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25 pages, 732 KiB  
Article
Long-Term Variability of Ice Phenomena in Selected Rivers of the Central Vistula River Catchment
by Agnieszka Hejduk and Michał Szalkowski
Water 2025, 17(17), 2523; https://doi.org/10.3390/w17172523 (registering DOI) - 24 Aug 2025
Abstract
The phenomenon of surface freezing in lakes, rivers, and reservoirs, has been an essential part of Poland’s winter landscape for centuries. It plays critical ecological roles, such as regulating heat balance and influencing the state of biocenoses. Due to progressive climate warming, we [...] Read more.
The phenomenon of surface freezing in lakes, rivers, and reservoirs, has been an essential part of Poland’s winter landscape for centuries. It plays critical ecological roles, such as regulating heat balance and influencing the state of biocenoses. Due to progressive climate warming, we have observed significant changes in ice cover duration, thickness, and timing in recent decades. Ice phenomena on rivers are temporary. They strongly depend on air temperature, which has recently been increasing worldwide. This paper analyzes the variability of ice phenomena formation in selected river profiles of the central Vistula River catchment, central Poland. The research period covers the years 1968–2016. The data come from the Institute of Meteorology and Water Management-State Research Institute (IMGW-PIB). The duration (including the dates of occurrence and disappearance of the phenomenon) and the frequency of occurrence of ice phenomena over the long-term were determined with particular attention to ice cover. The long-term occurrence of ice phenomena shows a decreasing trend (shorter duration, later onset dates) with a simultaneous increase in the average air temperature during the winter half of the hydrological year. Full article
(This article belongs to the Section Hydrology)
28 pages, 7744 KiB  
Article
Optimizing Random Forest with Hybrid Swarm Intelligence Algorithms for Predicting Shear Bond Strength of Cable Bolts
by Ming Xu, Yingui Qiu, Manoj Khandelwal, Mohammad Hossein Kadkhodaei and Jian Zhou
Machines 2025, 13(9), 758; https://doi.org/10.3390/machines13090758 (registering DOI) - 24 Aug 2025
Abstract
This study combines three optimization algorithms, Tunicate Swarm Algorithm (TSA), Whale Optimization Algorithm (WOA), and Jellyfish Search Optimizer (JSO), with random forest (RF) to predict the shear bond strength of cable bolts under different types and grouting conditions. Based on the original dataset, [...] Read more.
This study combines three optimization algorithms, Tunicate Swarm Algorithm (TSA), Whale Optimization Algorithm (WOA), and Jellyfish Search Optimizer (JSO), with random forest (RF) to predict the shear bond strength of cable bolts under different types and grouting conditions. Based on the original dataset, a database of 860 samples was generated by introducing random noise around each data point. After establishing three hybrid models (RF-WOA, RF-JSO, RF-TSA) and training them, the obtained models were evaluated using six metrics: coefficient of determination (R2), root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), variance account for (VAF), and A-20 index. The results indicate that the RF-JSO model exhibits superior performance compared to the other models. The RF-JSO model achieved an excellent performance on the testing set (R2 = 0.981, RMSE = 11.063, MAE = 6.457, MAPE = 9, VAF = 98.168, A-20 = 0.891). In addition, Shapley Additive exPlanations (SHAP), Partial Dependence Plot (PDP), and Local Interpretable Model-agnostic Explanations (LIME) were used to analyze the interpretability of the model, and it was found that confining pressure (Stress), elastic modulus (E), and a standard cable type (cable type_standard) contributed the most to the prediction of shear bond strength. In summary, the hybrid model proposed in this study can effectively predict the shear bond strength of cable bolts. Full article
(This article belongs to the Special Issue Key Technologies in Intelligent Mining Equipment)
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27 pages, 3909 KiB  
Review
Identifying Root Causes and Sustainable Solutions for Reducing Construction Waste Using Social Network Analysis
by Mona Salah, Emad Elbeltagi, Meshal Almoshaogeh, Fawaz Alharbi and Mohamed T. Elnabwy
Sustainability 2025, 17(17), 7638; https://doi.org/10.3390/su17177638 (registering DOI) - 24 Aug 2025
Abstract
The construction industry is a major contributor to environmental degradation, primarily due to the substantial volumes of construction waste (CW) generated on-site. As sustainability becomes a global imperative aligned with the UN 2030 Agenda, identifying and mitigating the root causes of CW is [...] Read more.
The construction industry is a major contributor to environmental degradation, primarily due to the substantial volumes of construction waste (CW) generated on-site. As sustainability becomes a global imperative aligned with the UN 2030 Agenda, identifying and mitigating the root causes of CW is essential. This study adopts a cross-disciplinary approach to explore the drivers of CW and support more effective, sustainable waste reduction strategies. A systematic literature review was conducted to extract 25 key CW source factors from academic publications. These were analyzed using Social Network Analysis (SNA) to reveal their structural relationships and relative influence. The results indicate that the lack of structured on-site waste management planning, accumulation of residual materials, and insufficient worker training are among the most influential CW drivers. Comparative analysis with industry data highlights theoretical–practical gaps and the need for improved alignment between research insights and site implementation. This paper recommends the adoption of tiered waste management protocols as part of contractual documentation, integrating Building Information Modeling (BIM)-based residual material traceability systems, and increasing attention to workforce training programs focused on material handling efficiency. Future research should extend SNA frameworks to sector-specific waste patterns (e.g., pavement or demolition projects) and explore the intersection between digital technologies and circular economy practices. The study contributes to enhancing waste governance, promoting resource efficiency, and advancing circularity in the built environment by offering data-driven prioritization of CW sources and actionable mitigation strategies. Full article
(This article belongs to the Section Waste and Recycling)
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28 pages, 10321 KiB  
Article
Influence of Spill Pressure and Saturation on the Migration and Distribution of Diesel Oil Contaminant in Unconfined Aquifers Using Three-Dimensional Numerical Simulations
by Alessandra Feo and Fulvio Celico
Appl. Sci. 2025, 15(17), 9303; https://doi.org/10.3390/app15179303 (registering DOI) - 24 Aug 2025
Abstract
Spilled hydrocarbons released from oil pipeline accidents can result in long-term environmental contamination and significant damage to habitats. In this regard, evaluating actions in response to vulnerability scenarios is fundamental to emergency management and groundwater integrity. To this end, understanding the trajectories and [...] Read more.
Spilled hydrocarbons released from oil pipeline accidents can result in long-term environmental contamination and significant damage to habitats. In this regard, evaluating actions in response to vulnerability scenarios is fundamental to emergency management and groundwater integrity. To this end, understanding the trajectories and their influence on the various parameters and characteristics of the contaminant’s fate through accurate numerical simulations can aid in developing a rapid remediation strategy. This paper develops a numerical model using the CactusHydro code, which is based on a high-resolution shock-capturing (HRSC) conservative method that accurately follows sharp discontinuities and temporal dynamics for a three-phase fluid flow. We analyze nine different emergency scenarios that represent the breaking of a diesel oil onshore pipeline in a porous medium. These scenarios encompass conditions such as dry season rupture, rainfall-induced saturation, and varying pipeline failure pressures. The influence of the spilled oil pressure and water saturation in the unsaturated zone is analyzed by following the saturation contour profiles of the three-phase fluid flow. We follow with the high-accuracy formation of shock fronts of the advective part of the migration. Additionally, the mass distribution of the expelled contaminant along the porous medium during the emergency is analyzed and quantified for the various scenarios. The results obtained indicate that the aquifer contamination strongly depends on the pressure outflow in the vertical flow. For a fixed pressure value, as water saturation increases, the mass of contaminant decreases, while the contamination speed increases, allowing the contaminant to reach extended areas. This study suggests that, even for LNAPLs, the distribution of leaked oil depends strongly on the spill pressure. If the pressure reaches 20 atm at the time of pipeline failure, then contamination may extend as deep as two meters below the water table. Additionally, different seasonal conditions can influence the spread of contaminants. This insight could directly inform guidelines and remediation measures for spill accidents. The CactusHydro code is a valuable tool for such applications. Full article
(This article belongs to the Section Environmental Sciences)
14 pages, 634 KiB  
Article
Existence and Uniqueness of Solutions for Cohen–Grossberg BAM Neural Networks with Time-Varying Leakage, Neutral, Distributed, and Transmission Delays
by Er-Yong Cong, Xian Zhang and Li Zhu
Mathematics 2025, 13(17), 2723; https://doi.org/10.3390/math13172723 (registering DOI) - 24 Aug 2025
Abstract
This paper establishes a rigorous theoretical framework for analyzing the existence and uniqueness of solutions to Cohen–Grossberg bidirectional associative memory neural networks (CGBAMNNs) incorporating four distinct types of time-varying delays: leakage, neutral, distributed, and transmission delays. This study makes three key contributions to [...] Read more.
This paper establishes a rigorous theoretical framework for analyzing the existence and uniqueness of solutions to Cohen–Grossberg bidirectional associative memory neural networks (CGBAMNNs) incorporating four distinct types of time-varying delays: leakage, neutral, distributed, and transmission delays. This study makes three key contributions to the field: First, it overcomes the fundamental challenge posed by the system’s inherent inability to be expressed in vector–matrix form, which previously limited the application of standard analytical techniques. Second, the work develops a novel and generalizable methodology that not only proves sufficient conditions for solution existence and uniqueness but also, for the first time in the literature, provides an explicit representation of the unique solution. Third, the proposed framework demonstrates remarkable extensibility, requiring only minor modifications to be applicable to a wide range of delayed system models. Theoretical findings are conclusively validated through numerical simulations, confirming both the robustness of the proposed approach and its practical relevance for complex neural network analysis. Full article
16 pages, 2447 KiB  
Article
Genesis Mechanism and Logging Evaluation Methods for Low-Resistivity Contrast Gas-Bearing Layers in Shallow Gas Reservoirs
by Ruijie Huang, Liang Xiao, Wei Zhang, Ruize Shi, Xiaopeng Liu and Ning Wu
Processes 2025, 13(9), 2695; https://doi.org/10.3390/pr13092695 (registering DOI) - 24 Aug 2025
Abstract
Shallow gas reservoirs exhibit low formation pressure and gas injection levels, leading to low-resistivity contrast between gas-bearing reservoirs and fully water-saturated layers. Gas-bearing formation identification and water saturation estimation face great challenges. To improve the accuracy of shallow gas reservoir identification and logging [...] Read more.
Shallow gas reservoirs exhibit low formation pressure and gas injection levels, leading to low-resistivity contrast between gas-bearing reservoirs and fully water-saturated layers. Gas-bearing formation identification and water saturation estimation face great challenges. To improve the accuracy of shallow gas reservoir identification and logging evaluation, it is essential to analyze the genesis mechanisms underlying the low-resistivity contrast. This study used the HJ Formation, a typical shallow gas reservoir located in the BY Sag of the eastern South China Sea Basin as an example. Combining the results of nuclear magnetic resonance (NMR), full rock mineral analysis and X-ray diffraction of clay minerals in the laboratory, it was determined that the genesis mechanism for the low-resistivity contrast in the gas-bearing reservoir was due to the high irreducible water saturation (Swi) and the cation-induced supplementary conductivity. Afterwards, we integrated three methods, density–neutron correlation, calculation of the apparent formation water resistivity, and cross-plots of conventional and gas-logging curves, to identify shallow gas reservoirs. In addition, we also established a Waxman–Smits-based model to estimate water saturation. Compared with the typical Archie’s equation, the predicted water saturation curve using the Waxman–Smits-based model was more reasonable. The established methods and models can be used in target shallow gas reservoir evaluations, and it also has reference value for other types of oilfields with similar physical characteristics. Full article
22 pages, 634 KiB  
Article
Bi-Level Sustainability Planning for Integrated Energy Systems Considering Hydrogen Utilization and the Bilateral Response of Supply and Demand
by Xiaofeng Li, Fangying Zhang, Yudai Huang and Gaohang Zhang
Sustainability 2025, 17(17), 7637; https://doi.org/10.3390/su17177637 (registering DOI) - 24 Aug 2025
Abstract
Under the background of “double carbon” and sustainable development, aimed at the problem of resource capacity planning in the integrated energy system (IES), at improving the economy of system planning operation and renewable energy (RE) consumption, and at reducing carbon emissions, this paper [...] Read more.
Under the background of “double carbon” and sustainable development, aimed at the problem of resource capacity planning in the integrated energy system (IES), at improving the economy of system planning operation and renewable energy (RE) consumption, and at reducing carbon emissions, this paper proposes a multi-objective bi-level sustainability planning method for IES considering the bilateral response of supply and demand and hydrogen utilization. Firstly, the multi-energy flow in the IES is analyzed, constructing the system energy flow framework, studying the support ability of hydrogen utilization and the bilateral response of supply and demand to system energy conservation, emission reduction and sustainable development. Secondly, a multi-objective bi-level planning model for IES is constructed with the purpose of optimizing economy, RE consumption, and carbon emission. The non-dominated sorting genetic algorithm II (NSGA-II) and commercial solver Gurobi are used to solve the model and, through the simulation, verify the model’s effectiveness. Finally, the planning results show that after introducing the hydrogen fuel cells, hydrogen storage tank, and bilateral response, the total costs and carbon emissions decreased by 29.17% and 77.12%, while the RE consumption rate increased by 16.75%. After introducing the multi-objective planning method considering the system economy, RE consumption, and carbon emissions, the system total cost increased by 0.34%, the consumption rate of RE increased by 0.6%, and the carbon emissions decreased by 43.61t, which effectively provides reference for resource planning and sustainable development of IES. Full article
24 pages, 623 KiB  
Article
Quantitative Ultrasound-Based Precision Diagnosis of Papillary, Follicular, and Medullary Thyroid Carcinomas Using Morphological, Structural, and Textural Features
by Hanna Piotrzkowska Wróblewska, Piotr Karwat, Agnieszka Żyłka, Katarzyna Dobruch Sobczak, Marek Dedecjus and Jerzy Litniewski
Cancers 2025, 17(17), 2761; https://doi.org/10.3390/cancers17172761 (registering DOI) - 24 Aug 2025
Abstract
Background/Objectives: Thyroid cancer encompasses distinct histological subtypes with varying biological behavior and treatment implications. Accurate preoperative subtype differentiation remains challenging. Although ultrasound (US) is widely used for thyroid nodule evaluation, qualitative assessment alone is often insufficient to distinguish between papillary (PTC), follicular [...] Read more.
Background/Objectives: Thyroid cancer encompasses distinct histological subtypes with varying biological behavior and treatment implications. Accurate preoperative subtype differentiation remains challenging. Although ultrasound (US) is widely used for thyroid nodule evaluation, qualitative assessment alone is often insufficient to distinguish between papillary (PTC), follicular (FTC), and medullary thyroid carcinoma (MTC). Methods: A retrospective analysis was performed on patients with histologically confirmed PTC, FTC, or MTC. A total of 224 standardized B-mode ultrasound images were analyzed. A set of fully quantitative features was extracted, including morphological characteristics (aspect ratio and perimeter-to-area ratio), internal echotexture (echogenicity and local entropy), boundary sharpness (gradient measures and KL divergence), and structural components (calcifications and cystic areas). Feature extraction was conducted using semi-automatic algorithms implemented in MATLAB. Statistical differences were assessed using the Kruskal–Wallis and Dunn–Šidák tests. A Random Forest classifier was trained and evaluated to determine the discriminatory performance of individual and combined features. Results: Significant differences (p < 0.05) were found among subtypes for key features such as perimeter-to-area ratio, normalized echogenicity, and calcification pattern. The full-feature Random Forest model achieved an overall classification accuracy of 89.3%, with F1-scores of 93.4% for PTC, 85.7% for MTC, and 69.1% for FTC. A reduced model using the top 10 features yielded an even higher accuracy of 91.8%, confirming the robustness and clinical relevance of the selected parameters. Conclusions: Subtype classification of thyroid cancer was effectively performed using quantitative ultrasound features and machine learning. The results suggest that biologically interpretable image-derived metrics may assist in preoperative decision-making and potentially reduce the reliance on invasive diagnostic procedures. Full article
(This article belongs to the Special Issue Thyroid Cancer: New Advances from Diagnosis to Therapy: 2nd Edition)
20 pages, 1635 KiB  
Review
Inclusive Healthcare System for Children with Disabilities: A Bibliometric Analysis and Visualization
by Erkan Gulgosteren, Yavuz Onturk, Abdullah Cuhadar, Mihaela Zahiu, Monica Stanescu and Rares Stanescu
Healthcare 2025, 13(17), 2106; https://doi.org/10.3390/healthcare13172106 (registering DOI) - 24 Aug 2025
Abstract
Background: Children with disabilities face complex, systemic health access barriers rooted in societal, institutional, and structural inequities, requiring urgent global policy attention. Publications on access to health services for this population category have been found to have a significant growth in both quantity [...] Read more.
Background: Children with disabilities face complex, systemic health access barriers rooted in societal, institutional, and structural inequities, requiring urgent global policy attention. Publications on access to health services for this population category have been found to have a significant growth in both quantity and content. The article aims to examine the structure and evolution of scientific literature in analyzing the healthcare system through the lens of inclusive services. Methods: We present the bibliometric profile of the global literature on access to health services for children with disabilities, the publication trends, the structure of research in this field concerning geographical distribution, methodological approaches, and interdisciplinary collaborations, and the core research topics, conceptual clusters, and future research directions in the field. The publications were screened from Web of Science databases, using PRISMA methodology. Finally, 1100 academic publications published between 1984 and 2025, obtained from a total of 432 different sources, the majority of which were peer-reviewed journals, were screened. Results: The calculated annual publication growth rate of 8.37% and the distinct upward trend observed, especially after 2015. The highest level was reached in 2023, with over 90 publications showing that the topic has become a focus of international academic interest. The USA (33.5%), the United Kingdom (15.7%), Australia (9.5%), and Canada (9.5%) stood out in publications, and there were strong collaborative networks among European nations (8.2%). Conclusions: Although high-income countries still appear to play a dominant role in research production, expanding international collaborations and distributing resources more equitably will contribute to the development of more inclusive solutions on a global scale. Temporal trends show an evolution toward diagnostic processes, family-centered approaches, and psychosocial dimensions. The results draw a clear picture of the current research landscape regarding access to health services for pediatric disability populations and identify potential directions for future research. Full article
(This article belongs to the Special Issue Disability Studies and Disability Evaluation)
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29 pages, 730 KiB  
Systematic Review
Legal Perspectives on Sexual Violence: A Cross-European Study
by Paola Frati, Stefania De Simone, Giovanni Pollice, Elena Giacani, Luigi Cipolloni and Francesco Orsini
Forensic Sci. 2025, 5(3), 38; https://doi.org/10.3390/forensicsci5030038 (registering DOI) - 24 Aug 2025
Abstract
Background: Sexual violence poses a significant challenge to European lawmakers, impacting the victim’s physical and psychological health. This study examines sexual violence legislation across EU member states, Switzerland, and the UK, analyzing similarities, differences, challenges, and potential solutions for effective policy development. [...] Read more.
Background: Sexual violence poses a significant challenge to European lawmakers, impacting the victim’s physical and psychological health. This study examines sexual violence legislation across EU member states, Switzerland, and the UK, analyzing similarities, differences, challenges, and potential solutions for effective policy development. The research was motivated by the adoption of EU Directive 2024/1385. Methods: This study analyzes sexual violence legislation across European countries in a comparative and qualitative way, highlighting differences, commonalities, and the potential for uniform regulation. The data were collected from the literature published between 2015 and 2024, focusing the EU member states, Switzerland, and the United Kingdom. Results: The examination of the norms governing sexual offenses in various European countries revealed significant differences in legislative frameworks, reflecting diverse cultural, ethical, and legal perspectives. Conclusions: Despite European countries sharing the goal of protecting victims and combating sexual violence, there are significant legislative disparities. Key recommendations include enhancing EU member state cooperation, implementing joint training programs, developing a specific EU directive, and creating coordinated prevention and education programs. While respecting national legal diversity, a unified approach is needed for effective prevention and prosecution of sexual violence across Europe. Full article
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38 pages, 5163 KiB  
Article
A Coordinated Adaptive Signal Control Method Based on Queue Evolution and Delay Modeling Approach
by Ruochen Hao, Yongjia Wang, Ziyu Wang, Lide Yang and Tuo Sun
Appl. Sci. 2025, 15(17), 9294; https://doi.org/10.3390/app15179294 (registering DOI) - 24 Aug 2025
Abstract
Coordinated adaptive signal control is a proven strategy for improving traffic efficiency and minimizing vehicular delays. First, we develop a Queue Evolution and Delay Model (QEDM) that establishes the relationship between detector-measured queue lengths and model parameters. QEDM accurately characterizes residual queue dynamics [...] Read more.
Coordinated adaptive signal control is a proven strategy for improving traffic efficiency and minimizing vehicular delays. First, we develop a Queue Evolution and Delay Model (QEDM) that establishes the relationship between detector-measured queue lengths and model parameters. QEDM accurately characterizes residual queue dynamics (accumulation and dissipation), significantly enhancing delay estimation accuracy under oversaturated conditions. Secondly, we propose a novel intersection-level signal optimization method that addresses key practical challenges: (1) pedestrian stages, overlap phases; (2) coupling effects between signal cycle and queue length; and (3) stochastic vehicle arrivals in undersaturated conditions. Unlike conventional approaches, this method proactively shortens signal cycles to reduce queues while avoiding suboptimal solutions that artificially “dilute” delays by extending cycles. Thirdly, we introduce an adaptive coordination control framework that maintains arterial-level green-band progression while maximizing intersection-level adaptive optimization flexibility. To bridge theory and practice, we design a cloud–edge–terminal collaborative deployment architecture for scalable signal control implementation and validate the framework through a hardware-in-the-loop simulation platform. Case studies in real-world scenarios demonstrate that the proposed method outperforms existing benchmarks in delay estimation accuracy, average vehicle delay, and travel time in coordinated directions. Additionally, we analyze the influence of coordination constraint update intervals on system performance, providing actionable insights for adaptive control systems. Full article
28 pages, 3698 KiB  
Systematic Review
Mesenchymal Stem Cells as Anti-Inflammatory Agents in Chronic Kidney Disease: A Systematic Review and Meta-Analysis
by Lukman Pura, Raeni Dwi Putri, Muh. Arya Prahmana, Muhammad Palar Wijaya, Ria Bandiara, Ahmad Faried and Rudi Supriyadi
Cells 2025, 14(17), 1313; https://doi.org/10.3390/cells14171313 (registering DOI) - 24 Aug 2025
Abstract
Background: Chronic kidney disease (CKD) is largely driven by inflammation. Mesenchymal stem cells (MSCs) show therapeutic potential; however, their efficacy across CKD etiologies remains unclear. Methods: Comprehensive searches were conducted in PubMed, Cochrane, ScienceDirect, Scopus and Google Scholar. Effect sizes for inflammation and [...] Read more.
Background: Chronic kidney disease (CKD) is largely driven by inflammation. Mesenchymal stem cells (MSCs) show therapeutic potential; however, their efficacy across CKD etiologies remains unclear. Methods: Comprehensive searches were conducted in PubMed, Cochrane, ScienceDirect, Scopus and Google Scholar. Effect sizes for inflammation and renal function outcomes were meta-analyzed. Results: Of 2514 studies screened, 52 met inclusion criteria (49 animal studies, 3 randomized controlled trials). In animal models, MSCs significantly reduced interleukin-6 (mean difference [MD] = −155.80; 95% CI: −249.10, −62.51; p = 0.001) and tumor necrosis factor-α (TNF-α) (MD = −35.53; 95% CI: −52.75, −18.30; p < 0.0001). In patients, TNF-α reduction was not significant (MD = −0.74; 95% CI: −2.20, 0.73; p = 0.32). Serum creatinine decreased in animals (MD = −0.38; 95% CI: −0.46, −0.29; p < 0.00001), but not in patients (MD = −0.59; 95% CI: −1.92, 0.74; p = 0.39). Blood urea nitrogen decreased in animals (MD = −19.27; 95% CI: −23.50, −15.04; p < 0.00001), and glomerular filtration rate improved (standardized MD = 1.83; 95% CI: 0.51, 3.15; p = 0.007), with no change in patients. Conclusion: MSCs improve inflammation and renal function in CKD animal models; however, evidence in patients remains inconclusive. Full article
(This article belongs to the Special Issue Immunoregulatory Functions of Mesenchymal Stem Cells (MSCs))
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18 pages, 943 KiB  
Article
Fecal Microbiota Transplantation in Patients with Alcohol-Associated Cirrhosis: A Clinical Trial
by Cristian Ichim, Adrian Boicean, Samuel Bogdan Todor, Paula Anderco and Victoria Bîrluțiu
J. Clin. Med. 2025, 14(17), 5981; https://doi.org/10.3390/jcm14175981 (registering DOI) - 24 Aug 2025
Abstract
Background: Gut microbiota dysregulation is increasingly recognized as a key contributor to the progression of liver cirrhosis and its complications, particularly hepatic encephalopathy. Fecal microbiota transplantation (FMT) has emerged as a novel therapeutic strategy aimed at restoring intestinal microbial homeostasis and modulating [...] Read more.
Background: Gut microbiota dysregulation is increasingly recognized as a key contributor to the progression of liver cirrhosis and its complications, particularly hepatic encephalopathy. Fecal microbiota transplantation (FMT) has emerged as a novel therapeutic strategy aimed at restoring intestinal microbial homeostasis and modulating systemic inflammation. Methods: This prospective, single-center clinical trial evaluated the short-term safety and efficacy of FMT in patients with alcohol-related liver cirrhosis. Clinical assessment, liver stiffness (via elastography), steatosis (controlled attenuation parameter), inflammatory biomarkers, and extended biochemical panels were analyzed at baseline, one week and one month post-FMT. A control group receiving standard medical therapy was used for comparison. Results: FMT was associated with a significant reduction in hepatic encephalopathy severity (p = 0.014), sustained improvements in liver stiffness (p = 0.027) and decreased steatosis (p = 0.025). At one month, C-reactive protein and neutrophil-to-lymphocyte ratio both declined significantly (p = 0.043), indicating a measurable anti-inflammatory effect. No serious adverse events were recorded. In comparison with controls, FMT recipients showed lower systemic inflammation and improved neuropsychiatric status. Conclusions: FMT demonstrated a favorable safety profile and yielded early clinical and biochemical benefits in patients with cirrhosis. These preliminary findings support the potential utility of microbiota-based interventions in chronic liver disease and warrant validation in larger, multicenter trials. Full article
(This article belongs to the Special Issue Alcohol-Related Liver Disease: Diagnosis, Treatment, and Management)
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24 pages, 5949 KiB  
Article
Green Smart Museums Driven by AI and Digital Twin: Concepts, System Architecture, and Case Studies
by Ran Bi, Chenchen Song and Yue Zhang
Smart Cities 2025, 8(5), 140; https://doi.org/10.3390/smartcities8050140 (registering DOI) - 24 Aug 2025
Abstract
In response to the urgent global call for “dual carbon” targets, the sustainable transformation of public museums has become a focal issue in both academic research and engineering practice. This study proposes and empirically validates an integrated management framework that unites digital twin [...] Read more.
In response to the urgent global call for “dual carbon” targets, the sustainable transformation of public museums has become a focal issue in both academic research and engineering practice. This study proposes and empirically validates an integrated management framework that unites digital twin modeling, artificial intelligence, and green energy systems for next-generation green smart museums. A unified, closed-loop platform for data-driven, adaptive management is implemented and statistically validated across distinct deployment scenarios. Empirical evaluation is conducted through the comparative analysis of three representative museum cases in China, each characterized by a distinct integration pathway: (A) advanced digital twin and AI management with moderate green energy adoption; (B) large-scale renewable energy integration with basic AI and digitalization; and (C) the comprehensive integration of all three dimensions. Multi-dimensional data on energy consumption, carbon emissions, equipment reliability, and visitor satisfaction are collected and analyzed using quantitative statistical techniques and performance indicator benchmarking. The results reveal that the holistic “triple synergy” approach in Case C delivers the most balanced and significant gains, achieving up to 36.7% reductions in energy use and 41.5% in carbon emissions, alongside the highest improvements in operational reliability and visitor satisfaction. In contrast, single-focus strategies show domain-specific advantages but also trade-offs—for example, Case B achieved high energy and carbon savings but relatively limited visitor satisfaction gains. These findings highlight that only coordinated, multi-technology integration can optimize performance across both environmental and experiential dimensions. The proposed framework provides both a theoretical foundation and practical roadmap for advancing the digital and green transformation of public cultural buildings, supporting broader carbon neutrality and sustainable development objectives. Full article
(This article belongs to the Special Issue Big Data and AI Services for Sustainable Smart Cities)
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14 pages, 680 KiB  
Article
Prevalence and Risk Factors for Salmonella spp. on Pig Carcass, Before and After Chilling, in Brazil
by Anna Carolina Massara Brasileiro, Mariana Avelino de Souza Santos, Cláudia Valéria Gonçalves Cordeiro de Sá, Carla Susana Rodrigues and João Paulo Amaral Haddad
Vet. Sci. 2025, 12(9), 803; https://doi.org/10.3390/vetsci12090803 (registering DOI) - 24 Aug 2025
Abstract
The Brazilian Ministry of Agriculture, Livestock and Food Supply conducted a microbiological baseline study to determine the national prevalence of Salmonella in pig carcasses, and characterize the contamination risk according to the abattoir size under federal inspection in 2014–2015. All establishments were size-classified [...] Read more.
The Brazilian Ministry of Agriculture, Livestock and Food Supply conducted a microbiological baseline study to determine the national prevalence of Salmonella in pig carcasses, and characterize the contamination risk according to the abattoir size under federal inspection in 2014–2015. All establishments were size-classified according to their slaughtering capacity per day. Sample weights were defined to increase the external validity of data. Samples were collected after the random selection of two half-carcasses from 76 slaughterhouses, one at the pre-chilling stage and the other at least 12 h after chilling. A total of 1544 samples were analyzed in official laboratories using Vidas SLM and SPT systems; for positive samples, we used the ISO 6579:2014. The results revealed that 10.00% (CI 7.50–13.22) of the pre-chilling carcasses tested positive for Salmonella spp. The medium-sized establishments exhibited the highest prevalence (18.51%). National and international market establishments had prevalences of 17.43%, and 9.05%, respectively. For carcass samples collected after chilling, the estimated prevalence was 4.58% (CI 3.13–6.65), with the highest prevalence recorded in the medium-sized establishments (7%), and values of 12.25% and 3.5% in the NM and IM, respectively. The information and knowledge thus acquired may support the further investigation and evaluation of surveillance programs, improving food safety. Full article
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