Celebrating
Peer Review
Week 2025
 
21 pages, 3489 KB  
Article
GA-YOLOv11: A Lightweight Subway Foreign Object Detection Model Based on Improved YOLOv11
by Ning Guo, Min Huang and Wensheng Wang
Sensors 2025, 25(19), 6137; https://doi.org/10.3390/s25196137 (registering DOI) - 4 Oct 2025
Abstract
Modern subway platforms are generally equipped with platform screen door systems to enhance safety, but the gap between the platform screen doors and train doors may cause passengers or objects to become trapped, leading to accidents. Addressing the issues of excessive parameter counts [...] Read more.
Modern subway platforms are generally equipped with platform screen door systems to enhance safety, but the gap between the platform screen doors and train doors may cause passengers or objects to become trapped, leading to accidents. Addressing the issues of excessive parameter counts and computational complexity in existing foreign object intrusion detection algorithms, as well as false positives and false negatives for small objects, this article introduces a lightweight deep learning model based on YOLOv11n, named GA-YOLOv11. First, a lightweight GhostConv convolution module is introduced into the backbone network to reduce computational resource waste in irrelevant areas, thereby lowering model complexity and computational load. Additionally, the GAM attention mechanism is incorporated into the head network to enhance the model’s ability to distinguish features, enabling precise identification of object location and category, and significantly reducing the probability of false positives and false negatives. Experimental results demonstrate that in comparison to the original YOLOv11n model, the improved model achieves 3.3%, 3.2%, 1.2%, and 3.5% improvements in precision, recall, mAP@0.5, and mAP@0.5: 0.95, respectively. In contrast to the original YOLOv11n model, the number of parameters and GFLOPs were reduced by 18% and 7.9%, respectfully, while maintaining the same model size. The improved model is more lightweight while ensuring real-time performance and accuracy, designed for detecting foreign objects in subway platform gaps. Full article
(This article belongs to the Special Issue Image Processing and Analysis for Object Detection: 3rd Edition)
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16 pages, 2720 KB  
Article
Shale Oil T2 Spectrum Inversion Method Based on Autoencoder and Fourier Transform
by Jun Zhao, Shixiang Jiao, Li Bai, Bing Xie, Yan Chen, Zhenguan Wu and Shaomin Zhang
Geosciences 2025, 15(10), 387; https://doi.org/10.3390/geosciences15100387 (registering DOI) - 4 Oct 2025
Abstract
Accurate inversion of the T2 spectrum of shale oil reservoir fluids is crucial for reservoir evaluation. However, traditional nuclear magnetic resonance inversion methods face challenges in extracting features from multi-exponential decay signals. This study proposed an inversion method that combines autoencoder (AE) [...] Read more.
Accurate inversion of the T2 spectrum of shale oil reservoir fluids is crucial for reservoir evaluation. However, traditional nuclear magnetic resonance inversion methods face challenges in extracting features from multi-exponential decay signals. This study proposed an inversion method that combines autoencoder (AE) and Fourier transform, aiming to enhance the accuracy and stability of T2 spectrum estimation for shale oil reservoirs. The autoencoder is employed to automatically extract deep features from the echo train, while the Fourier transform is used to enhance frequency domain features of multi-exponential decay information. Furthermore, this paper designs a customized weighted loss function based on a self-attention mechanism to focus the model’s learning capability on peak regions, thereby mitigating the negative impact of zero-value regions on model training. Experimental results demonstrate significant improvements in inversion accuracy, noise resistance, and computational efficiency compared to traditional inversion methods. This research provides an efficient and reliable new approach for precise evaluation of the T2 spectrum in shale oil reservoirs. Full article
(This article belongs to the Section Geophysics)
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22 pages, 2031 KB  
Review
Compressive Sensing for Multimodal Biomedical Signal: A Systematic Mapping and Literature Review
by Anggunmeka Luhur Prasasti, Achmad Rizal, Bayu Erfianto and Said Ziani
Signals 2025, 6(4), 54; https://doi.org/10.3390/signals6040054 (registering DOI) - 4 Oct 2025
Abstract
This study investigated the transformative potential of Compressive Sensing (CS) for optimizing multimodal biomedical signal fusion in Wireless Body Sensor Networks (WBSN), specifically targeting challenges in data storage, power consumption, and transmission bandwidth. Through a Systematic Mapping Study (SMS) and Systematic Literature Review [...] Read more.
This study investigated the transformative potential of Compressive Sensing (CS) for optimizing multimodal biomedical signal fusion in Wireless Body Sensor Networks (WBSN), specifically targeting challenges in data storage, power consumption, and transmission bandwidth. Through a Systematic Mapping Study (SMS) and Systematic Literature Review (SLR) following the PRISMA protocol, significant advancements in adaptive CS algorithms and multimodal fusion have been achieved. However, this research also identified crucial gaps in computational efficiency, hardware scalability (particularly concerning the complex and often costly adaptive sensing hardware required for dynamic CS applications), and noise robustness for one-dimensional biomedical signals (e.g., ECG, EEG, PPG, and SCG). The findings strongly emphasize the potential of integrating CS with deep reinforcement learning and edge computing to develop energy-efficient, real-time healthcare monitoring systems, paving the way for future innovations in Internet of Medical Things (IoMT) applications. Full article
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15 pages, 1917 KB  
Article
Test–Retest Reliability of Ankle Mobility, Balance, and Jump Tests in Amateur Trail Running Athletes
by Alberto Dominguez-Muñoz, José Carmelo Adsuar, Santos Villafaina, Juan Luis Leon-Llamas and Francisco Javier Dominguez-Muñoz
Sports 2025, 13(10), 352; https://doi.org/10.3390/sports13100352 (registering DOI) - 4 Oct 2025
Abstract
This study aimed to test the reliability of seven functional performance tests in amateur trail runners, including ankle mobility, balance, hopping, and countermovement jump (CMJ) tests. The sample consisted of 35 runners who were evaluated in two sessions separated by 7 to 14 [...] Read more.
This study aimed to test the reliability of seven functional performance tests in amateur trail runners, including ankle mobility, balance, hopping, and countermovement jump (CMJ) tests. The sample consisted of 35 runners who were evaluated in two sessions separated by 7 to 14 days, which varied due to participants’ scheduling constraints. Relative reliability was assessed using the Intraclass Correlation Coefficient (ICC, which indicates consistency between repeated measures), the Standard Error of Measurement (SEM, which reflects measurement precision), and the Minimal Detectable Change (MDC, which represents the smallest real change beyond measurement error). The results show high reliability in almost all tests. The Lunge Test obtained an ICC of 0.990 and 0.983 for distance, and 0.941 and 0.958 for angular measurements in both legs. The Hop Tests showed moderate reliability with ICC above 0.7 In contrast, the Y Balance Test demonstrated lower reliability, with ICC values ranging from 0.554 to 0.732. The CMJ test showed good reliability, with an ICC ranging from 0.753 to 0.894, an SEM between 5.79% and 11.3%, and an MDC ranging from 15.54% to 31.44%, making it useful for assessing lower limb explosive strength. Both tests presented comparatively higher error values, which should be considered when interpreting individual changes. These findings support the use of these tests as valid and reliable tools for evaluating ankle dorsiflexion, balance, functional symmetry, and lower limb explosive strength in amateur trail runners, prior to training programs or injury prevention strategies, provided that standardized protocols and validated measuring instruments are used. Full article
(This article belongs to the Special Issue Fostering Sport for a Healthy Life)
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17 pages, 2779 KB  
Article
Self-Reported Outcomes of Endocrine Therapy with or Without Ovarian Suppression in Premenopausal Breast Cancer Patients: A Brazilian Quality-of-Life Prospective Cohort
by Natália Nunes, Giselle Carvalho, Bernardo Ramos, Juliana Pecoraro, Lilian Lerner, Debora Azevedo, Thamirez Ferreira, Larissa Santiago de Moura, Carolina Galvão and Mariana Monteiro
Cancers 2025, 17(19), 3229; https://doi.org/10.3390/cancers17193229 (registering DOI) - 4 Oct 2025
Abstract
Background: Endocrine therapy (ET) with or without ovarian function suppression (OFS) is a cornerstone treatment for estrogen receptor-positive (ER+) breast cancer (BC) in premenopausal women, but its impact on quality of life (QoL) and sexual health remains a concern. Methods: We conducted a [...] Read more.
Background: Endocrine therapy (ET) with or without ovarian function suppression (OFS) is a cornerstone treatment for estrogen receptor-positive (ER+) breast cancer (BC) in premenopausal women, but its impact on quality of life (QoL) and sexual health remains a concern. Methods: We conducted a multicenter, prospective, observational study including premenopausal women (≤50 years) diagnosed with stage I–III ER+ BC and treated in private healthcare facilities in Brazil between 2013 and 2023. Patients received ET alone (ET-only) or combined with OFS (OFS-ET). QoL was assessed at baseline and 3, 6, 9, 12, and 24 months using the EORTC QLQ-BR23. Sexual functioning and sexual enjoyment were prespecified primary outcomes. Logistic regression identified factors associated with OFS use, and Fisher’s exact test was applied for categorical comparisons at 24 months. Results: Among 363 patients (80% ET-only, 20% ET + OFS), younger age, advanced stage, and chemotherapy were independently associated with OFS use. Both groups reported early declines in sexual functioning and enjoyment. By 24 months, ET-only patients had returned to baseline, whereas OFS patients remained below baseline. At the item level, no significant differences were observed in sexual desire (51.5% vs. 42.0%; p = 0.33) or enjoyment (26.0% vs. 13.5%; p = 0.20). Lack of sexual activity was more frequent in the OFS group (60.6% vs. 41.2%; p = 0.05). Body image was significantly more impaired with OFS, with a higher proportion of patients reporting feeling less attractive (38.2% vs. 19.9%; p = 0.04) and less feminine (26.5% vs. 11.7%; p = 0.05). Conclusions: ET impairs sexual health in young BC survivors, particularly when combined with OFS. These findings underscore the need for routine sexual health assessments and supportive interventions in survivorship care. Full article
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27 pages, 2297 KB  
Article
Artificial Intelligence Adoption in Non-Chemical Agriculture: An Integrated Mechanism for Sustainable Practices
by Arokiaraj A. Amalan and I. Arul Aram
Sustainability 2025, 17(19), 8865; https://doi.org/10.3390/su17198865 (registering DOI) - 4 Oct 2025
Abstract
Artificial Intelligence (AI) holds significant potential to enhance sustainable non-chemical agricultural methods (NCAM) by optimising resource management, automating precision farming practices, and strengthening climate resilience. However, its widespread adoption among farmers’ remains limited due to socio-economic, infrastructural, and justice-related challenges. This study investigates [...] Read more.
Artificial Intelligence (AI) holds significant potential to enhance sustainable non-chemical agricultural methods (NCAM) by optimising resource management, automating precision farming practices, and strengthening climate resilience. However, its widespread adoption among farmers’ remains limited due to socio-economic, infrastructural, and justice-related challenges. This study investigates AI adoption among NCAM farmers using an Integrated Mechanism for Sustainable Practices (IMSP) conceptual framework which combines the Technology Acceptance Model (TAM) with a justice-centred approach. A mixed-methods design was employed, incorporating Fuzzy-Set Qualitative Comparative Analysis (fsQCA) of AI adoption pathways based on survey data, alongside critical discourse analysis of thematic farmers narrative through a justice-centred lens. The study was conducted in Tamil Nadu between 30 September and 25 October 2024. Using purposive sampling, 57 NCAM farmers were organised into three focus groups: marginal farmers, active NCAM practitioners, and farmers from 18 districts interested in agricultural technologies and AI. This enabled an in-depth exploration of practices, adoption, and perceptions. The findings indicates that while factors such as labour shortages, mobile technology use, and cost efficiencies are necessary for AI adoption, they are insufficient without supportive extension services and inclusive communication strategies. The study refines the TAM framework by embedding economic, cultural, and political justice considerations, thereby offering a more holistic understanding of technology acceptance in sustainable agriculture. By bridging discourse analysis and fsQCA, this research underscores the need for justice-centred AI solutions tailored to diverse farming contexts. The study contributes to advancing sustainable agriculture, digital inclusion, and resilience, thereby supporting the United Nations’ Sustainable Development Goals (SDGs). Full article
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46 pages, 3080 KB  
Review
Machine Learning for Structural Health Monitoring of Aerospace Structures: A Review
by Gennaro Scarselli and Francesco Nicassio
Sensors 2025, 25(19), 6136; https://doi.org/10.3390/s25196136 (registering DOI) - 4 Oct 2025
Abstract
Structural health monitoring (SHM) plays a critical role in ensuring the safety and performance of aerospace structures throughout their lifecycle. As aircraft and spacecraft systems grow in complexity, the integration of machine learning (ML) into SHM frameworks is revolutionizing how damage is detected, [...] Read more.
Structural health monitoring (SHM) plays a critical role in ensuring the safety and performance of aerospace structures throughout their lifecycle. As aircraft and spacecraft systems grow in complexity, the integration of machine learning (ML) into SHM frameworks is revolutionizing how damage is detected, localized, and predicted. This review presents a comprehensive examination of recent advances in ML-based SHM methods tailored to aerospace applications. It covers supervised, unsupervised, deep, and hybrid learning techniques, highlighting their capabilities in processing high-dimensional sensor data, managing uncertainty, and enabling real-time diagnostics. Particular focus is given to the challenges of data scarcity, operational variability, and interpretability in safety-critical environments. The review also explores emerging directions such as digital twins, transfer learning, and federated learning. By mapping current strengths and limitations, this paper provides a roadmap for future research and outlines the key enablers needed to bring ML-based SHM from laboratory development to widespread aerospace deployment. Full article
(This article belongs to the Special Issue Feature Review Papers in Fault Diagnosis & Sensors)
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11 pages, 512 KB  
Article
Run-Based Tests Performed on an Indoor and Outdoor Surface Are Comparable in Adolescent Rugby League Players
by Michael A. Carron and Vincent J. Dalbo
Sports 2025, 13(10), 351; https://doi.org/10.3390/sports13100351 (registering DOI) - 4 Oct 2025
Abstract
At non-professional levels of rugby league, run-based tests are commonly performed on outdoor turfed fields and on indoor multipurpose sport surfaces, and results are monitored to gauge player performance and progression. However, test–retest reliability has not been conducted on indoor surfaces in adolescent [...] Read more.
At non-professional levels of rugby league, run-based tests are commonly performed on outdoor turfed fields and on indoor multipurpose sport surfaces, and results are monitored to gauge player performance and progression. However, test–retest reliability has not been conducted on indoor surfaces in adolescent rugby league players, and no research has examined if results obtained on outdoor and indoor surfaces are comparable for practitioners. Adolescent, male, rugby league players (N = 15; age = 17.1 ± 0.7 years) completed a 20 m linear sprint test (10- and 20 m splits), 505-Agility Test, and Multistage Fitness Test (MSFT) weekly for three consecutive weeks. Absolute (coefficient of variation (CV)) and relative (intraclass correlation coefficient (ICC)) reliability of each run-based test performed on the indoor surface was quantified. Dependent t-tests, Hedges g, and 95% confidence intervals were used to examine if differences in performance occurred between indoor and outdoor surfaces. Effect size magnitudes were determined as Trivial: <0.20, Small: 0.20–0.49, Medium: 0.50–0.79, and Large: ≥0.80. All tests were considered reliable on the indoor surface (CV < 5.0%; ICCs = moderate-good) except for the 505-Agility Test (CV = 4.6–5.1%; ICCs = poor). Non-significant (p > 0.05), trivial differences were revealed between surface types for 10 (g = 0.15, 95% CI = −0.41 to 0.70) and 20 m (g = 0.06, 95% CI = −0.49 to 0.61) sprint tests, the 505-Agility Test (Right: g = −0.53, 95% CI = −1.12 to 0.06; Left: g = −0.40, 95% CI = −0.97 to 0.17), and the MSFT (g = 0.25, 95% CI = −0.31 to 0.81). The 10 and 20 m linear sprint test and MSFT have acceptable test–retest reliability on an indoor multipurpose sport surface, and practitioners may compare results of run-based tests obtained on an outdoor and indoor surface. Full article
(This article belongs to the Special Issue Sport-Specific Testing and Training Methods in Youth)
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24 pages, 1040 KB  
Article
The SIOA Algorithm: A Bio-Inspired Approach for Efficient Optimization
by Vasileios Charilogis, Ioannis G. Tsoulos, Dimitrios Tsalikakis and Anna Maria Gianni
AppliedMath 2025, 5(4), 135; https://doi.org/10.3390/appliedmath5040135 (registering DOI) - 4 Oct 2025
Abstract
The Sporulation-Inspired Optimization Algorithm (SIOA) is an innovative metaheuristic optimization method inspired by the biological mechanisms of microbial sporulation and dispersal. SIOA operates on a dynamic population of solutions (“microorganisms”) and alternates between two main phases: sporulation, where new “spores” are generated through [...] Read more.
The Sporulation-Inspired Optimization Algorithm (SIOA) is an innovative metaheuristic optimization method inspired by the biological mechanisms of microbial sporulation and dispersal. SIOA operates on a dynamic population of solutions (“microorganisms”) and alternates between two main phases: sporulation, where new “spores” are generated through adaptive random perturbations combined with guided search towards the global best, and germination, in which these spores are evaluated and may replace the most similar and less effective individuals in the population. A distinctive feature of SIOA is its fully self-adaptive parameter control, where the dispersal radius and the probabilities of sporulation and germination are dynamically adjusted according to the progress of the search (e.g., convergence trends of the average fitness). The algorithm also integrates a special “zero-reset” mechanism, enhancing its ability to detect global optima located near the origin. SIOA further incorporates a stochastic local search phase to refine solutions and accelerate convergence. Experimental results demonstrate that SIOA achieves high-quality solutions with a reduced number of function evaluations, especially in complex, multimodal, or high-dimensional problems. Overall, SIOA provides a robust and flexible optimization framework, suitable for a wide range of challenging optimization tasks. Full article
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19 pages, 715 KB  
Article
Can Digital Finance Unleash the Potential for Household Consumption? A Comparison Based on the Inconsistency Between Income and Consumption Classes
by Ziqing Du and Guangming Lv
J. Theor. Appl. Electron. Commer. Res. 2025, 20(4), 275; https://doi.org/10.3390/jtaer20040275 (registering DOI) - 4 Oct 2025
Abstract
The high savings propensity of households has led to inconsistencies between income and consumption classes in China. The issue of unleashing the consumption potential of households arouses public concern. This paper explores the effect of digital finance DF on unleashing the consumption potential [...] Read more.
The high savings propensity of households has led to inconsistencies between income and consumption classes in China. The issue of unleashing the consumption potential of households arouses public concern. This paper explores the effect of digital finance DF on unleashing the consumption potential of households from the perspective of household consumption habits. By examining the inconsistency between income and consumption classes, the findings reveal that households in China have substantial untapped consumption potential, and that prioritizing household consumption potential is a more effective approach to stimulating consumption. The mechanism analysis shows that DF facilitates consumption growth through both reducing time costs and precautionary savings, as well as easing credit and liquidity constraints. Notably, these effects are more pronounced in underconsumption households compared to equal-consumption households. Full article
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19 pages, 1508 KB  
Article
The Digitalization–Performance Nexus in the European Union: A Country-Level Analysis of Heterogeneity and Complementarities
by Dragos Paun, Ciprian Adrian Paun and Nicolae Paun
J. Theor. Appl. Electron. Commer. Res. 2025, 20(4), 274; https://doi.org/10.3390/jtaer20040274 (registering DOI) - 4 Oct 2025
Abstract
This study investigates the multifaceted impact of digitalization on economic performance across the 27 European Union member states from 2017 to 2023. Using a comprehensive panel dataset, the analysis moves beyond aggregate metrics to dissect how specific digital levers contribute to trade performance [...] Read more.
This study investigates the multifaceted impact of digitalization on economic performance across the 27 European Union member states from 2017 to 2023. Using a comprehensive panel dataset, the analysis moves beyond aggregate metrics to dissect how specific digital levers contribute to trade performance and national income. A two-way fixed effects (FEs) regression model is employed to rigorously control for unobserved country-specific heterogeneity and common time-based shocks, with diagnostic tests confirming the suitability of this specification. The results reveal a complex and often counter-intuitive set of relationships. One key finding is a statistically significant negative association between the EU’s headline Digital Economy and Society Index (DESI) and goods exports, a paradox that emerges in the model once specific business-level digital tools are accounted for. This suggests that composite indices can be misleading for granular policy analysis. The marginal benefit of cloud adoption diminishes significantly in countries with higher levels of public investment in Research and Development (R&D), indicating a substitution rather than a complementary relationship between these two innovation channels. Full article
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19 pages, 2498 KB  
Article
Multi-Modal Biomarker Profiling of Tumor Microenvironment and Genomic Alterations to Enhance Immunotherapy Stratification in Melanoma
by Meshack Bida, Thabiso Victor Miya, Tebogo Marutha, Rodney Hull, Mohammed Alaouna and Zodwa Dlamini
Curr. Issues Mol. Biol. 2025, 47(10), 821; https://doi.org/10.3390/cimb47100821 - 3 Oct 2025
Abstract
Tumor mutational burden (TMB) and tumor-infiltrating lymphocytes (TILs) are key biomarkers for predicting immunotherapy responses in cutaneous melanoma. The discordance between brisk TIL morphology and absent cytokine signals complicates immune profiling. We examined the interactions between TMB, TIL patterns, cytokine expression, and genomic [...] Read more.
Tumor mutational burden (TMB) and tumor-infiltrating lymphocytes (TILs) are key biomarkers for predicting immunotherapy responses in cutaneous melanoma. The discordance between brisk TIL morphology and absent cytokine signals complicates immune profiling. We examined the interactions between TMB, TIL patterns, cytokine expression, and genomic alterations to uncover immune escape mechanisms and refine prognostic tools. A structure-based BRAF druggability analysis was performed to anchor the genomic findings in a therapeutic context. Primary cutaneous melanoma cases (N = 205) were classified as brisk (n = 65), non-brisk (n = 60), or absent TILs (n = 80) according to the American association for cancer research (AACR) guidelines. Inter-observer concordance was measured using intraclass correlation. Tumor necrosis factor alpha (TNF-α) and interferon gamma (IFN-γ) levels were graded using immunohistochemistry. Eleven brisk TIL cases lacking TNF-α expression were analyzed using the (Illumina TruSight Oncology 500, Illumina-San Diego, CA, USA). Dabrafenib docking to the BRAF ATP site was performed with Glide SP/XP and rescored with Prime MM-GBSA. Brisk TILs lacking cytokine signals suggested post-translational silencing of TNF-α/IFN-γ. Among the 11 profiled cases, eight exhibited high TMB and copy number alterations, with enrichment of nine metastasis/immune regulation genes. Inter-observer concordance was high (absent TILs, 95%; brisk TILs, 90.7%). BRAF docking yielded a canonical type-I pose and strong ATP pocket engagement (ΔG_bind −84.93 kcal·mol−1). Single biomarkers are insufficient for diagnosis. A multiparametric framework combining histology, cytokine immunohistochemistry (IHC), and genomic profiling enhances stratification and reveals immune escape pathways, with BRAF modeling providing a mechanistic anchor for the targeted therapy. Full article
(This article belongs to the Section Molecular Medicine)
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58 pages, 4299 KB  
Article
Optimisation of Cryptocurrency Trading Using the Fractal Market Hypothesis with Symbolic Regression
by Jonathan Blackledge and Anton Blackledge
Commodities 2025, 4(4), 22; https://doi.org/10.3390/commodities4040022 - 3 Oct 2025
Abstract
Cryptocurrencies such as Bitcoin can be classified as commodities under the Commodity Exchange Act (CEA), giving the Commodity Futures Trading Commission (CFTC) jurisdiction over those cryptocurrencies deemed commodities, particularly in the context of futures trading. This paper presents a method for predicting both [...] Read more.
Cryptocurrencies such as Bitcoin can be classified as commodities under the Commodity Exchange Act (CEA), giving the Commodity Futures Trading Commission (CFTC) jurisdiction over those cryptocurrencies deemed commodities, particularly in the context of futures trading. This paper presents a method for predicting both long- and short-term trends in selected cryptocurrencies based on the Fractal Market Hypothesis (FMH). The FMH applies the self-affine properties of fractal stochastic fields to model financial time series. After introducing the underlying theory and mathematical framework, a fundamental analysis of Bitcoin and Ethereum exchange rates against the U.S. dollar is conducted. The analysis focuses on changes in the polarity of the ‘Beta-to-Volatility’ and ‘Lyapunov-to-Volatility’ ratios as indicators of impending shifts in Bitcoin/Ethereum price trends. These signals are used to recommend long, short, or hold trading positions, with corresponding algorithms (implemented in Matlab R2023b) developed and back-tested. An optimisation of these algorithms identifies ideal parameter ranges that maximise both accuracy and profitability, thereby ensuring high confidence in the predictions. The resulting trading strategy provides actionable guidance for cryptocurrency investment and quantifies the likelihood of bull or bear market dominance. Under stable market conditions, machine learning (using the ‘TuringBot’ platform) is shown to produce reliable short-horizon estimates of future price movements and fluctuations. This reduces trading delays caused by data filtering and increases returns by identifying optimal positions within rapid ‘micro-trends’ that would otherwise remain undetected—yielding gains of up to approximately 10%. Empirical results confirm that Bitcoin and Ethereum exchanges behave as self-affine (fractal) stochastic fields with Lévy distributions, exhibiting a Hurst exponent of roughly 0.32, a fractal dimension of about 1.68, and a Lévy index near 1.22. These findings demonstrate that the Fractal Market Hypothesis and its associated indices provide a robust market model capable of generating investment returns that consistently outperform standard Buy-and-Hold strategies. Full article
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19 pages, 5024 KB  
Article
A Study on Geometrical Consistency of Surfaces Using Partition-Based PCA and Wavelet Transform in Classification
by Vignesh Devaraj, Thangavel Palanisamy and Kanagasabapathi Somasundaram
AppliedMath 2025, 5(4), 134; https://doi.org/10.3390/appliedmath5040134 - 3 Oct 2025
Abstract
The proposed study explores the consistency of the geometrical character of surfaces under scaling, rotation and translation. In addition to its mathematical significance, it also exhibits advantages over image processing and economic applications. In this paper, the authors used partition-based principal component analysis [...] Read more.
The proposed study explores the consistency of the geometrical character of surfaces under scaling, rotation and translation. In addition to its mathematical significance, it also exhibits advantages over image processing and economic applications. In this paper, the authors used partition-based principal component analysis similar to two-dimensional Sub-Image Principal Component Analysis (SIMPCA), along with a suitably modified atypical wavelet transform in the classification of 2D images. The proposed framework is further extended to three-dimensional objects using machine learning classifiers. To strengthen fairness, we benchmarked against both Random Forest (RF) and Support Vector Machine (SVM) classifiers using nested cross-validation, showing consistent gains when TIFV is included. In addition, we carried out a robustness analysis by introducing Gaussian noise to the intensity channel, confirming that TIFV degrades much more gracefully compared to traditional descriptors. Experimental results demonstrate that the method achieves improved performance compared to traditional hand-crafted descriptors such as measured values and histogram of oriented gradients. In addition, it is found to be useful that this proposed algorithm is capable of establishing consistency locally, which is never possible without partition. However, a reasonable amount of computational complexity is reduced. We note that comparisons with deep learning baselines are beyond the scope of this study, and our contribution is positioned within the domain of interpretable, affine-invariant descriptors that enhance classical machine learning pipelines. Full article
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16 pages, 392 KB  
Article
Investigating the Etiology and Demographic Distribution of Enamel Hypoplasia
by Claudia Moro, Lucie Biehler-Gomez, Giuseppe Lanza Attisano, Daniele Maria Gibelli, Federica Boschi, Danilo De Angelis and Cristina Cattaneo
Heritage 2025, 8(10), 420; https://doi.org/10.3390/heritage8100420 - 3 Oct 2025
Abstract
Enamel hypoplasia (EH) is a stress marker commonly used in bioarcheological research to investigate health during growth. However, its analysis in contemporary samples allows for additional avenues of research, including comparison with medical records. The aim of the present research is to explore [...] Read more.
Enamel hypoplasia (EH) is a stress marker commonly used in bioarcheological research to investigate health during growth. However, its analysis in contemporary samples allows for additional avenues of research, including comparison with medical records. The aim of the present research is to explore the influence of biological sex and socioeconomic status on the distribution of EH and examine the factors that contribute to the development of this defect. In this perspective, analysis of dentition was conducted on 132 individuals, with known information about age, biological sex, nationality, medical records, and socioeconomic status. Statistical analysis was conducted using Fisher’s test and the chi-square test. As a result, EH was observed more frequently among individuals from disadvantaged backgrounds, while a significant association was observed with socioeconomic status, evidencing a strong association between EH presence and structural vulnerability (chi-square, p = 0.04). The frequency of EH between sexes was not significant; however, a higher frequency was observed among males (chi-square, p = 0.94). We hypothesize that the impact of female biological buffering might be reduced in the European sample, as this result aligns with background information of the context. These results align with the research hypotheses and reinforce the multifactorial etiology of EH. Full article
(This article belongs to the Special Issue Advanced Analysis of Bioarchaeology, Skeletal Biology and Evolution)
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81 pages, 4442 KB  
Systematic Review
From Illusion to Insight: A Taxonomic Survey of Hallucination Mitigation Techniques in LLMs
by Ioannis Kazlaris, Efstathios Antoniou, Konstantinos Diamantaras and Charalampos Bratsas
AI 2025, 6(10), 260; https://doi.org/10.3390/ai6100260 - 3 Oct 2025
Abstract
Large Language Models (LLMs) exhibit remarkable generative capabilities but remain vulnerable to hallucinations—outputs that are fluent yet inaccurate, ungrounded, or inconsistent with source material. To address the lack of methodologically grounded surveys, this paper introduces a novel method-oriented taxonomy of hallucination mitigation strategies [...] Read more.
Large Language Models (LLMs) exhibit remarkable generative capabilities but remain vulnerable to hallucinations—outputs that are fluent yet inaccurate, ungrounded, or inconsistent with source material. To address the lack of methodologically grounded surveys, this paper introduces a novel method-oriented taxonomy of hallucination mitigation strategies in text-based LLMs. The taxonomy organizes over 300 studies into six principled categories: Training and Learning Approaches, Architectural Modifications, Input/Prompt Optimization, Post-Generation Quality Control, Interpretability and Diagnostic Methods, and Agent-Based Orchestration. Beyond mapping the field, we identify persistent challenges such as the absence of standardized evaluation benchmarks, attribution difficulties in multi-method systems, and the fragility of retrieval-based methods when sources are noisy or outdated. We also highlight emerging directions, including knowledge-grounded fine-tuning and hybrid retrieval–generation pipelines integrated with self-reflective reasoning agents. This taxonomy provides a methodological framework for advancing reliable, context-sensitive LLM deployment in high-stakes domains such as healthcare, law, and defense. Full article
(This article belongs to the Section AI Systems: Theory and Applications)
5 pages, 587 KB  
Editorial
Special Issue “Understanding Sports-Related Health Issues, 2nd Edition”
by Daniel Rojas-Valverde
J. Funct. Morphol. Kinesiol. 2025, 10(4), 386; https://doi.org/10.3390/jfmk10040386 - 3 Oct 2025
Abstract
Sports-related health issues represent a complex and multifactorial phenomenon that extends far beyond the immediate occurrence of an injury or the onset of an illness [...] Full article
(This article belongs to the Special Issue Understanding Sports-Related Health Issues, 2nd Edition)
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19 pages, 1377 KB  
Article
Effect of Pectin on the Quality Attributes and Phenolic Composition of Blackberry Jam from Wild and Cultivated Fruits at Different Altitudes
by Adis Veliu, Xhabir Abdullahi, Erhan Sulejmani, Omer Faruk Celik, Mehmet Ali Olcer and Burhan Ozturk
Foods 2025, 14(19), 3420; https://doi.org/10.3390/foods14193420 - 3 Oct 2025
Abstract
This study investigated the influence of different pectin concentrations (0%, 0.1%, and 0.5%) on the physicochemical, antioxidant, and sensory properties of blackberry jam (Rubus fruticosus L.) prepared from fruits harvested at three altitudinal locations (wild: 998 m; cultivated: 500 m and 1090 [...] Read more.
This study investigated the influence of different pectin concentrations (0%, 0.1%, and 0.5%) on the physicochemical, antioxidant, and sensory properties of blackberry jam (Rubus fruticosus L.) prepared from fruits harvested at three altitudinal locations (wild: 998 m; cultivated: 500 m and 1090 m). The jams were analyzed for phenolic profile, antioxidant capacity, color, texture, and sensory attributes. The results showed that altitude strongly affected the phenolic profile and antioxidant capacity, with wild blackberries exhibiting the highest levels of total phenolics, flavonoids, and anthocyanins. Pectin addition in moderate levels (0.1%) enhanced sensory acceptance, particularly in jams from higher altitudes. Furthermore, jams with added pectin showed improved vitamin C retention and reduced bitterness associated with phenolic compounds. Overall, the findings highlight the dual role of pectin in modulating the functional and sensory qualities of blackberry jam, while also demonstrating the impact of altitudinal variation on fruit-derived products. Full article
(This article belongs to the Special Issue Impact of Processing Technology on Food Quality and Safety)
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27 pages, 27375 KB  
Article
ComputationalAnalysis of a Towed Jumper During Static Line Airborne Operations: A Parametric Study Using Various Airdrop Configurations
by Usbaldo Fraire, Jr., Mehdi Ghoreyshi, Adam Jirasek, Keith Bergeron and Jürgen Seidel
Aerospace 2025, 12(10), 897; https://doi.org/10.3390/aerospace12100897 - 3 Oct 2025
Abstract
This study uses the CREATETM-AV/Kestrel simulation software to model a towed jumper scenario using standard aircraft settings to quantify paratrooper stability and risk of contact during static line airborne operations. The focus areas of this study include a review of the [...] Read more.
This study uses the CREATETM-AV/Kestrel simulation software to model a towed jumper scenario using standard aircraft settings to quantify paratrooper stability and risk of contact during static line airborne operations. The focus areas of this study include a review of the technical build-up, which includes aircraft, paratrooper and static line modeling, plus preliminary functional checkouts executed to verify simulation performance. This research and simulation development effort is driven by the need to meet the analysis demands required to support the US Army Personnel Airdrop with static line length studies and the North Atlantic Treaty Organization (NATO) Joint Airdrop Capability Syndicate (JACS) with airdrop interoperability assessments. Each project requires the use of various aircraft types, static line lengths and exit procedures. To help meet this need and establish a baseline proof of concept (POC) simulation, simulation setups were developed for a towed jumper from both the C-130J and C-17 using a 20-ft static line to support US Army Personnel Airdrop efforts. Concurrently, the JACS is requesting analysis to support interoperability testing to help qualify the T-11 parachute from an Airbus A400M Atlas aircraft, operated by NATO nations. Due to the lack of an available A400M geometry, the C-17 was used to demonstrate the POC, and plans to substitute the geometry are in order when it becomes available. The results of a nominal Computational Fluid Dynamics (CFD) simulation run using a C-17 and C-130J will be reviewed with a sample of the output to help characterize performance differences for the aircraft settings selected. The US Army Combat Capabilities Development Command Soldier Center (DEVCOM-SC) Aerial Delivery Division (ADD) has partnered with the US Air Force Academy (USAFA) High Performance Computing Research Center (HPCRC) to enable Modeling and Simulation (M&S) capabilities that support the Warfighter and NATO airdrop interoperability efforts. Full article
(This article belongs to the Special Issue Advancing Fluid Dynamics in Aerospace Applications)
17 pages, 15384 KB  
Article
Subterranean Biodiversity on the Brink: Urgent Framework for Conserving the Densest Cave Region in South America
by Robson de Almeida Zampaulo, Marconi Souza-Silva and Rodrigo Lopes Ferreira
Animals 2025, 15(19), 2899; https://doi.org/10.3390/ani15192899 - 3 Oct 2025
Abstract
Subterranean ecosystems represent some of the most unique and fragile habitats on Earth, yet they remain poorly understood and highly vulnerable to human-induced disturbances. Despite their ecological significance, these systems are rarely integrated into conservation planning, and surface-level protected areas alone are insufficient [...] Read more.
Subterranean ecosystems represent some of the most unique and fragile habitats on Earth, yet they remain poorly understood and highly vulnerable to human-induced disturbances. Despite their ecological significance, these systems are rarely integrated into conservation planning, and surface-level protected areas alone are insufficient to safeguard their biodiversity. In southeastern Brazil, a karst landscape spanning approximately 1200 km2, recognized as the region with the highest cave density in South America (approximately 2600 caves), is under increasing pressure from urban expansion, agriculture, and mining, all of which threaten the ecological integrity of subterranean habitats. This study sought to identify caves of high conservation priority by integrating species richness of non-troglobitic invertebrates, occurrence of troglobitic species, presence of endemic troglobitic taxa, and the degree of anthropogenic impacts, using spatial algebra and polygon-based mapping approaches. Agriculture and exotic forestry plantations (54%) and mining operations (15%) were identified as the most prevalent disturbances. A total of 32 troglobitic species were recorded, occurring in 63% of the 105 surveyed caves. Notably, seven caves alone harbor 25% of the region’s known cave invertebrate diversity and encompass 50% of its cave-restricted species. The findings highlight the global significance of this spot of subterranean biodiversity and reinforce the urgent need for targeted conservation measures. Without immediate action to mitigate unsustainable land use and resource exploitation, the persistence of these highly specialized communities is at imminent risk. Full article
(This article belongs to the Section Ecology and Conservation)
23 pages, 5798 KB  
Article
Application of Generative AI in Financial Risk Prediction: Enhancing Model Accuracy and Interpretability
by Kai-Chao Yao, Hsiu-Chu Hung, Ching-Hsin Wang, Wei-Lun Huang, Hui-Ting Liang, Tzu-Hsin Chu, Bo-Siang Chen and Wei-Sho Ho
Information 2025, 16(10), 857; https://doi.org/10.3390/info16100857 - 3 Oct 2025
Abstract
This study explores the application of generative artificial intelligence (AI) in financial risk forecasting, aiming to assess its potential in enhancing both the accuracy and interpretability of predictive models. Traditional methods often struggle with the complexity and nonlinearity of financial data, whereas generative [...] Read more.
This study explores the application of generative artificial intelligence (AI) in financial risk forecasting, aiming to assess its potential in enhancing both the accuracy and interpretability of predictive models. Traditional methods often struggle with the complexity and nonlinearity of financial data, whereas generative AI—such as large language models and generative adversarial networks (GANs)—offers novel solutions to these challenges. The study begins with a comprehensive review of current research on generative AI in financial risk prediction, with a focus on its roles in data augmentation and feature extraction. It then investigates techniques such as Generative Adversarial Explanation (GAX) to evaluate their effectiveness in improving model interpretability. Case studies demonstrate the practical value of generative AI in real-world financial forecasting and quantify its contribution to predictive accuracy. Furthermore, the study identifies key challenges—including data quality, model training costs, and regulatory compliance—and proposes corresponding mitigation strategies. The findings suggest that generative AI can significantly improve the accuracy and interpretability of financial risk models, though its adoption must be carefully managed to address associated risks. This study offers insights and guidance for future research in applying generative AI to financial risk forecasting. Full article
(This article belongs to the Special Issue Modeling in the Era of Generative AI)
24 pages, 6085 KB  
Article
Heat Pump Optimization—Comparative Study of Different Optimization Algorithms and Heat Exchanger Area Approximations
by Eivind Brodal
Energies 2025, 18(19), 5270; https://doi.org/10.3390/en18195270 - 3 Oct 2025
Abstract
More energy efficient heat pumps can be designed if the industry is able to identify reliable optimization schemes able to predict how a fixed amount of money is best spent on the different individual components. For example, how to optimally design and size [...] Read more.
More energy efficient heat pumps can be designed if the industry is able to identify reliable optimization schemes able to predict how a fixed amount of money is best spent on the different individual components. For example, how to optimally design and size the different heat exchangers (HEs) in a heat pump with respect to cost and performance. In this work, different optimization algorithms and HE area integral approximations are compared for heat pumps with two and three HEs, with or without ejectors. Since the main goal is to identify optimal numerical schemes, not optimal designs, heat transfer is simplified, assuming a constant U-value for all HEs, which reduces the computational work significantly. Results show that high-order HE area approximations are 10400 times faster than conventional trapezoidal and adaptive integral methods. High-order schemes with 45 grid points (N) obtained 80100% optimization success rates. For subcritical processes, the LMTD method produced accurate results with N5, but such schemes are unreliable and difficult to extend to real HE models with non-constant U. Results also show that constrained gradient-based optimizations are 10 times faster than particle swarm, and that conventional GA optimizations are extremely inefficient. This study therefore recommends applying high-order HE area approximations and gradient-based optimizations methods developing accurate optimization schemes for the industry, which include realistic heat transfer coefficients. Full article
211 pages, 28108 KB  
Review
The Impact of the Common Rail Fuel Injection System on Performance and Emissions of Modern and Future Compression Ignition Engines
by Alessandro Ferrari and Alberto Vassallo
Energies 2025, 18(19), 5259; https://doi.org/10.3390/en18195259 - 3 Oct 2025
Abstract
An overview of the Common Rail (CR) diesel engine challenges and of the promising state-of-the-art solutions for addressing them is provided. The different CR injector driving technologies have been compared, based on hydraulic, spray and engine performance for conventional diesel combustion. Various injection [...] Read more.
An overview of the Common Rail (CR) diesel engine challenges and of the promising state-of-the-art solutions for addressing them is provided. The different CR injector driving technologies have been compared, based on hydraulic, spray and engine performance for conventional diesel combustion. Various injection patterns, high injection pressures and nozzle design features are analyzed with reference to their advantages and disadvantages in addressing engine issues. The benefits of the statistically optimized engine calibrations have also been examined. With regard to the combustion strategy, the role of a CR engine in the implementation of low-temperature combustion (LTC) is reviewed, and the effect of the ECU calibration parameters of the injection on LTC steady-state and transition modes, as well as on an LTC domain, is illustrated. Moreover, the exploitation of LTC in the last generation of CR engines is discussed. The CR apparatus offers flexibility to optimize the engine calibration even for biofuels and e-fuels, which has gained interest in the last decade. The impact of the injection strategy on spray, ignition and combustion is discussed with reference to fuel consumption and emissions for both biodiesel and green diesel. Finally, the electrification of CR diesel engines is reviewed: the effects of electrically heated catalysts, electric supercharging, start and stop functionality and electrical auxiliaries on NOx, CO2, consumption and torque are analyzed. The feasibility of mild hybrid, strong hybrid and plug-in CR diesel powertrains is discussed. For the future, based on life cycle and manufacturing cost analyses, a roadmap for the automotive sector is outlined, highlighting the perspectives of the CR diesel engine for different applications. Full article
(This article belongs to the Topic Advanced Engines Technologies)
18 pages, 6513 KB  
Article
Analysis of Grain Growth Behavior of Intermetallic Compounds on Plated Pure Sn for Micropump Solder Caps
by Hwa-Sun Park, Chang-Yun Na, Jong-Wook Kim, Woon-Seok Jung, Jae-Hyuk Park, Jong-Woo Lim and Youn-Goo Yang
Materials 2025, 18(19), 4602; https://doi.org/10.3390/ma18194602 - 3 Oct 2025
Abstract
We evaluated for the morphology and growth behavior of IMC grain according to number of reflows of solder cap pure Sn microbumps. In the structure of Ni barrier/Cu layer between Cu pillar and pure Sn, solder cap pure Sn on the top layer [...] Read more.
We evaluated for the morphology and growth behavior of IMC grain according to number of reflows of solder cap pure Sn microbumps. In the structure of Ni barrier/Cu layer between Cu pillar and pure Sn, solder cap pure Sn on the top layer was analyzed for the behavior change of IMC grain according to the number of reflows. The height and diameter of the bumps on the wafer were designed to be 40 μm and 30 μm, respectively. The vertical structure of the microbump consisted of Ti/Cu (1000 Å/2000 Å), Cu pillar (20 µm), Ni barrier (3 µm), and Cu (1 µm). The overall height of the bump is about 40 μm. Additionally, the height of the solder cap pure Sn as the last layer is 20 μm. The diameter of the bump is 30 μm. It was formed using plating. After plating to solder cap Sn, it was finally formed for the microbump using reflow. Samples were prepared according to the number of reflows (1, 3, 5, 7, and 9). To observe the grain morphology of the IMC, the pure Sn on the upper layer (solder cap) was removed using SupraBond RO-22 etchant. In the removed state, the morphology of the IMC grain was evaluated to the inside surface of bump using SEM and a 3D scope. The average number of IMC grains decreased linearly during reflow cycles 1 to 5 and then gradually decreased during reflow cycles 7 to 10. The average surface area of IMC grains was 18.243 μm when reflow was performed once. The average surface area of IMC grains increased proportionally for reflow cycles 1 to 10. Based on the experimental results, when the count of reflow was performed more than 10 times, it was confirmed that the solder cap pure Sn was reduced by more than 50% due to the increase in the area of IMC grain. Full article
(This article belongs to the Section Metals and Alloys)
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21 pages, 365 KB  
Article
To Love and to Serve: Exploring the Strengths of Pacific Youth, and Mobilising Them for Community Wellbeing and Transformative Change
by Analosa Veukiso-Ulugia, Sarah McLean-Orsborn, Riki Nofo’akifolau and Terry Fleming
Youth 2025, 5(4), 105; https://doi.org/10.3390/youth5040105 - 3 Oct 2025
Abstract
Pacific youth in Aotearoa New Zealand are culturally diverse and deeply rooted in their families and communities. Despite facing socioeconomic inequities, systemic barriers, and limited decision-making opportunities, they maintain a positive perception of health and actively contribute to collective wellbeing. This paper explores [...] Read more.
Pacific youth in Aotearoa New Zealand are culturally diverse and deeply rooted in their families and communities. Despite facing socioeconomic inequities, systemic barriers, and limited decision-making opportunities, they maintain a positive perception of health and actively contribute to collective wellbeing. This paper explores the strengths of Pacific youth and how these can be harnessed to mobilise community wellbeing and transformative change. Using Pacific research methodologies—lalaga (weaving) and talanoa—we integrate findings from three key sources: the Talavou o le Moana Pacific Youth19 Report (quantitative data from 1130 Pacific youth), the Pacific Youth Home and Family Brief (open-text responses on family life), and insights from a panel of Pacific policy, research, and community experts presented in a webinar. These resources were reviewed and woven together by a team of three Pacific practitioners and one New Zealand European researcher, all with backgrounds in youth health, social work, and Pacific education. The lalaga reveals Pacific youth’s collective strength, cultural identity, and deep sense of responsibility. Their resilience and leadership, even amid adversity, highlight the urgent need for culturally grounded, youth-led, and community-responsive approaches. Empowering Pacific youth as agents of change is essential for fostering holistic wellbeing and transformative futures. Full article
21 pages, 2769 KB  
Article
Computational Intelligence-Based Modeling of UAV-Integrated PV Systems
by Mohammad Hosein Saeedinia, Shamsodin Taheri and Ana-Maria Cretu
Solar 2025, 5(4), 45; https://doi.org/10.3390/solar5040045 - 3 Oct 2025
Abstract
The optimal utilization of UAV-integrated photovoltaic (PV) systems demands accurate modeling that accounts for dynamic flight conditions. This paper introduces a novel computational intelligence-based framework that models the behavior of a moving PV system mounted on a UAV. A unique mathematical approach is [...] Read more.
The optimal utilization of UAV-integrated photovoltaic (PV) systems demands accurate modeling that accounts for dynamic flight conditions. This paper introduces a novel computational intelligence-based framework that models the behavior of a moving PV system mounted on a UAV. A unique mathematical approach is developed to translate UAV flight dynamics, specifically roll, pitch, and yaw, into the tilt and azimuth angles of the PV module. To adaptively estimate the diode ideality factor under varying conditions, the Grey Wolf Optimization (GWO) algorithm is employed, outperforming traditional methods like Particle Swarm Optimization (PSO). Using a one-year environmental dataset, multiple machine learning (ML) models are trained to predict maximum power point (MPP) parameters for a commercial PV panel. The best-performing model, Rational Quadratic Gaussian Process Regression (RQGPR), demonstrates high accuracy and low computational cost. Furthermore, the proposed ML-based model is experimentally integrated into an incremental conductance (IC) MPPT technique, forming a hybrid MPPT controller. Hardware and experimental validations confirm the model’s effectiveness in real-time MPP prediction and tracking, highlighting its potential for enhancing UAV endurance and energy efficiency. Full article
(This article belongs to the Special Issue Efficient and Reliable Solar Photovoltaic Systems: 2nd Edition)
24 pages, 3288 KB  
Article
Bioluminescent ATP-Metry in Assessing the Impact of Various Microplastic Particles on Fungal, Bacterial, and Microalgal Cells
by Olga Senko, Nikolay Stepanov, Aysel Aslanli and Elena Efremenko
Microplastics 2025, 4(4), 72; https://doi.org/10.3390/microplastics4040072 - 3 Oct 2025
Abstract
The concentration of intracellular adenosine triphosphate (ATP) is one of the most important characteristics of the metabolic state of the cells of microorganisms and their viability. This indicator, monitored by bioluminescent ATP-metry, and accumulation of the suspension biomass in the medium were used [...] Read more.
The concentration of intracellular adenosine triphosphate (ATP) is one of the most important characteristics of the metabolic state of the cells of microorganisms and their viability. This indicator, monitored by bioluminescent ATP-metry, and accumulation of the suspension biomass in the medium were used to assess the effect of particles of different synthetic microplastics (MPs) (non-biodegradable and biodegradable) on the cells of yeast, filamentous fungi, bacteria and phototrophic microorganisms (microalgae and cyanobacteria) co-exposed with polymer samples in different environments and concentrations. It was found that the effect of MPs on microorganisms depends on the concentration of MPs (1–5 g/L), as well as on the initial concentration of cells (104 or 107 cells/mL) in the exposure medium with polymers. It was shown that the lack of a sufficient number of nutrition sources in the medium with MPs is not fatal for the cells. The study of the effect of MPs on the photobacteria Photobacterium phosphoreum, widely used as a bioindicator for assessing the ecotoxicity of various environments, demonstrated a correlation between the residual bioluminescence of these cells and the level of their intracellular ATP in media with biodegradable polycaprolactone and polylactide, which had an inhibitory effect on these cells. Marine representatives of phototrophic microorganisms showed the greatest sensitivity to the presence of MPs, which was confirmed by both a decrease in the level of intracellular ATP and the concentration of their biomass. Among the eight microorganisms studied, bacteria of the genus Pseudomonas turned out to be not only the most tolerant to the presence of the seven MP samples used in the work, but also actively growing in their presence. Full article

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