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10 pages, 658 KiB

Open AccessArticle

Soil Nitrification Rate Is Affected by Plant Species and Nitrogen Levels

by Luca Vitale, Giuseppe Maglione, Francsico Garcia-Sanchez, Lourdes Yabor, Maria Riccardi, Lucia Ottaiano, Bruno Di Matteo, Rosario Nocerino, Antonio Manco and Anna Tedeschi

Agriculture 2025, 15(16), 1740; https://doi.org/10.3390/agriculture15161740 (registering DOI) - 14 Aug 2025

Abstract

The soil nitrification rate is significantly affected by plant species, and it is also modulated by different nitrogen levels in the soil. There are a wide range of plant species with the capacity to produce biological nitrification inhibitors (hereafter referred to as BNI [...] Read more.

The soil nitrification rate is significantly affected by plant species, and it is also modulated by different nitrogen levels in the soil. There are a wide range of plant species with the capacity to produce biological nitrification inhibitors (hereafter referred to as BNI species). The preliminary results of this study report the influence of three different plant species on the nitrification rates under soil supply with three (0 mM, 3.5 mM, and 7.0 mM) nitrogen levels. The aim was to evaluate the potential of hemp, ryegrass, and sorghum in mitigating nitrification, in order to define a sustainable strategy for improving the nitrogen use efficiency by crops and to limit the nitrogen loss from agroecosystems. Leaf gas exchange measurements were also carried out in this study. Photosynthesis was only affected by nitrogen supply in hemp, resulting in a reduction in CO₂ assimilation at nitrogen doses higher than the plant’s requirements. Ryegrass devotes more reductive power towards leaf nitrogen assimilation than sorghum and hemp do. The greatest variation in nitrification rate in response to N was observed in soil cultivated with hemp (which also showed the highest potential nitrification rate), followed by sorghum and ryegrass. We speculate that this occurred because the greater seed sowing density for ryegrass ensured a greater quantity in the soil of molecules acting on nitrification compared to sorghum and hemp, with these latter being sown at lower densities. Our results suggest that sorghum and ryegrass might directly affect nitrification by BNI molecules, whereas hemp might indirectly mitigate nitrification through the nitrogen uptake. However, further research is needed to evaluate the effects exerted by the studied plant species on nitrification rates. Full article

(This article belongs to the Section Agricultural Soils)

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28 pages, 968 KiB

Open AccessArticle

EVuLLM: Ethereum Smart Contract Vulnerability Detection Using Large Language Models

by Eleni Mandana, George Vlahavas and Athena Vakali

Electronics 2025, 14(16), 3226; https://doi.org/10.3390/electronics14163226 (registering DOI) - 14 Aug 2025

Abstract

Smart contracts have become integral to decentralized applications, yet their programmability introduces critical security risks, exemplified by high-profile exploits such as the DAO and Parity Wallet incidents. Existing vulnerability detection methods, including static and dynamic analysis, as well as machine learning-based approaches, often [...] Read more.

Smart contracts have become integral to decentralized applications, yet their programmability introduces critical security risks, exemplified by high-profile exploits such as the DAO and Parity Wallet incidents. Existing vulnerability detection methods, including static and dynamic analysis, as well as machine learning-based approaches, often struggle with emerging threats and rely heavily on large, labeled datasets. This study investigates the effectiveness of open-source, lightweight large language models (LLMs) fine-tuned using parameter-efficient techniques, including Quantized Low-Rank Adaptation (QLoRA), for smart contract vulnerability detection. We introduce the EVuLLM dataset to address the scarcity of diverse evaluation resources and demonstrate that our fine-tuned models achieve up to 94.78% accuracy, surpassing the performance of larger proprietary models, while significantly reducing computational requirements. Moreover, we emphasize the advantages of lightweight models deployable on local hardware, such as enhanced data privacy, reduced reliance on internet connectivity, lower infrastructure costs, and improved control over model behavior, factors that are especially critical in security-sensitive blockchain applications. We also explore Retrieval-Augmented Generation (RAG) as a complementary strategy, achieving competitive results with minimal training. Our findings highlight the practicality of using locally hosted LLMs for secure, efficient, and reproducible smart contract analysis, paving the way for broader adoption of AI-driven security in blockchain ecosystems. Full article

(This article belongs to the Special Issue Network Security and Cryptography Applications)

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12 pages, 900 KiB

Open AccessArticle

Quantitative DSA Analysis of MCA Aneurysms Using SymDIRECT Pixel Clustering: A Novel Framework for Objective Post-Treatment Evaluation

by Ante Rotim, Marina Raguž, Nikica Fulir, Darko Orešković, Vladimir Kalousek, Petar Marčinković, Krešimir Rotim, Bruno Splavski, Silva Butković Soldo and Tomislav Sajko

Diagnostics 2025, 15(16), 2036; https://doi.org/10.3390/diagnostics15162036 (registering DOI) - 14 Aug 2025

Abstract

Background: Digital subtraction angiography (DSA) remains the gold standard for assessing aneurysm morphology before and after treatment. While visual interpretation is common, quantitative image analysis remains underutilized in clinical practice. This study aimed to evaluate postoperative vascular changes in patients with middle cerebral [...] Read more.

Background: Digital subtraction angiography (DSA) remains the gold standard for assessing aneurysm morphology before and after treatment. While visual interpretation is common, quantitative image analysis remains underutilized in clinical practice. This study aimed to evaluate postoperative vascular changes in patients with middle cerebral artery (MCA) aneurysms using SymDIRECT-based pixel clustering on preoperative and postoperative DSA images. Methods: A total of 59 patients with unruptured MCA aneurysms were analyzed retrospectively. SymDIRECT clustering segmented angiographic images into four intensity clusters. Quantitative comparison of cluster pixel counts between pre- and postoperative images was performed. Results: Both neurosurgical clipping and endovascular treatment groups demonstrated significant reductions in medium- and high-intensity pixel clusters postoperatively, reflecting successful aneurysm occlusion. The background cluster increased post-treatment in most cases, with an average rise of over 14%, indicating effective anatomical exclusion of the aneurysm. Conclusions: SymDIRECT-based pixel clustering enables objective, pixel-level quantification of treatment response in DSA images. This approach may support standardized imaging follow-up protocols and improve reproducibility in neurovascular outcome assessment. Future integration with AI-based segmentation could facilitate real-time image interpretation. Full article

(This article belongs to the Special Issue Advances in the Diagnosis of Nervous System Diseases—3rd Edition)

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30 pages, 5536 KiB

Open AccessArticle

Explainable Artificial Intelligence for the Rapid Identification and Characterization of Ocean Microplastics

by Dimitris Kalatzis, Angeliki I. Katsafadou, Eleni I. Katsarou, Dimitrios C. Chatzopoulos and Yiannis Kiouvrekis

Microplastics 2025, 4(3), 51; https://doi.org/10.3390/microplastics4030051 (registering DOI) - 14 Aug 2025

Abstract

Accurate identification of microplastic polymers in marine environments is essential for tracing pollution sources, understanding ecological impacts, and guiding mitigation strategies. This study presents a comprehensive, explainable-AI framework that uses Raman spectroscopy to classify pristine and weathered microplastics versus biological materials. Using a [...] Read more.

Accurate identification of microplastic polymers in marine environments is essential for tracing pollution sources, understanding ecological impacts, and guiding mitigation strategies. This study presents a comprehensive, explainable-AI framework that uses Raman spectroscopy to classify pristine and weathered microplastics versus biological materials. Using a curated spectral library of 78 polymer specimens—including pristine, weathered, and biological materials—we benchmark seven supervised machine learning models (Decision Trees, Random Forest, k-Nearest Neighbours, Neural Networks, LightGBM, XGBoost and Support Vector Machines) without and with Principal Component Analysis for binary classification. Although k-Nearest Neighbours and Support Vector Machines achieved the highest single metric accuracy (82.5%), k NN also recorded the highest recall both with and without PCA, thereby offering the most balanced overall performance. To enhance interpretability, we employed SHapley Additive exPlanations, which revealed chemically meaningful spectral regions (notably near 700 cm⁻¹ and 1080 cm⁻¹) as critical to model predictions. Notably, models trained without Principal Component Analysis provided clearer feature attributions, suggesting improved interpretability in raw spectral space. This pipeline surpasses traditional spectral matching techniques and also delivers transparent insights into classification logic. Our findings can support scalable, real-time deployment of AI-based tools for oceanic microplastic monitoring and environmental policy development. Full article

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29 pages, 9110 KiB

Open AccessArticle

Wind Field Retrieval from Fengyun-3E Radar Based on a Backpropagation Neural Network

by Zhengxuan Zhao, Fang Pang, George P. Petropoulos, Yansong Bao, Qing Xiao, Yuanyuan Wang, Shiqi Li, Wanyue Gao and Tianhao Wang

Remote Sens. 2025, 17(16), 2813; https://doi.org/10.3390/rs17162813 (registering DOI) - 14 Aug 2025

Abstract

Ocean surface wind fields are crucial for marine environmental research and applications in weather forecasting, ocean disaster monitoring, and climate change studies. However, traditional wind retrieval methods often struggle with modeling complexity and ambiguity due to the nonlinear nature of geophysical model functions [...] Read more.

Ocean surface wind fields are crucial for marine environmental research and applications in weather forecasting, ocean disaster monitoring, and climate change studies. However, traditional wind retrieval methods often struggle with modeling complexity and ambiguity due to the nonlinear nature of geophysical model functions (GMFs), leading to increased computational costs and reduced accuracy. To tackle these challenges, this study establishes a sea surface wind field retrieval model employing a backpropagation (BP) neural network, which integrates multi-angular observations from the Wind Radar (WindRAD) sensor aboard the Fengyun-3E (FY-3E) satellite. Experimental results show that the proposed model achieves high precision in retrieving both wind speed and direction. The wind speed model achieves a root-mean-square error (RMSE) of 1.20 m/s for the training set and 1.00 m/s for the selected test set when using ERA5 data as the reference, outperforming the official WindRAD products. For wind direction, the model attains an RMSE of 23.99° on the training set and 24.58° on the test set. Independent validation using Tropical Atmosphere Ocean (TAO) buoy observations further confirms the model’s effectiveness, yielding an RMSE of 1.29 m/s for wind speed and 24.37° for wind direction, also surpassing official WindRAD products. The BP neural network effectively captures the nonlinear relationship between wind parameters and radar backscatter signals, showing significant advantages over traditional methods and maintaining good performance across different wind speeds, particularly in the moderate range (4–10 m/s). In summary, the method proposed herein significantly enhances wind field retrieval accuracy from space; it has the potential to optimize satellite wind field products and improve global wind monitoring and meteorological forecasting. Full article

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23 pages, 2744 KiB

Open AccessArticle

CASF: Correlation-Alignment and Significance-Aware Fusion for Multimodal Named Entity Recognition

by Hui Li, Yunshi Tao, Huan Wang, Zhe Wang and Qingzheng Liu

Algorithms 2025, 18(8), 511; https://doi.org/10.3390/a18080511 (registering DOI) - 14 Aug 2025

Abstract

With the increasing content richness of social media platforms, Multimodal Named Entity Recognition (MNER) faces the dual challenges of heterogeneous feature fusion and accurate entity recognition. Aiming at the key problems of inconsistent distribution of textual and visual information, insufficient feature alignment and [...] Read more.

With the increasing content richness of social media platforms, Multimodal Named Entity Recognition (MNER) faces the dual challenges of heterogeneous feature fusion and accurate entity recognition. Aiming at the key problems of inconsistent distribution of textual and visual information, insufficient feature alignment and noise interference fusion, this paper proposes a multimodal named entity recognition model based on dual-stream Transformer: CASF-MNER, which designs cross-modal cross-attention based on visual and textual features, constructs a bidirectional interaction mechanism between single-layer features, forms a higher-order semantic correlation modeling, and realizes the cross relevance alignment of modal features; construct a dynamic perception mechanism of multimodal feature saliency features based on multiscale pooling method, construct an entropy weighting strategy of global feature distribution information to adaptively suppress noise redundancy and enhance key feature expression; establish a deep semantic fusion method based on hybrid isomorphic model, design a progressive cross-modal interaction structure, and combine with contrastive learning to realize global fusion of the deep semantic space and representational consistency optimization. The experimental results show that CASF-MNER achieves excellent performance on both Twitter-2015 and Twitter-2017 public datasets, which verifies the effectiveness and advancement of the method proposed in this paper. Full article

(This article belongs to the Section Algorithms for Multidisciplinary Applications)

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10 pages, 1089 KiB

Open AccessCase Report

A Family Cluster of Imported Human Brucella melitensis Infection with Probable Breast Milk Transmission: A Case Series

by Christopher Loftus, Jessica Jervis, Victoria Owen, Tom Wingfield, Robert Ball, Waison Wong, Ceri Evans, Christopher Darlow, Francesca Liuzzi, Susan Batley, Rashika Fernando, Alessandro Gerada and Stephen D. Woolley

Trop. Med. Infect. Dis. 2025, 10(8), 227; https://doi.org/10.3390/tropicalmed10080227 (registering DOI) - 14 Aug 2025

Abstract

Human brucellosis is a zoonotic, bacterial infection caused by the intracellular, Gram-negative Brucella spp., which is common globally but rare in the United Kingdom, with approximately 20 imported cases per annum following travel to countries with high endemicity. Transmission typically occurs via the [...] Read more.

Human brucellosis is a zoonotic, bacterial infection caused by the intracellular, Gram-negative Brucella spp., which is common globally but rare in the United Kingdom, with approximately 20 imported cases per annum following travel to countries with high endemicity. Transmission typically occurs via the ingestion of infected animal products, including unpasteurised dairy products. Human-to-human transmission is rare, and routes include postpartum vertical transmission through breastfeeding. We report here on a familial cluster of three cases within a single UK-based Kurdish household of four, including a 11-month-old infant infected through the consumption of breast milk. Four months prior to presentation, the family had travelled together to northern Iraq for a 5-week holiday and all consumed local dairy products except for the children, including the 11-month-old, who was exclusively breastfed at the time. All three patients, including one adult male with complicated brucellosis, had a favourable outcome with medical therapy.: Brucellosis is an important differential diagnosis in returning travellers and specialist advice should be obtained early to prevent sequelae. It is also important for active case-finding, especially in family units with shared exposure. Paediatricians and adult physicians who may manage brucellosis should consider the possibility of vertical transmission in breastfeeding mothers. Full article

(This article belongs to the Section Neglected and Emerging Tropical Diseases)

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14 pages, 6190 KiB

Open AccessArticle

Effects of Transgenic Insect-Resistant Maize HGK60 on Rhizosphere Soil Bacterial Communities

by Yanjun Chen, Junyi Yang, Libo Pan, Meng Liu, Qiuming Wang, Nengwen Xiao and Xiao Guan

Microorganisms 2025, 13(8), 1892; https://doi.org/10.3390/microorganisms13081892 (registering DOI) - 14 Aug 2025

Abstract

While genetically modified crops bring significant economic benefits, the environmental safety issues they may pose have also received increasing attention. To study the impact of planting genetically modified insect-resistant crops on soil ecosystems, this research employed methods such as 16S rDNA amplicon full-length [...] Read more.

While genetically modified crops bring significant economic benefits, the environmental safety issues they may pose have also received increasing attention. To study the impact of planting genetically modified insect-resistant crops on soil ecosystems, this research employed methods such as 16S rDNA amplicon full-length sequencing, using transgenic Cry1Ah insect-resistant corn HGK60 and its conventional counterpart Zheng 58 as subjects for a three-year continuous survey to analyze the effects of planting transgenic Cry1Ah insect-resistant corn HGK60 on the rhizosphere bacterial community. The following results were obtained. (1) A total of 216 corn rhizosphere soil samples were annotated to 51 phyla, 119 orders, 221 families, and 549 genera. (2) Overall, there was no significant difference in the composition of the rhizosphere bacterial community between HGK60 and Zheng 58 at the phylum, class, order, or family levels (p > 0.05), and the planting of HGK60 did not significantly affect the relative abundance of rhizosphere probiotics (p > 0.05). Some differences appeared only briefly and were not reproducible. (3) Alpha and beta diversity analyses showed that overall, the planting of HGK60 had no significant impact on the structure of the rhizosphere bacterial community (p > 0.05). (4) Significant changes in the rhizosphere bacterial community were observed across different growth stages of corn. It can be concluded that the planting of HGK60 has no significant impact on the rhizosphere bacteria. This study provides valuable data support for the environmental safety assessment of genetically modified crops. Full article

(This article belongs to the Section Plant Microbe Interactions)

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14 pages, 550 KiB

Open AccessArticle

Systemic Governance of Rural Revitalization: Social Capital Transfer Through State-Owned Enterprise Interventions in China

by Xinhui Wu, Minsheng Li and Yaofu Huang

Systems 2025, 13(8), 695; https://doi.org/10.3390/systems13080695 (registering DOI) - 14 Aug 2025

Abstract

This study investigates how state-owned enterprises (SOEs) contribute to rural revitalization in China through systemic interventions that enable the transfer of social capital. Addressing the gap between external resource inputs and internal development needs, the study adopts a systems thinking framework to conceptualize [...] Read more.

This study investigates how state-owned enterprises (SOEs) contribute to rural revitalization in China through systemic interventions that enable the transfer of social capital. Addressing the gap between external resource inputs and internal development needs, the study adopts a systems thinking framework to conceptualize social capital as comprising structural, relational, and cognitive components. Drawing on multi-case evidence from assistance projects led by China Southern Power Grid, this study selects 11 assistance projects from a broader pool of 199 cases, to demonstrate how SOEs act as institutional nodes to reshape rural governance systems. They rebuild local organizational networks (structural capital), establish long-term trust through “strong commitment–weak contract” mechanisms (relational capital), and localize technical knowledge to align with rural contexts (cognitive capital). These interlinked processes form an integrated system that enhances rural governance capacity and promotes sustainable development. The findings highlight that SOEs are not merely resource providers but systemic catalysts that support cross-scalar collaboration and social infrastructure building. The study contributes a novel perspective by integrating social capital theory with a systemic governance lens and offer a actionable insights into the institutional design of assistance models for the future interventions by SOEs and similar entities in underdeveloped areas. Full article

(This article belongs to the Special Issue Systems Thinking: Insights and Solutions to Complex Societal Challenges)

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39 pages, 3230 KiB

Open AccessArticle

Decoding Wine Narratives with Hierarchical Attention: Classification, Visual Prompts, and Emerging E-Commerce Possibilities

by Vlad Diaconita, Anda Belciu, Alexandra Maria Ioana Corbea and Iuliana Simonca

J. Theor. Appl. Electron. Commer. Res. 2025, 20(3), 212; https://doi.org/10.3390/jtaer20030212 (registering DOI) - 14 Aug 2025

Abstract

Wine reviews can connect words to flavours; they entwine sensory experiences into vivid stories. This research explores the intersection of artificial intelligence and oenology by using state-of-the-art neural networks to decipher the nuances in wine reviews. For more accurate wine classification and to [...] Read more.

Wine reviews can connect words to flavours; they entwine sensory experiences into vivid stories. This research explores the intersection of artificial intelligence and oenology by using state-of-the-art neural networks to decipher the nuances in wine reviews. For more accurate wine classification and to capture the essence of what matters most to aficionados, we use Hierarchical Attention Networks enhanced with pre-trained embeddings. We also propose an approach to create captivating marketing images using advanced text-to-image generation models, mining a large review corpus for the most important descriptive terms and thus linking textual tasting notes to automatically generated imagery. Compared to more conventional models, our results show that hierarchical attention processes fused with rich linguistic embeddings better reflect the complexities of wine language. In addition to improving the accuracy of wine classification, this method provides consumers with immersive experiences by turning sensory descriptors into striking visual stories. Ultimately, our research helps modernise wine marketing and consumer engagement by merging deep learning with sensory analytics, proving how technology-driven solutions can amplify storytelling and shopping experiences in the digital marketplace. Full article

(This article belongs to the Topic Data Science and Intelligent Management)

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23 pages, 3875 KiB

Open AccessArticle

Edge AI for Industrial Visual Inspection: YOLOv8-Based Visual Conformity Detection Using Raspberry Pi

by Marcelo T. Okano, William Aparecido Celestino Lopes, Sergio Miele Ruggero, Oduvaldo Vendrametto and João Carlos Lopes Fernandes

Algorithms 2025, 18(8), 510; https://doi.org/10.3390/a18080510 (registering DOI) - 14 Aug 2025

Abstract

This paper presents a lightweight and cost-effective computer vision solution for automated industrial inspection using You Only Look Once (YOLO) v8 models deployed on embedded systems. The YOLOv8 Nano model, trained for 200 epochs, achieved a precision of 0.932, an mAP@0.5 of 0.938, [...] Read more.

This paper presents a lightweight and cost-effective computer vision solution for automated industrial inspection using You Only Look Once (YOLO) v8 models deployed on embedded systems. The YOLOv8 Nano model, trained for 200 epochs, achieved a precision of 0.932, an mAP@0.5 of 0.938, and an F1-score of 0.914, with an average inference time of ~470 ms on a Raspberry Pi 500, confirming its feasibility for real-time edge applications. The proposed system aims to replace physical jigs used for the dimensional verification of extruded polyamide tubes in the automotive sector. The YOLOv8 Nano and YOLOv8 Small models were trained on a Graphics Processing Unit (GPU) workstation and subsequently tested on a Central Processing Unit (CPU)-only Raspberry Pi 500 to evaluate their performance in constrained environments. The experimental results show that the Small model achieved higher accuracy (a precision of 0.951 and an mAP@0.5 of 0.941) but required a significantly longer inference time (~1315 ms), while the Nano model achieved faster execution (~470 ms) with stable metrics (precision of 0.932 and mAP@0.5 of 0.938), therefore making it more suitable for real-time applications. The system was validated using authentic images in an industrial setting, confirming its feasibility for edge artificial intelligence (AI) scenarios. These findings reinforce the feasibility of embedded AI in smart manufacturing, demonstrating that compact models can deliver reliable performance without requiring high-end computing infrastructure. Full article

(This article belongs to the Special Issue Advances in Computer Vision: Emerging Trends and Applications)

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18 pages, 1034 KiB

Open AccessArticle

Navigating the Future: A Novel PCA-Driven Layered Attention Approach for Vessel Trajectory Prediction with Encoder–Decoder Models

by Fusun Er and Yıldıray Yalman

Appl. Sci. 2025, 15(16), 8953; https://doi.org/10.3390/app15168953 (registering DOI) - 14 Aug 2025

Abstract

This study introduces a novel deep learning architecture for vessel trajectory prediction based on Automatic Identification System (AIS) data. The motivation stems from the increasing importance of maritime transport and the need for intelligent solutions to enhance safety and efficiency in congested waterways—particularly [...] Read more.

This study introduces a novel deep learning architecture for vessel trajectory prediction based on Automatic Identification System (AIS) data. The motivation stems from the increasing importance of maritime transport and the need for intelligent solutions to enhance safety and efficiency in congested waterways—particularly with respect to collision avoidance and real-time traffic management. Special emphasis is placed on river navigation scenarios that limit maneuverability with the demand of higher forecasting precision than open-sea navigation. To address these challenges, we propose a Principal Component Analysis (PCA)-driven layered attention mechanism integrated within an encoder–decoder model to reduce redundancy and enhance the representation of spatiotemporal features, allowing the layered attention modules to focus more effectively on salient positional and movement patterns across multiple time steps. This dual-level integration offers a deeper contextual understanding of vessel dynamics. A carefully designed evaluation framework with statistical hypothesis testing demonstrates the superiority of the proposed approach. The model achieved a mean positional error of 0.0171 nautical miles (SD: 0.0035), with a minimum error of 0.0006 nautical miles, outperforming existing benchmarks. These results confirm that our PCA-enhanced attention mechanism significantly reduces prediction errors, offering a promising pathway toward safer and smarter maritime navigation, particularly in traffic-critical riverine systems. While the current evaluation focuses on short-term horizons in a single river section, the methodology can be extended to complex environments such as congested ports or multi-ship interactions and to medium-term or long-term forecasting to further enhance operational applicability and generalizability. Full article

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23 pages, 1193 KiB

Open AccessArticle

Disability and Factors Associated with Disability in the Discharge Transition Phase After Acquired Brain Injury: An Observational Follow-Up Study

by Helene Honoré, Peter Preben Eggertsen, Asger Roer Pedersen and Jørgen Feldbæk Nielsen

Healthcare 2025, 13(16), 1989; https://doi.org/10.3390/healthcare13161989 (registering DOI) - 14 Aug 2025

Abstract

Background/Objectives: Patients with acquired brain injury (ABI) often face complex challenges during the transition from in-hospital rehabilitation to everyday life. This study aimed to investigate disability, health-related quality of life (HRQoL), work, and other aspects of functioning as indicators of a meaningful life [...] Read more.

Background/Objectives: Patients with acquired brain injury (ABI) often face complex challenges during the transition from in-hospital rehabilitation to everyday life. This study aimed to investigate disability, health-related quality of life (HRQoL), work, and other aspects of functioning as indicators of a meaningful life in this transition phase. Additionally, we assessed how disability three months post-discharge correlates with known risk factors. Methods: We conducted a prospective observational follow-up study including patients aged ≥18 years with ABI discharged from a specialized rehabilitation clinic. Patient-reported outcomes, including disability and HRQoL, were collected at discharge and three months later. Associations between disability and known risk factors were analyzed using multiple linear regression. Results: A total of 137 patients were included (mean age 63), with a follow-up completion rate of 59%. At follow-up, 11% reported complete recovery, while a moderate level of disability persisted overall, with no systematic change from discharge. HRQoL improved significantly, reaching a mean score of 0.83. Fatigue, sex, and time from injury to rehabilitation were significantly associated with disability levels. Conclusions: The transition phase after rehabilitation posed challenges for patients with ABI, with 38% experiencing moderate disability. Despite this, HRQoL improved to levels comparable with the general population. Fatigue, sex, and rehabilitation timing emerged as key factors influencing disability outcomes. Full article

(This article belongs to the Special Issue Exercise Therapy: Improving Functionality, Physical Health, and Quality of Life)

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13 pages, 1853 KiB

Open AccessArticle

Development and Validation of Differential Diagnosis Models and Nomograms Based on Serum D-Dimer and Other Multimodal Information for Borderline and Benign Epithelial Ovarian Tumors: A Multicenter Study

by Yiqing Zhang, Yayang Duan, Fang He, Chunhua Duan, Junli Wang, Chaoxue Zhang and Yi Zhou

Diagnostics 2025, 15(16), 2035; https://doi.org/10.3390/diagnostics15162035 (registering DOI) - 14 Aug 2025

Abstract

Background: It is difficult to make a definite diagnosis of borderline epithelial ovarian tumors before surgery. In order to avoid incorrectly classifying tumors as benign, a differential diagnosis model was developed to distinguish between benign and borderline epithelial tumors utilizing multimodal information. Method: [...] Read more.

Background: It is difficult to make a definite diagnosis of borderline epithelial ovarian tumors before surgery. In order to avoid incorrectly classifying tumors as benign, a differential diagnosis model was developed to distinguish between benign and borderline epithelial tumors utilizing multimodal information. Method: A multicenter study was conducted. A retrospective analysis of the transvaginal ultrasonography and clinical data of patients who underwent surgery and received pathological diagnoses of borderline and benign epithelial ovarian tumors was conducted. Both Univariate and multivariate logistic regression analyses were used to develop a diagnostic model for borderline epithelial tumors. The efficacy and feasibility of this model were assessed through examination of training, internal validation, and external test sets. Results: There was a significant difference in D-dimer levels between borderline and benign epithelial tumors. Abnormal CA125, D-dimer, maximum mass diameter > 10 cm, regular and irregular solid portions, and blood flow in the mass were independent risk factors for borderline epithelial ovarian tumors. The diagnostic model was evaluated by the Hosmer–Lemeshow test and demonstrated strong fitting capabilities. ROC curve analysis of the training set, verification set, and external test set confirmed the model’s predictive ability. Conclusions: These independent risk factors may be combined to assess the risk of borderline epithelial ovarian tumors. Our findings will assist novice gynecologic sonographers in distinguishing between benign and borderline epithelial tumors. Full article

(This article belongs to the Section Pathology and Molecular Diagnostics)

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4 pages, 160 KiB

Open AccessEditorial

Emerging Research on the Relationship Between Diet, Gut Microbes, and Human Health

by Jennifer J. Barb and Gwenyth R. Wallen

Nutrients 2025, 17(16), 2627; https://doi.org/10.3390/nu17162627 (registering DOI) - 14 Aug 2025

Abstract

The gut microbiome has emerged as a key player in nearly every aspect of human health, influencing not only physical well-being, but also emotional regulation and brain function, as well as dietary behaviors and cravings [...] Full article

(This article belongs to the Special Issue The Relationship between Diet, Gut Microbes and Human Health)

21 pages, 7083 KiB

Open AccessArticle

Green Low-Temperature Activation and Curing for High-Toughness Geopolymer Binders from Diabase Tailings

by Yanan Hu, Yong Yao, Lingling Zhang, Xianming Hu and Xinchun Yang

Materials 2025, 18(16), 3815; https://doi.org/10.3390/ma18163815 (registering DOI) - 14 Aug 2025

Abstract

This study addresses the low reactivity and poor toughness of diabase tailings (DT), a high-silica industrial byproduct, which restricts their large-scale application in geopolymer binders. To overcome these limitations, a dual-regulation strategy integrating stepwise low-temperature thermal activation (100, 200, and 300 °C) with [...] Read more.

This study addresses the low reactivity and poor toughness of diabase tailings (DT), a high-silica industrial byproduct, which restricts their large-scale application in geopolymer binders. To overcome these limitations, a dual-regulation strategy integrating stepwise low-temperature thermal activation (100, 200, and 300 °C) with standard curing (20 ± 2 °C, 95% RH) was developed. This approach aimed to enhance mineral dissolution kinetics and facilitate the formation of a dense, interconnected gel network. XRD, FTIR, and SEM analyses revealed significant decomposition of amphibole, pyroxene, and olivine, accompanied by increased release of reactive Si and Al species, leading to the formation of a compact N–A–S–H/C–A–S–H gel structure. Under optimized conditions (Si/Al = 2.6; activator modulus = 1.2), the geopolymer achieved a 7-day compressive strength of 42.3 ± 1.8 MPa, a flexural strength of 12.76 ± 1.6 MPa, and a flexural-to-compressive strength ratio of 0.308, demonstrating significant improvements in toughness compared with conventional binders. This green, energy-efficient strategy not only reduces energy consumption and CO₂ emissions but also provides a technically feasible pathway for the high-value reuse of silicate-rich mining wastes, contributing to the development of sustainable construction materials with enhanced mechanical performance. Full article

(This article belongs to the Special Issue Green and Sustainable Infrastructure Construction Materials (3rd Edition))

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7 pages, 10289 KiB

Open AccessCase Report

Brown Tumor in Jaw Associated with Hyperparathyroidism: A Case Report

by Verónica Montserrat Silva-Gutiérrez, Judith Berenice Macías-Jiménez, Adriana Molotla-Fragoso, Claudia Patricia Mejía-Velázquez, Gabriel Levi Estévez-González and Luis Fernando Jacinto-Alemán

Oral 2025, 5(3), 59; https://doi.org/10.3390/oral5030059 (registering DOI) - 14 Aug 2025

Abstract

Background/Objectives: Brown tumors are bone manifestations of hyperparathyroidism, and they are characterized by histologic similarities with Central Giant Cell Granuloma (CGCG). Their diagnosis requires clinical, microscopic, macroscopic, and serologic correlation, as there is usually an elevation in parathormone levels due to the [...] Read more.

Background/Objectives: Brown tumors are bone manifestations of hyperparathyroidism, and they are characterized by histologic similarities with Central Giant Cell Granuloma (CGCG). Their diagnosis requires clinical, microscopic, macroscopic, and serologic correlation, as there is usually an elevation in parathormone levels due to the underlying metabolic disorder. Methods: This case describes a patient with a left mandibular lesion and a history of CGCG. Results: Through the joint analysis of clinical, histologic, and serologic findings, the diagnosis of a brown tumor associated with hyperparathyroidism was confirmed. Conclusions: This case highlights the importance of a comprehensive evaluation of oral and systemic features for accurate diagnoses and appropriate patient management. Full article

(This article belongs to the Collection Oral and Systemic Health: Border Dentistry and the Borders of Dental Practice)

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15 pages, 241 KiB

Open AccessArticle

Addressing Childhood Obesity in Children in Need in Greece: Policy Implementers’ Knowledge, Perceptions and Lessons for Effective Implementation

by Theodora Balafouti, Dimitra E. Strongylou, Vaios Svolos, Matzourana Argyropoulou, Renos Roussos, Christina Mavrogianni, Alexios Manidis, Anela Halilagic, George Moschonis, Odysseas Androutsos, Yannis Manios and Theodora Mouratidou

Nutrients 2025, 17(16), 2629; https://doi.org/10.3390/nu17162629 (registering DOI) - 14 Aug 2025

Abstract

Background/Objectives: Policy implementers play a crucial role in the effective delivery of policies aiming at promoting a healthy lifestyle in the most vulnerable populations. This study aimed to explore (a) policy implementers’ knowledge and perceptions of the policy framework promoting physical activity and [...] Read more.

Background/Objectives: Policy implementers play a crucial role in the effective delivery of policies aiming at promoting a healthy lifestyle in the most vulnerable populations. This study aimed to explore (a) policy implementers’ knowledge and perceptions of the policy framework promoting physical activity and healthy nutrition among children in need in Greece, and (b) self-perceived barriers and facilitators of the framework implementation. The term children in need refers to children who are at risk of poverty and/or social exclusion. Methods: A qualitative study design was employed consisting of semi-structured interviews with 25 policy implementers, who represented four delivery systems (health, social protection, food, and education sectors) from three geographical regions in Greece. Interviews were completed between November and December 2023. Thematic analysis was conducted using inductive and deductive approaches to identify key themes, following data management in the N-VIVO 14 software. Results: Commonly mentioned policies that study participants were involved in included school- and/or community-level-based behavioral interventions. Participants perceived policy implementation efforts that often relied on individual initiatives as inconsistent. Most participants argued that existing policies were not tailored to the needs of children in need. Major self-perceived barriers included limited personnel training, limited facilities and infrastructure, and lack of incentives or opportunities to encourage active participation. Major self-perceived facilitators included personnel motivation, integration of nutrition and physical education into school curricula, and provision of free school meals, which was associated with regular school attendance of children from the Roma communities. Conclusions: Individual, sociocultural, and structural issues are shown to persist across different delivery systems indicating the complexity of tackling obesogenic environments, especially among children in need. This is the first study in Greece to provide evidence on self-perceived barriers and facilitators and could inform ongoing national and European efforts to address obesogenic environments in children in need. Full article

(This article belongs to the Section Nutritional Epidemiology)

12 pages, 3438 KiB

Open AccessArticle

Assessment of Hypertension in Hemodialysis Patients with the Concomitant Use of Peridialytic and Interdialytic Ambulatory Blood Pressure Measurements

by Kallistheni Leonidou, Ioannis Kontogiorgos, Christodoula Kourtidou, Eleni Georgianou, Vasileios Rafailidis, Stefanos Roumeliotis, Konstantinos Leivaditis, Elias V. Balaskas, Vassilios Liakopoulos and Panagiotis I. Georgianos

Life 2025, 15(8), 1290; https://doi.org/10.3390/life15081290 (registering DOI) - 14 Aug 2025

Abstract

Background: For patients on hemodialysis, routine blood pressure (BP) measurements taken shortly before or after dialysis provide inaccurate estimates of the BP load during the interdialytic period. In this study, we used peridialytic recordings in combination with interdialytic ambulatory BP monitoring (ABPM) aiming [...] Read more.

Background: For patients on hemodialysis, routine blood pressure (BP) measurements taken shortly before or after dialysis provide inaccurate estimates of the BP load during the interdialytic period. In this study, we used peridialytic recordings in combination with interdialytic ambulatory BP monitoring (ABPM) aiming to provide a more precise assessment of hypertension in a sample of 70 stable hemodialysis patients. Methods: The evaluation of hypertension in the study cohort was performed using the following approaches: (i) routine predialysis and postdialysis BP measurements taken by the dialysis-unit staff were prospectively recorded over six consecutive dialysis sessions; (ii) ABPM was performed using the Microlife WatchBPO3 device (20 min intervals during an entire 44 h interdialytic period). The diagnostic thresholds of hypertension were ≥140/90 mmHg for predialysis, ≥130/80 mmHg for postdialysis and ≥130/80 mmHg for 44 h ambulatory BP, respectively. Patients receiving ≥1 antihypertensive medication also were classified as hypertensives. Results: The prevalence of hypertension was 88.6% by predialysis, 92.9% by postdialysis and 90.0% by ambulatory BP measurements. In all, 87.1% of patients were being treated for hypertension. When the combination of predialysis and 44 h ambulatory BP was evaluated, the prevalence of sustained normotension, white-coat, masked and sustained hypertension was 52.9%, 21.4%, 5.7% and 20.0%, respectively. A similar distribution of patients into these phenotypes was observed when postdialysis BP was used for the classification of the severity of hypertension (50.0%, 24.3%, 5.7% and 20.0% for sustained normotension, white-coat, masked and sustained hypertension, respectively). Interdialytic ABPM revealed that just one patient had abnormal BP solely during the daytime period. Conversely, isolated nocturnal hypertension was diagnosed in 27.1% of patients. Conclusions: This study shows that among patients on hemodialysis, peridialytic BP is an inaccurate proxy of interdialytic ambulatory BP. In approximately 30% of patients, there is discordance between routine peridialytic recordings and interdialytic ABPM for the diagnosis of hypertension. ABPM also facilitates the diagnosis of isolated nocturnal hypertension, which is another frequent BP phenotype in this high-risk patient population. Full article

(This article belongs to the Section Epidemiology)

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17 pages, 4999 KiB

Open AccessArticle

Simulating the Phylogenetic Diversity Metrics of Plant Communities in Alpine Grasslands of Xizang, China

by Mingxue Xiang, Tao Ma, Wei Sun, Shaowei Li and Gang Fu

Diversity 2025, 17(8), 569; https://doi.org/10.3390/d17080569 (registering DOI) - 14 Aug 2025

Abstract

Phylogenetic diversity serves as a critical complement to traditional species diversity metrics. However, the performance variations among different computational models in simulating phylogenetic diversity within plant communities in the alpine grasslands of the Qinghai-Xizang Plateau remain insufficiently characterized. Here, we evaluated nine modeling [...] Read more.

Phylogenetic diversity serves as a critical complement to traditional species diversity metrics. However, the performance variations among different computational models in simulating phylogenetic diversity within plant communities in the alpine grasslands of the Qinghai-Xizang Plateau remain insufficiently characterized. Here, we evaluated nine modeling approaches—random forest (RF), generalized boosting regression (GBR), multiple linear regression (MLR), artificial neural network (ANN), generalized linear regression (GLR), conditional inference tree (CIT), extreme gradient boosting (eXGB), support vector machine (SVM), and recursive regression tree (RRT)—for predicting three key phylogenetic diversity metrics [Faith’s phylogenetic diversity (PD), mean pairwise distance (MPD), mean nearest taxon distance (MNTD)] using climate variables and NDVImax. Our comprehensive analysis revealed distinct model performance patterns under grazing vs. fencing regimes. The eXGB algorithm demonstrated superior accuracy for fencing conditions, achieving the lowest relative bias (−0.08%) and RMSE (9.54) for MPD, along with optimal performance for MNTD (bias = 2.95%, RMSE = 44.86). Conversely, RF emerged as the most robust model for grazing scenarios, delivering the lowest bias (−1.63%) and RMSE (16.89) for MPD while maintaining strong predictive capability for MNTD (bias = −1.09%, RMSE = 27.59). Notably, scatterplot analysis revealed that only RF, GBR, and eXGB maintained symmetrical distributions along the 1:1 line, while other models showed problematic one-to-many value mappings or asymmetric patterns. These findings show that machine learning (especially RF and eXGB) enhances phylogenetic diversity predictions by integrating climate and NDVI data, though model performance varies by metric and management context. This study offers a framework for ecological forecasting, emphasizing multi-metric validation in biodiversity modeling. Full article

(This article belongs to the Topic Effective Strategies for Rangeland Conservation and Sustainable Management)

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7 pages, 1359 KiB

Open AccessArticle

Using Count Regression to Investigate Millennial-Scale Vegetation and Fire Response from Multiple Sites Across the Northern Rocky Mountains, USA

by Jennifer Watt, Brian F. Codding, Jordin Hartley, Carlie Murphy and Andrea Brunelle

Fire 2025, 8(8), 321; https://doi.org/10.3390/fire8080321 (registering DOI) - 14 Aug 2025

Abstract

The Northern Rocky Mountains, USA contain a vast forested landscape, managed primarily by the federal government. This region contains some of the highest elevations forests and most iconic endangered and threatened species in the contiguous United States. The influence of human impacts and [...] Read more.

The Northern Rocky Mountains, USA contain a vast forested landscape, managed primarily by the federal government. This region contains some of the highest elevations forests and most iconic endangered and threatened species in the contiguous United States. The influence of human impacts and climate change are evident on the landscape today, with larger and more frequent fires impacting vegetation composition and recovery. This project uses paleoecological data from six lake sediment cores to investigate what drives fire across this region over the Holocene. Count regression was used to predict charcoal influx as a function of Pinus pollen accumulation rates (PAR) and percent. The results show that fire activity increases significantly with Pinus pollen, and that baseline fire activity varies significantly across sites, largely following an elevation gradient. The results of this analysis illustrate a novel way to use paleoecological data to provide valuable information to federal agencies as they prepare for future management of these ecologically valuable areas. Full article

(This article belongs to the Special Issue Fire Patterns, Driving Factors, and Multidimensional Impacts Under Climate Change and Human Activities)

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4 pages, 163 KiB

Open AccessEditorial

Gamma and X-Ray Technologies for Medical Research: Image Analysis and Disease Discovered

by Adam Konefał

Appl. Sci. 2025, 15(16), 8954; https://doi.org/10.3390/app15168954 (registering DOI) - 14 Aug 2025

Abstract

The continuous pursuit of innovative diagnostic and medical research methods plays a crucial role in improving public health [...] Full article

(This article belongs to the Special Issue Gamma and X-ray Technologies for Medical Research: Image Analysis and Disease Discovered)

3 pages, 124 KiB

Open AccessEditorial

New Evidence on Nutrition and Metabolic Influences on Pregnancy Outcomes: Insights from This Special Issue

by Jose Luis Bartha

Nutrients 2025, 17(16), 2628; https://doi.org/10.3390/nu17162628 (registering DOI) - 14 Aug 2025

Abstract

The complex relationship between maternal nutrition, metabolic status and pregnancy outcomes has long remained controversial in perinatal research [...] Full article

(This article belongs to the Special Issue Nutritional and Metabolic Factors in Pregnancy Complications)

24 pages, 5111 KiB

Open AccessArticle

The Use of Gas Dynamics to Estimate the Influence of Flanges on Gear Windage Power Loss

by Thibaut Torres, Yasser Diab, Christophe Changenet, Thomas Touret and Bérengère Guilbert

Dynamics 2025, 5(3), 33; https://doi.org/10.3390/dynamics5030033 (registering DOI) - 14 Aug 2025

Abstract

This study aims to develop a new model for windage losses, building upon existing formulation, complemented by dedicated experimental campaigns and a specific methodology designed to isolate and quantify windage losses. The model relies on an analytical approach to flow characterization, incorporating a [...] Read more.

This study aims to develop a new model for windage losses, building upon existing formulation, complemented by dedicated experimental campaigns and a specific methodology designed to isolate and quantify windage losses. The model relies on an analytical approach to flow characterization, incorporating a correction factor accounting for air density reduction. The experimental investigation was carried out on a dedicated test bench and includes both spur and helical gears. The results demonstrate good agreement between the proposed model and the experimental data, with and without the presence of nearby obstacles, such as side flanges, highlighting the model’s robustness across different configurations. The proposed windage loss model reproduces the experimental results with significantly greater accuracy than the original one, yielding relative deviations below 5% compared to almost 20% for spur gears, and below 9% compared to over 21%, and in some cases up to 50%, for helical gears. Full article

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20 pages, 2665 KiB

Open AccessArticle

Subjective Perception and Cooling Effect for Dynamic Ventilation with Fluctuating Air Velocity

by Chunfeng Lao, Jing Ling, Jing Li, Jinghua Jiang, Sheng Zhang, Yan Yan, Yue Yin and Mingliang Gu

Buildings 2025, 15(16), 2871; https://doi.org/10.3390/buildings15162871 (registering DOI) - 14 Aug 2025

Abstract

Dynamic ventilation has proven effective in enhancing indoor thermal comfort. However, previous studies often expose participants to inconsistent thermal environments, potentially compromising the accuracy of subjective evaluations. To address this limitation, this study implemented dynamic ventilation with fluctuating air velocity in an accurately [...] Read more.

Dynamic ventilation has proven effective in enhancing indoor thermal comfort. However, previous studies often expose participants to inconsistent thermal environments, potentially compromising the accuracy of subjective evaluations. To address this limitation, this study implemented dynamic ventilation with fluctuating air velocity in an accurately controlled environmental chamber. Objective measurements of indoor air velocity and air temperature distribution are conducted, and subjective thermal sensation votes are collected under thermally consistent environments among participants. During the experiment, all participants experience similar dynamic thermal environments. The results show that participants experience thermal comfort under dynamic ventilation. Dynamic ventilation enhances convective heat transfer between the human body and the surrounding air and stimulates cutaneous cold receptors. The pronounced cooling effect of dynamic airflow contributes to a reduction in skin temperature on the head, chest, upper arm, forearm, hand, and thigh, with a temperature drop ranging from 1.3% to 2.8%. In addition, dynamic ventilation significantly reduces draft risk, with the proportion of participants reporting a dissatisfied sensation decreasing from 10% to 0%. This study demonstrates the advantages of dynamic ventilation in improving thermal comfort and minimizing draft risk under controlled and uniform environmental conditions for all participants. Full article

(This article belongs to the Special Issue Development of Indoor Environment Comfort)

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20 pages, 533 KiB

Open AccessArticle

Digital Economy Promotes the Improvement of Urban Living Environment: Evidence from Provincial Panel Data of China

by Quanxin Zhu and Huiqin Yao

Sustainability 2025, 17(16), 7339; https://doi.org/10.3390/su17167339 (registering DOI) - 14 Aug 2025

Abstract

The digital economy has become an important aspect of global competition and a key force in restructuring global factor resources, reshaping economic structures, and changing the landscape of global competition. This paper defines the concept of urban living environment, analyzes the mechanisms through [...] Read more.

The digital economy has become an important aspect of global competition and a key force in restructuring global factor resources, reshaping economic structures, and changing the landscape of global competition. This paper defines the concept of urban living environment, analyzes the mechanisms through which the digital economy affects it, and empirically examines the effects and transmission mechanisms of the digital economy in enhancing urban living environments based on panel data from 30 Chinese provinces from 2012 to 2022. The study finds that the accelerated development of the digital economy significantly contributes to improvements in urban living environments. Specifically, the digital economy exerts its positive influence through the intermediary paths of technological innovation, government governance, and changes in resident behavior. Moreover, its effect is more pronounced in provinces with lower investment in industrial pollution control and relatively lax environmental regulations. From a regional perspective, the digital economy has a greater impact on improving urban living environments in the eastern region than in the central and western regions. Full article

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