Sign in to use this feature.

Years

Between: -

Subjects

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Journals

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Article Types

Countries / Regions

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Search Results (30,244)

Search Parameters:
Keywords = achievement test

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
15 pages, 2215 KiB  
Article
Detoxification of Ustiloxin A Through Oxidative Deamination and Decarboxylation by Endophytic Fungus Petriella setifera
by Peng Li, Gan Gu, Xuwen Hou, Dan Xu, Jungui Dai, Yu Kuang, Mingan Wang, Daowan Lai and Ligang Zhou
Toxins 2025, 17(2), 48; https://doi.org/10.3390/toxins17020048 (registering DOI) - 22 Jan 2025
Abstract
Ustiloxins are a group of cyclopeptide mycotoxins produced by rice false smut pathogen Villosiclava virens (anamorph: Ustilaginoidea virens) which seriously threaten the safety production of rice and the health of humans and livestock. Ustiloxin A, accounting for 60% of the total ustiloxins, [...] Read more.
Ustiloxins are a group of cyclopeptide mycotoxins produced by rice false smut pathogen Villosiclava virens (anamorph: Ustilaginoidea virens) which seriously threaten the safety production of rice and the health of humans and livestock. Ustiloxin A, accounting for 60% of the total ustiloxins, is the main toxic component. Biotransformation, a process of modifying the functional groups of compounds by means of regio- or stereo-specific reactions catalyzed by the enzymes produced by organisms, has been considered as an efficient way to detoxify mycotoxins. In this study, the endophytic fungus Petriella setifera Nitaf10 was found to be able to detoxify ustiloxin A through biotransformation. Two transformed products were obtained by using the cell-free extract (CFE) containing intracellular enzymes of P. setifera Nitaf10. They were structurally characterized as novel ustiloxin analogs named ustiloxins A1 (1) and A2 (2) by analysis of the 1D and 2D NMR and HRESIMS spectra as well as by comparison with known ustiloxins. The cytotoxic activity of ustiloxins A1 (1) and A2 (2) was much weaker than that of ustiloxin A. The biotransformation of ustiloxin A was found to proceed via oxidative deamination and decarboxylation and was possibly catalyzed by the intracellular amine oxidase and oxidative decarboxylase in the CFE. An appropriate bioconversion was achieved by incubating ustiloxin A with the CFE prepared in 0.5 mol/L phosphate buffer (pH 7.0) for 24 to 48 h. The optimum initial pH values for the bioconversion of ustiloxin A were 7–9. Among eight metal ions (Co2+, Cu2+, Fe3+, Zn2+, Ba2+, Ca2+, Mg2+ and Mn2+) tested at 5 mmol/L, Cu2+, Fe3+ and Zn2+ totally inhibited the conversion of ustiloxin A. In conclusion, detoxification of ustiloxin A through oxidative deamination and decarboxylation is an efficient strategy. Full article
(This article belongs to the Special Issue Mitigation and Detoxification Strategies of Mycotoxins)
Show Figures

Figure 1

19 pages, 3375 KiB  
Article
Enhancing Cross-Modal Camera Image and LiDAR Data Registration Using Feature-Based Matching
by Jennifer Leahy, Shabnam Jabari, Derek Lichti and Abbas Salehitangrizi
Remote Sens. 2025, 17(3), 357; https://doi.org/10.3390/rs17030357 (registering DOI) - 22 Jan 2025
Abstract
Registering light detection and ranging (LiDAR) data with optical camera images enhances spatial awareness in autonomous driving, robotics, and geographic information systems. The current challenges in this field involve aligning 2D-3D data acquired from sources with distinct coordinate systems, orientations, and resolutions. This [...] Read more.
Registering light detection and ranging (LiDAR) data with optical camera images enhances spatial awareness in autonomous driving, robotics, and geographic information systems. The current challenges in this field involve aligning 2D-3D data acquired from sources with distinct coordinate systems, orientations, and resolutions. This paper introduces a new pipeline for camera–LiDAR post-registration to produce colorized point clouds. Utilizing deep learning-based matching between 2D spherical projection LiDAR feature layers and camera images, we can map 3D LiDAR coordinates to image grey values. Various LiDAR feature layers, including intensity, bearing angle, depth, and different weighted combinations, are used to find correspondence with camera images utilizing state-of-the-art deep learning matching algorithms, i.e., SuperGlue and LoFTR. Registration is achieved using collinearity equations and RANSAC to remove false matches. The pipeline’s accuracy is tested using survey-grade terrestrial datasets from the TX5 scanner, as well as datasets from a custom-made, low-cost mobile mapping system (MMS) named Simultaneous Localization And Mapping Multi-sensor roBOT (SLAMM-BOT) across diverse scenes, in which both outperformed their baseline solutions. SuperGlue performed best in high-feature scenes, whereas LoFTR performed best in low-feature or sparse data scenes. The LiDAR intensity layer had the strongest matches, but combining feature layers improved matching and reduced errors. Full article
(This article belongs to the Special Issue Remote Sensing Satellites Calibration and Validation)
Show Figures

Figure 1

21 pages, 3622 KiB  
Article
Life Cycle Sustainability Assessment of Microbially Induced Calcium Carbonate Precipitation (MICP) Soil Improvement Techniques
by Alena J. Raymond, Jason T. DeJong, Michael G. Gomez, Alissa Kendall, Alexandra C. M. San Pablo, Minyong Lee, Charles M. R. Graddy and Douglas C. Nelson
Appl. Sci. 2025, 15(3), 1059; https://doi.org/10.3390/app15031059 (registering DOI) - 22 Jan 2025
Abstract
Microbially induced calcium carbonate precipitation (MICP) is a biomediated ground improvement technology that uses ureolytic bacteria to precipitate calcium carbonate minerals to improve the strength and stiffness of soils. MICP can be mediated by either augmented non-native or stimulated indigenous microorganisms, resulting in [...] Read more.
Microbially induced calcium carbonate precipitation (MICP) is a biomediated ground improvement technology that uses ureolytic bacteria to precipitate calcium carbonate minerals to improve the strength and stiffness of soils. MICP can be mediated by either augmented non-native or stimulated indigenous microorganisms, resulting in biocemented soils and generated aqueous ammonium (NH4+) byproducts. Although the process has been extensively investigated, the fate and transport of generated NH4+ byproducts has posed an environmental challenge and to date, their associated environmental impacts have remained poorly understood. In an effort to better quantify process impacts, a large-scale experiment was conducted involving three 3.7 m long soil columns, wherein three different ureolytic biocementation treatment approaches were employed. A life cycle sustainability assessment (LCSA) was performed to compare the environmental impacts and costs of these different MICP treatment approaches as well as evaluate the potential environmental benefits of NH4+ byproduct removal using post-treatment rinsing. The objective of this paper is to present the results of the LCSA study. LCSA results suggest that when treatments are consistent with those performed in this study, stimulation can be more sustainable than augmentation, and the use of lower ureolytic rates can further reduce process environmental impacts by achieving greater spatial uniformity and extent of biocementation. The LCSA outcomes also illustrate tension between the environmental benefits afforded by NH4+ byproduct removal and the life cycle impacts and costs associated with this removal. For the specific testing conditions, the injection of 1.8 pore volumes of rinse solutions to remove generated NH4+ byproducts following biocementation was found to minimize environmental impacts; however, further refinement of such approaches will likely result from future field-scale applications. Full article
Show Figures

Figure 1

12 pages, 17479 KiB  
Article
Epoxy as an Alternative Resin in Particleboard Production with Pine Wood Residues: Physical, Mechanical, and Microscopical Analyses of Panels at Three Resin Proportions
by Antonio José Santos Junior, Marjorie Perosso Herradon, Matheus Viana de Souza, Sergio Augusto Mello da Silva, Victor Almeida De Araujo, Diego Henrique de Almeida, Herisson Ferreira dos Santos and André Luis Christoforo
Forests 2025, 16(2), 196; https://doi.org/10.3390/f16020196 (registering DOI) - 22 Jan 2025
Abstract
Given the construction challenges and the impacts of industrial waste generation and the implications of using chemical adhesives, this study aims to evaluate epoxy as an alternative resin, whose application in the production of wood particleboards is still underexplored. In this regard, its [...] Read more.
Given the construction challenges and the impacts of industrial waste generation and the implications of using chemical adhesives, this study aims to evaluate epoxy as an alternative resin, whose application in the production of wood particleboards is still underexplored. In this regard, its results were compared with those of widely used adhesives, such as urea-formaldehyde (UF). Pine wood particles were used, and epoxy resin was applied as a binder in 5%, 10%, and 15% proportions. Panels were manufactured under pressing parameters of 5 N/mm2 for 10 min at 110 °C. Physical and mechanical properties of panels were evaluated using Brazilian, European, and American standards. The results showed that epoxy resin is potentially convenient for the particleboard industry, as the 15% trait panels met the P4 class criteria in the Brazilian and European standards and D-2 for the American code, and the 10% trait panels achieved the M-3i class for the American document. Although 5% adhesive was insufficient to envelop wood particles, these traits with greater percentages reached high enveloping ratings in the scanning electron microscopy (SEM) test, making epoxy resin viable for the panel industry as a potential alternative to formaldehyde-based adhesives. Full article
(This article belongs to the Special Issue Wood Quality and Mechanical Properties: 2nd Edition)
Show Figures

Figure 1

9 pages, 2251 KiB  
Article
Design of On-Site Calibration Device for Electricity Meter Based on Pulse Detection
by Yingchun Wang, Wenjing Yu, Cheng Zhang, Li Ye, Wei Wei and Zhixin Yang
Inventions 2025, 10(1), 6; https://doi.org/10.3390/inventions10010006 (registering DOI) - 22 Jan 2025
Abstract
At present, the error calibration of electricity meters in operation generally adopts an off-site method; that is, the electricity meter is taken out of operation and then calibrated in the laboratory. Off-site calibration, while beneficial, may not fully capture the operational error of [...] Read more.
At present, the error calibration of electricity meters in operation generally adopts an off-site method; that is, the electricity meter is taken out of operation and then calibrated in the laboratory. Off-site calibration, while beneficial, may not fully capture the operational error of the electricity meter due to potential differences in environmental conditions. An on-site calibration device for electricity meters based on pulse detection is designed, which obtains the error of the electricity meter under calibration by comparing the energy pulses of the standard electricity meter with those of the electricity meter under calibration. High-precision voltage and current sampling channels are designed, with a voltage measurement error of less than 0.02% and a current measurement error of less than 0.03%. In response to the non-synchronous sampling problem caused by frequency fluctuations in the on-site verification environment, a fast optimal frequency estimation algorithm is applied to accurately calculate the signal frequency within two cycles. The sampling time interval is adjusted to achieve lock-frequency synchronous sampling, and ensure the accurate calculation of electrical parameters. In order to reduce the complexity of the device circuit structure and equipment cost, a standard electric energy pulses generation method based on digital integration-to-frequency is proposed, which uses software to generate electric energy pulses, with a maximum output frequency of up to 10 kHz. Tests conducted in the laboratory on the developed on-site calibration device for electricity meters show that its accuracy is better than the 0.05 accuracy class, meeting the application requirements for on-site verification of electricity energy meters. Full article
Show Figures

Figure 1

22 pages, 646 KiB  
Article
No Planet-B Attitudes: The Main Driver of Gen Z Travelers’ Willingness to Pay for Sustainable Tourism Destinations
by Arthur Filipe de Araújo, Isabel Andrés-Marques and Lorenza López Moreno
Sustainability 2025, 17(3), 847; https://doi.org/10.3390/su17030847 - 21 Jan 2025
Abstract
With consumers becoming increasingly aware of the effects of human activity on the environment, tourism products and destinations are increasingly marketed as sustainable and socially responsible. As most sustainable practices lead to additional costs, and tourists’ decisions tend to be price sensitive, achieving [...] Read more.
With consumers becoming increasingly aware of the effects of human activity on the environment, tourism products and destinations are increasingly marketed as sustainable and socially responsible. As most sustainable practices lead to additional costs, and tourists’ decisions tend to be price sensitive, achieving sustainability goals necessarily involves understanding how much more tourists are willing to pay for sustainable practices as well as the antecedents of such willingness to pay (WTP). The present study aims to advance knowledge on the antecedents of WTP for sustainable destinations (WTP-4-SD), for which it builds on previous studies employing the Theory of Planned Behavior (TPB) and the New Environmental Paradigm (NEP). In this context, a theoretical model involving ecotourism attitudes, environmental beliefs, climate change-related risk perceptions (CC-RRP), environmental concern during trip (ECDT), and sustainable consumption behavior (SCB) as antecedents of WTP-4-SD is proposed. The model was tested based on data collected through an online survey from a sample of 847 Spanish and Portuguese Gen Z travelers and analyzed through Structural Equations Modeling (SEM). The findings suggest that a cohesive set of attitudes and beliefs regarding the man–nature relationship, the risks of climate change, and the role of tourism—which have been labeled “No Planet-B Attitudes”—is the main driver of WTP-4-SD. The effects of SCB and ECDT on WTP-4-SD have also been confirmed—although the latter is quite small—as well as those of No Planet-B Attitudes on both. The findings bring about insights into young travelers’ attitudes towards nature and the role of tourism in sustainable development, as well as useful implications for sustainable tourism planning and marketing. Full article
32 pages, 2845 KiB  
Article
Internal Model Control for Onboard Methanol-Reforming Hydrogen Production Systems
by Fengxiang Chen, Yuanyuan Duan, Yaowang Pei and Bo Zhang
Energies 2025, 18(3), 476; https://doi.org/10.3390/en18030476 - 21 Jan 2025
Abstract
Methanol reforming is considered to be one of the most promising hydrogen production technologies for hydrogen fuel cells. It is expected to solve the problem of hydrogen storage and transportation because of its high hydrogen production rate, low cost, and good safety. However, [...] Read more.
Methanol reforming is considered to be one of the most promising hydrogen production technologies for hydrogen fuel cells. It is expected to solve the problem of hydrogen storage and transportation because of its high hydrogen production rate, low cost, and good safety. However, the strong nonlinearity and slow response of the pressure and temperature subsystems pose challenges to the tracking control of the methanol reforming hydrogen production system. In this paper, two internal model-based temperature and pressure controllers are proposed, in which the temperature is adjusted by controlling the air flow and the pressure is adjusted by controlling the opening of the back-pressure valve. Firstly, a lumped parameter model of the methanol reforming hydrogen production system is constructed using MATLAB/Simulink® (produced by MathWorks in Natick, Massachusetts, USA). In addition, the transfer function model of the system is obtained by system identification at the equilibrium point, and the internal model controller is further designed. The simulation results show that the control method achieves the robustness of the system, and the temperature and pressure of the reforming reactor can quickly and accurately track the target value when the load changes. Small-load step tests indicate stable tracking of the temperature and pressure for the reforming reactor, without steady-state errors. Under large-temperature step signal testing, the response time for the reforming temperature is about 148 s, while the large-pressure step signal test shows that the response time for the reforming pressure is about 8 s. Compared to the PID controller, the internal model controller exhibits faster response, zero steady-state error, and no overshoot. The results show that the internal model control method has strong robustness and dynamic characteristics. Full article
(This article belongs to the Section A5: Hydrogen Energy)
22 pages, 19268 KiB  
Article
Key Characteristics and Controlling Factors of the Gas Reservoir in the Fourth Member of the Ediacaran Dengying Formation in the Penglai Gas Field, Sichuan Basin
by Hongwei Chen, Shilin Wang, Ahmed Mansour, Qirong Qin, Mohamed S. Ahmed, Yongjing Cen, Feng Liang, Yuan He, Yi Fan and Thomas Gentzis
Minerals 2025, 15(2), 98; https://doi.org/10.3390/min15020098 (registering DOI) - 21 Jan 2025
Abstract
This study focuses on the PS8 well in the Penglai Gas Field (Sichuan Basin), a newly identified key exploration area, where high-yield gas testing has been achieved from the Ediacaran Fourth Member of the Dengying Formation. Comprehensive analyses of drilling cores, cuttings, thin [...] Read more.
This study focuses on the PS8 well in the Penglai Gas Field (Sichuan Basin), a newly identified key exploration area, where high-yield gas testing has been achieved from the Ediacaran Fourth Member of the Dengying Formation. Comprehensive analyses of drilling cores, cuttings, thin sections, analytical data, well logging, and production testing data were conducted to investigate the main characteristics of the gas reservoir and the factors controlling the formation model of the reservoir. The results reveal that the reservoir rocks in the Fourth Member of the Dengying Formation are primarily algal-clotted dolomite, algal-laminated dolomite, and arenaceous dolomite. The reservoir porosity is dominated by secondary pores, such as algal-bonded framework pores, intergranular dissolved pores, and intercrystalline dissolved pores, which contribute to the overall low porosity and extremely low permeability. The gas reservoir is classified as a unified structural–lithological reservoir, with the upper sub-member of the Fourth Member serving as a completely gas-bearing unit. This unit is characterized as an ultra-deep, dry gas reservoir with medium sulfur and medium CO2 contents. The development of this gas reservoir follows a “laterally generated and laterally stored, upper generation and lower storage” reservoir formation model. Regional unconformities and fracture systems developed during the Tongwan II Episode tectonic movement provide efficient pathways for hydrocarbon migration and accumulation. The high-quality source rocks in the lower Cambrian Qiongzhusi Formation serve as both the direct cap rock and lateral seal of the gas reservoir, creating an optimal source–reservoir spatial configuration. This study provides valuable insights into the giant gas reservoir of the Dengying Formation, which can aid in optimizing exploration activities in the Sichuan Basin. Full article
(This article belongs to the Section Mineral Exploration Methods and Applications)
Show Figures

Graphical abstract

31 pages, 3947 KiB  
Article
Effect of TiO2 Morphology on the Properties and Photocatalytic Activity of g-C3N4/TiO2 Nanocomposites Under Visible-Light Illumination
by Matevž Roškarič, Gregor Žerjav, Janez Zavašnik, Matjaž Finšgar and Albin Pintar
Molecules 2025, 30(3), 460; https://doi.org/10.3390/molecules30030460 - 21 Jan 2025
Abstract
This study focused on the preparation and investigation of g-C3N4/TiO2 photocatalysts using different TiO2 morphologies (anatase nanoparticles (TPs), poorly crystalline nanotubes (aTTs), and well-crystalline anatase nanorods (TRs)) and self-synthesized g-C3N4 (CN). The synthesis of [...] Read more.
This study focused on the preparation and investigation of g-C3N4/TiO2 photocatalysts using different TiO2 morphologies (anatase nanoparticles (TPs), poorly crystalline nanotubes (aTTs), and well-crystalline anatase nanorods (TRs)) and self-synthesized g-C3N4 (CN). The synthesis of the g-C3N4/TiO2 composites was carried out using a mortar mixing technique and a g-C3N4 to TiO2 weight ratio of 1:1. In addition, the g-C3N4/TiO2 composites were annealed in a muffle furnace at 350 °C for 2 h in air. The successful formation of a g-C3N4/TiO2 composite with a mesoporous structure was confirmed using the results of XRD, N2 physisorption, and FTIR analyses, while the results of microscopic analysis techniques confirmed the preservation of TiO2 morphology in all g-C3N4/TiO2 composites investigated. UV-Vis DR measurements showed that the investigated g-C3N4/TiO2 composites exhibited visible-light absorption due to the presence of CN. The results of solid-state photoluminescence and electrochemical impedance spectroscopy showed that the composites exhibited a lower charge recombination compared to pure TiO2 and CN. For example, the charge transfer resistance (RCT) of the CNTR/2 composite of TR and CN calcined in air for 2 h was significantly reduced to 0.4 MΩ, compared to 0.9 MΩ for pure TR and 1.0 MΩ for pure CN. The CNTR/2 composite showed the highest photocatalytic performance of the materials tested, achieving 30.3% degradation and 25.4% mineralization of bisphenol A (BPA) dissolved in water under visible-light illumination. In comparison, the pure TiO2 and CN components achieved significantly lower BPA degradation rates (between 2.4 and 11.4%) and mineralization levels (between 0.6 and 7.8%). This was due to (i) the presence of Ti3+ and O-vacancies in the TR, (ii) enhanced heterojunction formation, and (iii) charge transfer dynamics enabled by a dual mixed type-II/Z scheme mechanism. Full article
(This article belongs to the Special Issue New Materials and Catalysis in Environmental Protection)
21 pages, 1793 KiB  
Article
A Computationally Efficient Method for the Diagnosis of Defects in Rolling Bearings Based on Linear Predictive Coding
by Mohammad Mohammad, Olga Ibryaeva, Vladimir Sinitsin and Victoria Eremeeva
Algorithms 2025, 18(2), 58; https://doi.org/10.3390/a18020058 - 21 Jan 2025
Abstract
Monitoring the condition of rolling bearings is a crucial task in many industries. An efficient tool for diagnosing bearing defects is necessary since they can lead to complete machine failure and significant economic losses. Traditional diagnosis solutions often rely on a complex artificial [...] Read more.
Monitoring the condition of rolling bearings is a crucial task in many industries. An efficient tool for diagnosing bearing defects is necessary since they can lead to complete machine failure and significant economic losses. Traditional diagnosis solutions often rely on a complex artificial feature extraction process that is time-consuming, computationally expensive, and too complex to deploy in practice. In actual working conditions, however, the amount of labeled fault data available is relatively small, so a deep learning model with good generalization and high accuracy is difficult to train. This paper proposes a solution that uses a simple feedforward artificial neural network (NN) for classification and adopts the linear predictive coding (LPC) algorithm for feature extraction. The LPC algorithm finds several coefficients for a given signal segment containing information about the signal spectrum, which is sufficient for further classification. The LPC-NN solution was tested on the Case Western Reserve University (CWRU) and South Ural State University (SUSU) datasets. The results demonstrated that, in most cases, LPC-NN yielded an accuracy of 100%. The proposed method achieves higher diagnostic accuracy and stability to load changes than other advanced techniques, has a significantly improved time performance, and is conducive to real-time industrial fault diagnosis. Full article
27 pages, 1555 KiB  
Article
Easy and Straightforward FPGA Implementation of Model Predictive Control Using HDL Coder
by Marziye Purraji, Elyas Zamiri and Angel de Castro
Electronics 2025, 14(3), 419; https://doi.org/10.3390/electronics14030419 - 21 Jan 2025
Abstract
Model Predictive Control (MPC) is widely adopted for power electronics converters due to its ability to optimize system performance under dynamic constraints. However, its FPGA implementation remains challenging due to the complexity of Hardware Description Language (HDL) programming. This paper addresses this challenge [...] Read more.
Model Predictive Control (MPC) is widely adopted for power electronics converters due to its ability to optimize system performance under dynamic constraints. However, its FPGA implementation remains challenging due to the complexity of Hardware Description Language (HDL) programming. This paper addresses this challenge by introducing a straightforward methodology that simplifies FPGA implementation using MATLAB Simulink HDL Coder. It is shown that HDL Coder yields favorable synthesis outcomes, both in terms of area and time, compared to hand-coded HDL. Notably, the proposed method achieves a significantly reduced sampling step for the MPC algorithm—down to 32 ns—marking a substantial improvement over state-of-the-art implementations. The Integrated Logic Analyzer (ILA) IP available in the Vivado tool is used during the HIL testing phase to facilitate the real-time observation and analysis required for debugging and confirming the FPGA-implemented controller performance. Additionally, this paper discusses the advantages of utilizing HDL Coder for simplifying the FPGA programming process in power electronics applications and addresses the design challenges encountered using this methodology. Full article
(This article belongs to the Section Industrial Electronics)
19 pages, 1844 KiB  
Article
Prediction of Projectile Interception Point and Interception Time Based on Harris Hawk Optimization–Convolutional Neural Network–Support Vector Regression Algorithm
by Zhanpeng Gao and Wenjun Yi
Mathematics 2025, 13(3), 338; https://doi.org/10.3390/math13030338 - 21 Jan 2025
Abstract
In modern warfare, the accurate prediction of the intercept time and intercept point of the interceptor is the key to achieving penetration. Aiming at this problem, firstly, a convolutional neural network (CNN) is used to automatically extract high-level features from the data, and [...] Read more.
In modern warfare, the accurate prediction of the intercept time and intercept point of the interceptor is the key to achieving penetration. Aiming at this problem, firstly, a convolutional neural network (CNN) is used to automatically extract high-level features from the data, and then these features are used as the input of support vector regression (SVR) for regression prediction. The Harris Hawk optimization (HHO) is used to optimize the hyperparameters of SVR, and the HHO-CNN-SVR algorithm is proposed. In order to verify the effectiveness of the algorithm for the prediction of the interception point and interception time, this paper constructs a dataset to test the method of simulating the missile interception maneuvering target. Compared with BP, ELM, SVR, HHO-SVR, and CNN-SVR models, the HHO-CNN-SVR model has outstanding performance in prediction accuracy and stability, especially for the interception time. The error is the smallest, and the error fluctuation is small. The MAE of the prediction result is only 0.0139 s; in the interception point prediction, the error of the range and elevation direction is significantly lower than that of the models used for comparison. The MAE in the range direction is 2.3 m, and the MAE in the elevation direction is 2.01 m, which meet the engineering requirements. The HHO-CNN-SVR model has strong prediction accuracy and stability in interception time and interception point prediction. In addition, different control strategies are used to construct a new prediction set, and noise is added to the prediction set. The HHO-CNN-SVR algorithm can maintain good prediction results. The results show that the HHO-CNN-SVR model proposed in this paper has strong generalization ability and high robustness, which can provide reliable support for penetration decision making and defense system optimization. Full article
Show Figures

Figure 1

27 pages, 997 KiB  
Article
Holistic Electric Powertrain Component Design for Battery Electric Vehicles in an Early Development Phase
by Nico Rosenberger, Silvan Deininger, Jan Koloch and Markus Lienkamp
World Electr. Veh. J. 2025, 16(2), 61; https://doi.org/10.3390/wevj16020061 - 21 Jan 2025
Abstract
As BEV gain significance in the automotive industry, manufacturers must diversify their vehicle portfolios with a wide range of electric vehicle models. Electric powertrains must be designed to meet the unique requirements and boundary conditions of different vehicle concepts to provide satisfying solutions [...] Read more.
As BEV gain significance in the automotive industry, manufacturers must diversify their vehicle portfolios with a wide range of electric vehicle models. Electric powertrains must be designed to meet the unique requirements and boundary conditions of different vehicle concepts to provide satisfying solutions for their customers. During the early development phases, it is crucial to establish an initial powertrain component design that allows the respective divisions to develop their components independently and minimize interdependencies, avoiding time- and cost-intensive iterations. This study presents a holistic electric powertrain component design model, including the high-voltage battery, power electronics, electric machine, and transmission, which is meant to be used as a foundation for further development. This model’s simulation results and performance characteristics are validated against a reference vehicle, which was torn down and tested on a vehicle dynamometer. This tool is applicable for an optimization approach, focusing on achieving optimal energy consumption, which is crucial for the design of battery electric vehicles. Full article
31 pages, 6185 KiB  
Article
A Framework for Market State Prediction with Ontological Asset Selection: A Multimodal Approach
by Igor Felipe Carboni Battazza, Cleyton Mário de Oliveira Rodrigues and João Fausto L. de Oliveira
Appl. Sci. 2025, 15(3), 1034; https://doi.org/10.3390/app15031034 - 21 Jan 2025
Abstract
In this study, we introduce a detailed framework for predicting market conditions and selecting stocks by integrating machine learning techniques with ontological financial analysis. The process starts with ontology-based stock selection, categorizing companies using fundamental financial indicators such as liquidity, profitability, debt ratios, [...] Read more.
In this study, we introduce a detailed framework for predicting market conditions and selecting stocks by integrating machine learning techniques with ontological financial analysis. The process starts with ontology-based stock selection, categorizing companies using fundamental financial indicators such as liquidity, profitability, debt ratios, and growth metrics. For instance, firms showcasing favorable debt-to-equity ratios along with robust revenue growth are identified as high-performing entities. This classification facilitates targeted analyses of market dynamics. To predict market states—categorizing them into bull, bear, or neutral phases—the framework utilizes a Non-Stationary Markov Chain (NMC), BERT, to assess sentiment in financial news articles and Long Short-Term Memory (LSTM) networks to identify temporal patterns. Key inputs like the Sentiment Index (SI) and Illiquidity Index (ILLIQ) play essential roles in dynamically influencing regime predictions within the NMC model; these inputs are supplemented by variables including GARCH volatility and VIX to enhance predictive precision further still. Empirical findings demonstrate that our approach achieves an impressive 97.20% accuracy rate for classifying market states, significantly surpassing traditional methods like Naive Bayes, Logistic Regression, KNN, Decision Tree, ANN, Random Forest, and XGBoost. The state-predicted strategy leverages this framework to dynamically adjust portfolio positions based on projected market conditions. It prioritizes growth-oriented assets during bull markets, defensive assets in bear markets, and maintains balanced portfolios in neutral states. Comparative testing showed that this approach achieved an average cumulative return of 13.67%, outperforming the Buy and Hold method’s return of 8.62%. Specifically, for the S&P 500 index, returns were recorded at 6.36% compared with just a 1.08% gain from Buy and Hold strategies alone. These results underscore the robustness of our framework and its potential advantages for improving decision-making within quantitative trading environments as well as asset selection processes. Full article
(This article belongs to the Section Computing and Artificial Intelligence)
Show Figures

Figure 1

12 pages, 1256 KiB  
Article
Investigation of Perceived Stress During COVID-19 Pandemic Self-Isolation Periods
by Paulius Ūselis, Živilė Jacikė, Audronė Šeibokaitė and Aušra Griciūtė
Medicina 2025, 61(2), 175; https://doi.org/10.3390/medicina61020175 - 21 Jan 2025
Abstract
Background and Objectives: The study purpose was to analyze possible health consequences of self-isolation during the COVID-19 pandemic, aiming to evaluate diagnostics methods. Specifically, we analyzed perceived stress of self-isolation with the aim of evaluating the suitability of psychological and laboratory diagnostics [...] Read more.
Background and Objectives: The study purpose was to analyze possible health consequences of self-isolation during the COVID-19 pandemic, aiming to evaluate diagnostics methods. Specifically, we analyzed perceived stress of self-isolation with the aim of evaluating the suitability of psychological and laboratory diagnostics methods for routine clinical practice. In order to achieve the aim of the study, the following objectives were formulated: to compare the results of psychological and laboratory diagnostic methods between case and control groups; and to evaluate associations between psychological and laboratory stress indicators separately in case and control groups. Materials and Methods: The research study consisted of control and case groups of 28 volunteers each. The main selection criterion for the case group was self-isolation due to COVID-19 and a maximum period of 3 months after post-isolation, while the control group had to be of a similar age but did not have to be isolated or self-isolated. Both groups consisted of young (18–24 years) individuals. All participants had to fill out a Perceived Stress Scale (PSS) questionnaire and were subjected to a laboratory test for stress indicators (alpha-amylase, secretory cortisol, and immunoglobulin A) from a saliva sample. Results: A comparison of the laboratory stress indicator scores for both study groups revealed statistically significant differences between the clinical subgroups, i.e., the distributions of the control and case groups were significantly different within the affected case group and control. The values obtained for study groups and PSS scores showed no discrepancies between the two investigation methods, i.e., PSS assessment and laboratory stress indicators results. The PSS values between the clinical groups were significantly different from each other, suggesting that the laboratory stress indicator scores differed but were consistent or complementary to the PSS results. A separate comparison of age and stress indicator levels in the control group revealed a correlation between age and PSS scores, indicating that younger individuals were more prone to subjective perception of moderate stress. Conclusions: The results showed that COVID-19 self-isolation during quarantine affected people’s psychological health. Using psychological examination and laboratory stress indicators, the results of the case group reliably differed from the results of the control group, allowing us to conclude that self-isolation more often caused moderate chronic stress, with or without decompensation. Besides the main study objective, we observed that laboratory stress biomarkers may be acceptable for broader clinical application during routine psychological treatment. The clinical application of laboratory stress biomarkers had been validated previously by another method, i.e., psychological investigation using PSS. Full article
(This article belongs to the Section Epidemiology & Public Health)
Show Figures

Figure 1

Back to TopTop