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Appl. Sci., Volume 15, Issue 4 (February-2 2025) – 92 articles

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17 pages, 1738 KiB  
Article
Onion Peel Powder’s Impact on the Leptin Receptors in the Hippocampus of Obese Rats
by Małgorzata Komar, Monika Michalak-Majewska, Radosław Szalak, Agata Wawrzyniak, Waldemar Gustaw, Wojciech Radzki and Marcin B. Arciszewski
Appl. Sci. 2025, 15(4), 1768; https://doi.org/10.3390/app15041768 (registering DOI) - 9 Feb 2025
Abstract
The bioactive components present in onion peel powder are a promising factor in preventing/treating obesity. Overweight/obesity causes metabolic changes, which can lead to leptin resistance in the central nervous system (CNS) and, thus, to structural and functional changes in the brain. Objectives: [...] Read more.
The bioactive components present in onion peel powder are a promising factor in preventing/treating obesity. Overweight/obesity causes metabolic changes, which can lead to leptin resistance in the central nervous system (CNS) and, thus, to structural and functional changes in the brain. Objectives: The presented study focused on evaluating the influence of a diet supplemented with onion peel powder on the immunoexpression of leptin receptors (LepRs) in the hippocampus in obese rats and the potential anti-obesity role of the onion in the brain. Methods: To induce obesity, the animals were given a high-energy chow containing lard and sucrose. Onion skin powder was used to modify the standard and high-energy diets (10.5 g per rat/week) of Wistar rats in a 14-week experiment followed by a brain IHC study. Results: The effect of the onion diet on the expression of neuronal LepRs and astrocytes in the hippocampus was analyzed. Obese animals receiving onion in the diet showed significant growth in the average number of immunoreactive LepR (LepR-IR) neurons (p = 0.00108) and their average size (p = 0.00168) in the CA1 field of the hippocampus. Meanwhile, in obese rats not given onion peel powder, a significant increase in the average density of astrocytes was observed (p< 0.0001). Conclusions: The increased density of astrocytes in the hippocampus of obese animals can probably have a beneficial effect on brain changes in overweight individuals. The inclusion of onion in the diet of overweight/obese individuals may lead to increased hippocampal neuroplasticity, manifested by changes in the immunoexpression of LepRs. It can be speculated that the observed changes have a protective effect on the CNS structures during obesity, but this undoubtedly requires further research. Full article
(This article belongs to the Special Issue Bioactive Compounds for Functional Foods and Sustainability)
36 pages, 3446 KiB  
Review
A Review on the Progress of Integrated Geophysical Exploration Techniques for Leakage Hazard Detection in Earth and Rock Dams
by Guochen Zhang, Liqun Xu, Fei Qiu, Zhiyuan Shen and Yin Zhang
Appl. Sci. 2025, 15(4), 1767; https://doi.org/10.3390/app15041767 (registering DOI) - 9 Feb 2025
Abstract
Earth and rock dams are an important part of the flood control system, and hidden dangers in such dams are a serious threat to project safety. The application of a single geophysical exploration technology is associated with multiple solutions and limitations, and research [...] Read more.
Earth and rock dams are an important part of the flood control system, and hidden dangers in such dams are a serious threat to project safety. The application of a single geophysical exploration technology is associated with multiple solutions and limitations, and research on an integrated technology is meaningful for the timely detection and management of hidden dangers in earth and rock dams. This paper summarizes the respective advantages and limitations of geophysical exploration techniques for leakage detection in dams by sorting out and analyzing their principles and application characteristics. The principles of the integrated technology are outlined, and a data analysis system for GIS-based integrated geophysical exploration is elaborated. The challenges and shortcomings of the development of integrated geophysical exploration techniques are summarized. The theoretical model of integrated geophysical exploration information fusion technology based on data fusion and joint inversion is proposed. The feasibility of establishing the theoretical model based on data fusion and joint inversion is demonstrated, providing a direction for the development and practical application of integrated geophysical exploration techniques in the field of geotechnical engineering. Full article
24 pages, 914 KiB  
Article
The Effects of High Hydrostatic Pressure Treatment on the Quality Characteristics and the Protein Structure of Vacuum-Packed Fresh Pork and Wild Boar Meats
by Koppány Majzinger, Bernadett Kovács-Bányász, Zsuzsanna Horváth-Mezőfi, Enikő Pósa, Barbara Csehi, Géza Hitka, Boglárka Alpár and Ildikó Csilla Nyulas-Zeke
Appl. Sci. 2025, 15(4), 1766; https://doi.org/10.3390/app15041766 (registering DOI) - 9 Feb 2025
Abstract
In this study, the effects of high hydrostatic pressure treatment on the quality characteristics and the protein structure of vacuum-packed fresh pork and wild boar meats were investigated. Based on the results, an optimal pressure value was determined that would not cause the [...] Read more.
In this study, the effects of high hydrostatic pressure treatment on the quality characteristics and the protein structure of vacuum-packed fresh pork and wild boar meats were investigated. Based on the results, an optimal pressure value was determined that would not cause the sensory properties of the treated flesh to differ from that of the untreated fresh meats but would effectively contribute to the extension of shelf life. The conclusion was made that high hydrostatic pressure treatment performed at 200 MPa did not lead to the denaturation of meat proteins and had no significant effect on the pH value or the color parameters. However, the shelf life of the meat could be extended by a week due to the two-orders-of-magnitude germicidal effect of the treatment. Full article
(This article belongs to the Special Issue Microbiology in Meat Production and Meat Processing)
31 pages, 7093 KiB  
Review
A Data-Driven Visualization Approach for Life-Cycle Cost Analysis of Open-Cut and Trenchless CIPP Methods for Sanitary Sewers: A PRISMA Systematic Review
by Gayatri Thakre, Vinayak Kaushal, Eesha Karkhanis and Mohammad Najafi
Appl. Sci. 2025, 15(4), 1765; https://doi.org/10.3390/app15041765 (registering DOI) - 9 Feb 2025
Abstract
The wastewater conveyance systems in the United States are facing severe structural challenges, with the nation’s overall wastewater infrastructure receiving a critically low grade of D- from the American Society of Civil Engineers (ASCE). Innovative trenchless technologies, such as Cured-in-Place Pipe Renewal Technology [...] Read more.
The wastewater conveyance systems in the United States are facing severe structural challenges, with the nation’s overall wastewater infrastructure receiving a critically low grade of D- from the American Society of Civil Engineers (ASCE). Innovative trenchless technologies, such as Cured-in-Place Pipe Renewal Technology (CIPPRT), offer a cost-efficient substitute for traditional open-cut construction methods (OCCM). However, the possibility of a comprehensive life-cycle cost analysis (LCCA) comparing these methods remains unexplored. LCCA examines the comprehensive financial impact, encompassing installation, operation, maintenance, rehabilitation, and replacement expenses, using net present value (NPV) over a set duration. The objective of this study is to systematically review the existing literature to explore advancements in calculating the LCCA for CIPPRT and compare the latter approach to OCCM. A rigorous PRISMA-guided methodology applied to academic databases identified 845 publications (1995–2024), with 83 documents being selected after stringent screening. The findings reveal limited use of artificial intelligence (AI) or machine learning (ML) in predicting CIPPRT costs. A bibliometric analysis using VOSviewer visualizes the results. The study underscores the potential of intelligent, data-driven approaches, such as spreadsheet models and AI, to enhance decision-making in selecting rehabilitation methods tailored to project conditions. These advancements promise more sustainable and cost-effective management of sanitary sewer systems, offering vital insights for decision-makers in addressing critical infrastructure challenges. Full article
(This article belongs to the Special Issue Advances in Underground Pipeline Technology, 2nd Edition)
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19 pages, 7816 KiB  
Article
Climatology, Diversity, and Variability of Quasi-Biweekly to Intraseasonal Extreme Temperature Events in Hong Kong from 1885 to 2022
by Hoiio Kong, Kechen Wu, Pak Wai Chan, Jinping Liu, Banglin Zhang and Jeremy Cheuk-Hin Leung
Appl. Sci. 2025, 15(4), 1764; https://doi.org/10.3390/app15041764 (registering DOI) - 9 Feb 2025
Abstract
In July 2023, 19 continuous days of very hot days in Hong Kong brought inconvenience to citizens and disasters to society. This long-lasting heat wave event is closely linked to the atmospheric variability on the quasi-biweekly to intraseasonal timescales. While extreme weather has [...] Read more.
In July 2023, 19 continuous days of very hot days in Hong Kong brought inconvenience to citizens and disasters to society. This long-lasting heat wave event is closely linked to the atmospheric variability on the quasi-biweekly to intraseasonal timescales. While extreme weather has aroused the attention of scientists and society, limited studies focus on quasi-biweekly to intraseasonal extreme (QBIE) weather. Thus, to address this issue, this study aims at examining the climatology and long-term variability of these QBIE events in Hong Kong. This study serves as one of the very few fundamental works that construct a century-long record of QBIE temperature events, based on in situ observation in Hong Kong, and further examines the climatology, diversity, and variability of these QBIE temperature events. A total of 382 QBIE heat waves and 510 QBIE cold surges are identified from 1885 to 2022, exhibiting various characteristics in their occurring time and seasonality. Based on ARIMA model and time series analyses, we find that while apparent interannual variability exists in QBIE heat wave and cold surge activity, short-term climate prediction of QBIE temperature events based on past patterns or common climate indices is largely unfeasible. This research provides a valuable historical reference for understanding QBIE weather in the Guangdong–Hong Kong–Macau Greater Bay Area and highlights the need for further studies on the predictability of QBIE weather in the future. Full article
(This article belongs to the Section Earth Sciences)
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12 pages, 2222 KiB  
Article
A Novel 3D-Printed Training Platform for Ossiculoplasty with Objective Performance Evaluation
by Nicolas Emiliani, Giulia Molinari, Barbara Bortolani, Cecilia Lotto, Arianna Burato, Rossana D’Azzeo, Lukas Anschuetz, Ignacio Javier Fernandez, Livio Presutti, Gabriele Molteni, Laura Cercenelli and Emanuela Marcelli
Appl. Sci. 2025, 15(4), 1763; https://doi.org/10.3390/app15041763 (registering DOI) - 9 Feb 2025
Abstract
Ossiculoplasty (OPL) aims to restore ossicular chain continuity to improve hearing in patients with conductive or mixed hearing loss, often performed during tympanoplasty. The current training methods, including cadaveric temporal bone models, face challenges such as limited availability, high costs, and biological risks, [...] Read more.
Ossiculoplasty (OPL) aims to restore ossicular chain continuity to improve hearing in patients with conductive or mixed hearing loss, often performed during tympanoplasty. The current training methods, including cadaveric temporal bone models, face challenges such as limited availability, high costs, and biological risks, prompting the exploration of alternative models. This study introduces a novel training platform for OPL using 3D-printed temporal bones and incudes, including a magnified (3:1) model to enhance skill acquisition. Sixty medical students were divided into two groups: one trained on magnified models before transitioning to real-sized ones, and the other used only real-sized models. Training performance was quantitatively assessed using post-remodeling cone-beam CT imaging and mesh distance analysis. The results showed a significant improvement in performance for students with preliminary training on magnified models (87% acceptable results vs. 37%, p = 0.001). Qualitative feedback indicated higher confidence and skill ratings in the magnified model group. This study highlights the effectiveness of scalable, anatomically accurate synthetic models for complex surgical training. While further validation is required with experienced trainees and broader scenarios, the findings support the integration of 3D printing technologies into otologic education, offering a cost-effective, reproducible, and innovative approach to enhancing surgical preparedness. Full article
(This article belongs to the Special Issue 3D Printing Technologies in Biomedical Engineering)
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30 pages, 1747 KiB  
Article
A Dual-Adaptive Perspective on PV Array Reconfiguration with Genetic Algorithms Under Partial Shading Conditions
by Özgür Karaduman and Koray Şener Parlak
Appl. Sci. 2025, 15(4), 1762; https://doi.org/10.3390/app15041762 (registering DOI) - 9 Feb 2025
Abstract
Photovoltaic systems are among the most popular renewable energy sources due to their ease of installation and low operating costs. However, they are characterized by low efficiency, non-linear electrical properties, and sensitivity to radiant intensity on the panels. To address these limitations, researchers [...] Read more.
Photovoltaic systems are among the most popular renewable energy sources due to their ease of installation and low operating costs. However, they are characterized by low efficiency, non-linear electrical properties, and sensitivity to radiant intensity on the panels. To address these limitations, researchers have focused on improving the efficiency of these systems. The most effective method for enhancing the maximum power point of a PV array is reconfiguration, which involves rearranging the connection structures of the panels. This study presents a method for determining the reconfiguration of panels based on their radiant intensity using a genetic algorithm (GA). The method matches the rows of the PV array to achieve similar radiant intensities, thereby increasing power efficiency. An algorithm was developed to enable the adaptive panels to connect to any row of the fixed section in a PV array divided into dual-adaptive and fixed sections, controlling this connection structure. This GA-based algorithm utilizes short-circuit currents obtained from specific points of the PV array to identify the most suitable connection structure within the solution space and generates control signals for reconfiguration. Simulation results with various array structures and shading scenarios demonstrate that the proposed method increases array efficiency and achieves results within a practically applicable cycle time. Full article
(This article belongs to the Special Issue Solar Energy Collection, Conversion and Utilization)
28 pages, 6455 KiB  
Article
Optimizing Bitumen Performance in Warm Mix Asphalt Using Cecabase RT BIO10: A Taguchi-Based Experimental Approach
by Mustafa Çakı and Fatih İrfan Baş
Appl. Sci. 2025, 15(4), 1761; https://doi.org/10.3390/app15041761 (registering DOI) - 9 Feb 2025
Abstract
Flexible pavements stand out as the most commonly used worldwide, compared to rigid and composite pavements, owing to their versatility and widespread application. The use of hot mix asphalt (HMA) in flexible pavements causes significant environmental concerns due to high CO2 emissions [...] Read more.
Flexible pavements stand out as the most commonly used worldwide, compared to rigid and composite pavements, owing to their versatility and widespread application. The use of hot mix asphalt (HMA) in flexible pavements causes significant environmental concerns due to high CO2 emissions and energy consumption, whereas warm mix asphalt (WMA) technologies have gained popularity in recent decades, offering a more sustainable alternative by enabling asphalt production at lower temperatures. WMA technologies can be categorized into three main groups: foaming, organic additives, and chemical additives, with each offering distinct benefits for performance and environmental impact. One of the chemical additives used in WMA production is Cecabase RT BIO10. In this study, virgin bitumen with 50/70 penetration was modified by adding Cecabase RT BIO10 at four levels: 0%, 0.3%, 0.4%, and 0.5% by weight. The experimental design employed a Taguchi L16 orthogonal array to systematically evaluate the effects of various factors on modified bitumen performance. Binders were prepared at four temperatures (110 °C, 120 °C, 130 °C, and 140 °C), four mixing durations (15, 20, 25, and 30 min), and four mixing speeds (1000, 2000, 3000, and 4000 rpm), enabling an efficient analysis of each parameter’s impact. The prepared binders were subjected to a series of tests, including penetration, softening point, flash point, rotational thin film oven test (RTFOT), elastic recovery, Marshall stability, ultrasonic pulse velocity (UPV), and FTIR analysis. These tests were conducted to investigate the effects of various parameters and levels on the binder properties. Additionally, stiffness and seismic modules were evaluated to provide a more comprehensive understanding of the binder’s performance. The experiment results revealed that the penetration, elastic recovery percentage, and Marshall stability increased with increasing additive content while the softening point and RTFOT mass loss decreased. At a high service temperature of 40 °C, the stiffness modulus of the modified bitumen decreased slightly. At a low service temperature of −10 °C, it decreased further. Additionally, the incorporation of Cecabase RT BIO10 led to an increase in the seismic modulus. Through optimization using the Taguchi method, the optimal levels were determined to be a 0.4% Cecabase RT BIO10 ratio, 140 °C mixing temperature, 30 min mixing time, and 1000 RPM mixing speed. The optimal responses for each test were identified and integrated into a unified optimal response, resulting in a comprehensive design guide with 95% confidence level estimates for all possible level combinations. Full article
(This article belongs to the Section Civil Engineering)
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29 pages, 15339 KiB  
Article
A Noise Reduction Algorithm for White Noise and Periodic Narrowband Interference Noise in Partial Discharge Signals
by Jiyuan Cao, Yanwen Wang, Weixiong Zhu and Yihe Zhang
Appl. Sci. 2025, 15(4), 1760; https://doi.org/10.3390/app15041760 (registering DOI) - 9 Feb 2025
Abstract
Partial discharge (PD) detection plays an important role in online condition monitoring of electrical equipment and power cables. However, the noise of PD measurement will significantly reduce the performance of the detection algorithm. In this paper, we focus on the study of a [...] Read more.
Partial discharge (PD) detection plays an important role in online condition monitoring of electrical equipment and power cables. However, the noise of PD measurement will significantly reduce the performance of the detection algorithm. In this paper, we focus on the study of a PD noise reduction algorithm based on improved singular value decomposition (SVD) and multivariate variational mode decomposition (MVMD) for white Gaussian noise (WGN) and periodic narrowband interference signal noise. The specific noise reduction algorithm is divided into three noise reduction processes: The first noise reduction completes the suppression of narrowband interference in the noisy PD signal by the SVD algorithm with the guidance signal. The guidance signal is composed of a sinusoidal signal of the accurately estimated narrowband interference frequency component, and the amplitude is twice the maximum amplitude of the noisy PD signal. The second noise reduction decomposes the noisy PD signal after filtering the narrowband interference signal into k optimal intrinsic mode function by the MVMD after parameter optimization. By calculating the kurtosis value of each intrinsic mode function, it is determined whether it is the PD dominant component or the noise dominant component, and the noise dominant component is subjected to 3σ filtering to obtain the reconstructed PD signal. The third noise reduction uses a new wavelet threshold algorithm to denoise the reconstructed PD signal to obtain the denoised PD signal. The overall noise reduction algorithm proposed in this paper is compared with some existing methods. The results show that this method has a good effect on reducing the noise of PD signals measured in simulation and field. Full article
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15 pages, 954 KiB  
Article
Lateral Load-Bearing Performance of a Long Pile in Layered Soils Based on the Modified Vlasov Foundation Model
by Fengjun Liu, Jianjun Ma and Da Li
Appl. Sci. 2025, 15(4), 1759; https://doi.org/10.3390/app15041759 (registering DOI) - 9 Feb 2025
Abstract
A mechanical model of a laterally loaded long pile in layered soils was established to accurately calculate the lateral load-bearing performance of the pile foundation, and attention was paid to the influence of the complete separation of the pile–soil contact surface in a [...] Read more.
A mechanical model of a laterally loaded long pile in layered soils was established to accurately calculate the lateral load-bearing performance of the pile foundation, and attention was paid to the influence of the complete separation of the pile–soil contact surface in a certain part of the pile on its lateral load-bearing performance. Based on the modified Vlasov foundation model, the displacement equation of the laterally loaded long pile embedded in layered soils was derived by the separation variable method. Using the solution method presented in this study, the deformation and internal force of the free-fixed pile were obtained. Then, the effects of the slenderness ratio of the pile and the complete separation of the pile–soil contact surface on the lateral load-bearing performance of the long pile in layered soils were analyzed. The results show that the deformation of the pile body increases with the increase in the slenderness ratio under the lateral load. Meanwhile, the position of the maximum bending moment and the negative shear force moves upward along the pile as the slenderness ratio increases. When the contact surface of the pile–upper stratum is separated, the deformation of the pile top doubles, and the negative shear force increases by three times compared to the case without the effect of separation of the pile–soil contact surface. When the contact surface between the pile and the middle layer soil is separated, the deformation and bending moment of the pile increase by 25%, and the maximum negative shear force decreases. Full article
(This article belongs to the Special Issue Advances and Challenges in Rock Mechanics and Rock Engineering)
23 pages, 8809 KiB  
Article
An Integrated Study of Highway Pavement Subsidence Using Ground-Based Geophysical and Satellite Methods
by Michael Frid, Amit Helman, Dror Sharf, Vladi Frid, Wafa Elias and Dan G. Blumberg
Appl. Sci. 2025, 15(4), 1758; https://doi.org/10.3390/app15041758 (registering DOI) - 9 Feb 2025
Viewed by 97
Abstract
This study investigates highway pavement subsidence along Road 431, Israel, using an integrated geophysical framework that combines Interferometric Synthetic Aperture Radar (InSAR), Ground Penetrating Radar (GPR), and Electrical Resistivity Tomography (ERT). These methods address the limitations of standalone techniques by correlating surface subsidence [...] Read more.
This study investigates highway pavement subsidence along Road 431, Israel, using an integrated geophysical framework that combines Interferometric Synthetic Aperture Radar (InSAR), Ground Penetrating Radar (GPR), and Electrical Resistivity Tomography (ERT). These methods address the limitations of standalone techniques by correlating surface subsidence patterns with subsurface anomalies. InSAR identified surface subsidence rates of up to −2.7 cm/year, pinpointing subsidence hotspots, while GPR detected disintegrated fill layers and air voids, and ERT revealed resistivity anomalies at depths of 50–100 m linked to karstic cavities and water infiltration. Validation through borehole drilling confirmed structural heterogeneity, specifically identifying karstic voids in limestone layers and weathered chalk layers that align with the geophysical findings. The findings highlight the complex interplay of geological and hydrological processes driving ground instability, exacerbated by groundwater fluctuations. This study demonstrates the novelty of combining surface and subsurface monitoring methods, offering a detailed diagnostic framework for understanding and mitigating geotechnical risks in transportation infrastructure. Full article
(This article belongs to the Special Issue New Technology for Road Surface Detection)
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16 pages, 3948 KiB  
Article
Spatial Evolution Analysis of Tailings Flow from Tailings Dam Failure Based on MacCormack-TVD
by Lei Ma, Chao Zhang, Changkun Ma and Xueting Li
Appl. Sci. 2025, 15(4), 1757; https://doi.org/10.3390/app15041757 (registering DOI) - 9 Feb 2025
Viewed by 101
Abstract
Adopting an appropriate method to analyze the spatial evolution process of tailings flow after tailings dam failure can provide a rational assessment of the inundation range and evaluate the subsequent disaster. Simultaneously, it can offer a foundation for tailings pond construction and safety [...] Read more.
Adopting an appropriate method to analyze the spatial evolution process of tailings flow after tailings dam failure can provide a rational assessment of the inundation range and evaluate the subsequent disaster. Simultaneously, it can offer a foundation for tailings pond construction and safety management. This paper, focusing on a specific iron mine in Xiagao, Guangdong, establishes a three-dimensional simulation of the tailings pond based on the design drawings of the raised tailings pond. Utilizing the depth integral method as the theoretical basis, this research references parameter values obtained through model experiments for numerical simulation. Through the numerical simulation method, the study simulates the disaster range, flow, and spatial state of the tailings flow after a dam break. The tailings flow velocity and the depth of the flow in the affected areas are derived, demonstrating the disasters resulting from dam failure. Moreover, the feasibility of raising the tailings dam is evaluated. The assessment extends to the damage risk of tailings dam failure to critical downstream facilities and provides disaster prevention and control suggestions for high-risk situations. This study ultimately offers technical support for the prevention and control of tailings dam failure accidents and the advancement of mine safety production. Full article
(This article belongs to the Special Issue GIS-Based Spatial Analysis for Environmental Applications)
16 pages, 1396 KiB  
Article
Study on the Effect of Plant Growth on the Power Generation Performance of CdTe Photovoltaic Glass Curtain Walls
by Dawei Mu, Xiaoyong Yang and Yixian Zhang
Appl. Sci. 2025, 15(4), 1756; https://doi.org/10.3390/app15041756 (registering DOI) - 9 Feb 2025
Viewed by 109
Abstract
The high summer temperatures of PV (photovoltaic) glass curtain walls lead to reduced power generation performance of PV modules and increased indoor temperatures. To address this issue, this study constructed a test platform for planted photovoltaic glass curtain walls to investigate the effect [...] Read more.
The high summer temperatures of PV (photovoltaic) glass curtain walls lead to reduced power generation performance of PV modules and increased indoor temperatures. To address this issue, this study constructed a test platform for planted photovoltaic glass curtain walls to investigate the effect of plants on their power generation performance. The study’s results indicate the following: (1) reducing the average surface temperature of the surface temperature measurement instrument for the photovoltaic glass curtain wall by 13.6 °C can increase its average power generation capacity by 76 w, demonstrating its power generation performance; (2) plant cultivation influences the micro-environmental temperature on the surface temperature of the photovoltaic glass curtain wall, resulting in a decrease in average micro-environmental temperature by 3.2 °C and average surface temperature by 10.1 °C; (3) compared to traditional PV glass curtain walls, the planted PV glass curtain wall increases cumulative PV power generation output by 21.5 kWh over 15 days and average daily power generation output by 1.4 kWh. Furthermore, during sunny weather with high temperatures, the PV power generation output of the planted PV glass curtain wall is significantly enhanced. Full article
22 pages, 2005 KiB  
Article
From Vine to Wine: Coloured Phenolics as Fingerprints
by Jesús Heras-Roger and Carlos Díaz-Romero
Appl. Sci. 2025, 15(4), 1755; https://doi.org/10.3390/app15041755 (registering DOI) - 9 Feb 2025
Viewed by 100
Abstract
Anthocyanins are important bioactive compounds crucial for the sensory characteristics of red wines. Anthocyanin profiles of 205 monovarietal red wines from the Canary Islands were investigated. Eleven anthocyanins were identified and determined using HPLC-DAD. Anthocyanin concentrations of red wines produced in Canary Islands [...] Read more.
Anthocyanins are important bioactive compounds crucial for the sensory characteristics of red wines. Anthocyanin profiles of 205 monovarietal red wines from the Canary Islands were investigated. Eleven anthocyanins were identified and determined using HPLC-DAD. Anthocyanin concentrations of red wines produced in Canary Islands fell within the usual range observed in red wines from other regions. Red wines elaborated with international grape cultivars presented, in general, higher mean concentrations than those elaborated using autochthonous cultivars. The influence of grape cultivar, production island, denomination of origin, and wine aging on the anthocyanin concentration was studied, leading to the deduction that aging was the parameter with the highest influence. A high number of significant correlations between the anthocyanins determined were found out supporting a common organic synthetic way for these coloured phenolics. Application of multivariate analysis techniques, such as principal component analysis and discriminant analysis, tended to classify the red wine samples according to grape cultivar, geographical production areas, and aging. This study could contribute to the quality control and verification within the wine industry, which is an interesting tool in the prevention of fraud and for increasing consumer confidence. Full article
(This article belongs to the Special Issue New Insights into Bioactive Compounds)
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21 pages, 2731 KiB  
Article
Risk Diagnosis Analysis of Ethane Storage Tank Leakage Based on Fault Tree and Fuzzy Bayesian Network
by Min Pang, Zheyuan Zhang, Zhaoming Zhou and Qing Li
Appl. Sci. 2025, 15(4), 1754; https://doi.org/10.3390/app15041754 (registering DOI) - 9 Feb 2025
Viewed by 110
Abstract
This study proposes a risk assessment method for ethane tank leakage based on Fault Tree Analysis (FTA) and the Fuzzy Bayesian Network (FBN). It aims to diagnose and probabilistically evaluate system risks in scenarios where leakage data are imprecise and insufficient. Initially, a [...] Read more.
This study proposes a risk assessment method for ethane tank leakage based on Fault Tree Analysis (FTA) and the Fuzzy Bayesian Network (FBN). It aims to diagnose and probabilistically evaluate system risks in scenarios where leakage data are imprecise and insufficient. Initially, a fault tree for ethane tank leakage risk is constructed using the connectivity of logical gates. Then, through the analysis of minimal cut sets, the fundamental causes of ethane tank leakage risk are identified, including cracking, instability, and corrosion perforation. Subsequently, the fault tree is mapped into a Bayesian network, which is then integrated to transform it into an FTA–FBN risk diagnostic probability model. Prior probabilities of parent nodes and conditional probability tables are obtained through fuzzy mathematics principles and expert guidance. These are combined with Bayesian inference to derive posterior probabilities, thereby determining the contribution of each basic event to the ethane tank leakage risk. By leveraging the advantages of the fuzzy Bayesian network in handling uncertain problems, the model and analysis effectively address the ambiguities encountered in real-world scenarios. In order to better cope with the uncertainty of leakage, the weakest t-norm algorithm and the similarity aggregation method are introduced for the parameter learning of the fuzzy Bayesian network to achieve an accurate solution of the model. Finally, this integrated model is used in a real case to study the causes of ethane storage tank leakage. The research results are of great scientific significance for revealing the evolution mechanism of ethane storage tank leakage accidents and ensuring system safety throughout the life cycle. Full article
(This article belongs to the Topic Oil and Gas Pipeline Network for Industrial Applications)
20 pages, 26727 KiB  
Article
A Supervised Approach for Land Use Identification in Trento Using Mobile Phone Data as an Alternative to Unsupervised Clustering Techniques
by Manuel Mendoza-Hurtado, Gonzalo Cerruela-García and Domingo Ortiz-Boyer
Appl. Sci. 2025, 15(4), 1753; https://doi.org/10.3390/app15041753 (registering DOI) - 9 Feb 2025
Viewed by 166
Abstract
This study explores land use classification in Trento using supervised learning techniques combined with call detail records (CDRs) as a proxy for human activity. Located in an alpine environment, Trento presents unique geographic challenges, including varied terrain and sparse network coverage, making it [...] Read more.
This study explores land use classification in Trento using supervised learning techniques combined with call detail records (CDRs) as a proxy for human activity. Located in an alpine environment, Trento presents unique geographic challenges, including varied terrain and sparse network coverage, making it an ideal case for testing the robustness of supervised learning approaches. By analyzing spatiotemporal patterns in CDRs, we trained and evaluated several classification algorithms, including k-nearest neighbors (kNN), support vector machines (SVM), and random forests (RF), to map land use categories, such as home, work, and forest. A comparative analysis highlights the performance of each method, emphasizing the strengths of RF in capturing complex patterns, its good generalization ability, and the usage of kNN with different distance measures. Our supervised machine-learning approach outperforms unsupervised clustering techniques by capturing complex patterns and achieving higher accuracy. Results demonstrate the potential of CDRs for urban planning, offering a cost-effective approach for fine-grained land use monitoring with the particularities of Trento, as its landscape combines urban areas, agricultural fields, and forested regions, reflecting its alpine setting, in contrast with other metropolitan regions. Full article
(This article belongs to the Special Issue Artificial Intelligence and the Future of Smart Cities)
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22 pages, 4590 KiB  
Article
Modelling Pollutant Dispersion in Urban Canyons to Enhance Air Quality and Urban Planning
by Francisco Ruda Sarria, MCarmen Guerrero Delgado, Rafael Monge Palma, Teresa Palomo Amores, José Sánchez Ramos and Servando Álvarez Domínguez
Appl. Sci. 2025, 15(4), 1752; https://doi.org/10.3390/app15041752 (registering DOI) - 9 Feb 2025
Viewed by 166
Abstract
Air pollution in urban street canyons presents a serious health risk, especially in densely populated areas. While previous research has explored airflow characteristics in these canyons, it often lacks detailed data on pollutant dispersion and the effects of wind speed on airflow patterns [...] Read more.
Air pollution in urban street canyons presents a serious health risk, especially in densely populated areas. While previous research has explored airflow characteristics in these canyons, it often lacks detailed data on pollutant dispersion and the effects of wind speed on airflow patterns and vortex formation. This study uses Computational Fluid Dynamics (CFD) to deliver quantitative measurements of pollutant dispersion rates and qualitative insights into airflow patterns across various street canyon morphologies. The analysis examines a range of aspect ratios (ARs), from wide (AR = 0.75) to narrow (AR = 4.5), and different wind speeds to evaluate their effects on pollutant dispersion. Findings indicate that purging flow rates decline as the AR increases, with a more pronounced decrease at lower AR values. In narrower streets, airflow patterns are particularly sensitive to wind velocity, leading to unexpected vortices that hinder effective pollutant dispersion. By incorporating these insights into urban design strategies, cities can enhance street ventilation, thereby reducing pollutant concentrations and improving public health. This study also tests a specific street layout in Seville to predict pollutant accumulation under various conditions, assessing health risks based on World Health Organization guidelines. Ultimately, this research aids in developing healthier, more sustainable urban environments. Full article
(This article belongs to the Section Green Sustainable Science and Technology)
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29 pages, 7485 KiB  
Article
SKVOS: Sketch-Based Video Object Segmentation with a Large-Scale Benchmark
by Ruolin Yang, Da Li, Conghui Hu and Honggang Zhang
Appl. Sci. 2025, 15(4), 1751; https://doi.org/10.3390/app15041751 (registering DOI) - 9 Feb 2025
Viewed by 177
Abstract
In this paper, we propose sketch-based video object segmentation (SKVOS), a novel task that segments objects consistently across video frames using human-drawn sketches as queries. Traditional reference-based methods, such as photo masks and language descriptions, are commonly used for segmentation. Photo masks provide [...] Read more.
In this paper, we propose sketch-based video object segmentation (SKVOS), a novel task that segments objects consistently across video frames using human-drawn sketches as queries. Traditional reference-based methods, such as photo masks and language descriptions, are commonly used for segmentation. Photo masks provide high precision but are labor intensive, limiting scalability. While language descriptions are easy to provide, they often lack the specificity needed to distinguish visually similar objects within a frame. Despite their simplicity, sketches capture rich, fine-grained details of target objects and can be rapidly created, even by non-experts, making them an attractive alternative for segmentation tasks. We introduce a new approach that utilizes sketches as efficient and informative references for video object segmentation. To evaluate sketch-guided segmentation, we introduce a new benchmark consisting of three datasets: Sketch-DAVIS16, Sketch-DAVIS17, and Sketch-YouTube-VOS. Building on a memory-based framework for semi-supervised video object segmentation, we explore effective strategies for integrating sketch-based references. To ensure robust spatiotemporal coherence, we introduce two key innovations: the Temporal Relation Module and Sketch-Anchored Contrastive Learning. These modules enhance the model’s ability to maintain consistency both across time and across different object instances. Our method is evaluated on the Sketch-VOS benchmark, demonstrating superior performance with overall improvements of 1.9%, 3.3%, and 2.0% over state-of-the-art methods on the Sketch-YouTube-VOS, Sketch-DAVIS 2016, and Sketch-DAVIS 2017 validation sets, respectively. Additionally, on the YouTube-VOS validation set, our method outperforms the leading language-based VOS approach by 10.1%. Full article
(This article belongs to the Special Issue Advances in Computer Vision and Semantic Segmentation, 2nd Edition)
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24 pages, 10180 KiB  
Article
Solution for Active and Passive Earth Pressure on Rigid Retaining Walls with Narrow Backfill
by Xu Wang, Faning Dang, Xiaoshan Cao, Le Zhang, Jun Gao and Haibin Xue
Appl. Sci. 2025, 15(4), 1750; https://doi.org/10.3390/app15041750 (registering DOI) - 9 Feb 2025
Viewed by 211
Abstract
For a retaining wall adjacent to rock or rigid structures, existing model test results indicate that the slip soil in the limit state can be approximated as a trapezoidal slip wedge. Based on the static equilibrium condition of the slip wedge, a calculation [...] Read more.
For a retaining wall adjacent to rock or rigid structures, existing model test results indicate that the slip soil in the limit state can be approximated as a trapezoidal slip wedge. Based on the static equilibrium condition of the slip wedge, a calculation method for active and passive earth pressures is proposed that considers the effect of backfill width through extreme value analysis. As the backfill width increases, the trapezoidal slip wedge transitions to a triangular slip wedge, introducing a critical width to distinguish between finite and semi-infinite soil conditions. For cohesionless soils, the proposed method converges to Coulomb theory at the critical width; when the backfill is clay, the critical width exceeds the width of Coulomb’s triangular slip wedge due to the stabilizing contribution of cohesion. Parameter analysis reveals that with increasing backfill width, the active earth pressure of cohesionless soil follows a non-linear upward trend, whereas the passive earth pressure decays exponentially. For clay, the active earth pressure initially increases with backfill width and then decreases, whereas the passive earth pressure first decays exponentially and then exhibits a slight increase. Variations in the friction angle significantly affect both active and passive earth pressures, while cohesion mainly influences active earth pressure, and wall-soil friction angle exerts a stronger impact on passive earth pressure. The effectiveness of the proposed method is verified by comparison with results from model tests and numerical simulations. Full article
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9 pages, 197 KiB  
Article
Establishment of Fatty Acid Profile and Comparative Analysis of Volatile Substances in Regular and DHA-Biofortified Raw Milk
by Shaohong Jin, Genna Ba, Jianmin Zou, Chong Chen, Jian He, Pengjie Wang and Yinhua Zhu
Appl. Sci. 2025, 15(4), 1749; https://doi.org/10.3390/app15041749 (registering DOI) - 9 Feb 2025
Viewed by 238
Abstract
This study aimed to establish fatty acid profiles of regular raw milk and docosahexaenoic acid (DHA)-biofortified raw milk and to compare the volatile substance composition of the two types of raw milk. The fatty acid composition of the two types of raw milk [...] Read more.
This study aimed to establish fatty acid profiles of regular raw milk and docosahexaenoic acid (DHA)-biofortified raw milk and to compare the volatile substance composition of the two types of raw milk. The fatty acid composition of the two types of raw milk was analyzed by gas chromatography–mass spectrometry (GC). The results revealed the absence of C15:1, C17:1, C18:2, C22:1, and C24:1 in both types of raw milk, while C20:3 and C22:6 were exclusively found in DHA-biofortified raw milk. The fatty acid levels generally followed a pattern of initial increase and subsequent decrease during lactation, with higher concentrations of short- and medium-chain fatty acids being observed in regular raw milk. The C16:0, C18:3, C20:3, and C20:5 contents in the two types of raw milk varied significantly at different lactation stages. The gas chromatography–mass spectrometry (GC-MS) analysis of the volatile substances revealed the presence of aldehydes, ketones, esters, acids, and sulfur-containing compounds. The volatile substance content in the DHA-biofortified raw milk was generally higher than that in the regular raw milk, which was attributed to the elevated levels of unsaturated fatty acids in biofortified DHA raw milk. Full article
18 pages, 3222 KiB  
Article
A New Design for Switched-Mode Dental Iontophoresis System Using a Dual-Return Probe
by Serkan Dişlitaş
Appl. Sci. 2025, 15(4), 1748; https://doi.org/10.3390/app15041748 (registering DOI) - 8 Feb 2025
Viewed by 460
Abstract
In practice, continuous and pulse direct current (DC) methods are embodied in classical dental iontophoresis systems (CDISs) for the treatment of dentin hypersensitivity (DH). Changes in body electrical resistance and polarization occurrence are the main problems in dental iontophoresis applications. Moreover, continuous DC [...] Read more.
In practice, continuous and pulse direct current (DC) methods are embodied in classical dental iontophoresis systems (CDISs) for the treatment of dentin hypersensitivity (DH). Changes in body electrical resistance and polarization occurrence are the main problems in dental iontophoresis applications. Moreover, continuous DC application may cause discomforts such as irritation, burning and itching on the skin. For these reasons, it is preferred to use pulse DC instead of continuous DC. However, in pulse DC applications, the treatment period is prolonged depending on the decrease in the electrical charge flow. On the other hand, the pain threshold of teeth when the electric current is applied varies from person to person. In this study, in order to reduce the problems caused by the use of CDIS methods for the treatment of DH, a microcontroller-based switched-mode dental iontophoresis system (SMDIS) using a dual-return probe (RP) is designed, and its performance is compared with CDIS methods. According to the results, the new SMDIS both reduces the polarization effect as in the classical pulse DC method and shortens the prolonged treatment duration in pulse DC by raising the pain threshold of teeth due to increased ion transfer, which is a great advantage over former methods. Full article
26 pages, 2813 KiB  
Article
Supply–Demand Dynamic Matching in Cloud Manufacturing Based on Hypernetwork Model
by Jiawen Zhang and Cheng Wang
Appl. Sci. 2025, 15(4), 1747; https://doi.org/10.3390/app15041747 (registering DOI) - 8 Feb 2025
Viewed by 475
Abstract
In response to the escalating demand for personalisation and customisation, the manufacturing industry is increasingly driven towards digital and intelligent transformation. The fluctuating market environment and variable consumer demands impose significant challenges on the resilience of manufacturing systems. With the advent of Industry [...] Read more.
In response to the escalating demand for personalisation and customisation, the manufacturing industry is increasingly driven towards digital and intelligent transformation. The fluctuating market environment and variable consumer demands impose significant challenges on the resilience of manufacturing systems. With the advent of Industry 5.0, developing a resilient manufacturing system has become even more critical. While existing studies have extensively explored participant satisfaction in cloud manufacturing, there remains a notable gap in understanding how network resilience impacts supply–demand matching, particularly in dynamic and personalised production scenarios. To address this gap, this study employs complex system modeling, integrating complex network characteristics for matching modeling and optimising dynamic adjustment strategies. By incorporating network resilience into the matching process, the proposed approach enhances both the robustness and operational efficiency of manufacturing systems. Experimental results demonstrate that the proposed approach significantly improves system adaptability and reduces the impact of disruptions, offering a practical solution for industry practitioners to design resilient cloud manufacturing platforms. For scholars, this study provides a new perspective on integrating network resilience into supply–demand matching models, creating new opportunities for future research in intelligent and resilient manufacturing systems. Full article
16 pages, 2713 KiB  
Article
Analysis of Surface Roughness After Ball Burnishing of Pure Titanium Under Environmentally Friendly Conditions
by Suleyman Cinar Cagan and Kamil Leksycki
Appl. Sci. 2025, 15(4), 1746; https://doi.org/10.3390/app15041746 (registering DOI) - 8 Feb 2025
Viewed by 360
Abstract
This study investigates the optimization of ball burnishing parameters for enhancing the surface quality of pure Titanium (Ti) grade 2 titanium alloy under dry and Minimum Quantity Lubrication (MQL) conditions. Using a Taguchi L18 experimental design, the research systematically examines the effects of [...] Read more.
This study investigates the optimization of ball burnishing parameters for enhancing the surface quality of pure Titanium (Ti) grade 2 titanium alloy under dry and Minimum Quantity Lubrication (MQL) conditions. Using a Taguchi L18 experimental design, the research systematically examines the effects of three critical parameters: burnishing force (50–200 N), feed rate (0.5–2 mm/min), and number of passes (1–4). Surface quality was evaluated through roughness measurements (Ra and Rz values), with Analysis of Variance (ANOVA) employed to determine the statistical significance of each parameter. The results demonstrate that MQL conditions consistently outperform dry burnishing, contributing 50.93% to the total variance in surface quality. The optimal surface finish (Ra = 0.306 μm) was achieved under MQL conditions with a burnishing force of 200 N, feed rate of 0.5 mm/min, and four passes. Statistical analysis revealed that the burnishing environment was the most influential factor, followed by the number of passes (23.87%) and burnishing force (9.97%). A regression model with an R-squared value of 87.66% was developed to predict surface roughness under various parameter combinations. These investigations will be helpful in the development of sustainable and efficient methods for the surface engineering of Ti-based materials for the aerospace and biomedical industries. Full article
(This article belongs to the Section Materials Science and Engineering)
14 pages, 543 KiB  
Article
Exergaming-Based Rehabilitation for Lateral Trunk Flexion in Parkinson’s Disease: A Pilot Study
by Laura Mazzari, Elena Zambon, Serena Tonzar, Miriam Martini, Raffaele Sabot, Alessandra Galmonte and Paolo Manganotti
Appl. Sci. 2025, 15(4), 1745; https://doi.org/10.3390/app15041745 (registering DOI) - 8 Feb 2025
Viewed by 335
Abstract
(1) Background: Axial postural deformities represent a more common disabling motor complication in Parkinson’s disease. This study aims to investigate the clinical and neurophysiological effect of a rehabilitation treatment based on exergaming. (2) Methods: A pilot observational study was conducted on nine subjects [...] Read more.
(1) Background: Axial postural deformities represent a more common disabling motor complication in Parkinson’s disease. This study aims to investigate the clinical and neurophysiological effect of a rehabilitation treatment based on exergaming. (2) Methods: A pilot observational study was conducted on nine subjects affected by Parkinson’s disease and lateral trunk flexion, as well as on nine healthy controls with regard to some clinical and neurophysiological outcomes (3) Results: Statistically significant improvements were observed in all clinical assessment outcomes taken in to consideration: Berg balance scale (p = 0.0078), timed up and go tests (p = 0.03), degrees of lateral trunk inclination (p = 0.0039), and anterior/posterior trunk inclination (p = 0.0039). Regarding neurophysiological outcomes, the pressure pain threshold was enhanced and statistically significant in all areas assessed. Moreover, tensiomyography highlighted a statistically significant improvement in the maximal radial displacement of the ipsilateral erector spinae muscles. (4) Conclusions: The clinical and neurophysiological outcomes suggest both peripheral and central effects of exergaming. Peripherally, exergaming seems to lead to a postural trunk correction through a reduction in muscle stiffness in the ipsilateral erector spinae. Centrally, exergaming seems to lead to a central pain modulation through an upregulation of cortical connectivity associated with cognitive tasks. Taken together, these results also indicate that exergaming can be a feasible and enjoyable complement to traditional rehabilitation, potentially enhancing patients’ motivation and adherence. Full article
(This article belongs to the Special Issue Exercise, Fitness, Human Performance and Health: 2nd Edition)
30 pages, 2086 KiB  
Review
Hybrid Renewable Energy Systems—A Review of Optimization Approaches and Future Challenges
by Akvile Giedraityte, Sigitas Rimkevicius, Mantas Marciukaitis, Virginijus Radziukynas and Rimantas Bakas
Appl. Sci. 2025, 15(4), 1744; https://doi.org/10.3390/app15041744 (registering DOI) - 8 Feb 2025
Viewed by 442
Abstract
The growing need for sustainable energy solutions has propelled the development of Hybrid Renewable Energy Systems (HRESs), which integrate diverse renewable sources like solar, wind, biomass, geothermal, hydropower and tidal. This review paper focuses on balancing economic, environmental, social and technical criteria to [...] Read more.
The growing need for sustainable energy solutions has propelled the development of Hybrid Renewable Energy Systems (HRESs), which integrate diverse renewable sources like solar, wind, biomass, geothermal, hydropower and tidal. This review paper focuses on balancing economic, environmental, social and technical criteria to enhance system performance and resilience. Using comprehensive methodologies, the review examines state-of-the-art algorithms such as Multi-Objective Particle Swarm Optimization (MOPSO) and Non-Dominated Sorting Genetic Algorithm II (NSGA-II), alongside Crow Search Algorithm (CSA), Grey Wolf Optimizer (GWO), Levy Flight-Salp Swarm Algorithm (LF-SSA), Mixed-Integer Linear Programming (MILP) and tools like HOMER Pro 3.12–3.16 and MATLAB 9.1–9.13, which have been instrumental in optimizing HRESs. Key findings highlight the growing role of advanced, multi-energy storage technologies in stabilizing HRESs and addressing the intermittency of renewable sources. Moreover, the integration of metaheuristic algorithms with machine learning has enabled dynamic adaptability and predictive optimization, paving the way for real-time energy management. HRES configurations for cost-effectiveness, environmental sustainability, and operational reliability while also emphasizing the transformative potential of emerging technologies such as quantum computing are underscored. This review provides critical insights into the evolving landscape of HRES optimization, offering actionable recommendations for future research and practical applications in achieving global energy sustainability goals. Full article
(This article belongs to the Special Issue Advances in New Sources of Energy and Fuels)
20 pages, 2232 KiB  
Article
Applying Machine Learning to Preselective Weighing of Moving Vehicles
by Paweł Kowaleczko, Tomasz Kamiński, Mariusz Rychlicki, Zbigniew Kasprzyk, Marek Stawowy and Jacek Trzeszkowski
Appl. Sci. 2025, 15(4), 1743; https://doi.org/10.3390/app15041743 (registering DOI) - 8 Feb 2025
Viewed by 462
Abstract
The paper presents the general characteristics of weighing systems for vehicles in motion. A number of problems and constraints that accompany these systems to ensure adequate accuracy in the operation of these systems are pointed out. The efficient operation of WIM systems is [...] Read more.
The paper presents the general characteristics of weighing systems for vehicles in motion. A number of problems and constraints that accompany these systems to ensure adequate accuracy in the operation of these systems are pointed out. The efficient operation of WIM systems is also related to the proper preselection of vehicles for weighing in motion. The next part of the paper presents the basic classification and characteristics of machine learning algorithms, as well as examples of applications and implementations of these algorithms in various industries. The paper presents a model based on the XGBoost algorithm for estimating the weight of vehicles in motion, taking into account key characteristics of vehicles. The model was tested on large datasets from two locations in Poland, achieving high accuracy rates. The results indicate the model’s potential in optimizing preselection systems, allowing for the effective identification of overloaded vehicles. Future work will focus on testing the model at other locations to verify its scalability and operational efficiency. Full article
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14 pages, 3098 KiB  
Article
Aesthetic Speech Therapy: A New Protocol of Exercises Against Facial Aging, Focusing on Facial Muscles
by Luca Levrini, Andrea Carganico, Margherita Caccia, Alessandro Deppieri, Federica Marullo, Stefano Saran, Giorgio Binelli, Marco Iera and Piero Antonio Zecca
Appl. Sci. 2025, 15(4), 1742; https://doi.org/10.3390/app15041742 (registering DOI) - 8 Feb 2025
Viewed by 311
Abstract
The increasing emphasis on appearance and well-being has underscored the significance of self-care. From an aesthetic perspective, this entails addressing the early onset of wrinkles and the initial signs of aging. In response, new techniques have been developed, supplementing existing methods, to mitigate [...] Read more.
The increasing emphasis on appearance and well-being has underscored the significance of self-care. From an aesthetic perspective, this entails addressing the early onset of wrinkles and the initial signs of aging. In response, new techniques have been developed, supplementing existing methods, to mitigate the signs of aging. Aesthetic speech therapy has emerged in recent years as a non-invasive procedure to combat facial aging. The objective of this study is to evaluate its effects on the signs of facial aging in participants subjected to an experimental exercise protocol over a three-month period, focusing on orbicularis and zygomatic muscles, using both a digital evaluation analysis and a self-assessment questionnaire. A cohort of 21 female subjects, aged between 50 and 65, was instructed to perform a series of 4 targeted exercises for 15 min daily over a span of three months. The participants underwent monthly evaluations, each involving the collection of standardized photographic documentation and a three-dimensional facial scan. These scans were subsequently overlaid and analyzed by a colorimetric assay at the conclusion of the study period. Statistical tests were carried out by two-way ANOVA. Additionally, during the final evaluation (T3), the participants completed a questionnaire assessing their satisfaction with their self-image and the non-invasive aesthetic treatment they received. The statistical analysis of the overlays of the collected three-dimensional scans revealed a significant volumetric change around the orbicularis oris muscle. The difference between green and blue pixels was statistically significant (p < 0.05), as was the difference between blue and yellow pixels (p < 0.05). This change did not achieve statistical significance around the zygomatic muscles. The analysis of the participants’ questionnaire responses indicated an increasing level of satisfaction with their self-image at the end of the study compared to T0. Personal confidence increased by 20%, and participants reported a 53% improvement in satisfaction with their appearance in photographs. The observed volumetric changes may be attributed to modifications in the facial muscles targeted by the exercise protocol undertaken by the participants. However, further studies are warranted to delve deeper into this issue, considering the intricate process of facial aging and the complex three-dimensional structure of the face with its various components. Full article
(This article belongs to the Special Issue Artificial Intelligence for Healthcare)
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21 pages, 5910 KiB  
Article
The Association Between Aggressive Driving Behaviors and Elderly Pedestrian Traffic Accidents: The Application of Explainable Artificial Intelligence (XAI)
by Minjun Kim, Dongbeom Kim and Jisup Shim
Appl. Sci. 2025, 15(4), 1741; https://doi.org/10.3390/app15041741 (registering DOI) - 8 Feb 2025
Viewed by 306
Abstract
This study investigates the association between aggressive driving behavior and elderly pedestrian traffic accidents using the Explainable Artificial Intelligence (XAI) method. This study focuses on Seoul, South Korea, where an aging population and urban challenges create a pressing need for pedestrian safety research. [...] Read more.
This study investigates the association between aggressive driving behavior and elderly pedestrian traffic accidents using the Explainable Artificial Intelligence (XAI) method. This study focuses on Seoul, South Korea, where an aging population and urban challenges create a pressing need for pedestrian safety research. The analysis reveals that aggressive driving behaviors, particularly rapid acceleration, rapid deceleration, and speeding, are the most influential factors on the frequency of and deaths from elderly pedestrian traffic accidents. In addition, several built environments and demographic factors such as the number of crosswalks and elderly population play varying roles depending on the spatial match or mismatch between risky driving areas and accident spots. The findings of this study underscore the importance of tailored interventions including well-lit crosswalks, traffic calming measures, and driver education, to reduce the vulnerabilities of elderly pedestrians. The integration of XAI methods provides transparency and interpretability, enabling policymakers to make data-driven decisions. Expanding this approach to other urban contexts with diverse characteristics could validate and refine the findings, contributing to a comprehensive strategy for improving pedestrian safety globally. Full article
(This article belongs to the Special Issue Traffic Safety Measures and Assessment)
21 pages, 2079 KiB  
Article
CFTL: System Log Parsing Method Driven from Clustering According to First Token and Length for Anomaly Detection
by Jie Hu, Lingchun Long, He Sui, Zhaojun Gu and Guangming Zheng
Appl. Sci. 2025, 15(4), 1740; https://doi.org/10.3390/app15041740 (registering DOI) - 8 Feb 2025
Viewed by 455
Abstract
Logs are extensively used to analyze the running states of systems in many fields, such as cyber security, performance monitoring, and fault diagnosis, by recording events representing real-time system states. Conventional system log parsing methods are time-consuming and are prone to overfitting or [...] Read more.
Logs are extensively used to analyze the running states of systems in many fields, such as cyber security, performance monitoring, and fault diagnosis, by recording events representing real-time system states. Conventional system log parsing methods are time-consuming and are prone to overfitting or underfitting. Thus, automatic parsing methods, known as log parsers, have been proposed. However, most log parsers do not exhibit both high accuracy and low running time. In addition, they typically overfit during log template generation; thus, log parsers cannot effectively be deployed in large-scale distributed systems. To solve these problems, this study proposes an efficient heuristic log parsing method. The proposed method first clusters log messages by the first token and their length, then uses specific separation rules to divide them into refined groups, and finally matches the corresponding log templates. The performance of the proposed method was evaluated using reliable datasets and tools. The experimental results demonstrate that the proposed method not only exhibits high accuracy but also a low running time. The number of log events parsed by log parsers of log templates generated by the proposed method is close to the real count, and the proposed method also exhibits good accuracy for subsequent anomaly detection tasks. In addition, the proposed method is lightweight because it employs the most valuable parsing rules. Thus, the proposed method is suitable for deployment in large-scale distributed systems. Full article
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18 pages, 4348 KiB  
Article
Computer Modelling of Heliostat Fields by Ray-Tracing Techniques: Simulating the Sun
by José Carlos Garcia Pereira, Gonçalo Domingos and Luís Guerra Rosa
Appl. Sci. 2025, 15(4), 1739; https://doi.org/10.3390/app15041739 (registering DOI) - 8 Feb 2025
Viewed by 339
Abstract
To computer-simulate solar-concentrating facilities, an accurate knowledge of the Sun’s position as a function of latitude, longitude, time and date is required. In this work, it is reported first a simplified description of a general algorithm, developed by the astronomy community to accomplish [...] Read more.
To computer-simulate solar-concentrating facilities, an accurate knowledge of the Sun’s position as a function of latitude, longitude, time and date is required. In this work, it is reported first a simplified description of a general algorithm, developed by the astronomy community to accomplish that. Our implementation of this algorithm (included in our Light Analysis Modelling package) has been successfully validated against well trusted astronomy data. The software was then used to produce a wide range of results for 2024, for two well-known research facilities, the most northern (Jülich, Germany) and the most southern (Protaras, Cyprus) heliostat fields listed in the official SFERA-III EU project. This includes altitude and azimuth data, sunrise and sunset data, analemma curves, angular speed data and geocentric Sun trajectories around the observer’s position. Other ray-tracing techniques are also reported to help simulate the Sun vectors reaching the solar devices. The truly inspiring results obtained show how important this type of software is, from the scientific and industrial point of view, to better understand our relationship with our neighbor star, the Sun. Full article
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