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Appl. Sci., Volume 15, Issue 24 (December-2 2025) – 35 articles

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21 pages, 1502 KB  
Article
Failure Analysis and Machine Learning-Based Prediction in Urban Drinking Water Systems
by Salih Yılmaz
Appl. Sci. 2025, 15(24), 12887; https://doi.org/10.3390/app152412887 - 5 Dec 2025
Abstract
This work illustrates a machine learning methodology to forecast pipe failure frequencies in drinking water systems to enhance asset management and operational planning. Three supervised regression models—Random Forest Regressor (RFR), Extreme Gradient Boosting (XGB), and Multi-Layer Perceptron (MLP)—were developed and evaluated using historical [...] Read more.
This work illustrates a machine learning methodology to forecast pipe failure frequencies in drinking water systems to enhance asset management and operational planning. Three supervised regression models—Random Forest Regressor (RFR), Extreme Gradient Boosting (XGB), and Multi-Layer Perceptron (MLP)—were developed and evaluated using historical failure data from Malatya, Türkiye. The primary predictive variables identified were pipe diameter, pipe type, pipe age, and seasonal average ambient air temperature. The MLP demonstrated superior performance compared to the other models, attaining the lowest RMSE (1.48) and the highest R2 (0.993) with respect to the training data, effectively capturing the nonlinear characteristics and failure patterns. The MLP was validated using two datasets from 24 District Metered Areas (DMAs) in Sakarya and Kayseri, Türkiye. The model’s anticipated failure frequencies exhibited strong concordance with the observed failure frequencies, even in regions of elevated failure density, indicating the model’s proficiency in identifying high-risk locations and facilitating the prioritization of maintenance activities. The work demonstrates the potential of machine learning in water infrastructure management. It emphasizes the importance of employing a hybrid method with Geographic Information Systems (GISs) in future research to enhance forecast accuracy and spatial analysis. Full article
(This article belongs to the Section Civil Engineering)
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26 pages, 3073 KB  
Article
Energy-Saving Method for Nearby Wireless Battery-Powered Trackers Based on Their Cooperation
by Nerijus Morkevičius, Agnius Liutkevičius, Laura Kižauskienė, Audronė Janavičiūtė and Roman Banakh
Appl. Sci. 2025, 15(24), 12886; https://doi.org/10.3390/app152412886 - 5 Dec 2025
Abstract
The tracking of assets or cargo is one of the main objectives of global logistics and transportation systems, ensuring operational efficiency, security, and timeliness. Currently, battery-operated GPS (Global Positioning System)-based tracking devices are used for this purpose. The main shortcoming of these devices [...] Read more.
The tracking of assets or cargo is one of the main objectives of global logistics and transportation systems, ensuring operational efficiency, security, and timeliness. Currently, battery-operated GPS (Global Positioning System)-based tracking devices are used for this purpose. The main shortcoming of these devices is the lifetime of the batteries because they cannot be replaced or recharged, or because this is simply not economically feasible. Therefore, efficient methods are needed to prolong battery life as much as possible. Various existing energy-saving techniques can be applied to solve this problem. However, none of these consider situations in which multiple tracking devices are transported together and can cooperate to further increase their energy efficiency. In this study, we propose and evaluate the novel lightweight peer-to-peer energy-saving method for nearby wireless battery-powered trackers based on their cooperation. The proposed method is based on the short-range BLE (Bluetooth Low Energy) device discovery mechanism and the dynamic election of the leader tracker (with the highest battery capacity) to report the location of its own and other neighboring trackers to the central server. The experimental evaluation of the proposed method shows that, compared to the traditional approach, where each tracker sends its location individually, the proposed method allows a reduction in the average battery charge required for one position report from 19% to 240% per each cooperating tracker. The average energy consumption for one location report per node decreased from 4.68 mWh using the traditional approach to 3.93 mWh for 2 cooperating devices and 1.92 mWh for 15 cooperating devices. Full article
(This article belongs to the Special Issue Efficient Networking Architectures for Next-Generation Smart and Connected Systems)
20 pages, 2736 KB  
Article
Multi-Level Evaluation for Flexible Load Regulation Potential in Distribution Network Based on Ensemble Clustering
by Wei Lou, Cheng Zhao, Min Pan, Chao Zhen, Hao Liu and Xianjun Qi
Appl. Sci. 2025, 15(24), 12885; https://doi.org/10.3390/app152412885 - 5 Dec 2025
Abstract
With the rapid increase in the renewable energy penetration rate in distribution networks, the volatility and uncertainty on the power supply side have become prominent; thus, it is urgent to fully utilize the regulation potential of the flexible load on the user side [...] Read more.
With the rapid increase in the renewable energy penetration rate in distribution networks, the volatility and uncertainty on the power supply side have become prominent; thus, it is urgent to fully utilize the regulation potential of the flexible load on the user side to maintain the dynamic balance of power. A multi-level evaluation method for flexible load regulation potential based on ensemble clustering is proposed in the paper. First, a data-driven approach based on ensemble clustering is adopted to quantify the user-level regulation potential of flexible load. Second, the bus-level regulation potential of the flexible load is obtained by aggregation calculation. Finally, a quantitative evaluation of the system-level regulation potential of flexible load in the distribution network is realized by constructing three optimization models with different objectives. Case studies show that the proposed method can effectively evaluate the regulation potential of flexible load in the distribution network from multiple levels, i.e., user level, bus level, and system level. Full article
17 pages, 12121 KB  
Article
Control Applications with FPGA: Case of Approaching FPGAs for Students in an Intelligent Control Class
by Dušan Fister, Alen Jakopič and Mitja Truntič
Appl. Sci. 2025, 15(24), 12884; https://doi.org/10.3390/app152412884 - 5 Dec 2025
Abstract
Experience shows that knowledge transfer and understanding of fundamental FPGA principles are greatly improved by exercising laboratory practices and manual hands-on operations. Hence, a case study was performed on two didactic platforms for students of intelligent control techniques that were upgraded with FPGAs [...] Read more.
Experience shows that knowledge transfer and understanding of fundamental FPGA principles are greatly improved by exercising laboratory practices and manual hands-on operations. Hence, a case study was performed on two didactic platforms for students of intelligent control techniques that were upgraded with FPGAs to be involved in laboratory practices. Among others, platforms allow implementation of traditional linear control algorithms, such as PID, or modern non-linear control algorithms, such as fuzzy logic or artificial neural networks. Initially, the underlying physics can be carefully studied, and the mathematical model can be derived. Then, such a model can be discretized into its digital form, an appropriate controller can be designed, and its performance can be compared to the known benchmark. Controllers and control parameters can be practiced by students themselves, offering underlying potential for improving students’ understanding of the fundamentals of FPGA. Full article
(This article belongs to the Special Issue Artificial Intelligence for Learning and Education)
42 pages, 2282 KB  
Review
An Overview of Heavy Metals in Cosmetic Products and Their Toxicological Impact
by Alexandra Jităreanu, Adriana Trifan, Ioana-Cezara Caba, Ioana Mârțu and Luminița Agoroaei
Appl. Sci. 2025, 15(24), 12883; https://doi.org/10.3390/app152412883 - 5 Dec 2025
Abstract
Awareness of cosmetic product safety has grown in recent years. Certain ingredients and impurities, including heavy metals, may pose health risks to consumers. Heavy metals can be present in cosmetics as either intentional ingredients or contaminants, making strict monitoring of these substances essential. [...] Read more.
Awareness of cosmetic product safety has grown in recent years. Certain ingredients and impurities, including heavy metals, may pose health risks to consumers. Heavy metals can be present in cosmetics as either intentional ingredients or contaminants, making strict monitoring of these substances essential. This review assesses the toxicological implications of heavy metals in cosmetics, with a focus on advancements in analytical quantification methods, health risk assessment, and emerging non-animal-based approaches for evaluating toxicological profiles. Recent studies have detected traces of toxic metals, some exceeding permissible levels, in various cosmetic products, highlighting the need for ongoing monitoring programs to address heavy metal contamination. Additionally, the review emphasizes the importance of reliable and validated exposure assessment models and non-animal methodologies for determining systemic toxicity. Full article
(This article belongs to the Special Issue Exposure Pathways and Health Implications of Environmental Chemicals)
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23 pages, 10660 KB  
Article
Noise-Aware Hybrid Compression of Deep Models with Zero-Shot Denoising and Failure Prediction
by Lizhe Zhang, Quan Zhou, Ruihua Liu, Lang Huyan, Juanni Liu and Yi Zhang
Appl. Sci. 2025, 15(24), 12882; https://doi.org/10.3390/app152412882 - 5 Dec 2025
Abstract
Deep learning-based image compression achieves remarkable average rate-distortion performance but is prone to failure on noisy, high-frequency, or high-entropy inputs. This work systematically investigates these failure cases and proposes a noise-aware hybrid compression framework to address them. A High-Frequency Vulnerability Index (HFVI) is [...] Read more.
Deep learning-based image compression achieves remarkable average rate-distortion performance but is prone to failure on noisy, high-frequency, or high-entropy inputs. This work systematically investigates these failure cases and proposes a noise-aware hybrid compression framework to address them. A High-Frequency Vulnerability Index (HFVI) is proposed, integrating frequency energy, encoder Jacobian sensitivity, and texture entropy into a unified measure of degradation susceptibility. Guided by HFVI, the system incorporates a selective zero-shot denoising module (P2PA) and a lightweight hybrid codec selector that determines, for each image, whether P2PA is necessary and selecting the more reliable codec (a learning-based model or JPEG2000) accordingly, without retraining any compression backbones. Experiments span a 200,000-image cross-domain benchmark incorporating general datasets, synthetic noise (eight levels), and real-noise datasets demonstrate that the proposed pipeline improves PSNR by up to 1.28 dB, raises SSIM by 0.02, reduces LPIPS by roughly 0.05, and decreases the failure-case rate by 6.7% over the best baseline (Joint-IC). Additional intensity-profile and cross-validation analyses further validate the robustness and deployment readiness of the method, showing that the hybrid selector provides a practical path toward reliable, noise-adaptive deep image compression. Full article
31 pages, 11595 KB  
Article
PCB-Faster-RCNN: An Improved Object Detection Algorithm for PCB Surface Defects
by Zhige He, Yuezhou Wu, Yang Lv and Yuanqing He
Appl. Sci. 2025, 15(24), 12881; https://doi.org/10.3390/app152412881 - 5 Dec 2025
Abstract
As a fundamental and indispensable component of modern electronic devices, the printed circuit board (PCB) has a complex structure and highly integrated functions, with its manufacturing quality directly affecting the stability and reliability of electronic products. However, during large-scale automated PCB production, its [...] Read more.
As a fundamental and indispensable component of modern electronic devices, the printed circuit board (PCB) has a complex structure and highly integrated functions, with its manufacturing quality directly affecting the stability and reliability of electronic products. However, during large-scale automated PCB production, its surfaces are prone to various defects and imperfections due to uncontrollable factors, such as diverse manufacturing processes, stringent machining precision requirements, and complex production environments, which not only compromise product functionality but also pose potential safety hazards. At present, PCB defect detection in industry still predominantly relies on manual visual inspection, the efficiency and accuracy of which fall short of the automation and intelligence demands in modern electronics manufacturing. To address this issue, in this paper, we have made improvements based on the classical Faster-RCNN object detection framework. Firstly, ResNet-101 is employed to replace the conventional VGG-16 backbone, thereby enhancing the ability to perceive small objects and complex texture features. Then, we extract features from images by using deformable convolution in the backbone network to improve the model’s adaptive modeling capability for deformed objects and irregular defect regions. Finally, the Convolutional Block Attention Module is incorporated into the backbone, leveraging joint spatial and channel attention mechanisms to improve the effectiveness and discriminative power of feature representations. The experimental results demonstrate that the improved model achieves a 4.5% increase in mean average precision compared with the original Faster-RCNN. Moreover, the proposed method exhibits superior detection accuracy, robustness, and adaptability compared with mainstream object detection models, indicating strong potential for engineering applications and industrial deployment. Full article
(This article belongs to the Special Issue Deep Learning Techniques for Object Detection and Tracking)
39 pages, 1016 KB  
Article
The Development and Experimental Evaluation of a Multilingual Speech Corpus for Low-Resource Turkic Languages
by Aidana Karibayeva, Vladislav Karyukin, Ualsher Tukeyev, Balzhan Abduali, Dina Amirova, Diana Rakhimova, Rashid Aliyev and Assem Shormakova
Appl. Sci. 2025, 15(24), 12880; https://doi.org/10.3390/app152412880 - 5 Dec 2025
Abstract
The development of parallel audio corpora for Turkic languages, such as Kazakh, Uzbek, and Tatar, remains a significant challenge in the development of multilingual speech synthesis, recognition systems, and machine translation. These languages are low-resource in speech technologies, lacking sufficiently large audio datasets [...] Read more.
The development of parallel audio corpora for Turkic languages, such as Kazakh, Uzbek, and Tatar, remains a significant challenge in the development of multilingual speech synthesis, recognition systems, and machine translation. These languages are low-resource in speech technologies, lacking sufficiently large audio datasets with aligned transcriptions that are crucial for modern recognition, synthesis, and understanding systems. This article presents the development and experimental evaluation of a speech corpus focused on Turkic languages, intended for use in speech synthesis and automatic translation tasks. The primary objective is to create parallel audio corpora using a cascade generation method, which combines artificial intelligence and text-to-speech (TTS) technologies to generate both audio and text, and to evaluate the quality and suitability of the generated data. To evaluate the quality of synthesized speech, metrics measuring naturalness, intonation, expressiveness, and linguistic adequacy were applied. As a result, a multimodal (Kazakh–Turkish, Kazakh–Tatar, Kazakh–Uzbek) corpus was created, combining high-quality natural Kazakh audio with transcription and translation, along with synthetic audio in Turkish, Tatar, and Uzbek. These corpora offer a unique resource for speech and text processing research, enabling the integration of ASR, MT, TTS, and speech-to-speech translation (STS). Full article
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21 pages, 2416 KB  
Article
Study on the Compression and Collapsibility Characteristics of Yangling Loess Under Different Wet and Dense States
by Xiaohong Sun, Xu Li, Meng Li, Yasheng Luo, Jinlong Wang, Zimin Yin and Haijun Hu
Appl. Sci. 2025, 15(24), 12879; https://doi.org/10.3390/app152412879 - 5 Dec 2025
Abstract
To investigate the deformation characteristics of loess in the Yangling region of Shaanxi Province, China, under different wet-dense states, a fully automatic air pressure consolidation apparatus was used to conduct compression and collapsibility tests. The compression and collapsible deformation mechanisms were revealed from [...] Read more.
To investigate the deformation characteristics of loess in the Yangling region of Shaanxi Province, China, under different wet-dense states, a fully automatic air pressure consolidation apparatus was used to conduct compression and collapsibility tests. The compression and collapsible deformation mechanisms were revealed from the evolution patterns of compression yield pressure, compression coefficient, and collapsible coefficient. The tests results indicate the following: (1) the greater the compaction degree and the smaller the initial water content, the smaller the amplitude of the compression curve change, the greater the compressive yield stress, and the smaller the compression coefficient; a compression curve model considering initial water content and compaction degree was constructed. (2) The collapsibility coefficient shows a trend of first increasing and then decreasing under low pressure compaction and high initial water content, while under high pressure compaction and low initial water content, it exhibits a continuous increase. The increase in compaction degree and initial water content will both lead to a decrease in the coefficient of collapse. The collapsibility coefficient exhibits a more pronounced response under high pressure compared to low pressure. Soil samples with low compaction and low initial water content demonstrate significantly greater collapsibility sensitivity. (3) A collapsible prediction model applicable to Yangling loess was established based on SPSS software, and the research findings can offer theoretical support for the rapid assessment of loess collapsibility in this region. Full article
14 pages, 1152 KB  
Article
Impact of Concurrent Appointment of Recycled Aggregate Quality Managers on Post-Certification Quality Audit Results in Korea
by Soo-Min Jeon, Myun-Jung Kim and Sung-Hoon Kang
Appl. Sci. 2025, 15(24), 12878; https://doi.org/10.3390/app152412878 - 5 Dec 2025
Abstract
This study assessed whether permitting certified recycled aggregate companies to assign both quality and environmental management responsibilities to a single individual affects the effectiveness of post-certification quality management. Using data from 242 post-certification audits conducted in 2023, six regulatory audit items were quantified [...] Read more.
This study assessed whether permitting certified recycled aggregate companies to assign both quality and environmental management responsibilities to a single individual affects the effectiveness of post-certification quality management. Using data from 242 post-certification audits conducted in 2023, six regulatory audit items were quantified using a binary scoring scheme to produce a six-point score for each company. Audit outcomes were compared between companies employing dedicated quality managers (n = 147) and those operating with concurrently appointed managers (n = 95). Before conducting hypothesis testing, skewness, kurtosis, and F-tests were used to verify approximate normality and homogeneity of variances. Two-sample t-tests assuming equal variances revealed no statistically significant differences between the two personnel structures, and the effect size (Cohen’s d = 0.072) indicated negligible practical differences. Additionally, 52 companies (22%) experienced changes in their quality management personnel during the audit period. A separate comparison between companies with and without such changes also showed no statistically significant differences, with a small effect size (d = 0.276). These results suggest that the 2022 regulatory revision authorizing concurrent appointments did not exert any discernible adverse influence on post-certification audit performance and that additional administrative requirements for managing personnel changes may be unnecessary. The findings also highlight recurring deficiencies—particularly in quality testing and equipment management—which warrant continued attention from policymakers, certification bodies, and certified companies seeking to enhance the effectiveness of the recycled aggregate quality certification system. Full article
30 pages, 5550 KB  
Article
Numerical Simulation Investigation of Cuttings Transport Patterns in Horizontal Branch Wells for the Intelligent Drilling Simulation Experimental System
by Bin He, Xingming Wang, Qiaozhu Wang and Zhipeng Xu
Appl. Sci. 2025, 15(24), 12877; https://doi.org/10.3390/app152412877 - 5 Dec 2025
Abstract
Branched horizontal wells are widely applied in oil and gas development. However, their complex structures make cuttings transport and deposition problems more pronounced. In this study, a three-dimensional branched wellbore model was established based on an intelligent drilling and completion simulation system. A [...] Read more.
Branched horizontal wells are widely applied in oil and gas development. However, their complex structures make cuttings transport and deposition problems more pronounced. In this study, a three-dimensional branched wellbore model was established based on an intelligent drilling and completion simulation system. A computational fluid dynamics (CFD) approach, incorporating the Eulerian–Eulerian two-fluid model and the kinetic theory of granular flow, was employed to investigate the effects of wellbore diameter, eccentricity, curvature, flow rate, and rheological parameters on cuttings transport behavior. Results from the steady-state simulations indicate that increasing the wellbore diameter and eccentricity intensifies cuttings deposition at the connection section, with the lower-region concentration rising significantly as the eccentricity increases from 0% to 60%. A larger curvature enhances local flow disturbance but reduces the overall cuttings transport efficiency. Increasing the flow rate improves hole cleaning but may promote cuttings accumulation near the bottom of the main wellbore. As the flow behavior index increases from 0.4 to 0.8, the average cuttings concentration rises from 0.0996 to 0.1008, and the pressure drop increases from 1,010,894 Pa to 1,042,880 Pa, indicating improved transport capacity but higher energy consumption. Experimental results are consistent with the numerical simulation trends, confirming the model’s reliability. This study provides both theoretical and experimental support for optimizing complex wellbore structures and drilling fluid parameters. Full article
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16 pages, 739 KB  
Article
Delivery Reliability Assessment for a Multistate Smart-Grid Network with Transmission-Loss Effect
by Ting-Hau Shih and Yi-Kuei Lin
Appl. Sci. 2025, 15(24), 12876; https://doi.org/10.3390/app152412876 - 5 Dec 2025
Abstract
Assessing the performance of the smart-grid system (SGS) under uncertainty is essential for ensuring a reliable energy supply from the perspective of the grid operator. In this study, a multistate smart-grid network (MSGN) is developed to evaluate the delivery capability of the SGS. [...] Read more.
Assessing the performance of the smart-grid system (SGS) under uncertainty is essential for ensuring a reliable energy supply from the perspective of the grid operator. In this study, a multistate smart-grid network (MSGN) is developed to evaluate the delivery capability of the SGS. An MSGN consists of multiple interconnected facilities, where nodes represent energy sources or converters and arcs denote feeders. The output of each facility in the MSGN is modeled as multistate, as maintenance activities and partial failures can result in multiple possible output levels. During power delivery, transmission losses may arise due to heat dissipation and feeder aging, potentially resulting in insufficient power supply at the demand side. From a smart-grid management perspective, delivery reliability, defined as the probability that the MSGN can successfully deliver sufficient power from energy sources to the destination under transmission loss, is adopted as a performance index for evaluating SGS capability. To compute delivery reliability, a minimal-path-based algorithm is developed. A practical SGS is presented to demonstrate the applicability of the proposed model and to provide managerial insights into smart-grid performance and operational decision-making. Full article
(This article belongs to the Special Issue Smart Service Technology for Industrial Applications, 3rd Edition)
14 pages, 1152 KB  
Article
Lifetime Prediction of Lithium-Ion Batteries Based on the Correlation Between Internal Resistance Growth and State of Health (SoH)
by Hongjong Lee, Byunghyun Lee, Junhee Lee, Junho Choi and Kwonse Kim
Appl. Sci. 2025, 15(24), 12875; https://doi.org/10.3390/app152412875 - 5 Dec 2025
Abstract
This study analyzes the lifetime characteristics and degradation behavior of lithium-ion batteries under increasing charge–discharge cycles. The experiment focused on RE (Real Part), IM (Imaginary Part), and DCIR Degradation% (Direct Current Internal Resistance Degradation). The RE increased from 0.0023 Ω at the initial [...] Read more.
This study analyzes the lifetime characteristics and degradation behavior of lithium-ion batteries under increasing charge–discharge cycles. The experiment focused on RE (Real Part), IM (Imaginary Part), and DCIR Degradation% (Direct Current Internal Resistance Degradation). The RE increased from 0.0023 Ω at the initial state to 0.00293 Ω after 1200 cycles, representing a 28% rise, with a sharp acceleration after 400 cycles due to interfacial resistance buildup and electrolyte decomposition. The IM shifted from negative to positive values, indicating delayed electrochemical reactions and enhanced inductive behavior. A pronounced transition occurred between 400 and 800 cycles, confirming this range as a critical phase of performance degradation. Correlation analysis between SoH (State of Health) and DCIR Degradation% showed that while SoH decreased slightly from 100% to 87.3%, DCIR Degradation% increased significantly to 137.8%, indicating that internal resistance growth is the dominant cause of aging. When SoH falls below 70%, the battery reaches its effective end-of-life, accompanied by severe heat generation and power loss. In conclusion, the combined analysis of RE, IM, and DCIR Degradation% demonstrates that accumulated internal resistance is the key factor determining battery lifetime. Stabilizing the SEI layer, reinforcing electrode structures, and improving electrolyte stability are essential strategies for extending battery durability. Full article
(This article belongs to the Section Applied Industrial Technologies)
19 pages, 5142 KB  
Article
Laser-Raman Analysis of Individual Fluid Inclusions and Hydrocarbon-Fluid Evolution in the Marine–Terrestrial Transitional Longtan Formation Shale, Southern Sichuan Basin
by Longyi Wang, Xizhe Li, Ya’na Chen, Nijun Qi, Wenxuan Yu, Yuchuan Chen, Sijie He, Yuhang Zhou, Yaqi Zhao and Jing Xiang
Appl. Sci. 2025, 15(24), 12874; https://doi.org/10.3390/app152412874 - 5 Dec 2025
Abstract
This study integrates microthermometry and laser-Raman spectroscopy of individual fluid inclusions with basin modelling to reconstruct the hydrocarbon-fluid evolution and multistage re-mobilisation of the Permian Longtan Formation transitional marine–terrestrial shale in the YJ-LJ area, southern Sichuan Basin. Systematic analysis of aqueous two-phase, methane-rich, [...] Read more.
This study integrates microthermometry and laser-Raman spectroscopy of individual fluid inclusions with basin modelling to reconstruct the hydrocarbon-fluid evolution and multistage re-mobilisation of the Permian Longtan Formation transitional marine–terrestrial shale in the YJ-LJ area, southern Sichuan Basin. Systematic analysis of aqueous two-phase, methane-rich, and associated bitumen inclusions hosted in fracture-fill veins and sandy partings identifies four fluid episodes, enabling subdivision of petroleum evolution into five stages. Results show trapping pressure increasing with temperature to a maximum of 107.77 MPa, equivalent vitrinite reflectance (EqVR) between 1.49% and 2.49%, and formation-water salinity that first rises then falls, remaining within the high-salinity continental-leaching brine field. Coupled with thermal-history modelling, shale-oil/gas evolution is divided into: (1) low-pressure slow burial, (2) over-pressured rapid burial, (3) sustained over-pressured deep burial, (4) high-pressure uplift adjustment, and (5) late uplift adjustment. The study demonstrates that over-pressure has been partially preserved, providing critical palaeo-fluid and pressure evidence for exploring transitional marine-terrestrial shale gas. Full article
(This article belongs to the Topic Formation Mechanism and Quantitative Evaluation of Deep to Ultra-Deep High-Quality Reservoirs)
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24 pages, 3447 KB  
Article
Laser Truncation of Silicon Nanowires Fabricated by Ag-Assisted Chemical Etching for Reliable Electrode Deposition in Solar Cells
by Grażyna Kulesza-Matlak, Ewa Sarna, Tomasz Kukulski, Anna Sypień, Mariusz Kuglarz and Kazimierz Drabczyk
Appl. Sci. 2025, 15(24), 12873; https://doi.org/10.3390/app152412873 - 5 Dec 2025
Abstract
Silicon nanowires (SiNWs) fabricated by Ag-assisted metal-assisted chemical etching (MACE) exhibit excellent light-trapping performance, yet their fragile high-aspect-ratio morphology severely limits reliable metallization in photovoltaic devices. Conventional electrode deposition methods often fail on dense SiNW arrays due to poor mechanical stability of the [...] Read more.
Silicon nanowires (SiNWs) fabricated by Ag-assisted metal-assisted chemical etching (MACE) exhibit excellent light-trapping performance, yet their fragile high-aspect-ratio morphology severely limits reliable metallization in photovoltaic devices. Conventional electrode deposition methods often fail on dense SiNW arrays due to poor mechanical stability of the nanowire tips, leading to delamination, inhomogeneous coverage, and high contact resistance. In this work, we introduce a maskless laser-based truncation technique that selectively shortens MACE-derived SiNWs to controlled residual heights of 300–500 nm exclusively within the regions intended for electrode formation, while preserving the full nanowire morphology in active areas. A detailed parametric study of laser power, scanning speed, and pulse repetition frequency allowed the identification of an optimal processing window enabling controlled tip melting without damaging the nanowire roots or the crystalline silicon substrate. High-resolution SEM imaging confirms uniform planarization, well-preserved structural integrity, and the absence of subsurface defects in the laser-processed tracks. Optical reflectance measurements further demonstrate that introducing 2% and 5% truncated surface fractions—corresponding to the minimum and maximum metallized front-grid coverage in industrial Si solar cells—results in only a minimal reflectance increase, preserving the advantageous the light-trapping behavior of the SiNW texture. The proposed laser truncation approach provides a clean, scalable, and industrially compatible route toward creating electrode-ready surfaces on nanostructured silicon, enabling reliable metallization while maintaining optical performance. This method offers strong potential for integration into silicon photovoltaics, photodetectors, and nanoscale electronic and sensing devices. Full article
(This article belongs to the Special Issue Advances in Manufacturing and Machining Processes)
17 pages, 895 KB  
Article
Eccentric Hamstring Strength Monitoring to Predict Injury Risk in Men’s Non-League Professional Football: An Exploratory Cox Regression Study
by Daniel T. Jackson, Richard C. Blagrove, Peter K. Thain, Anthony Weldon, Ferozkhan Jadhakhan, Cain C. T. Clark and Adam L. Kelly
Appl. Sci. 2025, 15(24), 12872; https://doi.org/10.3390/app152412872 - 5 Dec 2025
Abstract
Hamstring-strain injuries (HSIs) are the most prevalent time-loss injuries in professional football. While player monitoring of muscular strength is ubiquitous in professional football, the utility of in-season testing for predicting HSI in non-league football (NLF) settings is unclear. This study aimed to investigate [...] Read more.
Hamstring-strain injuries (HSIs) are the most prevalent time-loss injuries in professional football. While player monitoring of muscular strength is ubiquitous in professional football, the utility of in-season testing for predicting HSI in non-league football (NLF) settings is unclear. This study aimed to investigate if short-term, in-season changes in eccentric hamstring strength are associated with HSI risk and compare the predictive performance to a baseline model. This was a single-season prospective cohort study (36 weeks) in 20 male professional NLF players (nine HSI events). Eccentric hamstring strength was measured twice weekly during Nordic hamstring exercise (NHE) using a NordBord device. Cox proportional hazard models (Andersen–Gill) evaluated the association of HSI with bilateral peak force and inter-limb asymmetry as time-varying and baseline predictors. Nine HSIs occurred (29% of all time-loss injuries; n = 31). The predictive analysis revealed that the baseline model with hazard ratio (HR) of 0.20 (95% CI: 0.09–0.46; C-index = 0.824) outperformed the time-varying model (HR 0.29; 95% CI: 0.15–0.56; C-index = 0.776), with higher bilateral peak force protective across both models. Conversely, inter-limb asymmetry showed no association with HSI risk (HR 1.10; 95% CI: 0.95–1.27; C-index = 0.527). A key related finding was that while single test inter-limb asymmetry measurements were unreliable, stability across the season was good (ICC(1,k) = 0.895). In this cohort, a greater bilateral peak force was protective against HSI, with baseline testing more effective than twice-weekly in-season testing. Inter-limb asymmetry did not predict HSI, and the utility of its isolated use remains unclear despite the stability of players’ season-long profiles. These exploratory findings require confirmation in larger cohorts. Full article
(This article belongs to the Special Issue Advanced Technologies in Physical Therapy and Rehabilitation)
15 pages, 2701 KB  
Article
Design of an Orthogonally Stacked DD Coil-Split Capacitive Plate Hybrid Coupler for UAV Wireless Charging
by Jaehoon Kim and Sangwook Park
Appl. Sci. 2025, 15(24), 12871; https://doi.org/10.3390/app152412871 - 5 Dec 2025
Abstract
This study proposes a hybrid wireless power transfer (WPT) coupler that integrates a Double-D (DD) coil and a Split Capacitive Plate (SCP) for unmanned aerial vehicle (UAV) near-field charging stations. The proposed structure arranges the DD coil and SCP orthogonally in a stacked [...] Read more.
This study proposes a hybrid wireless power transfer (WPT) coupler that integrates a Double-D (DD) coil and a Split Capacitive Plate (SCP) for unmanned aerial vehicle (UAV) near-field charging stations. The proposed structure arranges the DD coil and SCP orthogonally in a stacked configuration, enabling simultaneous utilization of both magnetic and electric field coupling paths. The equivalent circuit is composed of integrated inductive and capacitive coupling branches. The overall network is divided into subcircuits to define transmission matrices, which are then converted into a 2 × 2 S-parameter matrix. To verify the analytical model, the equivalent circuit results were compared with 3D full-wave simulation outcomes, showing a discrepancy of less than 8%, which is acceptable considering circuit simplification and parasitic effects. Furthermore, simulation results under positional and rotational misalignment conditions confirm that the proposed coupler maintains stable power transfer efficiency even beyond a 25% offset range. These results demonstrate that the complementary coupling mechanism, where one dominant coupling mode compensates for the attenuation of the other, operates effectively under misalignment. Consequently, the proposed hybrid coupler provides a promising alternative for enhancing misalignment tolerance in UAV near-field wireless charging systems. Full article
20 pages, 1603 KB  
Article
Optical Studies of Al2O3:ZnO and Al2O3:TiO2 Bilayer Films in UV-VIS-NIR Spectral Range
by Maciej Tram, Natalia Nosidlak, Magdalena M. Szindler, Marek Szindler, Katarzyna Tokarczyk, Piotr Dulian and Janusz Jaglarz
Appl. Sci. 2025, 15(24), 12870; https://doi.org/10.3390/app152412870 - 5 Dec 2025
Abstract
In this work, the results of ellipsometric studies of bilayer films of broadband oxides (Al2O3:ZnO, Al2O3:TiO2) are presented. Thin layers of Al2O3,ZnO and TiO2 were deposited on silicon [...] Read more.
In this work, the results of ellipsometric studies of bilayer films of broadband oxides (Al2O3:ZnO, Al2O3:TiO2) are presented. Thin layers of Al2O3,ZnO and TiO2 were deposited on silicon substrate using the atomic layer deposition (ALD) method. The desired ranges of antireflective properties were selected, and then, based on optical modeling, the appropriate thicknesses of individual layers were determined. Optical constants were determined based on ellipsometric measurements in the spectral range of 193–1690 nm. For several selected samples, this range has been extended to 470–6500 cm−1. B-spline function, Tauc–Lorentz, Cody–Lorentz and Psemi-M0 oscillator models were used to describe the optical properties of the investigated films. Reflectance spectra for layers on a silicon substrate were determined in the range from 200 to 2500 nm. Additionally, complementary studies, SEM and EDS analyses, were also performed. The EDS investigations enabled the determination of the composition of the bilayer films. Spectrophotometric analysis demonstrated consistency between the obtained experimental data and theoretical predictions, confirming the validity of the applied model. The studies showed significant improvement in antireflective properties depending on the thickness of the prepared layers while maintaining an extinction coefficient close to zero, across much of the investigated spectral range, regardless of the layer thickness. Full article
(This article belongs to the Section Optics and Lasers)
15 pages, 1363 KB  
Article
Numerical Simulation Analysis of Foam Drainage Gas Recovery in Horizontal Shale Gas Wells
by Jincai Shen, Xiaogang Chen, Mu Li, Xiaolin Chen, Zonggui Yang, Meng Yang, Hao Huang, Yang Zhang, Meng Zhang, Bo Zhang, Qixing Zhang and Tengfei Sun
Appl. Sci. 2025, 15(24), 12869; https://doi.org/10.3390/app152412869 - 5 Dec 2025
Abstract
Liquid loading is almost inevitable in most shale gas well developments and has a significant impact on both the gas production rate and the overall recovery efficiency of the reservoir. Foam drainage gas recovery is an effective method for removing accumulated liquid and [...] Read more.
Liquid loading is almost inevitable in most shale gas well developments and has a significant impact on both the gas production rate and the overall recovery efficiency of the reservoir. Foam drainage gas recovery is an effective method for removing accumulated liquid and restoring production in gas wells. This technique is cost-effective, easy to operate, and does not interfere with normal gas production, making it widely applied in gas well liquid drainage. In this study, based on the two-phase level set method in COMSOL, numerical models were established for three key production stages of a horizontal shale gas well: early-stage natural flow, mid-stage gas–liquid interface formation, and late-stage foam drainage gas recovery. The gas production process under different stages was simulated to verify the production behavior of gas wells. By setting appropriate simulation parameters, the model can be adapted to simulate gas well performance under various geological and production conditions. Numerical simulation also enables the evaluation of key parameters in foam drainage gas recovery, laying the foundation for process parameter optimization. Full article
(This article belongs to the Special Issue New Technologies and Theories Applied in Oil and Gas Development Under Complex Conditions)
44 pages, 3715 KB  
Review
Recent Advances in Non-Isolated DC/DC Converter Topologies: A Review and Future Perspectives
by Rafael Antonio Acosta-Rodríguez, Javier Rosero-García, Marco Rivera and Knapoj Chaimanekorn
Appl. Sci. 2025, 15(24), 12868; https://doi.org/10.3390/app152412868 - 5 Dec 2025
Abstract
Continuous advancements in power conversion techniques address the growing need for efficiency and adaptability in contemporary energy applications, including e-mobility, renewable energy, and energy storage systems. This work presents a review grounded in the fundamental topologies of power converters and subsequently analyzes their [...] Read more.
Continuous advancements in power conversion techniques address the growing need for efficiency and adaptability in contemporary energy applications, including e-mobility, renewable energy, and energy storage systems. This work presents a review grounded in the fundamental topologies of power converters and subsequently analyzes their modern modifications and technological advances. Traditional structures such as Buck, Boost, Ćuk, and flyback converters remain effective solutions for voltage and current regulation; however, they exhibit limitations when extremely high voltage conversion ratios are required. These constraints have motivated the emergence of more sophisticated architectures capable of overcoming such challenges. In this context, the paper provides a novel characterization and comparative analysis of quadratic and bidirectional converter topologies, emphasizing their capability to efficiently achieve both high and low conversion ratios while minimizing component stress and avoiding extreme load cycles. Quadratic converters demonstrate high performance in nonlinear systems with significant energy demands, whereas bidirectional converters enhance energy management in applications requiring bidirectional power flow, such as electric vehicles and energy storage systems. Full article
16 pages, 3173 KB  
Article
GNSS Vector Networks in a Local Conventional Reference Frame
by Tadeusz Gargula
Appl. Sci. 2025, 15(24), 12867; https://doi.org/10.3390/app152412867 - 5 Dec 2025
Abstract
The paper presents a proposal for a simple method of transforming initial GNSS vectors into a spatial local conventional reference frame. This transformation can serve as an alternative to the complex traditional procedure, which involves projecting coordinates onto a reference ellipsoid, mapping them [...] Read more.
The paper presents a proposal for a simple method of transforming initial GNSS vectors into a spatial local conventional reference frame. This transformation can serve as an alternative to the complex traditional procedure, which involves projecting coordinates onto a reference ellipsoid, mapping them onto a plane of an official local reference frame, and converting ellipsoidal heights into a system of orthometric heights. Local vectors (increments in horizontal coordinates and height differences) are often used in land surveying to analyse relative ground displacement, for example. The article offers a detailed definition of a local conventional reference frame and discusses its potential value for surveying practice. The proposed computation procedure was verified using a control network established to monitor displacement in a mining area. The calculated values of vector components in the local conventional reference frame were compared with the results of the traditional method for transforming GNSS vectors into official local reference frames (the PL-2000 coordinate system and the PL-EVRF2007-NH vertical reference frame). The results of both methods were verified against reference values from typical terrestrial surveys (electronic distance measurement and high-precision geometric levelling). The analysis demonstrates that the proposed numerical procedure is appropriate for control networks with certain areal limitations (up to about 300 m). Full article
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42 pages, 2339 KB  
Article
Machine Learning for Out-of-Sample Prediction of Industry Portfolio Returns Within Multi-Factor Asset Pricing Models
by Esra Sarıoğlu Duran, Turhan Korkmaz and Irem Ersöz Kaya
Appl. Sci. 2025, 15(24), 12866; https://doi.org/10.3390/app152412866 - 5 Dec 2025
Abstract
Accurately predicting asset returns remains a central challenge in finance, with significant implications for portfolio optimization and risk management. In response to the challenge, this study evaluates the predictive performance of machine learning algorithms in estimating excess returns of U.S. industry portfolios, within [...] Read more.
Accurately predicting asset returns remains a central challenge in finance, with significant implications for portfolio optimization and risk management. In response to the challenge, this study evaluates the predictive performance of machine learning algorithms in estimating excess returns of U.S. industry portfolios, within the out-of-sample prediction framework of the Fama–French three-, four-, five- and six-factor asset pricing models. In the analysis, Support Vector Regression, Multilayer Perceptron, Linear Regression, and k-Nearest Neighbor were employed using monthly return data from 1992 to 2022, covering 5-, 10-, 12-, 17-, 30-, 38-, 48-, and 49-portfolio configurations composed of NYSE, AMEX, and NASDAQ-listed firms. The findings reveal that support vector regression achieved the highest number of top-ranked results, producing the most successful outcomes in 305 out of 836 model–portfolio combinations. However, multilayer perceptron achieved the best fit in the largest number of portfolios, ranking first in all groups except the 5-industry configuration. Furthermore, the Fama–French five-factor model outperformed other specifications across all groupings, confirming the value of incorporating profitability and investment information. Predictive performance also varied by industry, as wholesale and manufacturing sectors exhibited strong alignment, whereas utilities and energy-related sectors, likely constrained by structural or regulatory features, remained less responsive and exposed to long-term risks. Full article
16 pages, 2927 KB  
Article
Development of a Laboratory-Integrated Microwave-Assisted Cutting Machine and Its Use in Carbonate Rocks
by Masoud Rostami Ghabankandi, Sair Kahraman, Ramazan Comakli, Mustafa Fener, Andrei Andras and Florin Dumitru Popescu
Appl. Sci. 2025, 15(24), 12865; https://doi.org/10.3390/app152412865 - 5 Dec 2025
Abstract
Full-face tunnel boring machines (TBMs) can be used to excavate hard rocks, but there are still significant issues with low advance rate and high cutter wear. However, roadheaders and other mechanical excavators are unable to cut hard rocks. Research on microwave-assisted excavation has [...] Read more.
Full-face tunnel boring machines (TBMs) can be used to excavate hard rocks, but there are still significant issues with low advance rate and high cutter wear. However, roadheaders and other mechanical excavators are unable to cut hard rocks. Research on microwave-assisted excavation has gained attention recently in an effort to decrease cutter wear, increase the TBM advance rate in hard rock excavation, and enable the mechanical excavation of hard rocks using machines other than TBMs. Nevertheless, neither a laboratory nor a field-based integrated microwave-assisted excavation machine is currently in use. The present paper aims to examine the cuttability of carbonate rock samples in the laboratory using an integrated microwave-assisted linear cutting machine which has just been developed. Samples of carbonate rock were gathered from Türkiye and Romania, and both untreated and treated samples underwent cutting tests with a pointed pick. For both non-microwave and microwave-assisted cutting experiments, optimum specific energy values for each sample were determined. During microwaving at 15 kW, when comparing optimum specific energy values to those of non-microwave cutting, notable decreases were seen. The evaluation of the findings demonstrated the economic viability of cutting carbonate rocks with microwave assistance. Full article
(This article belongs to the Section Earth Sciences)
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22 pages, 3762 KB  
Article
Optimized Quaternary Binder Systems for Sustainable High-Performance Concrete: Insights from Taguchi Design
by Tan-Khoa Nguyen, Khanh-Dung Tran Thi, Duy-Hai Vo and Woubishet Zewdu Taffese
Appl. Sci. 2025, 15(24), 12864; https://doi.org/10.3390/app152412864 - 5 Dec 2025
Abstract
The use of high-volume industrial by-products in high-performance concrete (HPC) production offers a promising and sustainable strategy for reducing ordinary Portland cement (OPC) consumption. However, each pozzolanic material has a unique chemical composition and physical characteristics, making ternary and quaternary binder systems an [...] Read more.
The use of high-volume industrial by-products in high-performance concrete (HPC) production offers a promising and sustainable strategy for reducing ordinary Portland cement (OPC) consumption. However, each pozzolanic material has a unique chemical composition and physical characteristics, making ternary and quaternary binder systems an effective approach for optimizing performance. In this study, quaternary binders comprising OPC partially replaced with Class F fly ash (FA), ground granulated blast-furnace slag (GGBFS), and silica fume (SF) were designed using the Taguchi method, and the mechanical and durability properties of fine-grained HPC were evaluated. Sixteen concrete mixtures were developed considering three factors—FA, GGBFS, and SF replacement levels—each at four dosage levels. The results show that incorporating SF significantly enhanced both mechanical performance and durability. An optimal blend containing 60% OPC, 30% GGBFS, and 10% SF exhibited superior performance compared with the 100% OPC control mix. Additionally, a mixture of 40% OPC, 40% GGBFS, 10% Class F FA, and 10% SF achieved comparable compressive strength to the control, exceeding 100 MPa at 28 days. SEM observations confirmed the dense microstructure of this HPC mix. ANOVA analysis indicated that FA and SF had a significantly greater influence on HPC strength development than GGBFS. Overall, these findings demonstrate the potential of high-volume industrial by-products to produce fine-grained HPC, providing a high-performance and environmentally friendly alternative to conventional OPC-based concrete. Full article
(This article belongs to the Special Issue Latest Advances in Cement and Concrete Composites: 2nd Edition)
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17 pages, 2846 KB  
Article
Air Quality Prediction Affected by Different Activation Functions and Hidden Layer Nodes in Artificial Neural Network Models
by Soo-Min Choi
Appl. Sci. 2025, 15(24), 12863; https://doi.org/10.3390/app152412863 - 5 Dec 2025
Abstract
The effects of different activation functions (sigmoid and hyperbolic tangent), and node numbers in a hidden layer of artificial neural network (ANN) models on urban air quality forecasting were investigated in a coastal city of Korea. The ANN models of multilayer perceptron (MLP) [...] Read more.
The effects of different activation functions (sigmoid and hyperbolic tangent), and node numbers in a hidden layer of artificial neural network (ANN) models on urban air quality forecasting were investigated in a coastal city of Korea. The ANN models of multilayer perceptron (MLP) with a back-propagation training algorithm for error calculation in cases of 13, 15, and 17 nodes in each hidden layer were performed using 15 input independent variables (PM, gas, and meteorological data of Gangneung city (Republic of Korea)), affected by PM and gas of an upwind Beijing city (China). Root mean square error (RMSE) and the coefficient of determination (R2; Pearson R) were evaluated to assess the two models’ forecasting abilities between the predicted and measured values. The values of R by ANN-sig (ANN-tanh) with 13, 15, and 17 hidden neuron numbers were 0.930 (0.950), 0.920 (0.947), 0.926(0.953) on PM10, 0.953 (0.956), 0.927 (0.938), 0.949 (0.960) on PM2.5, and 0.880 (0.959), 0.917 (0.886), and 0.882 (0.939) on NO2. Regardless of node numbers and activation functions, the prediction abilities of the two models were excellent, showing the highest values of R in the ANN-tanh model with more neuron numbers in the hidden layer. Unlike previous studies’ insistence that smaller nodes (larger) in the hidden layer produce the overfit (underfit) result in the ANN model, the present study proves that more nodes in its hidden layer than the input layer can yield the best prediction than any other, as shown in their temporal distributions and scatter plots of predicted and measured data. Future-time air quality forecasting at Gangneung city can be calculated sequentially using its current time data and previous time data from Beijing city, using the suggested empirical formulas. Full article
(This article belongs to the Special Issue Air Quality Monitoring, Analysis and Modeling)
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33 pages, 2753 KB  
Article
Effects of High-Resistance Strength Training and Curcumin-Based Formulation Supplementation on Oxidative Stress, Inflammation, Bone Health, and Muscle Function in Older Adults
by Angel Saez-Berlanga, Javier Gene-Morales, Alvaro Juesas, Pablo Jiménez-Martínez, Carlos Alix-Fages, Julio Fernandez-Garrido, Oscar Caballero, Danica Janicijevic, Veronica Gallo and Juan C. Colado
Appl. Sci. 2025, 15(24), 12862; https://doi.org/10.3390/app152412862 - 5 Dec 2025
Abstract
Objective: The aim was to evaluate the effects of two high-resistance training (RT) protocols combined with curcumin supplementation on antioxidant capacity, systemic inflammation, bone and muscle health, and body composition. Methods: Eighty-one apparently healthy older adults [(68.2 ± 4.6 years (57% women); BMI [...] Read more.
Objective: The aim was to evaluate the effects of two high-resistance training (RT) protocols combined with curcumin supplementation on antioxidant capacity, systemic inflammation, bone and muscle health, and body composition. Methods: Eighty-one apparently healthy older adults [(68.2 ± 4.6 years (57% women); BMI 26.4 ± 4.8 kg/m2; minimally active according to IPAQ] were randomly allocated to accentuated eccentric (Aecc), maximal strength (Max), or a non-training control (C). Additionally, participants received either a bio-optimized curcumin formulation (Cur) or a placebo (Pla), resulting in six study groups: Aecc-Cur, Aecc-Pla, Max-Cur, Max-Pla, C-Cur, and C-Pla. Participants underwent pre- and post-intervention assessments of oxidative stress, inflammation, and bone health parameters, whole-body composition, and muscle function. Aecc and Max performed six familiarization sessions and a 16-week intervention. Participants in the curcumin groups received 500 mg/day of a bio-optimized curcumin formulation (CursolTM; 2 × 250 mg capsules per day, corresponding to 10.50 mg/day of curcumin) throughout the intervention. Data were analyzed using three-way repeated-measures ANOVA/ANCOVA with time (pre–post) as the within-subject factor and training group and supplementation as between-subject factors, with Least Significant Difference post hoc comparisons and effect sizes (Hedges’ g, ηp2) reported, and the significance level set at p < 0.05. Results: Aecc was the most effective in improving antioxidant capacity (glutathione; F = 25.57, p ≤ 0.001, ηp2 = 0.262) and bone biomarkers (serum-procollagen type I N-propeptide—P1NP, p ≤ 0.001, ηp2 = 0.504; serum beta C-terminal cross-linked telopeptide of type I collagen—β-CTX—p = 0.022, ηp2 = 0.074, and their ratio—P1NP/β-CTX—p ≤ 0.001, ηp2 = 0.605). Interleukin-6 (IL-6) decreased more in Aecc (p ≤ 0.001, ηp2 = 0.584) and tumor necrosis factor-alpha (TNF-α) in Max (p ≤ 0.001, ηp2 = 0.471). Both groups similarly improved body composition and muscle function. Bone mineral density was generally unchanged. Overall, curcumin supplementation enhanced the benefits of high-RT programs (further glutathione increase in Aecc [Hedge’s g: 0.49]; IL-6 decrease in both modalities [Hedge’s g: 0.48–1.27]; decrease in TNF-α in controls [Hedge’s g: 0.47]; better outcomes in P1NP/β-CTX in all groups [Hedge’s g: 0.46–1.46]; among others). Conclusions: Aecc is recommended for supporting antioxidant capacity and bone health, while the choice between Aecc and Max may depend on the individual’s inflammatory profile. Curcumin supplementation further amplifies the benefits of both RT protocols across most outcome variables. Full article
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18 pages, 4290 KB  
Article
A Traveling Multi-Analyte Chemosensor Based on Wet-Chemical Colorimetry for Shipboard Seawater Analysis
by Jianzhang Wang, Yingxia Wu, Jian Zhang, Shengli Wang and Hongliang Wang
Appl. Sci. 2025, 15(24), 12861; https://doi.org/10.3390/app152412861 - 5 Dec 2025
Abstract
Continuous monitoring of seawater nutrients is crucial for marine resource research and conservation, yet it faces challenges due to the constraints of offshore working conditions. We developed a multi-analyte sensor based on flow analysis technology, which integrates wet-chemical colorimetry/fluorometry for the simultaneous in [...] Read more.
Continuous monitoring of seawater nutrients is crucial for marine resource research and conservation, yet it faces challenges due to the constraints of offshore working conditions. We developed a multi-analyte sensor based on flow analysis technology, which integrates wet-chemical colorimetry/fluorometry for the simultaneous in situ determination of nitrite, nitrate, ammonium, silicate, and phosphate in seawater. To mitigate bubble interference, an integrated gas-trapping cavity was designed, and a data-cleaning algorithm based on the interquartile range method was implemented. In June 2025, a sea trial was conducted at two stations in the northern South China Sea, the results of which showed high consistency with laboratory standard methods: the maximum absolute relative errors were 1.79% for nitrite, 5.01% for nitrate, 1.42% for ammonium, 5.93% for phosphate, and 2.95% for silicate. The performance under real marine conditions is demonstrated by relative errors below 6% and linear correlation coefficients exceeding 0.999 for all parameters. This research demonstrates a practical approach for in situ marine observation. Full article
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21 pages, 15109 KB  
Article
The Effect of Heat Treatment on the Abrasive Wear Resistance of Boron-Alloyed Armor Steel Welded Joints
by Martyna Zemlik, Beata Białobrzeska, Mateusz Stachowicz and Łukasz Konat
Appl. Sci. 2025, 15(24), 12860; https://doi.org/10.3390/app152412860 - 5 Dec 2025
Abstract
As a result of welding processes in boron-alloyed martensitic armor steels, unfavorable microstructural changes occur, leading to a significant reduction in the mechanical properties of both the weld metal and the base material. The dendritic structure of the weld metal and the partial [...] Read more.
As a result of welding processes in boron-alloyed martensitic armor steels, unfavorable microstructural changes occur, leading to a significant reduction in the mechanical properties of both the weld metal and the base material. The dendritic structure of the weld metal and the partial tempering in the heat-affected zone contribute to the decreased durability of structural components, thereby deteriorating their performance. This issue is particularly important since such steels are widely used not only in the defense industry but also in the mining, construction, transportation, and metallurgical sectors, where they operate under conditions of intensive abrasive wear. For this reason, the authors attempted to improve the mechanical properties of welded joints of boron-alloyed martensitic armor steel (with a nominal hardness of 500 HBW) through post-weld heat treatment. The welded joint was evaluated based on metallographic examinations using light microscopy and scanning electron microscopy, as well as abrasive wear tests carried out on a T-07 tribotester. The conducted investigations demonstrated that, under loose abrasive conditions (using electrofused alumina), heat treatment increased the wear resistance of the joints by 55% compared to the as-welded condition. The obtained results were compared with selected grades of Hardox steel commonly used in industrial applications. Full article
(This article belongs to the Special Issue Advanced Welding Technology and Its Applications)
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20 pages, 3620 KB  
Article
EMS-UKAN: An Efficient KAN-Based Segmentation Network for Water Leakage Detection of Subway Tunnel Linings
by Meide He, Lei Tan, Xiaohui Yang, Fei Liu, Zhimin Zhao and Xiaochun Wu
Appl. Sci. 2025, 15(24), 12859; https://doi.org/10.3390/app152412859 - 5 Dec 2025
Abstract
Water leakage in subway tunnel linings poses significant risks to structural safety and long-term durability, making accurate and efficient leakage detection a critical task. Existing deep learning methods, such as UNet and its variants, often suffer from large parameter sizes and limited ability [...] Read more.
Water leakage in subway tunnel linings poses significant risks to structural safety and long-term durability, making accurate and efficient leakage detection a critical task. Existing deep learning methods, such as UNet and its variants, often suffer from large parameter sizes and limited ability to capture multi-scale features, which restrict their applicability in real-world tunnel inspection. To address these issues, we propose an Efficient Multi-Scale U-shaped KAN-based Segmentation Network (EMS-UKAN) for detecting water leakage in subway tunnel linings. To reduce computational cost and enable edge-device deployment, the backbone replaces conventional convolutional layers with depthwise separable convolutions, and an Edge-Enhanced Depthwise Separable Convolution Module (EEDM) is incorporated in the decoder to strengthen boundary representation. The PKAN Block is introduced in the bottleneck to enhance nonlinear feature representation and improve the modeling of complex relationships among latent features. In addition, an Adaptive Multi-Scale Feature Extraction Block (AMS Block) is embedded within early skip connections to capture both fine-grained and large-scale leakage features. Extensive experiments on the newly collected Tunnel Water Leakage (TWL) dataset demonstrate that EMS-UKAN outperforms classical models, achieving competitive segmentation performance. In addition, it effectively reduces computational complexity, providing a practical solution for real-world tunnel inspection. Full article
(This article belongs to the Special Issue AI- and Digital Twin-Driven Intelligent Diagnostics and Predictive Maintenance for Transportation Systems)
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20 pages, 16525 KB  
Article
Fault Diagnosis of Core Drilling Rig Gearbox Based on Transformer and DCA-xLSTM
by Xiaolong Wu, Yaosen Du, Pengju Gao, Xiaoren Tang, Jianxun Liu and Hanchen Ma
Appl. Sci. 2025, 15(24), 12858; https://doi.org/10.3390/app152412858 - 5 Dec 2025
Abstract
The gearbox is a core component of drilling rigs, valued for its high efficiency and load capacity. However, prolonged operation under heavy loads makes it prone to wear and failure. Complicating diagnosis, the vibration signals generated are highly complex and nonlinear. To achieve [...] Read more.
The gearbox is a core component of drilling rigs, valued for its high efficiency and load capacity. However, prolonged operation under heavy loads makes it prone to wear and failure. Complicating diagnosis, the vibration signals generated are highly complex and nonlinear. To achieve accurate fault diagnosis under varying operating conditions, we propose a novel method named T-DCAx, which integrates a Dual-path Convolutional Attention network, an extended Long Short-Term Memory network (xLSTM), and a Transformer. Our model leverages the complementary strengths of these components: the xLSTM module, enhanced with exponential gating and a novel memory mechanism, excels at modeling long-term temporal dependencies and mitigating gradient vanishing issues. The Transformer module effectively captures global contextual information through self-attention. These are synergized with a dual-path convolutional attention structure to ensure effective joint learning of both local–temporal and global patterns. Finally, a dedicated gearbox test platform was established to collect vibration signals under various conditions and fault types. The proposed T-DCAx method was validated on this dataset and demonstrated superior performance against several benchmark methods in comparative analyses. Full article
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