 13870 KiB

Open AccessArticle

Mechatronic Device Used to Evaluate the Performance of a Compliant Mechanism and Image Processing System in Determining Optometric Parameters

by Victor Constantin, Daniel Comeagă, Bogdan Grămescu, Daniel Besnea, Adrian Cartal and Edgar Moraru

Appl. Sci. 2024, 14(12), 5345; https://doi.org/10.3390/app14125345 - 20 Jun 2024

Viewed by 246

Abstract

The work presented in the paper describes a mechatronic test stand and technique employed to determine the accuracy of a system developed by the authors to assist optometrists in measuring parameters used in the customization of progressive lenses, as well as regular lenses. [...] Read more.

The work presented in the paper describes a mechatronic test stand and technique employed to determine the accuracy of a system developed by the authors to assist optometrists in measuring parameters used in the customization of progressive lenses, as well as regular lenses. The system aims to offer information about interpupillary distance, pantoscopic angle, and vertex distance, as well as measurements useful in correctly mounting the lenses in the frames. This is conducted by attaching a marker support system to the user’s frame and determining the user’s dimensions by using image acquisition techniques performed via a custom application built for this purpose. In this paper, a test mannequin is used to determine the accuracy of the system, with measurements being compared to those obtained by using classic methods. This method is used to determine the accuracy of the measurements in a controlled environment. Following the good results obtained in this paper and pending some improvements to the application, clinical tests will be performed on a small scale in selected optometrist offices. Full article

(This article belongs to the Special Issue Mechatronics System Design in Medical Engineering)

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15 pages, 5493 KiB

Open AccessArticle

Investigating the Impact of Circular Sector Pole Head Structure on the Performance of a Multipole Magnetorheological Brake

by Yaojung Shiao and Manichandra Bollepelly

Appl. Sci. 2024, 14(12), 5344; https://doi.org/10.3390/app14125344 - 20 Jun 2024

Viewed by 255

Abstract

The magnetorheological brake (MRB) epitomized a revolutionary modification in the braking systems because of its extremely efficient and well-controlled performance. To increase the safety and controllability of automotive braking system, researchers have developed a different MRB structures. Although much research on magnetorheological brakes [...] Read more.

The magnetorheological brake (MRB) epitomized a revolutionary modification in the braking systems because of its extremely efficient and well-controlled performance. To increase the safety and controllability of automotive braking system, researchers have developed a different MRB structures. Although much research on magnetorheological brakes has shown positive results in terms of brake torque, braking time, thermal efficiency, etc., the ability to increase braking force by expanding the disc surface, through which the magnetic field operates in a compact structure, is restricted. To address this issue, a new multipole MRB configuration with a unique pole head design that maintains compactness. Initially, the conceptual design was achieved by leveraging the combined impact of the twin disc-type structure and multipole concept. The model was used in a dynamic simulation to show how the braking torque of a magnetorheological braking system varies with coil current. The effects of circular sector pole head shape on braking performance were investigated using COMSOL Multiphysics software (version 5.5). A three-dimensional electromagnetic model of the proposed MRB was developed to examine the magnetic flux intensity and the impact of magnetic field dispersion on the proposed pole head structure of a magnetorheological brake. Based on simulation results, the circular sector pole head configuration is capable of increasing the active chaining regions for the MR fluid on the rotor surface, allowing for a more effective use of magnetic flux throughout the whole surface of a rotating brake disc, thereby increasing the magnetic field usage rate. The acquired simulation results show an increase in braking torque while keeping a compact and practical design structure. Full article

(This article belongs to the Special Issue Advanced Engine Technologies and Innovative Vehicle Driving Systems: 2nd Edition)

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22 pages, 4416 KiB

Open AccessArticle

Three-Dimensional Dead-Reckoning Based on Lie Theory for Overcoming Approximation Errors

by Da Bin Jeong, Boeun Lee and Nak Yong Ko

Appl. Sci. 2024, 14(12), 5343; https://doi.org/10.3390/app14125343 - 20 Jun 2024

Viewed by 347

Abstract

This paper proposes a dead-reckoning (DR) method for vehicles using Lie theory. This approach treats the pose (position and attitude) and velocity of the vehicle as elements of the Lie group

{SE}_{2} (3)

and follows the computations based on Lie [...] Read more.

This paper proposes a dead-reckoning (DR) method for vehicles using Lie theory. This approach treats the pose (position and attitude) and velocity of the vehicle as elements of the Lie group

{SE}_{2} (3)

and follows the computations based on Lie theory. Previously employed DR methods, which have been widely used, suffer from cumulative errors over time due to inaccuracies in the calculated changes from velocity during the motion of the vehicle or small errors in modeling assumptions. Consequently, this results in significant discrepancies between the estimated and actual positions over time. However, by treating the pose and velocity of the vehicle as elements of the Lie group, the proposed method allows for accurate solutions without the errors introduced by linearization. The incremental updates for pose and velocity in the DR computation are represented in the Lie algebra. Experimental results confirm that the proposed method improves the accuracy of DR. In particular, as the motion prediction time interval of the vehicle increases, the proposed method demonstrates a more pronounced improvement in positional accuracy. Full article

(This article belongs to the Section Robotics and Automation)

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19 pages, 8293 KiB

Open AccessArticle

A Frequency Domain Kernel Function-Based Manifold Dimensionality Reduction and Its Application for Graph-Based Semi-Supervised Classification

by Zexiao Liang, Ruyi Gong, Guoliang Tan, Shiyin Ji and Ruidian Zhan

Appl. Sci. 2024, 14(12), 5342; https://doi.org/10.3390/app14125342 - 20 Jun 2024

Viewed by 373

Abstract

With the increasing demand for high-resolution images, handling high-dimensional image data has become a key aspect of intelligence algorithms. One effective approach is to preserve the high-dimensional manifold structure of the data and find the accurate mappings in a lower-dimensional space. However, various [...] Read more.

With the increasing demand for high-resolution images, handling high-dimensional image data has become a key aspect of intelligence algorithms. One effective approach is to preserve the high-dimensional manifold structure of the data and find the accurate mappings in a lower-dimensional space. However, various non-sparse, high-energy occlusions in real-world images can lead to erroneous calculations of sample relationships, invalidating the existing distance-based manifold dimensionality reduction techniques. Many types of noise are difficult to capture and filter in the original domain but can be effectively separated in the frequency domain. Inspired by this idea, a novel approach is proposed in this paper, which obtains the high-dimensional manifold structure according to the correlationships between data points in the frequency domain and accurately maps it to a lower-dimensional space, named Frequency domain-based Manifold Dimensionality Reduction (FMDR). In FMDR, samples are first transformed into frequency domains. Then, interference is filtered based on the distribution in the frequency domain, thereby emphasizing discriminative features. Subsequently, an innovative kernel function is proposed for measuring the similarities between samples according to the correlationships in the frequency domain. With the assistance of these correlationships, a graph structure can be constructed and utilized to find the mapping in a low-dimensional space. To further demonstrate the effectiveness of the proposed algorithm, FMDR is employed for the semi-supervised classification problems in this paper. Experiments using public image datasets indicate that, compared to baseline algorithms and state-of-the-art methods, our approach achieves superior recognition performance. Even with very few labeled data, the advantages of FMDR are still maintained. The effectiveness of FMDR in dimensionality reduction and feature extraction of images makes it widely applicable in fields such as image processing and image recognition. Full article

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16 pages, 33374 KiB

Open AccessArticle

SGST-YOLOv8: An Improved Lightweight YOLOv8 for Real-Time Target Detection for Campus Surveillance

by Gang Cheng, Peizhi Chao, Jie Yang and Huan Ding

Appl. Sci. 2024, 14(12), 5341; https://doi.org/10.3390/app14125341 - 20 Jun 2024

Viewed by 244

Abstract

Real-time target detection plays an important role in campus intelligent surveillance systems. This paper introduces Soft-NMS, GSConv, Triplet Attention, and other advanced technologies to propose a lightweight pedestrian and vehicle detection model named SGST-YOLOv8. In this paper, the improved YOLOv8 model is trained [...] Read more.

Real-time target detection plays an important role in campus intelligent surveillance systems. This paper introduces Soft-NMS, GSConv, Triplet Attention, and other advanced technologies to propose a lightweight pedestrian and vehicle detection model named SGST-YOLOv8. In this paper, the improved YOLOv8 model is trained on the self-made dataset, and the tracking algorithm is combined to achieve an accurate and efficient real-time pedestrian and vehicle tracking detection system. The improved model achieved an accuracy of 88.6%, which is 1.2% higher than the baseline model YOLOv8. Additionally, the mAP0.5:0.95 increased by 3.2%. The model parameters and GFLOPS reduced by 5.6% and 7.9%, respectively. In addition, this study also employed the improved YOLOv8 model combined with the bot sort tracking algorithm on the website for actual detection. The results showed that the improved model achieves higher FPS than the baseline YOLOv8 model when detecting the same scenes, with an average increase of 3–5 frames per second. The above results verify the effectiveness of the improved model for real-time target detection in complex environments. Full article

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10 pages, 793 KiB

Open AccessArticle

Heart Rate Dynamics and Quantifying Physical Fatigue in Canadian Football

by Abdullah Zafar, Samuel Guay, Sophie-Andrée Vinet, Francine Pilon, Géraldine Martens, François Prince and Louis De Beaumont

Appl. Sci. 2024, 14(12), 5340; https://doi.org/10.3390/app14125340 - 20 Jun 2024

Viewed by 279

Abstract

The cardiac response to physical exertion is linked to factors such as age, work intensity, and fitness levels. The primary objective of this study was to characterize within-session changes in cardiac response to running in Canadian football athletes, which may indicate physical fatigue. [...] Read more.

The cardiac response to physical exertion is linked to factors such as age, work intensity, and fitness levels. The primary objective of this study was to characterize within-session changes in cardiac response to running in Canadian football athletes, which may indicate physical fatigue. Performance profiles were collected from GPS and heart rate (HR) sensors worn by 30 male Canadian varsity football players (20–26 years old) over 28 games and practices. Running efforts with 60–180 s of rest were detected, and the maximum HR (HRmax) and peak HR recovery (HRRpk) during rest were extracted. Additionally, a new metric of cardiovascular cost (CVC) was developed to reflect the efficiency of the HR response to physical workload. HRmax was higher in games (p < 0.001) and in linemen (p < 0.001), and it increased over time (p < 0.001). HRRpk was higher in skilled players (p < 0.001) and changed over time (p < 0.001) depending on the rest period. CVC was higher in linemen (p < 0.001) and increased over time (p < 0.001). This study demonstrated the utility of HR response metrics to quantify ongoing fatigue experienced by Canadian football athletes and proposed a novel fatigue metric capable of monitoring an athlete’s fatigue state in real time. Full article

(This article belongs to the Section Applied Biosciences and Bioengineering)

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54 pages, 4151 KiB

Open AccessReview

Food and Drinking Water as Sources of Pathogenic Protozoans: An Update

by Franca Rossi, Serena Santonicola, Carmela Amadoro, Lucio Marino and Giampaolo Colavita

Appl. Sci. 2024, 14(12), 5339; https://doi.org/10.3390/app14125339 - 20 Jun 2024

Viewed by 539

Abstract

This narrative review was aimed at collecting updated knowledge on the risk factors, illnesses caused, and measures for the prevention of protozoan infections transmitted by food and drinking water. Reports screened dated from 2019 to the present and regarded global prevalence in food [...] Read more.

This narrative review was aimed at collecting updated knowledge on the risk factors, illnesses caused, and measures for the prevention of protozoan infections transmitted by food and drinking water. Reports screened dated from 2019 to the present and regarded global prevalence in food handlers, occurrence in food and drinking water, impact on human health, and recently reported outbreaks and cases of severe infections attributable to the dietary route. Cryptosporidium spp., Cyclospora cayetanensis, Entamoeba histolytica, and Cystoisospora belli were the protozoans most frequently involved in recently reported waterborne and foodborne outbreaks and cases. Blastocystis hominis was reported to be the most widespread intestinal protozoan in humans, and two case reports indicated its pathogenic potential. Dientamoeba fragilis, Endolimax nana, and Pentatrichomonas hominis are also frequent but still require further investigation on their ability to cause illness. A progressive improvement in surveillance of protozoan infections and infection sources took place in developed countries where the implementation of reporting systems and the application of molecular diagnostic methods led to an enhanced capacity to identify epidemiological links and improve the prevention of foodborne and waterborne protozoan infections. Full article

(This article belongs to the Section Applied Microbiology)

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18 pages, 895 KiB

Open AccessArticle

Reliability Prediction for New Prefabricated Track Structures Based on the Fuzzy Failure Modes, Effects, and Criticality Analysis Method

by Chao Huang, Jun Wu, Zhi Shan, Qing’e Wang and Zhiwu Yu

Appl. Sci. 2024, 14(12), 5338; https://doi.org/10.3390/app14125338 - 20 Jun 2024

Viewed by 340

Abstract

This paper aims to address the problems of safety and durability in China’s ballastless track structures, particularly the lack of accurate analysis and methods for predicting the reliability of the new type of prefabricated track structure during the design phase. We propose a [...] Read more.

This paper aims to address the problems of safety and durability in China’s ballastless track structures, particularly the lack of accurate analysis and methods for predicting the reliability of the new type of prefabricated track structure during the design phase. We propose a reliability prediction method for a new prefabricated track structure, the modular assembled track structure with built-in position retention. By adopting the fuzzy Failure Modes, Effects, and Criticality Analysis (fuzzy FMECA) method, a comprehensive assessment of fault severity, fault occurrence probability, and fault detection difficulty is conducted on the CRTS II slab track structure and the modular assembled track structure with built-in position retention. Consequently, a fault mode hazard assessment model for the new prefabricated track structure is constructed. Based on the assessment model and using a similar product method, a reliability prediction model for the new prefabricated track structure is established, and reliability prediction for the track structure is conducted. The research results indicate that the modular assembled track structure with built-in position retention has lower hazard levels and higher reliability compared to the CRTS II slab track structure. This study provides a scientific basis for the design optimization of new prefabricated track structures, helping to improve their safety and reliability, reduce operating and maintenance costs, and thereby promote the green and low-carbon development of the railway. Full article

(This article belongs to the Special Issue Green and Low-Carbon Concrete Technology and Construction)

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13 pages, 2340 KiB

Open AccessArticle

AST-PG: Attention-Based Spatial–Temporal Point-of-Interest-Group Model for Real-Time Point-of-Interest Recommendation

by Huarui Yu and Zesheng Cheng

Appl. Sci. 2024, 14(12), 5337; https://doi.org/10.3390/app14125337 - 20 Jun 2024

Viewed by 243

Abstract

Research on next-point-of-interest (POI) recommendation has become a new focus in the field of POI recommendation in recent years. The goal of POI recommendation tasks is to predict a user’s future movement trajectory based on their current state and historical behavioral information. Recent [...] Read more.

Research on next-point-of-interest (POI) recommendation has become a new focus in the field of POI recommendation in recent years. The goal of POI recommendation tasks is to predict a user’s future movement trajectory based on their current state and historical behavioral information. Recent studies have shown the effectiveness of neural network-based next-POI recommendation engines. However, most existing models only consider the correlation between consecutive visits, neglecting the complex dependencies of the POIs in the area and category features, as well as the processing of unstructured time series. This paper presents a new Attention-Based Spatial–Temporal Point-of-Interest-Group (AST-PG) model for POI recommendation. The model consists of a spatial module and a temporal module combined with each other by a multiple-attention mechanism. The spatial module in the proposed model groups the POIs based on geographic and category features, while the temporal module develops a uniform-length time trajectory vector for the unstructured temporal features. Comprehensive experimental results on two real datasets demonstrate that the proposed model of this study is superior to the state-of-the-art POI recommendation models in terms of performance. Full article

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11 pages, 2391 KiB

Open AccessArticle

IndustrialNeRF: Accurate 3D Industrial Digital Twin Based on Integrating Neural Radiance Fields Using Unsupervised Learning

by Hui Zhou, Juangui Xu, Hongbin Lin, Zhenguo Nie and Li Zheng

Appl. Sci. 2024, 14(12), 5336; https://doi.org/10.3390/app14125336 - 20 Jun 2024

Viewed by 244

Abstract

In the era of Industry 4.0, digital twin technology is revolutionizing traditional manufacturing paradigms. However, the adoption of this technology in modern manufacturing systems is fraught with challenges due to the scarcity of labeled data. Specifically, existing supervised machine learning algorithms, with their [...] Read more.

In the era of Industry 4.0, digital twin technology is revolutionizing traditional manufacturing paradigms. However, the adoption of this technology in modern manufacturing systems is fraught with challenges due to the scarcity of labeled data. Specifically, existing supervised machine learning algorithms, with their reliance on voluminous training data, find their applicability constrained in real-world production settings. This paper introduces an unsupervised 3D reconstruction approach tailored for industrial applications, aimed at bridging the data void in creating digital twin models. Our proposed model, by ingesting high-resolution 2D images, autonomously reconstructs precise 3D digital twin models without the need for manual annotations or prior knowledge. Through comparisons with multiple baseline models, we demonstrate the superiority of our method in terms of accuracy, speed, and generalization capabilities. This research not only offers an efficient approach to industrial 3D reconstruction but also paves the way for the widespread adoption of digital twin technology in manufacturing. Full article

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17 pages, 4413 KiB

Open AccessArticle

Super-Resolution Reconstruction of an Array Lidar Range Profile

by Xuelian Liu, Xulang Zhou, Guan Xi, Rui Zhuang, Chunhao Shi and Chunyang Wang

Appl. Sci. 2024, 14(12), 5335; https://doi.org/10.3390/app14125335 - 20 Jun 2024

Viewed by 308

Abstract

Aiming at the problem that the range profile of the current array lidar has a low resolution and contains few target details and little edge information, a super-resolution reconstruction method based on projection onto convex sets (POCS) combining the Lucas–Kanade (LK) optical flow [...] Read more.

Aiming at the problem that the range profile of the current array lidar has a low resolution and contains few target details and little edge information, a super-resolution reconstruction method based on projection onto convex sets (POCS) combining the Lucas–Kanade (LK) optical flow method with a Gaussian pyramid was proposed. Firstly, the reference high-resolution range profile was obtained by the nearest neighbor interpolation of the single low-resolution range profile. Secondly, the LK optical flow method was introduced to achieve the motion estimation of low-resolution image sequences, and the Gaussian pyramid was used to perform multi-scale correction on the estimated vector, effectively improving the accuracy of motion estimation. On the basis of data consistency constraints, gradient constraints were introduced based on the distance value difference between the target edge and the background to enhance the reconstruction ability of the target edge. Finally, the residual between the estimated distance and the actual distance was calculated, and the high-resolution reference range profile was iteratively corrected by using the point spread function according to the residual. Bilinear interpolation, bicubic interpolation, POCS, POCS with adaptive correction threshold, and the proposed method were used to reconstruct the range profile of the datasets and the real datasets. The effectiveness of the proposed method was verified by the range profile reconstruction effect and objective evaluation index. The experimental results show that the index of the proposed method is improved compared to the interpolation method and the POCS method. In the redwood-3dscan dataset experiments, compared to the traditional POCS, the average gradient (AG) of the proposed method is increased by at least 8.04%, and the edge strength (ES) is increased by at least 4.84%. In the real data experiments, compared to the traditional POCS, the AG of the proposed method is increased by at least 5.85%, and the ES is increased by at least 7.01%, which proves that the proposed method can effectively improve the resolution of the reconstructed range map and the quality of the detail edges. Full article

(This article belongs to the Special Issue Applications of Advanced Deep Learning Technology in Control and Intelligent Systems)

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13 pages, 2365 KiB

Open AccessArticle

Advancements in Gaze Coordinate Prediction Using Deep Learning: A Novel Ensemble Loss Approach

by Seunghyun Kim, Seungkeon Lee and Eui Chul Lee

Appl. Sci. 2024, 14(12), 5334; https://doi.org/10.3390/app14125334 - 20 Jun 2024

Viewed by 253

Abstract

Recent advancements in deep learning have enabled gaze estimation from images of the face and eye areas without the need for precise geometric locations of the eyes and face. This approach eliminates the need for complex user-dependent calibration and the issues associated with [...] Read more.

Recent advancements in deep learning have enabled gaze estimation from images of the face and eye areas without the need for precise geometric locations of the eyes and face. This approach eliminates the need for complex user-dependent calibration and the issues associated with extracting and tracking geometric positions, making further exploration of gaze position performance enhancements challenging. Motivated by this, our study focuses on an ensemble loss function that can enhance the performance of existing 2D-based deep learning models for gaze coordinate (x, y) prediction. We propose a new function and demonstrate its effectiveness by applying it to models from prior studies. The results show significant performance improvements across all cases. When applied to ResNet and iTracker models, the average absolute error reduced significantly from 7.5 cm to 1.2 cm and from 7.67 cm to 1.3 cm, respectively. Notably, when implemented on the AFF-Net, which boasts state-of-the-art performance, the average absolute error was reduced from 4.21 cm to 0.81 cm, based on our MPIIFaceGaze dataset. Additionally, predictions for ranges never encountered during the training phase also displayed a very low error of 0.77 cm in terms of MAE without any personalization process. These findings suggest significant potential for accuracy improvements while maintaining computational complexity similar to the existing models without the need for creating additional or more complex models. Full article

(This article belongs to the Special Issue Machine Vision and Machine Learning in Interdisciplinary Research)

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20 pages, 2936 KiB

Open AccessReview

Application of Quantum Dots for Photocatalytic Hydrogen Evolution Reaction

by Xia Gui, Yao Lu, Qin Wang, Mengdie Cai and Song Sun

Appl. Sci. 2024, 14(12), 5333; https://doi.org/10.3390/app14125333 - 20 Jun 2024

Viewed by 271

Abstract

There is increased interest in the conversion of solar energy into green chemical energy because of the depletion of fossil fuels and their unpleasant environmental effect. Photocatalytic hydrogen generation from water involves the direct conversion of solar energy into H₂ fuels, which [...] Read more.

There is increased interest in the conversion of solar energy into green chemical energy because of the depletion of fossil fuels and their unpleasant environmental effect. Photocatalytic hydrogen generation from water involves the direct conversion of solar energy into H₂ fuels, which exhibits significant advantages and immense promise. Nevertheless, photocatalytic efficiency is considerably lower than the standard range of industrial applications. Low light absorption efficiency, the rapid recombination of photogenerated electrons and holes, slow surface redox reaction kinetics and low photostability are well known to be key factors negatively affecting photocatalytic hydrogen production. Therefore, to construct highly efficient and stable photocatalysts is important and necessary for the development of photocatalytic hydrogen generation technology. In this review, quantum dots (QDs)-based photocatalysts have emerged with representative achievements. Due to their excellent light-harvesting ability, low recombination efficiency of photogenerated electrons and holes, and abundant surface active sites, QDs have attracted remarkable interest as photocatalysts and/or cocatalyst for developing highly efficient photocatalysts. In this review, the application of QDs for photocatalytic H₂ production is emphatically introduced. First, the special photophysical properties of QDs are briefly described. Then, recent progress into the research on QDs in photocatalytic H₂ production is introduced, in three types: semiconductor QDs (e.g., CdS, CdMnS, and InP QDs), metal QDs (e.g., Au, Pt and Ag QDs), and MXene QDs and carbon QDs (CDQs). Finally, the challenges and prospects of photocatalytic H₂ evolution with QDs in the future are discussed. Full article

(This article belongs to the Section Quantum Science and Technology)

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17 pages, 1529 KiB

Open AccessArticle

The Impact of a Virtual Educational Cooking Class on the Inflammatory Potential of Diet in Cancer Survivors

by Mariah Kay Jackson, Diane K. Ehlers, Laura D. Bilek, Laura Graeff-Armas, Melissa Acquazzino, James R. Hébert, Sherry Price, Rebecca Beaudoin and Corrine K. Hanson

Appl. Sci. 2024, 14(12), 5332; https://doi.org/10.3390/app14125332 - 20 Jun 2024

Viewed by 250

Abstract

(1) Background. Cognitive dysfunction is prevalent among cancer survivors. Inflammation may contribute to impaired cognition, and diet represents a novel strategy to mitigate cognitive decline. The purpose was to (1) assess the impact of an educational cooking class on cancer survivor eating habits [...] Read more.

(1) Background. Cognitive dysfunction is prevalent among cancer survivors. Inflammation may contribute to impaired cognition, and diet represents a novel strategy to mitigate cognitive decline. The purpose was to (1) assess the impact of an educational cooking class on cancer survivor eating habits and their inflammatory potential and (2) determine the relationship between diet and cognitive function. (2) Methods. This was a non-randomized interventional study of a virtual educational cooking class in post-treatment, adult cancer survivors. Energy-adjusted Dietary Inflammatory Index (E-DII™) scores and subjective cognitive function were assessed at baseline and 1 month post-intervention. (3) Results. Of 22 subjects, all were female, White, and primarily had breast cancer (64%). There was a significant decrease in E-DII scores, which became more anti-inflammatory, one month after intervention (−2.3 vs. −2.7, p = 0.005). There were significant increases in cognition, including perceived cognitive impairment (COG-PCI, p < 0.001), comments from others (COG-OTH, p < 0.001), and quality of life (COG-QOL, p < 0.001). A change in calories was a significant predictor of a change in perceived cognitive ability (COG-PCA) after adjustment (β = 0.007, p = 0.04; 95% CI (0.000, 0.014)). (4) Conclusions. Educational cooking classes may be an effective way to impact diet-derived inflammation; additional research is needed to assess the long-term effects of dietary changes on cognition. Full article

(This article belongs to the Section Applied Biosciences and Bioengineering)

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18 pages, 3419 KiB

Open AccessArticle

Deep Learning-Based Super-Resolution Reconstruction and Segmentation of Photoacoustic Images

by Yufei Jiang, Ruonan He, Yi Chen, Jing Zhang, Yuyang Lei, Shengxian Yan and Hui Cao

Appl. Sci. 2024, 14(12), 5331; https://doi.org/10.3390/app14125331 - 20 Jun 2024

Viewed by 284

Abstract

Photoacoustic imaging (PAI) is an emerging imaging technique that offers real-time, non-invasive, and radiation-free measurements of optical tissue properties. However, image quality degradation due to factors such as non-ideal signal detection hampers its clinical applicability. To address this challenge, this paper proposes an [...] Read more.

Photoacoustic imaging (PAI) is an emerging imaging technique that offers real-time, non-invasive, and radiation-free measurements of optical tissue properties. However, image quality degradation due to factors such as non-ideal signal detection hampers its clinical applicability. To address this challenge, this paper proposes an algorithm for super-resolution reconstruction and segmentation based on deep learning. The proposed enhanced deep super-resolution minimalistic network (EDSR-M) not only mitigates the shortcomings of the original algorithm regarding computational complexity and parameter count but also employs residual learning and attention mechanisms to extract image features and enhance image details, thereby achieving high-quality reconstruction of PAI. DeepLabV3+ is used to segment the images before and after reconstruction to verify the network reconstruction performance. The experimental results demonstrate average improvements of 19.76% in peak-signal-to-noise ratio (PSNR) and 4.80% in structural similarity index (SSIM) for the reconstructed images compared to those of their pre-reconstructed counterparts. Additionally, mean accuracy, mean intersection and union ratio (IoU), and mean boundary F1 score (BFScore) for segmentation showed enhancements of 8.27%, 6.20%, and 6.28%, respectively. The proposed algorithm enhances the effect and texture features of PAI and makes the overall structure of the image restoration more complete. Full article

(This article belongs to the Special Issue Application of Machine Vision and Deep Learning Technology)

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