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30 pages, 2308 KB

Open AccessArticle

Forecasting Installation Demand Using Machine Learning: Evidence from a Large PV Installer in Poland

by Anna Zielińska and Rafał Jankowski

Energies 2025, 18(18), 4998; https://doi.org/10.3390/en18184998 (registering DOI) - 19 Sep 2025

The dynamic growth of the photovoltaic (PV) market in Poland, driven by declining technology costs, government support programs, and the decentralization of energy generation, has created a strong demand for accurate short-term forecasts to support sales planning, logistics, and resource management. This study [...] Read more.

The dynamic growth of the photovoltaic (PV) market in Poland, driven by declining technology costs, government support programs, and the decentralization of energy generation, has created a strong demand for accurate short-term forecasts to support sales planning, logistics, and resource management. This study investigates the application of long short-term memory (LSTM) recurrent neural networks to forecast two key market indicators: the monthly number of completed PV installations and their average unit capacity. The analysis is based on proprietary two-year data from one of the largest PV companies in Poland, covering both sales and completed installations. The dataset was preprocessed through cleaning, filtering, and aggregation into a consistent monthly time series. Results demonstrate that the LSTM model effectively captured seasonality and temporal dependencies in the PV market, outperforming multilayer perceptron (MLP) models in forecasting installation counts and providing robust predictions for average capacity. These findings confirm the potential of LSTM-based forecasting as a valuable decision-support tool for enterprises and policymakers, enabling improved market strategy, optimized resource allocation, and more effective design of support mechanisms in the renewable energy sector. The originality of this study lies in the use of a unique, proprietary dataset of over 12,000 completed PV micro-installations, rarely available in the literature, and in its direct focus on market demand forecasting rather than energy production. This perspective highlights the practical value of the model for companies in sales planning, logistics, and resource allocation. Full article

(This article belongs to the Special Issue From Consumer Behavior to Business Innovation: Paths to a Sustainable Energy Future)

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14 pages, 961 KB

Open AccessArticle

Optoelectronic Properties of Hydrogen-Terminated Silicon Nanowires via Aliphatic C₈ Moieties: Impact of C–C Bond Order from First Principles

by Francesco Buonocore, Barbara Ferrucci, Sara Marchio, Simone Giusepponi, Sumesh Sadhujan, Musa Abu-Hilu, Muhammad Y. Bashouti and Massimo Celino

Appl. Sci. 2025, 15(18), 10235; https://doi.org/10.3390/app151810235 (registering DOI) - 19 Sep 2025

Abstract

In the present work we investigate by first principles calculations the structural, electronic, and optical properties of alkyl, 1-alkenyl and 1-alkynyl C₈ moieties chemisorbed on hydrogen-terminated silicon nanowire oriented along the ⟨112⟩ direction. Our results disclose how the nature of the carbon–carbon [...] Read more.

In the present work we investigate by first principles calculations the structural, electronic, and optical properties of alkyl, 1-alkenyl and 1-alkynyl C₈ moieties chemisorbed on hydrogen-terminated silicon nanowire oriented along the ⟨112⟩ direction. Our results disclose how the nature of the carbon–carbon bond contiguous to the Si surface influences the behavior of the system. While 1-alkynyl groups exhibit the strongest Si–C bonding, it is 1-alkenyl functionalization that induces the most significant enhancement in optical absorption within the visible range due to charge transfer. The charge transferred from the nanowire to the moiety confirms the electronic coupling of the two systems. We found that the highest occupied molecular orbital of the 1-alkenyl moiety lies only 0.3 eV below the valence band edge of the hydrogen-terminated silicon nanowire, enabling new low-energy optical transitions which are absent in both the unmodified silicon nanowire and the isolated molecule. These findings demonstrate a synergistic effect of functionalization. Our study provides valuable insights into the design of functionalized silicon nanostructures with tailored optical properties, with potential implications for applications in sensing, photonics, and energy conversion. Full article

(This article belongs to the Special Issue Nanostructured Materials: From Surface to Porous Solids, 2nd Edition)

44 pages, 1929 KB

Open AccessReview

Review of Uneven Road Surface Information Perception Methods for Suspension Preview Control

by Yujie Shen, Kai Jing, Kecheng Sun, Changning Liu, Yi Yang and Yanling Liu

Sensors 2025, 25(18), 5884; https://doi.org/10.3390/s25185884 (registering DOI) - 19 Sep 2025

Abstract

Accurate detection of road surface information is crucial for enhancing vehicle driving safety and ride comfort. To overcome the limitation that traditional suspension systems struggle to respond to road excitations in real time due to time delays in signal acquisition and control, suspension [...] Read more.

Accurate detection of road surface information is crucial for enhancing vehicle driving safety and ride comfort. To overcome the limitation that traditional suspension systems struggle to respond to road excitations in real time due to time delays in signal acquisition and control, suspension preview control technology has attracted significant attention for its proactive adjustment capability, with efficient road surface information perception being a critical prerequisite for its implementation. This paper systematically reviews road surface information detection technologies for suspension preview, focusing on the identification of potholes and speed bumps. Firstly, it summarizes relevant publicly available datasets. Secondly, it sorts out mainstream detection methods, including traditional dynamic methods, 2D image processing, 3D point cloud analysis, machine/deep learning methods, and multi-sensor fusion methods, while comparing their applicable scenarios and evaluation metrics. Furthermore, it emphasizes the core role of elevation information (e.g., pothole depth, speed bump height) in suspension preview control and summarizes elevation reconstruction technologies based on LiDAR, stereo vision, and multi-modal fusion. Finally, it prospects future research directions such as optimizing robustness, improving real-time performance, and reducing labeling costs. This review provides technical references for enhancing the accuracy of road surface information detection and the control efficiency of suspension preview systems, and it is of great significance for promoting the development of intelligent chassis. Full article

(This article belongs to the Special Issue Advanced Environment Perception, Decision Planning, and Intelligent Control of Transport Equipment)

25 pages, 9998 KB

Open AccessArticle

A Study on the Soil Seismic Liquefaction Artificial Neural Network Probabilistic Assessment Method Based on Standard Penetration Test Data

by Jingjun Li, Meng Fan, Zhengquan Yang, Xiaosheng Liu and Jianming Zhao

Appl. Sci. 2025, 15(18), 10229; https://doi.org/10.3390/app151810229 (registering DOI) - 19 Sep 2025

Abstract

Constructing a probabilistic assessment method is the primary task and key step in liquefaction research. This paper presents a systematic investigation into liquefaction potential evaluation methods. Through a comparative analysis of three conventional assessment methods, we identify critical limitations in existing approaches regarding [...] Read more.

Constructing a probabilistic assessment method is the primary task and key step in liquefaction research. This paper presents a systematic investigation into liquefaction potential evaluation methods. Through a comparative analysis of three conventional assessment methods, we identify critical limitations in existing approaches regarding accuracy and adaptability. A probabilistic ANN model was developed using field-collected standard penetration test (SPT) data from 311 liquefaction case histories. The model demonstrates superior performance with an overall accuracy of 86.17%, achieving 83.33% and 90.00% recognition rates for liquefied and non-liquefied cases, respectively. Key metrics, including precision (91.84%), recall (83.33%), and F1-score (87.38%), indicate robust discriminative capability. Comparative studies confirm the ANN model’s advantages over traditional methods in terms of prediction reliability and operational practicality. The research outcomes offer significant value for improving current liquefaction hazard assessment protocols in geotechnical engineering practice. Full article

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14 pages, 384 KB

Open AccessArticle

Assessment of Gait Disorders in Cerebral Small Vessel Disease: Advantages of Different Clinical Scales

by Larisa A. Dobrynina, Elina T. Bitsieva, Kamila V. Shamtieva, Maryam R. Zabitova and Marina V. Krotenkova

J. Clin. Med. 2025, 14(18), 6626; https://doi.org/10.3390/jcm14186626 (registering DOI) - 19 Sep 2025

Abstract

Background/Objectives: Cerebral small vessel disease (cSVD) is one of the leading causes of gait disorders (GDs) in the elderly. Clinical diversity and lack of standardization in assessment of GDs in cSVD patients are associated with late diagnosis. The comparative value of clinical rating [...] Read more.

Background/Objectives: Cerebral small vessel disease (cSVD) is one of the leading causes of gait disorders (GDs) in the elderly. Clinical diversity and lack of standardization in assessment of GDs in cSVD patients are associated with late diagnosis. The comparative value of clinical rating scales used for gait assessment in clinical studies of cSVD has not been previously clarified. The purpose of the study was to assess GDs in cSVD patients with different scales and evaluate the advantages of their usage in clinical practice. Materials and methods: The study included 124 cSVD patients (STRIVE, 2013) (average age 62.2 ± 7.9, women—53.2%) and 30 healthy volunteers (average age 59.77 ± 6.361, women—56.7%). Gait and balance function were assessed with the Tinetti test, “6-m walk” test, and the Clinical Scale for Assessing the Severity of Gait Disorders in SVD (RCN, 2019). Results: In total, 85 (68.5%) patients had gait disturbances. The “6-MWT” showed a general tendency to decrease gait speed, step length, and increase in base width. ROC analysis established their thresholds for GD diagnosis. Moderate- or high-risk of falls was found in 52 (41.9%) patients. Gait parameters assessed by two tests (Tinneti and 6-WMT) showed a high degree of intercorrelations. Comparative analysis of the quantitative parameters of Tinneti and 6-WMT tests revealed significant differences depending on the severity of the GD assessed by the Clinical Scale for Assessing the Severity of GDs in cSVD (RCN, 2019). Conclusion: GDs in cSVD are characterized by slowness, changes in step length, base width, and a high risk of falls. The Tinetti test and “6-MWT” have good reproducibility in сSVD, high correlations between the tests, as well as significant differences between the categories of GD severity, which justifies their use in cSVD patients. The advantage of the Tinetti test is the ability to perform a fall risk assessment, while “6-MWT” allows for the diagnosis of GD based on gait parameter thresholds, which is important in the early stages of the disease and in dynamic observation. The Clinical Scale for Assessing the Severity of Gait Disorders in cSVD is a convenient screening tool for assessing the severity of GDs in clinical practice. Full article

(This article belongs to the Section Clinical Neurology)

36 pages, 2691 KB

Open AccessReview

Advanced Electrochemical Sensors for Rapid and Sensitive Monitoring of Tryptophan and Tryptamine in Clinical Diagnostics

by Janani Sridev, Arif R. Deen, Md Younus Ali, Wei-Ting Ting, M. Jamal Deen and Matiar M. R. Howlader

Biosensors 2025, 15(9), 626; https://doi.org/10.3390/bios15090626 (registering DOI) - 19 Sep 2025

Abstract

Tryptophan (Trp) and tryptamine (Tryp), critical biomarkers in mood regulation, immune function, and metabolic homeostasis, are increasingly recognized for their roles in both oral and systemic pathologies, including neurodegenerative disorders, cancers, and inflammatory conditions. Their rapid, sensitive detection in biofluids such as saliva—a [...] Read more.

Tryptophan (Trp) and tryptamine (Tryp), critical biomarkers in mood regulation, immune function, and metabolic homeostasis, are increasingly recognized for their roles in both oral and systemic pathologies, including neurodegenerative disorders, cancers, and inflammatory conditions. Their rapid, sensitive detection in biofluids such as saliva—a non-invasive, real-time diagnostic medium—offers transformative potential for early disease identification and personalized health monitoring. This review synthesizes advancements in electrochemical sensor technologies tailored for Trp and Tryp quantification, emphasizing their clinical relevance in diagnosing conditions like oral squamous cell carcinoma (OSCC), Alzheimer’s disease (AD), and breast cancer, where dysregulated Trp metabolism reflects immune dysfunction or tumor progression. Electrochemical platforms have overcome the limitations of conventional techniques (e.g., enzyme-linked immunosorbent assays (ELISA) and mass spectrometry) by integrating innovative nanomaterials and smart engineering strategies. Carbon-based architectures, such as graphene (Gr) and carbon nanotubes (CNTs) functionalized with metal nanoparticles (Ni and Co) or nitrogen dopants, amplify electron transfer kinetics and catalytic activity, achieving sub-nanomolar detection limits. Synergies between doping and advanced functionalization—via aptamers (Apt), molecularly imprinted polymers (MIPs), or metal-oxide hybrids—impart exceptional selectivity, enabling the precise discrimination of Trp and Tryp in complex matrices like saliva. Mechanistically, redox reactions at the indole ring are optimized through tailored electrode interfaces, which enhance reaction kinetics and stability over repeated cycles. Translational strides include 3D-printed microfluidics and wearable sensors for continuous intraoral health surveillance, demonstrating clinical utility in detecting elevated Trp levels in OSCC and breast cancer. These platforms align with point-of-care (POC) needs through rapid response times, minimal fouling, and compatibility with scalable fabrication. However, challenges persist in standardizing saliva collection, mitigating matrix interference, and validating biomarkers across diverse populations. Emerging solutions, such as AI-driven analytics and antifouling coatings, coupled with interdisciplinary efforts to refine device integration and manufacturing, are critical to bridging these gaps. By harmonizing material innovation with clinical insights, electrochemical sensors promise to revolutionize precision medicine, offering cost-effective, real-time diagnostics for both localized oral pathologies and systemic diseases. As the field advances, addressing stability and scalability barriers will unlock the full potential of these technologies, transforming them into indispensable tools for early intervention and tailored therapeutic monitoring in global healthcare. Full article

(This article belongs to the Special Issue Nanomaterial-Based Biosensors for Point-of-Care Testing)

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14 pages, 2235 KB

Open AccessArticle

On the Feasibility of Localizing Transformer Winding Deformations Using Optical Sensing and Machine Learning

by Najmeh Seifaddini, Meysam Beheshti Asl, Sekongo Bekibenan, Simplice Akre, Issouf Fofana, Mohand Ouhrouche and Abdellah Chehri

Photonics 2025, 12(9), 939; https://doi.org/10.3390/photonics12090939 (registering DOI) - 19 Sep 2025

Abstract

Mechanical vibrations induced by electromagnetic forces during transformer operation can lead to winding deformation or failure, an issue responsible for over 12% of all transformer faults. While previous studies have predominantly relied on accelerometers for vibration monitoring, this study explores the use of [...] Read more.

Mechanical vibrations induced by electromagnetic forces during transformer operation can lead to winding deformation or failure, an issue responsible for over 12% of all transformer faults. While previous studies have predominantly relied on accelerometers for vibration monitoring, this study explores the use of an optical sensor for real-time vibration measurement in a dry-type transformer. Experiments were conducted using a custom-designed single-phase transformer model specifically developed for laboratory testing. This experimental setup offers a unique advantage: it allows for the interchangeable simulation of healthy and deformed winding sections without causing permanent damage, enabling controlled and repeatable testing scenarios. The transformer’s secondary winding was short-circuited, and three levels of current (low, intermediate, and high) were applied to simulate varying stress conditions. Vibration displacement data were collected under load to assess mechanical responses. The primary goal was to classify this vibration data to localize potential winding deformation faults. Five supervised learning algorithms were evaluated: Random Forest, Support Vector Machine, K-Nearest Neighbors, Logistic Regression, and Decision Tree classifiers. Hyperparameter tuning was applied, and a comparative analysis among the top four models yielded average prediction accuracies of approximately 60%. These results, achieved under controlled laboratory conditions, highlight the promise of this approach for further development and future real-world applications. Overall, the combination of optical sensing and machine learning classification offers a promising pathway for proactive monitoring and localization of winding deformations, supporting early fault detection and enhanced reliability in power transformers. Full article

(This article belongs to the Special Issue Science and Applications of Optical Fiber Sensors: Recent Advances and Future Trends)

58 pages, 11317 KB

Open AccessArticle

Territorial Rebalancing from an Axiological Perspective: A Reaction Capacity Index of Sicily’s Inner Areas

by Maria Rosa Trovato and Ludovica Nasca

Land 2025, 14(9), 1916; https://doi.org/10.3390/land14091916 (registering DOI) - 19 Sep 2025

Abstract

The marginalisation of the inner areas due to increased social, material, economic and infrastructural vulnerability is a growing phenomenon affecting many countries today. Although, specific policies, measures, and funding have recently been proposed to address this issue, they have been slow to produce [...] Read more.

The marginalisation of the inner areas due to increased social, material, economic and infrastructural vulnerability is a growing phenomenon affecting many countries today. Although, specific policies, measures, and funding have recently been proposed to address this issue, they have been slow to produce the expected results. Those responsible for decision-makers regarding the prospect of territorial rebalancing need support in identifying the residual value of these marginal areas. This will help them recognise where and how this value can be emphasised in an integrated, long-term redevelopment process. Based on an axiological perspective of territorial capital forms, the research project has developed a “Geo-referenced Value-based Knowledge Model” using Multi-attribute Value Theory (MAVT). It plays a key role in estimating the Reaction Capability Index (I_RCI) of Sicily’s “inner areas”. The results demonstrate the reaction capability of the municipalities in these areas. As a measure of the overall endowment of territorial capital, the I_RCI index can help decision-makers National Strategy Inner Areas (NSIA), promote the efficient use of resources, and encourage the effective implementation of policies aimed at rebalancing the territory. Full article

(This article belongs to the Special Issue The Other Perspective. City Networks and Territorial Justice towards a Landscape Ethics)

14 pages, 885 KB

Open AccessArticle

Association Between Multi-Dimensional Sleep Health and Breakfast Skipping in Japanese High School Students

by Suzune Nagao, Yuh Sasawaki, Hitoshi Inokawa, Nobuko Kitagawa, Naoyuki Takashima and Kazuhiro Yagita

Nutrients 2025, 17(18), 3005; https://doi.org/10.3390/nu17183005 (registering DOI) - 19 Sep 2025

Abstract

Background/Objectives: Breakfast skipping has been associated with a wide range of adverse health outcomes, including metabolic disorders, disrupted circadian rhythm, and impairments of memory and attention in adolescents and adults. Although partial associations between sleep and breakfast behaviors have been reported, few [...] Read more.

Background/Objectives: Breakfast skipping has been associated with a wide range of adverse health outcomes, including metabolic disorders, disrupted circadian rhythm, and impairments of memory and attention in adolescents and adults. Although partial associations between sleep and breakfast behaviors have been reported, few studies have examined multi-dimensional sleep health simultaneously in relation to breakfast skipping, especially comprehensive studies systematically examining this relationship, particularly under controlled social conditions, remain insufficient. Methods: We here demonstrate the association between sleep health and breakfast skipping among 2969 Japanese high school students. Participants provided between one and eight days of sleep diary data, including meal timing records; most (78.1%) completed all eight days, while the remainder contributed fewer days. Additionally, the Pittsburgh Sleep Quality Index (PSQI) was used to assess sleep quality, and the Pediatric Daytime Sleepiness Scale (PDSS) was used to evaluate daytime sleepiness. Results: Later wake-up times, lower sleep quality, and stronger daytime sleepiness were each associated with a higher likelihood of breakfast skipping. In additional analyses, no significant pairwise interactions were detected among wake-up time, PSQI, and PDSS, suggesting that these factors may be separately associated with breakfast skipping. Conclusions: These findings suggest that multi-dimensional sleep health, including wake-up time, sleep quality, and daytime sleepiness, is relevant to breakfast skipping. This study offers a novel contribution by linking multiple downstream indicators influenced by sleep health to breakfast behavior. Full article

(This article belongs to the Special Issue Body Image and Nutritional Status from Childhood to Adulthood)

48 pages, 3420 KB

Open AccessReview

Uncovering Analytical Patterns for Hazardous Components in Agricultural Production Systems

by Shiyu Deng, Xinxin Wu, Yongqiang Shi, Hany S. El-Mesery and Xinai Zhang

Foods 2025, 14(18), 3261; https://doi.org/10.3390/foods14183261 (registering DOI) - 19 Sep 2025

Abstract

Global food safety concerns underscore the critical importance of detecting hazardous components in agricultural production. This systematic review uncovers the prevalence and health impacts of common hazardous agents in agricultural commodities, including pesticide residues, heavy metals, mycotoxins, microbial contaminants, antibiotic residues, and genetically [...] Read more.

Global food safety concerns underscore the critical importance of detecting hazardous components in agricultural production. This systematic review uncovers the prevalence and health impacts of common hazardous agents in agricultural commodities, including pesticide residues, heavy metals, mycotoxins, microbial contaminants, antibiotic residues, and genetically modified material. It thoroughly analyzes research progress in conventional detection methodologies. Furthermore, the review critically examines current challenges and future trajectories in analysis patterns, with particular emphasis on integrated technological approaches, field-deployable rapid detection devices, and the development of global standardized frameworks. This work aims to provide comprehensive technical guidance for the efficient and precise detection of hazardous components in agricultural products and to inform the advancement of robust food safety regulatory systems. Full article

(This article belongs to the Section Food Quality and Safety)

15 pages, 512 KB

Open AccessStudy Protocol

Using Participatory Action Research to Enhance Physical Education Interventions for Promoting Active Lifestyles in Schools: A Study Design and Protocol

by Jorge Lizandra, Alexandra Valencia-Peris, Roberto Ferriz and Carmen Peiró-Velert

Healthcare 2025, 13(18), 2362; https://doi.org/10.3390/healthcare13182362 (registering DOI) - 19 Sep 2025

Abstract

Promoting active lifestyles among adolescents is essential due to their short-, medium-, and long-term contributions to young people’s holistic development and overall health. Beyond physical well-being, Physical Education foster physical activity, autonomy, social connectedness, motivation and emotional well-being, thus constituting a key dimension [...] Read more.

Promoting active lifestyles among adolescents is essential due to their short-, medium-, and long-term contributions to young people’s holistic development and overall health. Beyond physical well-being, Physical Education foster physical activity, autonomy, social connectedness, motivation and emotional well-being, thus constituting a key dimension of quality education. Background/Objectives: The “Estilos de Vida Activos (EVA)” project is a school-based intervention designed to foster adolescent agency and motivation in adopting active habits. Grounded in the salutogenic model, self-determination theory, and the health-based Physical Education pedagogical model, this protocol describes the design and implementation strategies of a participatory intervention in secondary schools. Methods: A variety of research methods will be used to collect quantitative and qualitative data before, during, and after the intervention. Validated questionnaires will assess active commuting, socioeconomic status, satisfaction of basic psychological needs, motivation, levels and intention to engage in physical activity. Qualitative data include interviews with teachers, Photovoice sessions with students, observation notes, and programme materials. Intervention: The EVA intervention is collaboratively developed by students, teachers, and researchers using participatory action research. It includes needs analysis, participatory activities, and co-design of tailored physical activity programmes. The intervention is described using the Template for Intervention Description and Replication checklist (TIDieR) to enhance transparency and replicability. Conclusions: This protocol presents a theoretically grounded and participatory approach to school-based health promotion. By integrating educational and collaborative strategies, it offers a replicable model that promotes adolescent active lifestyles, from contextual relevance, and pedagogical coherence, serving as a guide for inclusive and sustainable interventions in school settings. Full article

(This article belongs to the Special Issue Future Trends of Physical Activity in Health Promotion)

24 pages, 4279 KB

Open AccessArticle

Automated Detection of Shading Faults in Photovoltaic Modules Using Convolutional Neural Networks and I–V Curves

by Jesus A. Arenas-Prado, Angel H. Rangel-Rodriguez, Juan P. Amezquita-Sanchez, David Granados-Lieberman, Guillermo Tapia-Tinoco and Martin Valtierra-Rodriguez

Processes 2025, 13(9), 2999; https://doi.org/10.3390/pr13092999 (registering DOI) - 19 Sep 2025

Abstract

Renewable energy technologies play a key role in mitigating climate change and advancing sustainable development. Among these, photovoltaic (PV) systems have experienced significant growth in recent years. However, shading, one of the most common faults in PV modules, can drastically degrade their performance. [...] Read more.

Renewable energy technologies play a key role in mitigating climate change and advancing sustainable development. Among these, photovoltaic (PV) systems have experienced significant growth in recent years. However, shading, one of the most common faults in PV modules, can drastically degrade their performance. This study investigates the application of convolutional neural networks (CNNs) for the automated detection and classification of shading faults, including multiple severity levels, using current–voltage (I–V) curves. Four scenarios were simulated in Simulink: a healthy module and three levels of shading severity (light, moderate, and severe). The resulting I–V curves were transformed into grayscale images and used to train and evaluate several custom-designed CNN architectures. The goal is to assess the capability of CNN-based models to accurately identify shading faults and discriminate between severity levels. Multiple network configurations were tested, varying image resolution, network depth, and filter parameters, to explore their impact on classification accuracy. Furthermore, robustness was evaluated by introducing Gaussian noise at different levels. The best-performing models achieved classification accuracies of 99.5% under noiseless conditions and 90.1% under a 10 dB noise condition, demonstrating that CNN-based approaches can be both effective and computationally lightweight. These results underscore the potential of this methodology for integration into automated diagnostic tools for PV systems, particularly in applications requiring fast and reliable fault detection. Full article

(This article belongs to the Special Issue Control Process, Fault Detection and Classification in Power Systems and Industrial Machinery)

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15 pages, 1194 KB

Open AccessArticle

Micro-Nano Aeration Oxygenation Irrigation Has Increased Soil Nitrogen and Cotton Yield in Arid Areas

by Jiayue Wang, Qiqi Chai, Ze Wang, Yanbo Fu, Zhiguo Wang, Qingyong Bian, Junhui Cheng, Yupeng Zhao, Jinquan Zhu and Yanhong Wei

Water 2025, 17(18), 2778; https://doi.org/10.3390/w17182778 (registering DOI) - 19 Sep 2025

Abstract

To explore the effects of micro-nano aeration and oxygenation irrigation on soil characteristics and cotton growth in cotton fields in arid areas, this study was conducted at the National Soil Quality Aksu Observation and Experiment Station in Baicheng County, Xinjiang. “Xinluzao 78” cotton [...] Read more.

To explore the effects of micro-nano aeration and oxygenation irrigation on soil characteristics and cotton growth in cotton fields in arid areas, this study was conducted at the National Soil Quality Aksu Observation and Experiment Station in Baicheng County, Xinjiang. “Xinluzao 78” cotton was used as the experimental material, and the soil column cultivation method was adopted. Four nitrogen concentration gradients (N0: 0 kg·hm⁻², NL: 112.5 kg·hm⁻², NM: 225 kg·hm⁻², and NH: 337.5 kg·hm⁻²) and two irrigation methods (micro-nano aeration and oxygenation irrigation Y: DO15 mg/L, conventional irrigation C: DO7.6 mg/L) were set up to systematically analyze the total nitrogen content of the soil, enzyme activity, microbial community structure, and the response characteristics of cotton growth and yield. The results show that aeration treatment significantly increases the total nitrogen content in the soil. The total nitrogen content in the 0–15 cm and 15–30 cm soil layers treated with YNM (aeration + local conventional nitrogen application rate) increased by 9.14% and 8.53%, respectively, compared with CNM. YNM treatment significantly increased the activities of soil urease, sucrase, and β-glucosidase, among which total nitrogen had the strongest correlation with the activity of β-glucosidase. Oxygenation significantly increased the richness of soil microorganisms. The Chao1 index of YNM-treated bacteria was 75.7% higher than that of CNM-treated bacteria. YNM treatment increased cotton yield by 26.73% compared with CNM treatment. Moreover, the number of bells formed per plant and the weight of the bells increased by 44.44% and 29.6%, respectively. In conclusion, micro-nano aeration and oxygenation irrigation effectively increase cotton yield. By optimizing the activities of soil enzymes and microorganisms, micro-nano aeration and oxygenation irrigation enhance the ability of cotton to utilize and transform nitrogen, and alleviate the impact of insufficient nitrogen utilization by cotton in arid areas. Full article

(This article belongs to the Special Issue Impact of Biochar Additions on Soil Hydraulic Properties)

19 pages, 3616 KB

Open AccessFeature PaperArticle

Effects of Partial Replacement of Alfalfa Hay with Alfalfa Silage in Dairy Cows: Impacts on Production Performance and Rumen Microbiota

by Tian Xia, Zixin Liu, Ziyan Yang, Aoyu Jiang, Chuanshe Zhou and Zhiliang Tan

Animals 2025, 15(18), 2748; https://doi.org/10.3390/ani15182748 (registering DOI) - 19 Sep 2025

Abstract

As an important feed source for ruminants, alfalfa’s rational and efficient utilization is of great significance for the production and economic benefits of pastures. This study focuses on Sanhe dairy cows and includes a control group (CON group, alfalfa in the diet is [...] Read more.

As an important feed source for ruminants, alfalfa’s rational and efficient utilization is of great significance for the production and economic benefits of pastures. This study focuses on Sanhe dairy cows and includes a control group (CON group, alfalfa in the diet is hay) and an experimental group (AS group, alfalfa silage partially replaces alfalfa hay of equal dry weight). The feeding experiment lasted for 60 days. The results revealed that, compared with the CON group, the AS group exhibited increased milk yield, milk protein, and milk fat. There were no significant differences in apparent digestibility, serum biochemical indicators, and volatile fatty acid (VFA) levels between the two groups. However, the microbial composition of the rumen differed significantly between the two groups of cows based on β-diversity. On the genus level, compared with the CON group, the relative abundance of Erysipelatoclostridium, Pseudoflavonifractor, and Candidatus Saccharimonas in the AS group was significantly reduced. In summary, partially replacing alfalfa hay with alfalfa silage feed is beneficial for improving the production performance of cows and changing rumen microbial diversity. These findings provide a basis for the effective utilization of alfalfa. Full article

(This article belongs to the Special Issue Maximizing Fiber Utilization for Sustainable, Efficient Ruminant Production)

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49 pages, 3211 KB

Open AccessReview

Nanomedicine-Driven Modulation of the Gut–Brain Axis: Innovative Approaches to Managing Chronic Inflammation in Alzheimer’s and Parkinson’s Disease

by Antea Krsek, Lou Marie Salomé Schleicher, Ana Jagodic and Lara Baticic

Int. J. Mol. Sci. 2025, 26(18), 9178; https://doi.org/10.3390/ijms26189178 (registering DOI) - 19 Sep 2025

Abstract

Chronic inflammation plays a crucial role in the pathogenesis and progression of neurodegenerative diseases such as Alzheimer’s disease (AD) and Parkinson’s disease (PD), where sustained neuroinflammatory responses contribute to neuronal damage and functional decline. Recent advances in nanomedicine offer novel therapeutic strategies aimed [...] Read more.

Chronic inflammation plays a crucial role in the pathogenesis and progression of neurodegenerative diseases such as Alzheimer’s disease (AD) and Parkinson’s disease (PD), where sustained neuroinflammatory responses contribute to neuronal damage and functional decline. Recent advances in nanomedicine offer novel therapeutic strategies aimed at modulating inflammation, with a focus on targeting the gut–brain axis, a key mediator in the interplay between systemic inflammation and neurodegeneration. Artificial intelligence (AI) has emerged as a transformative tool in this context, facilitating the integration of large, complex datasets to better understand the intricate relationship between gut microbiota dysbiosis, chronic neuroinflammation, the exposome (cumulative impact of lifelong environmental exposures), and disease manifestation. AI-driven approaches and integrating exposome data with AI enable deeper insights into exposure–microbiome–inflammation interactions, enhance our understanding of the inflammatory pathways involved, support the development of predictive models for disease progression, and optimize the delivery of nanomedicine-based therapeutics. Additionally, AI applications in neuroimaging and personalized therapy planning have shown promise in addressing both motor and non-motor symptoms. This review provides a comprehensive synthesis of current knowledge, highlighting the convergence of AI, nanomedicine, and chronic inflammation in neurodegenerative disease care. Full article

(This article belongs to the Special Issue Nanomedicine Advances in the Treatment of Chronic Inflammation)

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21 pages, 3487 KB

Open AccessSystematic Review

IDH Mutations and Intraoperative 5-ALA Fluorescence in Gliomas: A Systematic Literature Review with Novel Exploratory Hypotheses on the Modulatory Effect of Vorasidenib

by Magdalena Rybaczek, Marek Jadeszko, Aleksander Lebejko, Magdalena Sawicka, Zenon Mariak, Tomasz Łysoń, Halina Car and Przemysław Wielgat

Cancers 2025, 17(18), 3075; https://doi.org/10.3390/cancers17183075 (registering DOI) - 19 Sep 2025

Abstract

Background: Fluorescence-guided surgery using 5-aminolevulinic acid (5-ALA) enables the intraoperative visualization of glioma. However, its effectiveness varies based on tumor subtype and molecular profile, posing challenges for achieving complete resection. Our systematic review aims to explore the relationship between IDH mutation status and [...] Read more.

Background: Fluorescence-guided surgery using 5-aminolevulinic acid (5-ALA) enables the intraoperative visualization of glioma. However, its effectiveness varies based on tumor subtype and molecular profile, posing challenges for achieving complete resection. Our systematic review aims to explore the relationship between IDH mutation status and intraoperative fluorescence visualization. Importantly, this is the first study to propose that vorasidenib, an emerging IDH-targeting agent, could enhance 5-ALA-guided surgery, marking a novel direction for translational research. Methods: A systematic literature search was conducted using the PubMed, Cochrane Library, Scopus and Web of Science databases up to May 2025, following PRISMA guidelines. The primary outcomes included fluorescence detection rates across different glioma subtypes and their correlation with IDH mutation status. Secondary outcomes comprised surgical efficacy measures such as gross total resection (GTR), overall survival (OS), and progression-free survival (PFS). Additionally, we analyzed the metabolic consequences of IDH mutations and evaluated the potential role of vorasidenib in enhancing 5-ALA-induced fluorescence. Results: Seven studies including 621 patients included in the final analysis. Fluorescence detection was nearly universal in WHO grade 4 gliomas (94–100%), but lower in grade 3 (43–85%) and rare in grade 2 (7–26%). Several cohorts reported reduced fluorescence in IDH-mutant gliomas, although this was not consistent across all studies. In high-grade gliomas, visible fluorescence correlated with higher GTR rates and, in some series, longer OS. Conversely, in lower-grade IDH-mutant gliomas, fluorescence did not increase GTR and was associated with worse PFS and OS. Conclusions: The effectiveness of 5-ALA-guided fluorescence in glioma surgery is significantly influenced by both tumor grade and IDH mutation status. Vorasidenib may represent a potential avenue for modulating tumor metabolism and enhancing intraoperative fluorescence in IDH-mutant gliomas, a hypothesis that warrants further experimental validation. Full article

(This article belongs to the Section Systematic Review or Meta-Analysis in Cancer Research)

17 pages, 2048 KB

Open AccessArticle

Clinical Characteristics and Management of Statin-Associated Anti-3-Hydroxy-3-Methylglutaryl-Coenzyme A Reductase Immune-Mediated Necrotizing Myopathy

by Jiyeol Yoon, Seung Woo Kim, Se Hoon Kim, Jason Jungsik Song, Yong-Beom Park, Hee Jin Park, Ha Young Shin, Se Hee Park and Yumie Rhee

J. Clin. Med. 2025, 14(18), 6610; https://doi.org/10.3390/jcm14186610 (registering DOI) - 19 Sep 2025

Abstract

Background: Immune-mediated necrotizing myopathy (IMNM) associated with anti-3-hydroxy-3-methylglutaryl-coenzyme A reductase (HMGCR) antibody is a rare but critical complication usually triggered by statin use. However, the comprehensive characterization and long-term outcomes of anti-HMGCR-positive IMNM remain underexplored. This study aimed to examine the clinical [...] Read more.

Background: Immune-mediated necrotizing myopathy (IMNM) associated with anti-3-hydroxy-3-methylglutaryl-coenzyme A reductase (HMGCR) antibody is a rare but critical complication usually triggered by statin use. However, the comprehensive characterization and long-term outcomes of anti-HMGCR-positive IMNM remain underexplored. This study aimed to examine the clinical characteristics, diagnostic challenges, treatment responses, and long-term outcomes of patients with anti-HMGCR-positive IMNM. Methods: A retrospective review was conducted at a single institution between 2019 and 2025 to analyze the data of patients diagnosed with anti-HMGCR-positive IMNM. Diagnoses were confirmed by detecting anti-HMGCR antibodies and meeting the criteria for IMNM of the European Neuromuscular Center. The analyzed data included demographics, clinical presentation, laboratory findings, imaging results, muscle biopsy characteristics, treatment regimens, and follow-up outcomes. Results: Ten patients (six women and four men) with a median age of 58 (range, 33–86) years were included. Nine patients had a history of statin use for a median duration of two years. The average diagnostic delay was 233 days after the onset of symptoms. The initial creatine kinase (CK) levels ranged from 1438 to over 13,000 IU/L. Muscle biopsies revealed necrosis and regeneration of muscle fibers. CK levels fluctuated and trended downward over 180 days post-treatment. Treatment included corticosteroids, methotrexate, azathioprine, tacrolimus, mycophenolate, intravenous immunoglobulin, and rituximab. Delayed treatment initiation from symptom onset was correlated with prolonged treatment time until the first remission. Conclusions: The prognosis of anti-HMGCR-positive IMNM is less favorable when treatment is delayed after symptom onset. Further research is warranted to identify poor prognostic markers and develop relevant treatments. Full article

(This article belongs to the Section Clinical Laboratory Medicine)

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18 pages, 835 KB

Open AccessArticle

The Diagnostic and Prognostic Value of Neutrophil-to-Lymphocyte and Platelet-to-Lymphocyte Ratios in Urosepsis

by Petru Octavian Drăgoescu, Bianca Liana Grigorescu, Andreea Doriana Stănculescu, Andrei Pănuș, Nicolae Dan Florescu, Monica Cara, Maria Andrei, Mihai Radu, George Mitroi and Alice Nicoleta Drăgoescu

Medicina 2025, 61(9), 1713; https://doi.org/10.3390/medicina61091713 (registering DOI) - 19 Sep 2025

Abstract

Background and Objectives: The severe systemic response to urinary tract infections known as urosepsis is associated with significant morbidity and mortality rates. The neutrophil-to-lymphocyte ratio (NLR) and the platelet-to-lymphocyte ratio (PLR) are simple blood tests that could be useful in predicting the outcome [...] Read more.

Background and Objectives: The severe systemic response to urinary tract infections known as urosepsis is associated with significant morbidity and mortality rates. The neutrophil-to-lymphocyte ratio (NLR) and the platelet-to-lymphocyte ratio (PLR) are simple blood tests that could be useful in predicting the outcome of sepsis. Materials and Methods: A prospective observational study was conducted at a tertiary care hospital, where our team studied 223 patients with urosepsis. The patients underwent Sepsis-3 criteria-based urosepsis and septic shock stratification followed by survivor and non-survivor classification. Clinical scores (Sequential Organ Failure Assessment-SOFA, National Early Warning Score-NEWS), laboratory markers (NLR, PLR, PCT-procalcitonin), and patient outcomes were then analysed. Results: An admission NLR ≥ 13 was a strong predictor of septic shock (adjusted Odds Ratio (OR) 2.10, 95% Confidence Interval (CI) 1.25–3.54) and in-hospital mortality (adjusted OR 2.45, 95% CI 1.40–4.28). While the prognostic value of the PLR remained moderate, the NLR demonstrated superior predictive power. As easily measurable biomarkers, the NLR and PLR provide valuable information to help clinicians identify at-risk patients during the early stages of urosepsis. Conclusions: The NLR is an independent predictor with high predictive value for both septic shock and mortality, performing as well as established clinical scores. The combination of these parameters with clinical assessments could lead to better early decisions and improved outcomes for patients with urosepsis. Full article

(This article belongs to the Special Issue Global Perspectives on Sepsis: Epidemiology, Awareness and Treatment Strategies)

29 pages, 4582 KB

Open AccessArticle

Hybrid FEM/SPH Modeling and CT Analysis of Dynamic Damage in Structural Steel Under Impact Loading

by Dariusz Pyka, Adam Kurzawa, Grzegorz Ziółkowski, Maciej Roszak and Martyna Strąg

Appl. Sci. 2025, 15(18), 10234; https://doi.org/10.3390/app151810234 (registering DOI) - 19 Sep 2025

Abstract

This study analyzed the dynamic behavior of EN C45 structural steel under impulse loading generated by a pressure wave. The experiments were conducted on a special test rig using two load configurations: (I) direct contact of the load with the sample surface and [...] Read more.

This study analyzed the dynamic behavior of EN C45 structural steel under impulse loading generated by a pressure wave. The experiments were conducted on a special test rig using two load configurations: (I) direct contact of the load with the sample surface and (II) detonation at a distance of 30 mm. Depending on the loading conditions, the specimens were fragmented or developed extensive internal cracks and plastic deformations. To complement the experimental program, hybrid numerical simulations were performed using the finite element method (FEM), smoothed particles hydrodynamics (SPH), and coupled Euler–Lagrange (CEL) approach. A modified Johnson–Cook (JC) model was used to account for dynamic damage and cracks. Computed tomography (CT) and metallographic analyses provided detailed information on the formation of cracks in MnS inclusions, brittle cracks near the sample axis, and shear deformation zones away from the axis. These observations allowed direct correlation with the predicted numerical deformation and damage fields. The innovative nature of this work lies in the combination of three complementary computational techniques with computed tomography analysis and microstructure analysis, providing a comprehensive framework for describing and confirming the mechanisms of damage and fragmentation of structural steels under explosive loading. Full article

6 pages, 162 KB

Open AccessEditorial

Precision Nutrition for Public Health

by Sabina Lachowicz-Wiśniewska and Agata Kotowska

Nutrients 2025, 17(18), 3004; https://doi.org/10.3390/nu17183004 (registering DOI) - 19 Sep 2025

Abstract

Public health—understood as both a science and a practice aimed at preventing disease, prolonging life, and promoting health—has become one of the most critical domains of institutional action, shaped by both nation-states and international organizations [...] Full article

(This article belongs to the Special Issue Public Health, Nutritional Behavior and Nutritional Status)

9 pages, 1578 KB

Open AccessArticle

Towards MRI-Only Mandibular Resection Planning: CT-like Bone Segmentation from Routine T1 MRI Images Using Deep Learning

by Reinier S. A. ten Brink, Bram J. Merema, Marith E. den Otter, Willemina A. van Veldhuizen, Max J. H. Witjes and Joep Kraeima

Craniomaxillofac. Trauma Reconstr. 2025, 18(3), 40; https://doi.org/10.3390/cmtr18030040 (registering DOI) - 19 Sep 2025

Abstract

We present a deep learning-based approach for accurate bone segmentation directly from routine T1-weighted MRI scans, with the goal of enabling MRI-only virtual surgical planning in head and neck oncology. Current workflows rely on CT for bone modeling and MRI for tumor delineation, [...] Read more.

We present a deep learning-based approach for accurate bone segmentation directly from routine T1-weighted MRI scans, with the goal of enabling MRI-only virtual surgical planning in head and neck oncology. Current workflows rely on CT for bone modeling and MRI for tumor delineation, introducing challenges related to image registration, radiation exposure, and resource use. To address this, we trained a deep neural network using CT-based segmentations of the mandible, cranium, and inferior alveolar nerve as ground truth. A dataset of 100 patients with paired CT and MRI scans was collected. MRI scans were resampled to the voxel size of CT, and corresponding CT segmentations were rigidly aligned to MRI. The model was trained on 80 cases and evaluated on 20 cases using Dice similarity coefficient, Intersection over Union (IoU), precision, and recall. The network achieved a mean Dice of 0.86 (SD ± 0.03), IoU of 0.76 (SD ± 0.05), and both precision and recall of 0.86 (SD ± 0.05). Surface deviation analysis between CT- and MRI-derived bone models showed a median deviation of 0.21 mm (IQR 0.05) for the mandible and 0.30 mm (IQR 0.05) for the cranium. These results demonstrate that accurate CT-like bone models can be derived from standard MRI, supporting the feasibility of MRI-only surgical planning. Full article

(This article belongs to the Special Issue Innovation in Oral- and Cranio-Maxillofacial Reconstruction)

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12 pages, 1090 KB

Open AccessArticle

The Impact of Cystic Fibrosis Algorithm Changes: A Case Study of Challenges and Strategies

by Jerusalem Alleyne, Kenneth Coursey, Kimberly Noble Piper, Cynthia Cass and Michael Pentella

Int. J. Neonatal Screen. 2025, 11(3), 82; https://doi.org/10.3390/ijns11030082 (registering DOI) - 19 Sep 2025

Abstract

The State Hygienic Lab at the University of Iowa (SHL) performs newborn blood spot screening (NBS) for IA, AK, ND, and SD. In October 2022, we halted in-house CFTR DNA testing due to the unexpected nonperformance of our newly expanded variant panel. Samples [...] Read more.

The State Hygienic Lab at the University of Iowa (SHL) performs newborn blood spot screening (NBS) for IA, AK, ND, and SD. In October 2022, we halted in-house CFTR DNA testing due to the unexpected nonperformance of our newly expanded variant panel. Samples were sent to a reference laboratory to ensure uninterrupted testing and by December 2022, SHL had selected an alternative test that enabled CFTR panel expansion as envisioned. However, due to circumstances beyond our control, test implementation was severely delayed, and in-house testing was paused. These events were consequential. Firstly, our prolonged utilization of reference labs and fees was a financial strain on the lab. Secondly, our timeliness decreased significantly, and lastly, these issues were burdensome for staff. The lab overcame these problems using three strategies: effective communication; technical expertise; and staff perseverance. Finally, in Aug 2023, SHL successfully resumed in-house testing. As state labs ponder major CFTR algorithm changes, such as the addition of next generation sequencing, the strategies we utilized can be useful during sudden setbacks. Our experience of replacing our CFTR assay underscores the importance of emergency preparedness and partnership within the NBS community. Full article

(This article belongs to the Special Issue Advances in Cystic Fibrosis Newborn Screening: From Laboratory Testing to Diagnosis)

18 pages, 393 KB

Open AccessArticle

Existence and Uniqueness in Fractional Boundary Value Problems: A Refined Criterion

by Saleh S. Almuthaybiri and Abdelhamid Zaidi

Axioms 2025, 14(9), 708; https://doi.org/10.3390/axioms14090708 (registering DOI) - 19 Sep 2025

Abstract

This paper is devoted to the study of the existence and uniqueness of solutions for a class of differential problems previously investigated by Laadjal. Our main contribution lies in deriving sharper estimates for the associated Green’s functions and employing Rus’s fixed-point theorem within [...] Read more.

This paper is devoted to the study of the existence and uniqueness of solutions for a class of differential problems previously investigated by Laadjal. Our main contribution lies in deriving sharper estimates for the associated Green’s functions and employing Rus’s fixed-point theorem within a suitably defined metric framework. Notably, the conditions under which our results hold are less restrictive, thereby encompassing a wider class of problems for which the existence and uniqueness of solutions can be rigorously guaranteed. This theoretical advancement is supported by numerical evidence presented in the final stage of our analysis. A key strength of the proposed approach is that it does not rely on stringent contraction conditions, which enhances its potential applicability to more general classes of fractional differential systems. Full article

(This article belongs to the Special Issue Advances in Nonlinear Analysis and Boundary Value Problems)

16 pages, 1519 KB

Open AccessArticle

Integrative Transcriptomic and Metabolomic Analyses Elucidate the Molecular Mechanisms Underlying Enhanced Yield and Bacterial Blight Resistance in the RXN2 Rice Cultivar

by Ji’an Bi, Jingqi Wang, Xuan Huang, Jiefeng Jiang, Xianbo Shi, Genliang Bao, Qiufeng Meng and Chengqi Yan

Plants 2025, 14(18), 2921; https://doi.org/10.3390/plants14182921 (registering DOI) - 19 Sep 2025

Abstract

Achieving high yield while maintaining disease resistance is a crucial goal in rice breeding programs. In this research, two cultivated rice varieties, Jia58 and Runxiang3, were selected as parental lines. A new variety, designated as the new variety RXN2, was generated and identified [...] Read more.

Achieving high yield while maintaining disease resistance is a crucial goal in rice breeding programs. In this research, two cultivated rice varieties, Jia58 and Runxiang3, were selected as parental lines. A new variety, designated as the new variety RXN2, was generated and identified through a breeding process that involved hybridization of the parental lines followed by irradiation-induced mutagenesis of the offspring. Compared with its parental lines, RXN2 shows increased plant height, higher yield, and stronger resistance to bacterial blight. Comprehensive transcriptomic and metabolic analyses indicate that pathways associated with growth, such as gibberellin and auxin signaling, are upregulated in RXN2. Meanwhile, defense-related pathways, especially those involving jasmonic acid and peroxidase metabolism, are significantly enhanced. These results provide new insights into the trade-offs between growth and defense and elucidate the genetic and metabolic underpinnings of the simultaneous improvement in grain yield and disease resistance in rice. Full article

(This article belongs to the Special Issue Metabolic Pathways and Hormonal Regulation in Plant Defense and Growth)

30 pages, 696 KB

Open AccessArticle

SPADR: A Context-Aware Pipeline for Privacy Risk Detection in Text Data

by Sultan Asiri, Randa Alshehri, Fatima Kamran, Hend Laznam, Yang Xiao and Saleh Alzahrani

Electronics 2025, 14(18), 3725; https://doi.org/10.3390/electronics14183725 (registering DOI) - 19 Sep 2025

Abstract

Large language models (LLMs) are powerful, but they can unintentionally memorize and leak sensitive information found in their training or input data. To address this issue, we propose SPADR, a semantic privacy anomaly detection and remediation pipeline designed to detect and remove privacy [...] Read more.

Large language models (LLMs) are powerful, but they can unintentionally memorize and leak sensitive information found in their training or input data. To address this issue, we propose SPADR, a semantic privacy anomaly detection and remediation pipeline designed to detect and remove privacy risks from text. SPADR addresses limitations in existing redaction methods by identifying deeper forms of sensitive content, including implied relationships, contextual clues, and non-standard identifiers that traditional NER systems often overlook. SPADR combines semantic anomaly scoring using a denoising autoencoder with named entity recognition and graph-based analysis to detect both direct and hidden privacy risks. It is flexible enough to work on both training data (to prevent memorization) and user input (to prevent leakage at inference time). We evaluate SPADR on the Enron Email Dataset, where it significantly reduces document-level privacy leakage while maintaining strong semantic utility. The enhanced version, SPADR (S2), reduces the PII leak rate from 100% to 16.06% and achieves a BERTScore F1 of 88.03%. Compared to standard NER-based redaction systems, SPADR offers more accurate and context-aware privacy protection. This work highlights the importance of semantic and structural understanding in building safer, privacy-respecting AI systems. Full article

(This article belongs to the Special Issue Advancements in Cross-Disciplinary AI: Theory and Application, 3rd Edition)

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32 pages, 40932 KB

Open AccessReview

Engineering Metal-Organic Frameworks for Enhanced Antimicrobial Efficacy: Synthesis Methodologies, Mechanistic Perspectives, and Versatile Applications

by Zaixiang Zheng, Junnan Cui, Shutong Wu, Zhimin Cao and Pan Cao

J. Funct. Biomater. 2025, 16(9), 353; https://doi.org/10.3390/jfb16090353 (registering DOI) - 19 Sep 2025

Abstract

Bacterial contamination and the escalating crisis of antibiotic resistance represent pressing global public health threats, with approximately 4.95 million deaths linked to antimicrobial resistance (AMR) in 2019 and projections estimating up to 10 million annual fatalities by 2050. As third-generation antimicrobial materials, metal–organic [...] Read more.

Bacterial contamination and the escalating crisis of antibiotic resistance represent pressing global public health threats, with approximately 4.95 million deaths linked to antimicrobial resistance (AMR) in 2019 and projections estimating up to 10 million annual fatalities by 2050. As third-generation antimicrobial materials, metal–organic frameworks (MOFs) have emerged as promising alternatives to conventional agents, leveraging their unique attributes such as high specific surface areas, tunable porosity, and controlled metal ion release kinetics. This review provides a systematic analysis of the foundational principles and core antibacterial mechanisms of MOFs, which include the sustained release of metal ions (e.g., Ag⁺, Cu²⁺, Zn²⁺), the generation of reactive oxygen species (ROS), and synergistic effects with encapsulated functional molecules. We highlight how these mechanisms underpin their efficacy across a range of applications. Rather than offering an exhaustive list of synthesis methods and metal compositions, this review focuses on clarifying structure–function relationships that enable MOF-based materials to outperform conventional antimicrobials. Their potential is particularly evident in several key areas: wound dressings and medical coatings that enhance tissue regeneration and prevent infections; targeted nanotherapeutics against drug-resistant bacteria; and functional coatings for food preservation and water disinfection. Despite existing challenges, including gaps in clinical translation, limited efficacy in complex multi-species infections, and incomplete mechanistic understanding, MOFs hold significant promise to revolutionize antimicrobial therapy. Through interdisciplinary optimization and advancements in translational research, MOFs are poised to drive a paradigm shift from “passive defense” to “active ecological regulation,” offering a critical solution to mitigate the global AMR crisis. Full article

(This article belongs to the Section Antibacterial Biomaterials)

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