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The present study investigated the acute effects of different stretching modalities applied within a warm-up on flexibility and vertical jump height. Thirty-seven young adults participated in four randomized experimental sessions, each corresponding to a different condition: static stretch (SS), dynamic stretch (DS), contract–relax with antagonist contraction (CRAC) or a control condition with no stretch (CTRL). Conditions were five min in total duration, including 2 × 15 s stretches for each muscle group (knee flexor, knee extensor, and plantar flexor muscles). Ten min and five min of cycling preceded and followed these procedures, respectively. Hamstring flexibility and a series of countermovement jump (CMJ) measurements were interspersed within this procedure. Except for CTRL, hamstring flexibility significantly increased (p < 0.01) after all experimental procedures (7.5 ± 6.6%, 4.1 ± 4.9%, and 2.7 ± 6.0% for CRA, SS, and DS, respectively). The relative increase was significantly greater for CRAC as compared CTRL (p < 0.001). Vertical jump height significantly decreased (p < 0.05) immediately after SS (−2.3 ± 3.9%), CTRL (−2.3 ± 3.5%), and CRAC (−3.2 ± 3.3%). Jump height was unchanged after DS (0.4 ± 4.5%). Whatever the condition, no additional jump height alteration was obtained after the re-warm-up. The main findings of the present study revealed that DS is more appropriate for maintaining vertical jump height. However, stretching has no major effect when performed within a warm-up. In contrast, if the main objective is to increase flexibility, CRAC is recommended. Full article

(This article belongs to the Special Issue Neuromechanical Adaptations to Exercise and Sports Training)

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23 pages, 2819 KiB

Open AccessArticle

Autonomous Mobile Station for Artificial Intelligence Monitoring of Mining Equipment and Risks

by Gabriel País Cerna, Germán Herrera-Vidal and Jairo R. Coronado-Hernández

Appl. Sci. 2025, 15(8), 4197; https://doi.org/10.3390/app15084197 (registering DOI) - 10 Apr 2025

Abstract

Artificial intelligence in the mining industry is key to improving safety, optimizing resources, and ensuring sustainable operations in complex environments. The main objective of this research is to develop an autonomous mobile station equipped with artificial vision and artificial intelligence to identify and track equipment, people, and animals in critical areas of mining operations, issuing real-time alerts to reduce occupational risks and improve operational control. The research is applied with an experimental approach, designed to validate the effectiveness of the proposed system in real open-pit mining environments. The proposed methodology consisted of five stages: (i) Selection of data collection equipment, (ii) Definition of the positioning scheme, (iii) Incorporation of the communication system, (iv) Data processing and transformation, and (v) Equipment identification and tracking. The results showed an average accuracy of 98% in the validation and 95% in the test, achieving perfect performance (100%) in key categories such as excavators and drills, highlighting the potential of this technology to transform mining towards safer and more efficient standards. Full article

15 pages, 37842 KiB

Open AccessArticle

First-Principles Calculations, Machine Learning and Monte Carlo Simulations of the Magnetic Coercivity of Fe_xCo_1−x Bulks and Nanoclusters

by Dou Du, Youwei Zhang, Xingwu Li and Namin Xiao

Nanomaterials 2025, 15(8), 577; https://doi.org/10.3390/nano15080577 (registering DOI) - 10 Apr 2025

Abstract

FeCo alloys, renowned for their exceptional magnetic properties, such as high saturation magnetization and elevated Curie temperatures, hold significant potential for various technological applications. This study combines density-functional theory (DFT) and Monte Carlo (MC) simulations to investigate the magnetic properties of FeCo alloys [...] Read more.

$J_{i j}$ ) and magnetic anisotropy (

$K_{i}$ ) along with machine learning (ML) predicted spin vectors (

$S_{i}$ ) serve as inputs for the Monte Carlo framework, enabling a detailed exploration of magnetic coercivity (

$H_{c}$ ) across different compositions and temperatures. The simulations reveal an optimal Fe concentration, particularly around Fe_0.65Co_0.35, where magnetic coercivity reaches its peak, aligning with experimental trends. A similar simulation procedure was conducted for a Fe₅₈Co₃₂ nanocluster at 300 K and 500 K, demonstrating magnetic behavior comparable to bulk materials. This integrative computational approach provides a powerful tool for simulating and understanding the magnetic properties of alloys and nanomaterials, thus aiding in the design of advanced magnetic materials. Full article

(This article belongs to the Special Issue Applications of 2D Materials in Nanoelectronics)

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29 pages, 10882 KiB

Open AccessReview

Renovation Strategies for Green Spaces in Aging Residential Communities in Cold Regions to Enhance Carbon Sequestration and Wellness

by Xia Rong, Haonian Fang and Chunlin He

Buildings 2025, 15(8), 1257; https://doi.org/10.3390/buildings15081257 (registering DOI) - 10 Apr 2025

Abstract

This study explores renovation strategies for green spaces in aging residential communities in cold regions, with a particular focus on enhancing carbon sequestration capacity and residents’ well-being. Under the framework of the “dual carbon” goals, a combination of literature analysis and resident surveys reveals that (1) the existing layouts of green spaceand plant selections have not fully considered their carbon sequestration potential, leaving significant room for optimization; (2) low outdoor temperatures, the lack of heating facilities, and monotonous winter landscapes contribute to reduced green space utilization, limiting outdoor activities and diminishing the health benefits of green spaces; and (3) the integration of glass sunrooms with renewable energy systems, such as photovoltaic power generation, can effectively improve winter green space utilization, regulate micro climates, and enhance vegetation-based carbon sequestration while also providing residents with comfortable spaces for social interaction and wellness activities. The findings indicate that scientifically optimizing green space layouts, selecting plant species with high carbon sequestration potential, and incorporating climate-adaptive architectural designs can significantly enhance the ecological value of green spaces and residents’ quality of life. It is recommended that future community renewal initiatives integrate green technologies, policy support, and interdisciplinary collaboration to promote low-carbon and livable urban development. Full article

(This article belongs to the Section Architectural Design, Urban Science, and Real Estate)

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19 pages, 1062 KiB

Open AccessArticle

Dynamic Multi-Fault Diagnosis-Based Root Cause Tracing for Assembly Production Lines of Liquid Storage Tanks

by You Teng, Donghui Li, Hongkai Xue, Yunkai Zhou, Kefu Wang and Qi Wu

Electronics 2025, 14(8), 1546; https://doi.org/10.3390/electronics14081546 (registering DOI) - 10 Apr 2025

Abstract

Tracing the root cause of defective products in liquid storage tank (LST) production poses a formidable challenge due to the complex dependencies between production and inspection processes. With associated coupling existing among multiple production processes, and the correspondence between the faults in production processes and inspection links being non-unique, these faults are usually difficult to be directly located via a single inspection process. In this paper, the problem of tracing the root cause of defective LST products, which is caused by process parameter deviations or human operation errors during production, is studied. A root cause tracing method that is based on the dynamic multi-fault diagnosis (DMFD) framework is proposed. First, a factorial hidden Markov model (FHMM) is established to depict the state transition process of the LST product, where its status changes over time and across production processes. This is achieved by considering the product state at each production process as a hidden state and the outcomes of each inspection process as an observation state. Then, the Viterbi algorithm is employed to solve the hidden state transition matrix and diagnostic matrix within the framework of the FHMM. Finally, experimental verification is carried out on a real LST assembly production line, and the influence of imperfect testing on the model accuracy is also considered. The experiment is carried out on an LST assembly line that encompasses three discrete links, including the welding of the upper and lower bodies, the installation of check valves, and the installation of sensors. Experimental results demonstrate that the proposed method achieves significantly more superior performance when compared to existing algorithms. Full article

21 pages, 6003 KiB

Open AccessArticle

Competitive Bio-Accumulation Between Ammonia and Nitrite Results in Their Antagonistic Toxicity to Hypophthalmichthys molitrix: Antioxidant and Immune Responses and Metabolic Detoxification Evidence

by Honghui Guo, Yiwen Li, Heng Ge, Hang Sha, Xiangzhong Luo, Guiwei Zou and Hongwei Liang

Antioxidants 2025, 14(4), 453; https://doi.org/10.3390/antiox14040453 (registering DOI) - 10 Apr 2025

Abstract

Ammonia and nitrite, as major aquatic pollutants, exhibit significant toxicity toward aquatic organisms. However, their interactive effects on fish are unclear. Aiming to determine their interactive effects, silver carp (Hypophthalmichthys molitrix) were exposed to ammonia, nitrite or ammonia + nitrite for 72 h. Silver carp exhibited pathological damage in the liver and spleen and significant increases in MDA, SOD and CAT in the liver and plasma after ammonia or nitrite exposure. Thus, ammonia and nitrite caused significant histology damage through inducing oxidative stress, and the antioxidative response of SOD−CAT was initiated by silver carp to defend them. A transcriptomic analysis suggested that disruptions in immune responses and metabolism were the main toxic effects caused by ammonia and nitrite. Specifically, nitrite decreased splenic TNF-α and IL-1β but increased splenic C4. Ammonia decreased splenic TNF-α and C4 but increased splenic IL-1β. We noted significant interactions between ammonia and nitrite, and the pathological changes and IBR in the co-exposure groups were less severe than those in the single-factor exposure groups, indicating that ammonia and nitrite have an antagonistic effect. Significant decreases in plasmatic ammonia and NO₂⁻+NO₃⁻ were induced by nitrite and ammonia, respectively. Moreover, the plasmatic glutamine, urea-N, and glutamine synthetase and glutamate dehydrogenase activities increased significantly under ammonia and nitrite exposure, while T-NOS decreased significantly. These results suggest an antagonistic interaction between ammonia and nitrite in silver carp, possibly resulting from competitive bioaccumulation. Consequently, the simultaneous monitoring and control of both ammonia and nitrite concentrations are essential to mitigate their compounded toxic effects, which might be exacerbated under isolated exposure conditions. Full article

(This article belongs to the Special Issue The Role of Oxidative Stress in Environmental Toxicity)

24 pages, 336 KiB

Open AccessFeature PaperArticle

Almost k-Step Opacity Enforcement in Stochastic Discrete-Event Systems via Differential Privacy

by Rong Zhao, Murat Uzam and Zhiwu Li

Mathematics 2025, 13(8), 1255; https://doi.org/10.3390/math13081255 (registering DOI) - 10 Apr 2025

Abstract

This paper delves into current-state opacity enforcement in partially observed discrete event systems through an innovative application of differential privacy, which is fundamental for security-critical cyber–physical systems. An opaque system implies that an external agent cannot infer the predefined system secret via its observational output, such that the important system information flow cannot be leaked out. Differential privacy emerges as a robust framework that is pivotal for the protection of individual data integrity within these systems. Motivated by the differential privacy mechanism for information protection, this research proposes the secret string adjacency relation as a novel concept, assessing the similarity between potentially compromised strings and system-generated alternatives, thereby shielding the system’s confidential data from external observation. The development of secret string differential privacy is achieved by substituting sensitive strings. These substitution strings are generated by a modified Levenshtein automaton, following exponentially distributed generation probabilities. The verification and illustrative examples of the proposed mechanism are provided. Full article

(This article belongs to the Special Issue Modeling, Simulation and Control of Dynamical Systems)

16 pages, 5597 KiB

Open AccessArticle

Seismic Non-Limited Active Earth Pressure Analysis of Retaining Walls Under Rotation-About-the-Base Mode

by Zhiliang Sun and Wei Wang

Appl. Sci. 2025, 15(8), 4202; https://doi.org/10.3390/app15084202 (registering DOI) - 10 Apr 2025

Abstract

Under seismic loading conditions, the backfill soil behind retaining walls does not fully reach the limit state, while seismic earth pressure is influenced by wall displacement. The RB (rotation about the base) displacement pattern represents a prevalent deformation mode in retaining walls during operational service. To calculate the seismic non-limited active earth pressure under RB mode, this study first establishes the relationship between critical horizontal displacement (corresponding to a fully mobilized wall–soil interface friction angle) and depth based on numerical simulations, revealing a linear correlation. Subsequently, nonlinear distribution relationships for the mobilized soil internal friction angle and wall–soil interface friction angle with wall-top displacement are derived. Building upon this foundation and considering the failure mechanism of backfill soil under RB displacement, the soil mass is divided into inclined slices. A pseudo-static analytical framework is proposed to calculate both the magnitude and application point of non-limited seismic earth pressure for rigid walls under RB displacement. Validation against experimental data from referenced studies demonstrates the method’s rationality. Earth pressure transitions from an initially concave triangular distribution to a linear pattern as displacement progresses. The application point descends from the initial at-rest position (1/3 H) with increasing wall-top displacement, subsequently rising as the soil approaches full active limit states, ultimately stabilizing at 1/3 H under linear pressure distribution. The parameter sensitivity analysis section summarizes that the horizontal seismic coefficient dominates influencing factors, followed by wall displacement, while soil internal friction angle and soil–wall interface friction angle exhibit relatively minor effects. These findings provide critical insights for optimizing seismic design methodologies of retaining structures. Full article

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16 pages, 2615 KiB

Open AccessArticle

Evaluation of Leaf Water Content in Watermelon Based on Hyperspectral Reflectance

by Dan Wu, Penghui Wang, Bing Chen, Licong Yi, Zhaoyi Dai and Bo Xiao

Water 2025, 17(8), 1142; https://doi.org/10.3390/w17081142 (registering DOI) - 10 Apr 2025

Abstract

Water is a crucial element for the growth of watermelon plants, making rapid and non-destructive monitoring of plant water content vital for precision irrigation in watermelon farming. While previous research has demonstrated the sensitivity of short-wave infrared (SWIR) bands to plant water content, their high costs limit widespread application. In contrast, visible and near-infrared (VNIR) spectral instruments offer significant advantages in terms of affordability, compactness, and spectral resolution. However, their potential for predicting the leaf water content (LWC) of watermelon plants has yet to be fully investigated. This study aims to assess the efficacy of hyperspectral reflectance measured with VNIR spectral instruments in estimating the LWC of watermelon plants at various leaf layers. Hyperspectral reflectance data (350−1100 nm) were collected from three leaf layers (upper, middle, and lower) under various drought treatments. Models for estimating LWC were developed using both spectral indices and full wavelength data. The results indicated that the middle leaf layer was the most effective for estimating LWC, and using full wavelength data achieved higher accuracy in LWC estimation. Furthermore, compared to the simple regression model, the AdaBoost-based machine learning model demonstrated superior performance, achieving an R² of 0.9636 in estimating LWC through five-fold cross-validation, which indicates high predictive accuracy. Ensemble learning significantly outperforms traditional methods, providing a substantial improvement in model accuracy. The findings offer important technical assistance for the spectral monitoring of LWC and precision irrigation in watermelon cultivation. Full article

(This article belongs to the Section Water, Agriculture and Aquaculture)

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22 pages, 2629 KiB

Open AccessArticle

Robust Wetting and Drying with Discontinuous Galerkin Flood Model on Unstructured Triangular Meshes

by Rabih Ghostine, Georges Kesserwani and Ibrahim Hoteit

Water 2025, 17(8), 1141; https://doi.org/10.3390/w17081141 (registering DOI) - 10 Apr 2025

Abstract

Godunov-based finite volume (FV) methods are widely employed to numerically solve the Shallow-Water Equations (SWEs) with application to simulate flood inundation over irregular geometries and real-field, where unstructured triangular meshing is favored. Second-order extensions have been devised, mostly on the MUSCL reconstruction and the discontinuous Galerkin (DG) approaches. In this paper, we introduce a novel second-order Runge–Kutta discontinuous Galerkin (RKDG) solver for flood modeling, specifically addressing positivity preservation and wetting and drying on unstructured triangular meshes. To enhance the RKDG model, we adapt and refine positivity-preserving and wetting and drying techniques originally developed for the MUSCL-based finite volume (FV) scheme, ensuring its effective integration within the RKDG framework. Two analytical test problems are considered first to validate the proposed model and assess its performance in comparison with the MUSCL formulation. The performance of the model is further explored in real flooding scenarios involving irregular topographies. Our findings indicate that the added complexity of the RKDG model is justified, as it delivers higher-quality results even on very coarse meshes. This reveals that there is a promise in deploying RKDG-based flood models in real-scale applications, in particular when field data are sparse or of limited resolution. Full article

(This article belongs to the Special Issue Recent Advances in Flood Risk Analysis and Management Practice)

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26 pages, 9834 KiB

Open AccessReview

Evaluating the Bidirectional Causal Effects of Alzheimer’s Disease Across Multiple Conditions: A Systematic Review and Meta-Analysis of Mendelian Randomization Studies

by Haoning Zhu, Huitong Ni, Qiuling Yang, Jiaqi Ni, Jianguang Ji, Shu Yang and Fu Peng

Int. J. Mol. Sci. 2025, 26(8), 3589; https://doi.org/10.3390/ijms26083589 (registering DOI) - 10 Apr 2025

Abstract

This study systematically evaluates and meta-analyzes Mendelian randomization studies on the bidirectional causal relationship between Alzheimer’s disease (AD) and systemic diseases. We searched five databases, assessed study quality, and extracted data. Diseases were classified using ICD-11, and the meta-analysis was performed with RevMan 5.4. A total of 56 studies identified genetic links between AD susceptibility and systemic diseases. Notably, genetic proxies for hip osteoarthritis (OR = 0.80; p = 0.007) and rheumatoid arthritis (OR = 0.97; p = 0.004) were inversely associated with AD risk, while gout (OR = 1.02; p = 0.049) showed a positive association. Genetic liability to depression (OR = 1.03; p = 0.001) elevated AD risk, and AD genetic risk increased susceptibility to delirium (OR = 1.32; p = 0.0005). Cardiovascular traits, including coronary artery disease (OR = 1.07; p = 0.021) and hypertension (OR = 4.30; p = 0.044), were causally linked to a higher AD risk. Other conditions, such as insomnia, chronic periodontitis, migraine, and certain cancers, exhibited significant genetic correlations. Intriguingly, herpes zoster (OR = 0.87; p = 0.005) and cataracts (OR = 0.96; p = 0.012) demonstrated inverse genetic associations with AD. These findings suggest potential therapeutic targets and preventive strategies, emphasizing the need to address comorbid systemic diseases to reduce AD risk and progression. Full article

(This article belongs to the Section Molecular Neurobiology)

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14 pages, 2201 KiB

Open AccessArticle

Two-Stage Process for Understanding Summer Monsoon Impact on Ozone over Eastern China

by Tianyu Zhu, Wei Dai, Yuhang Wang and Mingjie Xie

Atmosphere 2025, 16(4), 444; https://doi.org/10.3390/atmos16040444 (registering DOI) - 10 Apr 2025

Abstract

The ozone levels over eastern China show a distinct two-stage process, with an inter-seasonal low (ISL) between May and September, unlike other polluted northern low-to-mid-latitude regions. The timing and progression of this low from southern to northern China align with the East Asian summer monsoon (EASM). The EASM leads to a decrease (ΔISL1) during the first stage and an increase (ΔISL2) during the second stage. The response varies by region, with the ΔISL1 (25 to 60 ppbv) greater than the ΔISL2 (20 to 30 ppbv) in the North China Plain (NCP), and the ΔISL1 (20 to 35 ppbv) less than the ΔISL2 (35 to 55 ppbv) in the Pearl River Delta (PRD). The ozone levels are inversely related to the monsoon index (MI) during stage 1 (r = −0.69, p < 0.05), while during stage 2, the ozone levels are anticorrelated with the maximum MI in the NCP and PRD (r = −0.73 and −0.80, p < 0.05). And the average ozone levels are anticorrelated with the MI during stage 2 in the Yangtze River Delta (YRD) (r = −0.71, p < 0.05). The simulations using CMIP6 suggest that intensified EASM caused by greenhouse emissions may help reduce summertime ozone pollution. The results show that different regions require different pollution control policies during pre- and post-monsoon seasons. Full article

(This article belongs to the Section Air Quality)

21 pages, 1601 KiB

Open AccessArticle

Liptinite Segmentation in Microscopic Images via Deep Networks

by Sebastian Iwaszenko and Leokadia Róg

Minerals 2025, 15(4), 401; https://doi.org/10.3390/min15040401 (registering DOI) - 10 Apr 2025

Abstract

Maceral identification in images obtained with an immersive microscopy is one of the most important techniques for coal quality characterization. The objective of this paper is to explore the potential of semantic segmentation for the classification of liptinite macerals within microscope images. The following U-Net-based architectures were proposed for the task: a U-Net with a varying depth and feature map numbers, a U-Net extended with a proposed feature map attention mechanism, and a U-Net architecture with an encoder part replaced with a ResNet backbone. Two resolutions of input images were examined: 256 × 256 and 512 × 512 pixels. The training was conducted using constant and scheduled learning rate values. The results show a superior performance of the networks using a ResNet-based encoder, with the best IoU measure, equal 0.91, obtained with ResNet50. The other networks achieved worse results, but attention-supported U-Nets were considerably better than the basic versions. Both training approaches (constant and scheduled learning rates) yielded comparable results. The best results were better than those reported in the literature for other architectures of deep neural networks. It was also observed that the images presenting the greatest challenges to the networks were highly imbalanced, with the liptinite present only in a small area of the image. The architectures employing ResNet-based encoders were the only ones capable of surmounting these challenges. Full article

15 pages, 1596 KiB

Open AccessArticle

Bacterial Volatile Organic Compounds as Potential Caries and Periodontitis Disease Biomarkers

by Maisa Haiek, Vladislav Dvoyris, Yoav Y. Broza, Hossam Haick, Ervin Weiss and Yael Houri-Haddad

Int. J. Mol. Sci. 2025, 26(8), 3591; https://doi.org/10.3390/ijms26083591 (registering DOI) - 10 Apr 2025

Abstract

Oral diseases represent a significant global health and economic burden, necessitating the development of effective diagnostic tools. This study investigates the volatile organic compound (VOC) profiles of bacteria associated with dental caries and periodontal disease to explore their potential as diagnostic biomarkers. Four microbial strains—Streptococcus mutans (700610), Streptococcus sanguis (NCO 2863), Porphyromonas gingivalis (ATCC 33277), and Fusobacterium nucleatum (PK1594)—were cultured (N = 24), alongside intraoral samples (N = 60), from individuals with common oral diseases. Headspace VOCs were analyzed using gas chromatography-mass spectrometry (GC-MS), and statistical analyses were conducted by applying non-parametric Wilcoxon and Kruskal–Wallis tests. VOC identification was performed using the NIST14 database. Strain-specific VOC signatures were identified, with P. gingivalis and F. nucleatum exhibiting distinct profiles from each other and from Streptococcus strains. Comparative analysis of disease cohorts revealed statistically significant differences at multiple retention times between caries, gingivitis, and periodontitis. These findings suggest that VOC profiling enables differentiation between bacterial strains and disease phenotypes, supporting their potential application as diagnostic biomarkers for oral diseases. This study establishes a foundational framework for VOC-based diagnostic methodologies in dental pathology. Full article

(This article belongs to the Special Issue Molecular Engineering in Dental Restorative and Regenerative Materials)

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17 pages, 1498 KiB

Open AccessArticle

Energy Consumption Analysis and Optimization of Substation Building in Cold Regions Considering Various Influence Factors

by Wen Sun, Qi Zhang, Ou Zhang, Ruyu Zhang, Junru Lin and Heng Chen

Energies 2025, 18(8), 1948; https://doi.org/10.3390/en18081948 (registering DOI) - 10 Apr 2025

Abstract

Building-energy consumption constitutes a pivotal component of global energy systems, with the heating and cooling loads during the operational phase being particularly significant. Substation building, as nodes in the transmission and transformation network, deserve attention for their building-operating loads. This study investigates heating and cooling loads during substation operation in severe cold climates. By integrating energy consumption simulations with one-factor-at-a-time and orthogonal multivariate analyses, optimization strategies under key influencing factors are systematically explored. The impact analysis identifies the following order of influence magnitude on substation total loads: indoor equipment heat generation, ventilation rate, roof U-value, exterior wall U-value, and window U-value. The heating- and cooling-load characteristics exhibit distinct patterns depending on indoor equipment heat generation. The total building load can be reduced by 61.23 per cent under multifactor optimal de-sign conditions, highlighting the critical role of systemic design coordination. This study provides a case study reference for energy efficient design of heating and cooling loads in substations, especially where significant changes in equipment heat occur, and highlights the importance of controlling indoor heat sources to achieve optimal energy efficiency. Full article

(This article belongs to the Special Issue Energy Efficiency of the Buildings: 3rd Edition)

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