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45 pages, 3087 KB

Open AccessReview

A Comparative Overview of Technological Advances in Fall Detection Systems for Elderly People

by Omar Flor-Unda, Rafael Arcos-Reina, Cristina Estrella-Caicedo, Carlos Toapanta, Freddy Villao, Héctor Palacios-Cabrera, Susana Nunez-Nagy and Bernardo Alarcos

Sensors 2025, 25(24), 7423; https://doi.org/10.3390/s25247423 (registering DOI) - 5 Dec 2025

Abstract

Population ageing is a growing global trend. It was estimated that by 2050, people over 60 years of age will represent 35% of the population in industrialised countries. This context demands strategies that incorporate technologies, such as fall detection systems, to facilitate remote [...] Read more.

Population ageing is a growing global trend. It was estimated that by 2050, people over 60 years of age will represent 35% of the population in industrialised countries. This context demands strategies that incorporate technologies, such as fall detection systems, to facilitate remote monitoring and the automatic activation of risk alarms, thus improving quality of life. This article presents a scoping review of the leading technological solutions developed over the last decade for detecting falls in older adults, describing their principles of operation, effectiveness, advantages, limitations, and future trends in their development. The review was conducted under the PRISMA^® methodology, including articles indexed in SCOPUS, ScienceDirect, Web of Science, PubMed, IEEE Xplore and Taylor & Francis. There is a predominance in the use of inertial systems that use accelerometers and gyroscopes, valued for their low cost and wide availability. However, those approaches that combine image analysis with artificial intelligence and machine learning algorithms show superiority in terms of accuracy and robustness. Similarly, progress has been made in the development of multisensory solutions based on IoT technologies, capable of integrating information from various sources, which optimises decision-making in real time. Full article

(This article belongs to the Section Wearables)

32 pages, 5719 KB

Open AccessFeature PaperReview

Recent Progress in the Theory of Flat Bands and Their Realization

by Izumi Hase

Condens. Matter 2025, 10(4), 64; https://doi.org/10.3390/condmat10040064 (registering DOI) - 5 Dec 2025

Abstract

Flat electronic bands, characterized by a nearly dispersionless energy spectrum, have emerged as fertile ground for exploring strong correlation effects, unconventional magnetism, and topological phases. This review paper provides an overview of the theoretical basis, material realization, and emergent phenomena associated with flat [...] Read more.

Flat electronic bands, characterized by a nearly dispersionless energy spectrum, have emerged as fertile ground for exploring strong correlation effects, unconventional magnetism, and topological phases. This review paper provides an overview of the theoretical basis, material realization, and emergent phenomena associated with flat bands. We begin by discussing the geometric and topological origins of flat bands in lattice systems, emphasizing mechanisms such as destructive interference and compact localized states. We will also explain the relationship between quantum metrics and flat bands, which are recent theoretical findings. We then survey various classes of materials—ranging from engineered lattices and Moiré structures to transition metal compounds—where flat bands have been theoretically predicted or experimentally observed. The interplay between flat-band physics and strong correlations is explored through recent developments in ferromagnetism, superconductivity, and various Hall effects. Finally, we outline open questions and potential directions for future research, including the quest for ideal flat-band systems, the role of spin–orbit coupling, and the impact of disorder. This review aims to bridge fundamental concepts with cutting-edge advances, highlighting the rich physics and material prospects of flat bands. Full article

37 pages, 1982 KB

Open AccessArticle

A Quantum-Hybrid Framework for Urban Environmental Forecasting Integrating Advanced AI and Geospatial Simulation

by Janis Peksa, Andrii Perekrest, Kyrylo Vadurin and Dmytro Mamchur

Sensors 2025, 25(24), 7422; https://doi.org/10.3390/s25247422 (registering DOI) - 5 Dec 2025

Abstract

The paper examines the development of forecasting and modeling technologies for environmental processes using classical and quantum data analysis methods. The main focus is on the integration of deep neural networks and classical algorithms, such as AutoARIMA and BATS, with quantum approaches to [...] Read more.

The paper examines the development of forecasting and modeling technologies for environmental processes using classical and quantum data analysis methods. The main focus is on the integration of deep neural networks and classical algorithms, such as AutoARIMA and BATS, with quantum approaches to improve the accuracy of forecasting environmental parameters. The research is aimed at solving key problems in environmental monitoring, particularly insufficient forecast accuracy and the complexity of processing small data with high discretization. We developed the concept of an adaptive system for predicting environmental conditions in urban agglomerations. Hybrid forecasting methods were proposed, which include the integration of quantum layers in LSTM, Transformer, ARIMA, and other models. Approaches to spatial interpolation of environmental data and the creation of an interactive air pollution simulator based on the A* algorithm and the Gaussian kernel were considered. Experimental results confirmed the effectiveness of the proposed methods. The practical significance lies in the possibility of using the developed models for operational monitoring and forecasting of environmental threats. The results of the work can be applied in environmental information systems to increase the accuracy of forecasts and adaptability to changing environmental conditions. Full article

(This article belongs to the Section Environmental Sensing)

17 pages, 21162 KB

Open AccessArticle

Effect of Sc/Y Co-Doping on Initial Alumina Growth of Electron Beam Physical Vapor Deposited FeCoNiCrAl High-Entropy Coating

by Dongqing Li, Shuhui Zheng, Jian Gu and Jiajun Si

Coatings 2025, 15(12), 1436; https://doi.org/10.3390/coatings15121436 (registering DOI) - 5 Dec 2025

Abstract

FeCoNiCrAl and FeCoNiCrAlScY high-entropy coatings were fabricated via electron beam physical vapor deposition. The microstructure and short-term isothermal oxidation behavior of the coatings were compared. Sc and Y inhibited coating element diffusion to the superalloy substrate and formed co-precipitated phases during coating manufacturing. [...] Read more.

FeCoNiCrAl and FeCoNiCrAlScY high-entropy coatings were fabricated via electron beam physical vapor deposition. The microstructure and short-term isothermal oxidation behavior of the coatings were compared. Sc and Y inhibited coating element diffusion to the superalloy substrate and formed co-precipitated phases during coating manufacturing. The Sc/Y co-doped coating exhibited accelerated phase transformation from θ- to α-Al₂O₃ as compared to the undoped one. The effect mechanism associated with the nucleation of α-Al₂O₃ was discussed. The preferential formation of Sc/Y-rich oxides promoted the nucleation of α-Al₂O₃ beneath them, and the θ-α phase evolution process was directly skipped, which suppressed the rapid growth of θ-Al₂O₃ and the initial formation of cracks in the alumina film and provided the FeCoNiCrAl high-entropy coating with an improved oxidation property in the early oxidation stage. Full article

(This article belongs to the Section Corrosion, Wear and Erosion)

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17 pages, 1190 KB

Open AccessArticle

Temporal Profiling of SARS-CoV-2 Variants Using BioEnrichPy: A Network-Based Insight into Host Disruption and Neurodegeneration

by Sreelakshmi Kalayakkattil, Ananthakrishnan Anil Indu, Punya Sunil, Haritha Nekkanti, Smitha Shet and Ranajit Das

COVID 2025, 5(12), 203; https://doi.org/10.3390/covid5120203 (registering DOI) - 5 Dec 2025

Abstract

SARS-CoV-2, the virus responsible for COVID-19, disrupts human cellular pathways through complex protein–protein interaction, contributing to disease progression. As the virus has evolved, emerging variants have exhibited differences in transmissibility, immune evasion, and pathogenicity, underscoring the need to investigate their distinct molecular interactions [...] Read more.

SARS-CoV-2, the virus responsible for COVID-19, disrupts human cellular pathways through complex protein–protein interaction, contributing to disease progression. As the virus has evolved, emerging variants have exhibited differences in transmissibility, immune evasion, and pathogenicity, underscoring the need to investigate their distinct molecular interactions with host proteins. In this study, we constructed a comprehensive SARS–CoV–2–human protein–protein interaction network and analyzed the temporal evolution of pathway perturbations across different variants. We employed computational approaches, including network-based clustering and functional enrichment analysis, using our custom-developed Python (v3.13) pipeline, BioEnrichPy, to identify key host pathways perturbed by each SARS-CoV-2 variant. Our analyses revealed that while the early variants predominantly targeted respiratory and inflammatory pathways, later variants such as Delta and Omicron exerted more extensive systemic effects, notably impacting neurological and cardiovascular systems. Comparative analyses uncovered distinct, variant-specific molecular adaptations, underscoring the dynamic and evolving nature of SARS-CoV-2–host interactions. Furthermore, we identified host proteins and pathways that represent potential therapeutic vulnerabilities, which appear to have co-evolved with viral mutations. Full article

(This article belongs to the Special Issue Artificial Intelligence and Machine Learning Applications for Developing the Diagnosis of COVID-19, 3rd Edition)

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21 pages, 1502 KB

Open AccessArticle

Failure Analysis and Machine Learning-Based Prediction in Urban Drinking Water Systems

by Salih Yılmaz

Appl. Sci. 2025, 15(24), 12887; https://doi.org/10.3390/app152412887 (registering DOI) - 5 Dec 2025

Abstract

This work illustrates a machine learning methodology to forecast pipe failure frequencies in drinking water systems to enhance asset management and operational planning. Three supervised regression models—Random Forest Regressor (RFR), Extreme Gradient Boosting (XGB), and Multi-Layer Perceptron (MLP)—were developed and evaluated using historical [...] Read more.

This work illustrates a machine learning methodology to forecast pipe failure frequencies in drinking water systems to enhance asset management and operational planning. Three supervised regression models—Random Forest Regressor (RFR), Extreme Gradient Boosting (XGB), and Multi-Layer Perceptron (MLP)—were developed and evaluated using historical failure data from Malatya, Türkiye. The primary predictive variables identified were pipe diameter, pipe type, pipe age, and seasonal average ambient air temperature. The MLP demonstrated superior performance compared to the other models, attaining the lowest RMSE (1.48) and the highest R² (0.993) with respect to the training data, effectively capturing the nonlinear characteristics and failure patterns. The MLP was validated using two datasets from 24 District Metered Areas (DMAs) in Sakarya and Kayseri, Türkiye. The model’s anticipated failure frequencies exhibited strong concordance with the observed failure frequencies, even in regions of elevated failure density, indicating the model’s proficiency in identifying high-risk locations and facilitating the prioritization of maintenance activities. The work demonstrates the potential of machine learning in water infrastructure management. It emphasizes the importance of employing a hybrid method with Geographic Information Systems (GISs) in future research to enhance forecast accuracy and spatial analysis. Full article

(This article belongs to the Section Civil Engineering)

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16 pages, 12040 KB

Open AccessArticle

Milk Powder Fortified with Folic Acid and Colostrum Basic Protein Promotes Linear Growth and Improves Bone Microarchitecture in Juvenile Mice Without Adverse Metabolic Effects

by Hongjuan Liu, Yixin Zhang, Yuanjue Wu, Wenbo Wan, Jiawen Liang, Hui Xiong, Liping Hao and Ting Xiong

Nutrients 2025, 17(24), 3819; https://doi.org/10.3390/nu17243819 (registering DOI) - 5 Dec 2025

Abstract

Background: The juvenile-pubertal period is a critical window for linear growth and bone mass accumulation. This study investigated the joint effects of folic acid (FA) and colostrum basic protein (CBP)-fortified milk powder on growth, bone health, and metabolic safety in juvenile mice. Methods: [...] Read more.

Background: The juvenile-pubertal period is a critical window for linear growth and bone mass accumulation. This study investigated the joint effects of folic acid (FA) and colostrum basic protein (CBP)-fortified milk powder on growth, bone health, and metabolic safety in juvenile mice. Methods: Three-week-old C57BL/6J mice (n = 120) were acclimatized for 1 week and then randomly assigned to three isocaloric diet groups for an 8-week intervention starting at 4 weeks of age: Control (AIN-93M), Milk (AIN-93M + FA/CBP-fortified milk powder), and Positive Control (AIN-93G). Body length and weight were measured twice weekly. Bone microarchitecture was assessed by micro-computed tomography, and bone remodeling was evaluated through histology and serum biomarkers. The GH–IGF-1 axis and related metabolic parameters were also assessed. Results: FA–CBP–fortified milk powder significantly accelerated linear growth at intervention week 2, with body length higher in the Milk group than in the Control group (p < 0.01). After 8 weeks, the Milk group showed improved trabecular bone mass and microarchitecture compared with Control, especially in males (p < 0.01). Bone remodeling was transiently elevated at intervention week 4, as indicated by higher serum osteocalcin and CTX-I, and by increased osteoclast and cartilage matrix formation versus Control (p < 0.05). The GH–IGF-1 axis was also temporarily activated at week 4, with elevated serum GH and IGF-1/IGFBP-3 ratio compared with Control (p < 0.05). These skeletal benefits occurred without excess weight gain or adverse metabolic effects compared with Control (all p > 0.05). Conclusions: FA-CBP-fortified milk significantly enhanced linear growth during puberty and improved bone mass and microstructure in early adulthood. These skeletal benefits are consistent with the transient activation of the GH–IGF-1 axis. Importantly, no adverse metabolic effects were detected from early intervention through adulthood, supporting its potential application in growth-promoting nutritional strategies. Full article

(This article belongs to the Special Issue Nutrition in Children's Growth and Development)

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22 pages, 476 KB

Open AccessArticle

Economic Analysis of Global Catastrophic Risks Under Uncertainty

by Wei-Chun Tseng, Chi-Chung Chen and Tsung-Ling Hwang

Risks 2025, 13(12), 241; https://doi.org/10.3390/risks13120241 (registering DOI) - 5 Dec 2025

Abstract

Background: Despite the apparent importance of global catastrophe risks (GCRs), human society has invested relatively little to reduce them. One possible reason is that we do not understand the significance of reducing GCRs, especially when measured in the monetary terms that we typically [...] Read more.

Background: Despite the apparent importance of global catastrophe risks (GCRs), human society has invested relatively little to reduce them. One possible reason is that we do not understand the significance of reducing GCRs, especially when measured in the monetary terms that we typically use to make decisions. Consequently, we cannot compare them to other issues that influence our decision making and well-being. Purpose: In this study, we quantified the benefits of reducing all non-natural GCRs to highlight their importance. Method: We used a probabilistic model for simulation. Due to limited information, we introduced concepts and assumptions to aid the calculations, such as steady-state economics and sensitivity analyses. In addition, we converted expert opinions to help us focus on a narrower range of risk levels. Results: Within a considerably plausible range of the GCR, we found the following: 1. The benefits of halving the overall non-natural GCR over the next 100 years are substantial. 2. The expected human survival years are sensitive to the mitigation effort but robust to the horizon length. 3. The higher the population growth rate, the larger the expected life years saved. 4. The expected monetary benefits are positively related to the GWP per capita growth rate, mitigation period, and magnitude of natural GCRs but are negatively related to the discounting rate. Significance: The human species is actually facing multiple GCRs simultaneously. In the literature, there is still a gap in quantifying the benefits of reducing all non-natural GCRs/ERs in the coming century while accounting for the very long run on a million-year scale. This article fills such a gap, and the results may serve as a reference for global policymaking to handle this global public issue. Full article

(This article belongs to the Special Issue Tail Risk Analysis and Management)

38 pages, 8927 KB

Open AccessArticle

An Ongoing Search for Multitarget Ligands as Potential Agents for Diabetes Mellitus and Its Long-Term Complications: New Insights into (5-Arylidene-4-oxothiazolidin-3-yl)alkanoic Acid Derivatives

by Rosanna Maccari, Rosaria Ottanà, Valerij Talagayev, Roberta Moschini, Francesco Balestri, Francesca Felice, Francesca Iannuccilli, Gemma Sardelli, Rebecca Sodano, Gerhard Wolber, Paolo Paoli and Antonella Del Corso

Pharmaceuticals 2025, 18(12), 1863; https://doi.org/10.3390/ph18121863 (registering DOI) - 5 Dec 2025

Abstract

Background: Diabetes mellitus is a multifactorial disease characterized by complex metabolic dysfunctions and chronic complications induced by hyperglycaemia. The design of multitarget ligands, capable of simultaneously controlling different pathogenic processes, was proposed as a promising approach to identify novel antidiabetic drugs endowed [...] Read more.

Background: Diabetes mellitus is a multifactorial disease characterized by complex metabolic dysfunctions and chronic complications induced by hyperglycaemia. The design of multitarget ligands, capable of simultaneously controlling different pathogenic processes, was proposed as a promising approach to identify novel antidiabetic drugs endowed with improved efficacy. Methods: (5-Arylidene-4-oxothiazolidin-3-yl)alkanoic acid derivatives 1a–g and 2a–g were synthesized as potential multitarget antidiabetic agents. They were tested in vitro as inhibitors of both human recombinant AKR1B1 and PTP1B, and kinetic studies and molecular docking simulations for both enzymes were performed. Their effects on cellular glucose uptake, insulin signalling, and mitochondrial potential were assayed in cultures of murine C2C12 myocytes. A lipid accumulation assay was performed in HepG2 liver cells. The effects on high glucose-induced sorbitol accumulation were evaluated in lens HLE and retinal MIO-M1 cells. Results: All compounds displayed excellent AKR1B1 inhibitory activity (IC₅₀ 0.03–0.46 μM 1a–g; IC₅₀ 0.48–6.30 μM 2a–g); 1g and 2e–g also appreciably inhibited PTP1B at micromolar concentrations. Propanoic derivatives 2e–g significantly stimulated glucose uptake in C2C12 myocytes, in an insulin-independent way, reduced lipid accumulation in HepG2 liver cells, and caused hyperpolarization of C2C12 mitochondria at 10 μM concentration. Derivative 2e significantly reduced sorbitol accumulation in both HLE and MIO-M1 cells at a 5 μM concentration. Conclusions: The results reported here provided new insights into the mechanisms of action and structure/activity relationships of 4-thiazolidinone derivatives, underscoring the capability of compounds 2e–g of eliciting insulin-mimetic effects independent of hormone signalling. Among them, compound 2e also proved to inhibit AKR1B1-dependent sorbitol accumulation and, thus, emerged as a promising multitarget agent that can be considered for further investigations. Full article

(This article belongs to the Special Issue Antidiabetic Agents: New Drug Discovery Insights and Prospects)

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28 pages, 1633 KB

Open AccessSystematic Review

A Systematic Review and Meta-Analysis of Physical Activity Interventions in Colorectal Cancer Survivors: An Evidence Evaluation Attempt Across Racial/Ethnic Groups

by Yves Paul Vincent Mbous, Rowida Mohamed, George A. Kelley and Kimberly Michelle Kelly

Healthcare 2025, 13(24), 3198; https://doi.org/10.3390/healthcare13243198 (registering DOI) - 5 Dec 2025

Abstract

Aims: Recommendations for cancer survivors concur regarding physical activity (PA), and elucidating factors governing PA uptake among colorectal cancer (CRC) survivors is needed. This study examined the impact of PA interventions and investigated the variation in PA across several characteristics, including race/ethnicity. Design: [...] Read more.

Aims: Recommendations for cancer survivors concur regarding physical activity (PA), and elucidating factors governing PA uptake among colorectal cancer (CRC) survivors is needed. This study examined the impact of PA interventions and investigated the variation in PA across several characteristics, including race/ethnicity. Design: We performed a systematic review and aggregate data meta-analysis of randomized controlled trials (RCTs) of PA interventions. Data Sources: We used studies from CENTRAL, PubMed (NCBI), PsycINFO (EBSCOhost), CINAHL (EBSCOhost) with full text, Scopus (ELSEVIER), and the Web of Science (CLARIVATE) (1 May 1993–1 September 2023). Methods: For the meta-analysis, the inverse variance heterogeneity (IVhet) model was used to pool standardized mean difference effect sizes (Hedge’s g) for our primary outcome, changes in PA. Results: Sixteen studies representing 1668 participants were included in the meta-analysis. A moderate, statistically significant increase in PA was observed (g = 0.44, 95% CI 0.12–0.76; p = 0.01). However, a large amount of inconsistency was observed (I² = 80.8%, 95% CI, 36.1% to 90.9%), as well as major asymmetry suggestive of small-study effects (publication bias, LFK = 3.04). Only 28% of trials reported race/ethnicity, limiting equity analyses. Subgroups comparing atheoretical vs. theory-based interventions did not differ statistically. Meta-regression results suggested associations with specific behavior change theories and delivery features. Based on the Grading of Recommendations Assessment, Development and Evaluation (GRADE) assessment, the overall certainty of evidence was considered low. Conclusions: There is low-certainty evidence that PA interventions may improve PA among CRC survivors. Future trials should (i) recruit and report diverse samples in a clear and transparent manner, (ii) explicitly map theory constructs to techniques and test mechanisms, and (iii) report fidelity and clinically meaningful thresholds alongside behavioral outcomes. Full article

(This article belongs to the Special Issue Physical Activity, Healthy Lifestyle and Well-Being Across the Lifespan: Second Edition)

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24 pages, 1889 KB

Open AccessArticle

Inverse Problem Solving for a Porous Acoustical Multilayered System Based on the Transfer Matrix Approach

by Yassine Moradi, Julien Bustillo, Lionel Haumesser, Marc Lethiecq and Khalid Chikh

Acoustics 2025, 7(4), 79; https://doi.org/10.3390/acoustics7040079 (registering DOI) - 5 Dec 2025

Abstract

The acoustical modelling of multilayered systems is crucial for researchers and engineers aiming to evaluate and control the behaviour of complex media and to determine their internal properties. In this work, we first develop a forward model describing the propagation of acoustic waves [...] Read more.

The acoustical modelling of multilayered systems is crucial for researchers and engineers aiming to evaluate and control the behaviour of complex media and to determine their internal properties. In this work, we first develop a forward model describing the propagation of acoustic waves through various types of materials, including fluids, solids, and poroelastic media. The model relies on the classical theoretical frameworks of Thomson and Haskell for non-porous layers, while Biot’s theory is employed to describe wave propagation in poroelastic materials. The propagation is mathematically treated using the transfer matrix method, which links the acoustic displacement and stress at the extremities of each layer. Appropriate boundary conditions are applied at each interface to assemble all local matrices into a single global matrix representing the entire multilayer system. This forward model allows the calculation of theoretical transmission coefficients, which are then compared to experimental measurements to validate the approach proposed. Secondly, this modelling framework is used as the basis for solving inverse problems, where the goal is to retrieve unknown internal parameters, such as mechanical or acoustic properties, by minimizing the discrepancy between simulated and experimental transmission spectra. This inverse problem approach is essential in non-destructive evaluation applications, where direct measurements are often unfeasible. Full article

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19 pages, 6163 KB

Open AccessArticle

Analysis of Application of Design Standards for Future Climate Change Adaptive Agricultural Reservoirs Using Cluster Analysis

by Dong-Hyuk Joo, Ra Na, Hayoung Kim, Seung-Hwan Yoo and Sang-Hyun Lee

Water 2025, 17(24), 3463; https://doi.org/10.3390/w17243463 (registering DOI) - 5 Dec 2025

Abstract

This study aimed to assess the impact and vulnerability of climate change by classifying 26 clusters of meteorologically homogeneous regions. To determine the optimal clustering method, both K-means and Gaussian Mixture Model (GMM) clustering were analyzed using the effective storage capacity to watershed [...] Read more.

This study aimed to assess the impact and vulnerability of climate change by classifying 26 clusters of meteorologically homogeneous regions. To determine the optimal clustering method, both K-means and Gaussian Mixture Model (GMM) clustering were analyzed using the effective storage capacity to watershed area ratio. The optimal number of clusters was derived based on several evaluation metrics, including the Silhouette Score, Calinski-Harabasz Index, Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). Ultimately, GMM clustering was identified as the optimal method, with the best clustering results obtained at k = 4 for an effective storage capacity of 100,000 to 400,000 tons and k = 5 for an effective storage capacity of 400,000 to 10,000,000 tons. Additionally, standard reservoirs applicable to agricultural production infrastructure design standards were identified based on homogeneous weather region clusters, the optimal clustering method, and centroid results. The findings of this study can serve as fundamental data for the development and revision of design standards, contributing to more climate-resilient agricultural infrastructure. Full article

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12 pages, 3538 KB

Open AccessArticle

Computed Tomographic Features and Prevalence of Orbital Ligament Mineralization in Dogs

by Ying-Ying Lo, Amélie Montenon, Aurélien Jeandel and Anne-Sophie Bedu

Animals 2025, 15(24), 3522; https://doi.org/10.3390/ani15243522 (registering DOI) - 5 Dec 2025

Abstract

Mineralization within the orbital ligament (OL) is occasionally observed on canine head computed tomography (CT) examinations, typically without associated clinical signs. This feature has been only briefly mentioned in the veterinary literature. The present retrospective descriptive study evaluated 402 dogs to determine the [...] Read more.

Mineralization within the orbital ligament (OL) is occasionally observed on canine head computed tomography (CT) examinations, typically without associated clinical signs. This feature has been only briefly mentioned in the veterinary literature. The present retrospective descriptive study evaluated 402 dogs to determine the prevalence and CT characteristics of OL mineralization, including its location, morphology, margins, symmetry, size, and attenuation. Associations with signalment, medical history and concurrent mineralization were also assessed. Orbital ligament mineralization was identified in 157 of 402 dogs (39.1%). The lesion was consistently located dorsally (100%), and was most often symmetrical, triangular, well-defined and heterogenous. The presence of OL mineralization was significantly associated with increasing age and body weight, as well as with concurrent mineralization in other sites, such as lungs and ears. The lesion was significantly less frequent in brachycephalic dogs. No associations were found with facial trauma, orbital disease or other pathological conditions. Orbital ligament mineralization appears to be a common incidental finding in canine head CT studies, most likely representing a benign, age-related, and non-pathological change. Full article

(This article belongs to the Section Veterinary Clinical Studies)

27 pages, 5379 KB

Open AccessReview

Myocutaneous Flaps and Muscle Flaps for Management of Limbs’ Defects in Dogs and Cats: A Review

by Mandalena Markou, Eleftheria Dermisiadou, Konstantina Karagianni, Eugenia Flouraki and Vassiliki Tsioli

Pets 2025, 2(4), 41; https://doi.org/10.3390/pets2040041 - 5 Dec 2025

Abstract

The objective of the present study is to review the anatomical considerations, surgical techniques, clinical applications, and outcomes of myocutaneous and muscle flaps used in the reconstruction of limb defects in dogs and cats. Limb wounds in small animals often result from trauma, [...] Read more.

The objective of the present study is to review the anatomical considerations, surgical techniques, clinical applications, and outcomes of myocutaneous and muscle flaps used in the reconstruction of limb defects in dogs and cats. Limb wounds in small animals often result from trauma, neoplasia, or infection and can involve significant soft tissue loss. Reconstruction of these defects is challenging due to limited local skin availability, particularly in distal regions, and the need to preserve function while preventing complications. Muscle and myocutaneous flaps provide well-vascularized tissue suitable for covering complex wounds, especially those with exposed bone, joints, or tendons. This review synthesizes current literature on commonly used flaps—including latissimus dorsi, cutaneous trunci, trapezius, sartorius, semitendinosus, and flexor carpi ulnaris; focusing on their anatomical basis, vascular supply, arc of rotation, surgical technique, indications, and complication rates. Comparative data between dogs and cats are highlighted, and experimental as well as clinical applications are discussed. Myocutaneous flaps offer durable and reliable coverage with lower infection and necrosis rates compared to skin grafts, particularly in contaminated or poorly vascularized wounds. Common complications include distal flap necrosis, wound dehiscence, seroma, and, occasionally, functional deficits. Muscle and myocutaneous flaps remain essential tools in limb reconstruction. Successful outcomes require careful flap planning, surgical expertise, and vigilant postoperative care. Further prospective studies are needed to optimize flap selection and reduce complication rates in both species. Full article

25 pages, 4986 KB

Open AccessArticle

A Deep Hybrid CNNDBiLSTM Model for Short-Term Wind Speed Forecasting in Wind-Rich Regions of Tasmania, Australia

by Ananta Neupane, Nawin Raj and Ravinesh Deo

Energies 2025, 18(24), 6390; https://doi.org/10.3390/en18246390 (registering DOI) - 5 Dec 2025

Abstract

Accurate and reliable short-term wind speed forecasting plays a crucial role in efficient operation and integration of wind energy generation. This research study introduces an innovative deep hybrid model that combines Convolutional Neural Networks (CNN) with Double Bidirectional Long Short-Term Memory (DBiLSTM) networks [...] Read more.

Accurate and reliable short-term wind speed forecasting plays a crucial role in efficient operation and integration of wind energy generation. This research study introduces an innovative deep hybrid model that combines Convolutional Neural Networks (CNN) with Double Bidirectional Long Short-Term Memory (DBiLSTM) networks to enhance wind speed forecasting accuracy in Australia. Thirteen years of hourly wind speed data were collected from two wind-rich potential sites in Tasmania, Australia. The CNN component effectively captures local temporal patterns, while the DBiLSTM layers model long-range dependencies in both forward and backward directions. The proposed CNNDBiLSTM model was compared against three traditional benchmark models: Multiple Linear Regression (MLR), Support Vector Regression (SVR), and Categorical Boosting (CatBoost). The proposed framework can effectively support wind farm planning, operational reliability, and grid integration strategies within the renewable energy sector. A comprehensive evaluation framework across both Australian study sites (Flinders Island Airport, Scottsdale) showed that the CNNDBiLSTM consistently outperformed the baseline models. It achieved the highest correlation coefficients (r = 0.987–0.988), the lowest error rates (RMSE = 0.392–0.402, MAE = 0.294–0.310), and superior scores across multiple efficiency metrics (ENS, WI, LM). The CNNDBiLSTM demonstrated strong adaptability across coastal and inland environments, showing potential for real-world use in renewable-energy resource forecasting. The wind speed analysis and forecasting show Flinders with higher and consistent wind speed as a more viable option for large-scale wind energy generation than Scottsdale in Tasmania. Full article

(This article belongs to the Section A3: Wind, Wave and Tidal Energy)

21 pages, 2065 KB

Open AccessArticle

Machine Learning-Assisted Simultaneous Measurement of Salinity and Temperature Using OCHFI Cascaded Sensor Structure

by Anirban Majee, Koustav Dey, Nikhil Vangety and Sourabh Roy

Photonics 2025, 12(12), 1203; https://doi.org/10.3390/photonics12121203 - 5 Dec 2025

Abstract

A compact offset-coupled hybrid fiber interferometer (OCHFI) is designed and experimentally demonstrated for simultaneous measurement of salinity and temperature. The sensor integrates multimode fiber (MMF) and offset no-core fiber (NCF) through an intermediate single-mode fiber (SMF), producing distinct interference patterns for multi-parameter sensing. [...] Read more.

A compact offset-coupled hybrid fiber interferometer (OCHFI) is designed and experimentally demonstrated for simultaneous measurement of salinity and temperature. The sensor integrates multimode fiber (MMF) and offset no-core fiber (NCF) through an intermediate single-mode fiber (SMF), producing distinct interference patterns for multi-parameter sensing. The optimal SMF length was determined through COMSOL simulations (version 6.2) and fixed at 50 cm to achieve stable and well-separated interference dips. Fast Fourier Transform analysis confirmed that the modal behavior originates from the single-mode-multimode-single-mode (SMS) and single-mode-no-core-single-mode (SNS) segments. Experimentally, Dip 1 exhibits salinity sensitivity of 0.62206 nm/‰, while Dip 2 shows temperature sensitivity of 0.09318 nm/°C, both with linearity (R² > 0.99), excellent repeatability, and stability, with fluctuations within 0.15 nm over 60 min. To remove cross-sensitivity, both the transfer matrix method and an Artificial Neural Network (ANN) model were employed. The ANN approach significantly enhanced prediction accuracy (R² = 0.9999) with RMSE improvement approximately 539-fold for salinity and 56-fold for temperature, compared with the analytical model. The proposed OCHFI sensor provides a compact, low-cost, and intelligent solution for precise simultaneous salinity and temperature measurement, with strong potential for applications in marine, chemical, and industrial process control. Full article

(This article belongs to the Special Issue Optical Fiber Sensors: Shedding More Light with Machine Learning)

22 pages, 3542 KB

Open AccessArticle

Dual Resource Scheduling Method of Production Equipment and Rail-Guided Vehicles Based on Proximal Policy Optimization Algorithm

by Nengqi Zhang, Bo Liu and Jian Zhang

Technologies 2025, 13(12), 573; https://doi.org/10.3390/technologies13120573 - 5 Dec 2025

Abstract

In the context of intelligent manufacturing, the integrated scheduling problem of dual rail-guided vehicles (RGVs) and multiple parallel processing equipment in flexible manufacturing systems has gained increasing importance. This problem exhibits spatiotemporal coupling and dynamic constraint characteristics, making traditional optimization methods ineffective at [...] Read more.

In the context of intelligent manufacturing, the integrated scheduling problem of dual rail-guided vehicles (RGVs) and multiple parallel processing equipment in flexible manufacturing systems has gained increasing importance. This problem exhibits spatiotemporal coupling and dynamic constraint characteristics, making traditional optimization methods ineffective at finding optimal solutions. At the problem formulation level, the dual resource scheduling task is modeled as a mixed-integer optimization problem. An intelligent scheduling framework based on action mask-constrained Proximal Policy Optimization (PPO) deep reinforcement learning is proposed to achieve integrated decision-making for production equipment allocation and RGV path planning. The approach models the scheduling problem as a Markov Decision Process, designing a high-dimensional state space, along with a multi-discrete action space that integrates machine selection and RGV motion control. The framework employs a shared feature extraction layer and dual-head Actor-Critic network architecture, combined with parallel experience collection and synchronous parameter update mechanisms. In computational experiments across different scales, the proposed method achieves an average makespan reduction of 15–20% compared with numerical methods, while exhibiting excellent robustness under uncertain conditions including processing time fluctuations. Full article

(This article belongs to the Section Manufacturing Technology)

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24 pages, 1272 KB

Open AccessReview

CA19-9 as a Dynamic Biomarker for Continuous Monitoring of Therapeutic Efficacy in Pancreatic Adenocarcinoma

by Luigi Brancato, Damar Osok, Laurent Van den Bossche, Eric Van Cutsem, Susan E. Bates, Johan Van den Bossche and Johannes Bogers

Cancers 2025, 17(24), 3902; https://doi.org/10.3390/cancers17243902 - 5 Dec 2025

Abstract

Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal malignancies, largely due to late-stage diagnosis and limited therapeutic efficacy. The carbohydrate antigen 19-9 (CA19-9) is the most widely used serum biomarker in the management of PDAC. While CA19-9 has significant limitations as [...] Read more.

Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal malignancies, largely due to late-stage diagnosis and limited therapeutic efficacy. The carbohydrate antigen 19-9 (CA19-9) is the most widely used serum biomarker in the management of PDAC. While CA19-9 has significant limitations as a screening or diagnostic tool, including low sensitivity for early-stage disease and a lack of expression in the Lewis antigen-negative population, its value in the post-diagnostic setting is well established. This review examines the production and clearance dynamics of CA19-9. It critically evaluates how these factors impact its role as a biomarker for prognosis, assessment of resectability, and real-time monitoring of therapeutic response and recurrence in patients with PDAC. We explore how the relatively short half-life and correlation with tumor burden make CA19-9 a dynamic tool for tracking disease progression and treatment efficacy, often providing insights that precede radiographic changes. This review concludes that, despite its limitations, CA19-9 remains an important, cost-effective, and widely accessible biomarker for the longitudinal management of patients with established pancreatic cancer. Its dynamic changes allow continuous real-time disease monitoring providing critical information for clinical decision-making. Full article

(This article belongs to the Special Issue Advances in Pancreatic Cancer: Exploring Biomarkers and Prognostic Predictors)

40 pages, 4012 KB

Open AccessReview

Soil Moisture Monitoring Method and Data Products: Current Research Status and Future Development Trends

by Ruihao Liu, Cun Chang, Ruisen Zhong and Shiyang Lu

Remote Sens. 2025, 17(24), 3945; https://doi.org/10.3390/rs17243945 - 5 Dec 2025

Abstract

Soil moisture (SM) is a key variable regulating land–atmosphere energy exchange, hydrological processes, and ecosystem functioning. Though important, there are still unresolved problems in accurate SM monitoring and the practical application and validation of existing methods. In this review, we integrate mechanistic classification [...] Read more.

Soil moisture (SM) is a key variable regulating land–atmosphere energy exchange, hydrological processes, and ecosystem functioning. Though important, there are still unresolved problems in accurate SM monitoring and the practical application and validation of existing methods. In this review, we integrate mechanistic classification and applicability and constraint discussions to develop a coherent understanding of current SM monitoring approaches. Within this framework, in situ measurements, optical and thermal infrared methods, active and passive microwave remote sensing (RS) techniques, and model-based simulations are compared, and publicly accessible SM dataset products are comparatively analyzed in terms of product characteristics and application limitations. Different from other published reviews, this study covers a large scope of SM monitoring methods varying from in situ observation to RS inversion, and classifies them based on their mechanisms, thereby constructing a complete comparative framework for SM research. Moreover, three types of open-access SM dataset products are investigated, optical and microwave RS products, model simulation and data fusion products, and reanalysis dataset products, and evaluated according to their resolution, depth, applicability, advantages, and limitations. By doing so, it is concluded that in situ observations remain essential for calibration and validation but are spatially limited. Optical and thermal infrared methods are restricted by atmospheric conditions and a shallow penetration depth, while microwave techniques exhibit varying performances under different vegetation and soil conditions. Existing datasets differ significantly in resolution, consistency, and coverage, making no single product universally applicable. Future research should focus on multi-source and spatiotemporal data fusions, the integration of machine learning with physical mechanisms, enhancement for cross-sensor consistency, the establishment of standardized uncertainty evaluation frameworks, and the refinement of high-order RTMs and parameterization. Full article

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20 pages, 7168 KB

Open AccessArticle

A Multi-Granularity Gated Image-Level Supervised Network (MGG-ISCNet) for Spike Counting in Agropyron cristatum (L.) Gaertn

by Lihua Guan, Ziyu Ding, Meng’an Song, Xinyuan He, Qiqi Wang, Ruopu Zan, Zhangru Gao, Xiang Li, Yan Zhao and Dongyan Zhang

Agronomy 2025, 15(12), 2805; https://doi.org/10.3390/agronomy15122805 - 5 Dec 2025

Abstract

Accurate counting of spikes in crested wheatgrass, an important forage resource, is essential for breeding and yield evaluation. However, traditional manual counting is inefficient, and instance-level supervised methods face challenges such as high annotation costs and counting errors caused by overlapping targets in [...] Read more.

Accurate counting of spikes in crested wheatgrass, an important forage resource, is essential for breeding and yield evaluation. However, traditional manual counting is inefficient, and instance-level supervised methods face challenges such as high annotation costs and counting errors caused by overlapping targets in complex field scenes. To address these issues, this study proposes the Multi-Granularity Gating Image-Level Supervision Count Network (MGG-ISCNet), a lightweight image-level supervised counting network. The network integrates multi-granularity features adaptively and employs a lightweight regression head with two 1D convolution layers and global average pooling for efficient feature compression, greatly reducing parameter complexity. Requiring only image-level count labels without positional annotations, the proposed approach substantially lowers labeling costs. On a self-constructed crested wheatgrass dataset, the MGG-ISCNet achieved an MAE of 2.73, RMSE of 3.86, and R² of 0.81. Furthermore, transfer experiments on the wheat spike dataset GWHD2020 demonstrated strong generalization. The proposed method achieved the best accuracy among both instance-level and image-level supervised approaches, with MAE = 3.63, RMSE = 4.73, and R² = 0.95, while featuring significantly fewer parameters (61.08 M) compared to the existing image-level method. Overall, this work provides an efficient and lightweight solution for spike counting in crested wheatgrass and other cereal crops, offering valuable support for breeding and forage production. Full article

(This article belongs to the Section Precision and Digital Agriculture)

20 pages, 316 KB

Open AccessArticle

Frequency of HLA Alleles in a Cohort of 100 Romanian Late-Life Adults: An Academic Insight into Genetic Longevity

by Radu-Alexandru Truică, Adriana Tălăngescu, Ion Mărunțelu, Alexandra-Elena Constantinescu and Ileana Constantinescu

Curr. Issues Mol. Biol. 2025, 47(12), 1018; https://doi.org/10.3390/cimb47121018 - 5 Dec 2025

Abstract

The human leukocyte antigen (HLA) system plays a crucial role in regulating the immune response and is significant in organ transplantation, disease association studies, and population genetics. But does it influence longevity? The present study aims to explore the frequency of HLA alleles [...] Read more.

The human leukocyte antigen (HLA) system plays a crucial role in regulating the immune response and is significant in organ transplantation, disease association studies, and population genetics. But does it influence longevity? The present study aims to explore the frequency of HLA alleles in a cohort of 100 individuals in the 65–90 age bracket from Romania, providing insights into genetic diversity and potential implications in longevity. High-resolution HLA typing was performed using next-generation sequencing (NGS) technology, allowing for precise identification of HLA alleles with a high degree of accu Full article

(This article belongs to the Section Molecular Medicine)

17 pages, 3818 KB

Open AccessArticle

Water and Soil Salinization Mechanism in the Arid Barkol Inland Basin in NW China

by Ziyue Wang, Chaoyao Zan, Yajing Zhao, Bo Xu, Rui Long, Xiaoyong Wang, Jun Zhang and Tianming Huang

Water 2025, 17(24), 3462; https://doi.org/10.3390/w17243462 - 5 Dec 2025

Abstract

Identifying the dominant mechanisms of water and soil salinization in arid and semi-arid endorheic basins is fundamental for our understanding of basin-scale water–salt balance and supports water resources management. In many inland basins, mineral dissolution, evaporation, and transpiration govern salinization, but disentangling these [...] Read more.

Identifying the dominant mechanisms of water and soil salinization in arid and semi-arid endorheic basins is fundamental for our understanding of basin-scale water–salt balance and supports water resources management. In many inland basins, mineral dissolution, evaporation, and transpiration govern salinization, but disentangling these processes remains difficult. Using the Barkol Basin in northwestern China as a representative endorheic system, we sampled waters and soils along a transect from the mountain front through alluvial fan springs and rivers to the terminal lake. We integrated δ¹⁸O–δ²H with hydrochemical analyses, employing deuterium excess (d-excess) to partition salinity sources and quantify contributions. The results showed that mineral dissolution predominated, contributing 65.8–81.8% of groundwater salinity in alluvial fan settings and ~99.7% in the terminal lake, whereas direct evapoconcentration was minor (springs and rivers ≤ 4%; lake ≤ 0.2%). Water chemistry types evolved from Ca-HCO₃ in mountainous runoff, to Ca·Na-HCO₃·SO₄ in groundwater and groundwater-fed rivers, and finally to Na-SO₄·Cl in the terminal lake. The soil profiles showed that groundwater flow and vadose-zone water–salt transport control spatial patterns: surface salinity rises from basin margins (<1 mg/g) to the lakeshore and is extremely high near the lake (23.85–244.77 mg/g). In spring discharge belts and downstream wetlands, the sustained evapotranspiration of groundwater-supported soil moisture drives surface salt accumulation, making lakeshores and wetlands into terminal sinks. The d-excess-based method can robustly separate the salinization processes despite its initial isotopic variability. Full article

(This article belongs to the Special Issue Isotope Hydrology: Tracing Water’s Journey and Water–Rock Interactions in a Changing World)

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26 pages, 3073 KB

Open AccessArticle

Energy-Saving Method for Nearby Wireless Battery-Powered Trackers Based on Their Cooperation

by Nerijus Morkevičius, Agnius Liutkevičius, Laura Kižauskienė, Audronė Janavičiūtė and Roman Banakh

Appl. Sci. 2025, 15(24), 12886; https://doi.org/10.3390/app152412886 - 5 Dec 2025

Abstract

The tracking of assets or cargo is one of the main objectives of global logistics and transportation systems, ensuring operational efficiency, security, and timeliness. Currently, battery-operated GPS (Global Positioning System)-based tracking devices are used for this purpose. The main shortcoming of these devices [...] Read more.

The tracking of assets or cargo is one of the main objectives of global logistics and transportation systems, ensuring operational efficiency, security, and timeliness. Currently, battery-operated GPS (Global Positioning System)-based tracking devices are used for this purpose. The main shortcoming of these devices is the lifetime of the batteries because they cannot be replaced or recharged, or because this is simply not economically feasible. Therefore, efficient methods are needed to prolong battery life as much as possible. Various existing energy-saving techniques can be applied to solve this problem. However, none of these consider situations in which multiple tracking devices are transported together and can cooperate to further increase their energy efficiency. In this study, we propose and evaluate the novel lightweight peer-to-peer energy-saving method for nearby wireless battery-powered trackers based on their cooperation. The proposed method is based on the short-range BLE (Bluetooth Low Energy) device discovery mechanism and the dynamic election of the leader tracker (with the highest battery capacity) to report the location of its own and other neighboring trackers to the central server. The experimental evaluation of the proposed method shows that, compared to the traditional approach, where each tracker sends its location individually, the proposed method allows a reduction in the average battery charge required for one position report from 19% to 240% per each cooperating tracker. The average energy consumption for one location report per node decreased from 4.68 mWh using the traditional approach to 3.93 mWh for 2 cooperating devices and 1.92 mWh for 15 cooperating devices. Full article

(This article belongs to the Special Issue Efficient Networking Architectures for Next-Generation Smart and Connected Systems)

14 pages, 278 KB

Open AccessArticle

Do Nurses Thrive in Their Organization? Validation of the Short Form of Nurses’ Organizational Health Questionnaire

by Alessandro Sili, Maddalena De Maria, Valerio Della Bella, Jacopo Fiorini and Claudio Barbaranelli

Nurs. Rep. 2025, 15(12), 432; https://doi.org/10.3390/nursrep15120432 - 5 Dec 2025

Abstract

Background/Aim: The quality of care provided to patients was closely related to the nursing staff’s well-being and their experience within the organization. This study aimed to evaluate the psychometric properties of the short form of the Nurses’ Organizational Health Questionnaire (QISO-SF), with a [...] Read more.

Background/Aim: The quality of care provided to patients was closely related to the nursing staff’s well-being and their experience within the organization. This study aimed to evaluate the psychometric properties of the short form of the Nurses’ Organizational Health Questionnaire (QISO-SF), with a focus on its relevance for assessing nurses’ organizational well-being in healthcare environments. The study examined the instrument’s structural validity and internal consistency. Methods: A secondary analysis was conducted using data from three cross-sectional studies, including 1279 nurses providing direct patient care across various Italian healthcare settings. Dimensionality of the QISO-SF was tested via confirmatory factor analysis (CFA), and reliability was assessed using ordinal omega coefficients (ω). Results: The QISO-SF comprises 48 items across 11 dimensions, grouped into 5 scales: Comfort, Organizational Context and Relational Processes, Workload, Positive and Negative Indicators, and Psychophysical Distress. The instrument demonstrated good structural validity (RMSEA = 0.048–0.094; CFI = 0.967–0.994) and satisfactory reliability (ω = 0.644–0.857). By maintaining the theoretical framework of the original questionnaire while reducing completion time, the short form is suitable for evaluating nurses’ work-related quality of life and organizational well-being. Conclusions: The QISO-SF is a concise, reliable, and valid tool to assess work-related quality of life and Organizational health in nursing professionals. Its use can support interventions aimed at promoting well-being in healthcare settings. Full article

(This article belongs to the Special Issue Health Questionnaires in Nursing)

15 pages, 634 KB

Open AccessArticle

Evaluation of Antimicrobial and Antibiofilm Activity of Eucalyptus urograndis (Clone I144) Pyroligneous Extract on Bovine Mastitis Isolate of Multiple-Drug-Resistant Staphylococcus aureus Strains

by Isadora Karoline de Melo, Caio Sergio Santos, Nilza Dutra Alves, Gustavo Lopes Araujo, Aline Maciel Clarindo, Alexandre Santos Pimenta, Denny Parente de Sá Barreto Maia Leite, Rinaldo Aparecido Mota and Francisco Marlon Carneiro Feijó

Microorganisms 2025, 13(12), 2771; https://doi.org/10.3390/microorganisms13122771 - 5 Dec 2025

Abstract

Milk is an important agricultural product and is consumed worldwide. However, the dairy sector faces a significant challenge due to bovine mastitis, a common disease that has a substantial impact on the dairy industry. In more severe cases, it leads to the culling [...] Read more.

Milk is an important agricultural product and is consumed worldwide. However, the dairy sector faces a significant challenge due to bovine mastitis, a common disease that has a substantial impact on the dairy industry. In more severe cases, it leads to the culling of chronically infected cows. Mastitis poses a risk due to the frequent use of antibiotics in treatment, which contributes to the spread of bacteria with antimicrobial resistance. The present study aimed to evaluate the antimicrobial and antibiofilm potential of a pyroligneous extract of Eucalyptus urograndis (clone I144) against multidrug-resistant Staphylococcus aureus, the causative agent of mastitis. Sensitivity profiles to various conventional antibiotics were assessed, including the minimum inhibitory concentration (MIC), the minimum bactericidal concentration (MBC), and biofilm inhibition, in ten Staphylococcus aureus strains using the crystal violet method. The results showed that the multidrug-resistant strains were sensitive to the pyroligneous extract of Eucalyptus urograndis (clone I144) at a concentration of 12.5% and exhibited antibiofilm activity starting at a concentration of 3.13%. In conclusion, our findings show that the pyroligneous extract of Eucalyptus urograndis (clone I144), at 12.5%, inhibited different multidrug-resistant S. aureus and MRSA strains isolated from bovine mastitis. These results indicate that the extract represents an effective preventive strategy against mastitis-causing pathogens that are difficult to treat, making it a promising alternative to reduce the dependence on synthetic antibiotics. In vivo studies are needed to confirm these findings and provide a basis for evidence-based clinical guidelines. Full article

(This article belongs to the Special Issue Advances in Veterinary Microbiology)

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31 pages, 5969 KB

Open AccessArticle

Assessing the Impact of Multi-Decadal Land Use Change on Agricultural Water–Energy Dynamics in the Awash Basin, Ethiopia: Insights from Remote Sensing and Hydrological Modeling

by Tewekel Melese Gemechu, Huifang Zhang, Jialong Sun and Baozhang Chen

Agronomy 2025, 15(12), 2804; https://doi.org/10.3390/agronomy15122804 - 5 Dec 2025

Abstract

Sustainable agriculture in semi-arid regions like the Awash Basin is critically dependent on water availability, which is increasingly threatened by rapid land use and land cover (LULC) change. This study assesses the impact of multi-decadal LULC changes on water resources essential for agriculture. [...] Read more.

Sustainable agriculture in semi-arid regions like the Awash Basin is critically dependent on water availability, which is increasingly threatened by rapid land use and land cover (LULC) change. This study assesses the impact of multi-decadal LULC changes on water resources essential for agriculture. Using satellite-derived LULC scenarios (2001, 2010, 2020) to drive the WRF-Hydro/Noah-MP modeling framework, we provide a holistic assessment of water dynamics in Ethiopia’s Awash Basin. The model was calibrated and validated with observed streamflow (R² = 0.80–0.89). Markov analysis revealed rapid cropland expansion and urbanization (2001–2010), followed by notable woodland recovery (2010–2020) linked to national initiatives. Simulations show that early-period changes increased surface runoff, potentially enhancing reservoir storage for large-scale irrigation. In contrast, later changes promoted subsurface flow, indicating a shift towards enhanced groundwater recharge, which is critical for small-scale and well-based irrigation. Evapotranspiration (ET) trends, validated against GLEAM (monthly R² = 0.88–0.96), reflected these shifts, with urbanization suppressing water fluxes and woodland recovery fostering their resurgence. This research demonstrates that land use trajectories directly alter the partitioning of agricultural water sources. The findings provide critical evidence for designing sustainable land and water management strategies that balance crop production with forest conservation to secure irrigation water and support initiatives like Ethiopia’s Green Legacy Initiative. Full article

(This article belongs to the Section Water Use and Irrigation)

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