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Recent Articles  

43 pages, 10806 KB  
Article
An MD View of Ligand Binding
by Adrian Calderon, Eric Harbinson, Rüdiger Ettrich, Natalia Kulik and Jannette Carey
Molecules 2025, 30(24), 4678; https://doi.org/10.3390/molecules30244678 (registering DOI) - 6 Dec 2025
Abstract
Protein–ligand complexes in crystal structures are well described by an array of bonding interactions among precisely defined functional groups. The present work examines how one representative complex behaves in one-microsecond molecular dynamics simulations, starting from a crystal structure with a native biological ligand [...] Read more.
Protein–ligand complexes in crystal structures are well described by an array of bonding interactions among precisely defined functional groups. The present work examines how one representative complex behaves in one-microsecond molecular dynamics simulations, starting from a crystal structure with a native biological ligand bound, and proceeding to simulations of structures derived by docking of that native ligand, and then to docking of selected ligand analogs. The MD behaviors and system energies calculated in RMSD plateau regions using MM/GBSA are similar when initiated from the crystal structure or the structure with the docked native ligand, although independent replicate simulations differ. Despite these similarities, interatomic contact frequencies indicate that some contacts observed in the crystal structure are rarely sampled again; others are sampled only intermittently; and new contacts are recruited that can be more persistent. Docked structures of non-native ligand analogs were chosen for simulation by screening manually for features consistent with known binding interactions, and these displayed behaviors similar to those for the native ligand and, in some cases, similar calculated energies. Overall, ligands appear to cooperate dynamically with the protein in forming the observed interactions. Full article
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10 pages, 1760 KB  
Article
Enhanced Detection of SARS-CoV-2 Using Platinum-Decorated Poly(2-vinylpyridine) Nanoparticle-Based Lateral Flow Immunoassay
by Yayoi Kimura, Yasushi Enomoto, Yasufumi Matsumura, Kazuo Horikawa, Hideaki Kato, Atsushi Goto, Kei Miyakawa and Akihide Ryo
Biomedicines 2025, 13(12), 2993; https://doi.org/10.3390/biomedicines13122993 (registering DOI) - 6 Dec 2025
Abstract
Background: Rapid and high-throughput diagnostic methods are essential for controlling the spread of infectious diseases, such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Lateral flow immunoassay (LFIA) strips provide a cost-effective and user-friendly platform for point-of-care testing. However, the sensitivity of conventional [...] Read more.
Background: Rapid and high-throughput diagnostic methods are essential for controlling the spread of infectious diseases, such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Lateral flow immunoassay (LFIA) strips provide a cost-effective and user-friendly platform for point-of-care testing. However, the sensitivity of conventional LFIA kits is often limited by the performance of their detection probes. This study reports a highly sensitive LFIA strip for detecting the SARS-CoV-2 nucleocapsid (NP) protein using platinum-decorated poly(2-vinylpyridine) nanoparticles (Pt-P2VPs) as probes. Methods: Monoclonal antibodies against SARS-CoV-2 NP were conjugated with Pt-P2VPs and incorporated into LFIA strips. The test line was coated with anti–SARS-CoV-2 NP monoclonal antibody, and the control line with goat anti-mouse IgG. Recombinant proteins, viral strains, and nasopharyngeal swab specimens from patients were used to evaluate assay performance, with reverse transcription polymerase chain reaction (RT-PCR) as the reference standard. Diagnostic accuracy was assessed using nonparametric statistical tests. Results: Pt-P2VP-based LFIA strips enabled sensitive detection of recombinant NP and inactivated SARS-CoV-2, with minimal cross-reactivity. In 200 clinical specimens (100 PCR-negative and 100 PCR-positive), the assay achieved 74% sensitivity and 100% specificity, with strong correlation to viral RNA load. Compared with conventional LFIA kits, Pt-P2VP strips demonstrated superior sensitivity at lower viral loads. Conclusions: Pt-P2VPs represent a promising probe material for enhancing LFIA performance and may facilitate the development of rapid, sensitive, and scalable immunoassays for infectious disease diagnostics in biomedical applications. Full article
(This article belongs to the Section Microbiology in Human Health and Disease)
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22 pages, 8713 KB  
Article
The Characteristics of the South Yellow Sea Foreland Basin and Its Tectonic Evolution
by Hong Xu, Yazeng Ma, Guangyou Zhu, Dapeng Su, Baohua Lei, Guoqing Zhang, Bolin Zhang, Weiwei Zhang and Wei Yan
J. Mar. Sci. Eng. 2025, 13(12), 2314; https://doi.org/10.3390/jmse13122314 (registering DOI) - 6 Dec 2025
Abstract
The first oil and gas well in the South Yellow Sea Basin was completed in 1961. In 1984, 2.45 tons of light oil were obtained from the Cenozoic strata. However, it remains the only large oil and gas basin in China’s offshore area [...] Read more.
The first oil and gas well in the South Yellow Sea Basin was completed in 1961. In 1984, 2.45 tons of light oil were obtained from the Cenozoic strata. However, it remains the only large oil and gas basin in China’s offshore area without industrial oil and gas discoveries. Although the consensus is that the South Yellow Sea Basin is a foreland basin, and the oil and gas exploration prospects are promising, the research on the regional structure and the tectonic evolution of the foreland basin system is weak, which seriously hinders the process of industrial oil and gas discoveries. This paper reports the results of over 30 years of onshore and offshore investigations and well-seismic joint interpretation in the study area: for the first time, the mountains and basins formed by the collision of the North China and Yangtze plates were discovered in the geological survey of the northern islands of the South Yellow Sea Basin; the C-type eclogite chronology of Qianliyan Island, the characteristics of the foreland basins and intracontinental foreland basins around the South Yellow Sea, and the tectonic evolution characteristics and models of the basins were clarified. Through the zircon/phosphate fission track analysis of the deep black Jurassic strata in the Qianyuan S-2 well, it was revealed that the collision and subduction of the Pacific Plate against the Eurasian Plate since the Late Cretaceous–Paleogene led to large-scale uplift movements, and more than 3000 m of strata were eroded in the basin area. This is consistent with the multiple unconformities of E/N, K/N, and T2/N identified by well-seismic joint interpretation, and is also the main reason why oil and gas have been difficult to preserve in the South Yellow Sea Basin since the Middle Triassic–Jurassic. Deep prototype oil and gas exploration in the basin may be the preferred option for current oil and gas exploration deployment, which is conducive to achieving industrial oil and gas discoveries. Full article
(This article belongs to the Section Geological Oceanography)
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22 pages, 12089 KB  
Article
A Brain–Computer Interface for Control of a Virtual Prosthetic Hand
by Ángel del Rosario Zárate-Ruiz, Manuel Arias-Montiel and Christian Eduardo Millán-Hernández
Computation 2025, 13(12), 287; https://doi.org/10.3390/computation13120287 (registering DOI) - 6 Dec 2025
Abstract
Brain–computer interfaces (BCIs) have emerged as an option that allows better communication between humans and some technological devices. This article presents a BCI based on the steady-state visual evoked potentials (SSVEP) paradigm and low-cost hardware to control a virtual prototype of a robotic [...] Read more.
Brain–computer interfaces (BCIs) have emerged as an option that allows better communication between humans and some technological devices. This article presents a BCI based on the steady-state visual evoked potentials (SSVEP) paradigm and low-cost hardware to control a virtual prototype of a robotic hand. A LED-based device is proposed as a visual stimulator, and the Open BCI Ultracortex Biosensing Headset is used to acquire the electroencephalographic (EEG) signals for the BCI. The processing and classification of the obtained signals are described. Classifiers based on artificial neural networks (ANNs) and support vector machines (SVMs) are compared, demonstrating that the classifiers based on SVM have superior performance to those based on ANN. The classified EEG signals are used to implement different movements in a virtual prosthetic hand using a co-simulation approach, showing the feasibility of BCI being implemented in the control of robotic hands. Full article
(This article belongs to the Special Issue Kinematics, Dynamics and Control for Rehabilitation Robotics and Prostheses)
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20 pages, 3959 KB  
Article
Pulsed Electric Field Inactivation of Acetobacter aceti: Mechanisms and Efficacy Elucidation
by Yongxin Teng, Boru Chen, Runyu Yao, Langhong Wang, Zhong Han and Xin-An Zeng
Foods 2025, 14(24), 4188; https://doi.org/10.3390/foods14244188 (registering DOI) - 6 Dec 2025
Abstract
The spoilage bacterium Acetobacter aceti poses a major threat to wine quality by causing acetification, driving the need for effective control methods. This study investigated the inactivation of A. aceti using pulsed electric field (PEF) and elucidated the multi-target mechanisms. The results demonstrated [...] Read more.
The spoilage bacterium Acetobacter aceti poses a major threat to wine quality by causing acetification, driving the need for effective control methods. This study investigated the inactivation of A. aceti using pulsed electric field (PEF) and elucidated the multi-target mechanisms. The results demonstrated that PEF efficacy was highly dependent on the electric field intensity. PEF treatment at 30 kV/cm achieved a >3-log reduction in viable cell counts, with a Weibull model analysis indicating a critical inactivation threshold of 21.64 kV/cm. Mechanistic investigations revealed that PEF induced irreversible damage to the cell membrane, evidenced by morphological rupture (SEM) and a 4-fold increased permeability (flow cytometry), which led to a massive leakage of intracellular contents. Critically, this physical damage was correlated with profound physiological disruption, including the inactivation of key spoilage enzymes alcohol dehydrogenase (ADH, 80.0% relative activity loss) and aldehyde dehydrogenase (ALDH, 93.1% relative activity loss). Furthermore, PEF induced severe oxidative stress (4.25-fold increase in ROS and 3.30-fold increase in MDA) and a collapse in energy metabolism. Collectively, these findings reveal a synergistic inactivation mechanism, which establishes a strong theoretical foundation for the potential development of PEF as a non-thermal strategy to control acetic spoilage in winemaking. Full article
(This article belongs to the Special Issue Food Preservation and Safety: Innovative Applications of Physical, Chemical, and Biological Methods and Their Interactions with Foodborne Microorganisms)
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32 pages, 1525 KB  
Review
Energy Efficiency Strategies in Latin American University Buildings: A Critical Review of Simulation Models, Technologies, and Implementation Pathways for Highland Climates
by Luis Contreras-Vásquez, Rubén Nogales-Portero, Jorge Guevara-Robalino, José Cabrera-Escobar and Alberto Ríos-Villacorta
Energies 2025, 18(24), 6391; https://doi.org/10.3390/en18246391 (registering DOI) - 6 Dec 2025
Abstract
This systematic review analyzed energy efficiency strategies in Latin American university buildings, with emphasis on highland climates. Following PRISMA guidelines, 225 documents were screened from Scopus, Web of Science, and Google Scholar, yielding 36 studies published between 2015 and 2025. Reported interventions achieved [...] Read more.
This systematic review analyzed energy efficiency strategies in Latin American university buildings, with emphasis on highland climates. Following PRISMA guidelines, 225 documents were screened from Scopus, Web of Science, and Google Scholar, yielding 36 studies published between 2015 and 2025. Reported interventions achieved 10–40% energy savings (median 18.5%), annual cost savings of USD 5672–USD 218,426 per building, with substantial variation reflecting differences in building size, intervention scope, and technology selection and carbon mitigation of 79–497 tons CO2e annually. Common measures included LED retrofits, building automation, and solar photovoltaics, while integrated approaches reached up to 60% savings but required longer payback periods. Only six studies validated simulations with field data, and six addressed highland climates, limiting regional applicability. Free modeling tools such as EnergyPlus and OpenStudio increased accessibility but faced adoption barriers due to steep learning curves and scarce documentation in Spanish and Portuguese. Key barriers included inadequate metering (53%), limited funding (61%), and policy gaps (53%), while enablers involved ISO 50001 adoption and strong institutional leadership. Overall, evidence remains fragmented, highlighting the need for integrated frameworks linking validated models, technology, governance, and regional collaboration. Full article
(This article belongs to the Special Issue Smart Optimization and Renewable Integrated Energy System)
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18 pages, 712 KB  
Article
Can Anti-Racist Civic Engagement Be Dialogic? A Dialogic Analysis of Decolonial Discourse in Belgian Higher Education
by Hari Prasad Sacré
Genealogy 2025, 9(4), 147; https://doi.org/10.3390/genealogy9040147 (registering DOI) - 6 Dec 2025
Abstract
Universities have become central arenas in which the terms of racial justice are negotiated, contested, and at times sanctioned. This article examines how decolonial discourse in Belgian higher education navigates the tension between dialogic and authoritative discourse. Decolonial discourse in Belgium tackles racial [...] Read more.
Universities have become central arenas in which the terms of racial justice are negotiated, contested, and at times sanctioned. This article examines how decolonial discourse in Belgian higher education navigates the tension between dialogic and authoritative discourse. Decolonial discourse in Belgium tackles racial illiteracy or the lack of institutional capacity to engage with the histories and contemporary realities of race. The study draws on a qualitative analysis of thirteen publicly available documents, including open letters, manifestos, and institutional responses produced between 2017 and 2021, with a dialogic analysis of five key texts within the Ghent University Association. Using Bakhtin’s framework of dialogic and authoritative discourse, operationalised through Matusov and von Duyke’s concept of internally persuasive discourse (IPD), the analysis identifies three modes of responding to racial illiteracy: appeals to personal conviction (IPD1), the formulation of new institutional norms (IPD2), and dialogic inquiry that treats illiteracy as a shared pedagogical problem (IPD3). The findings show that while decolonial movements expose the structural and epistemic conditions that sustain racial illiteracy, institutional responses from students, staff, and governing bodies often address these critiques by enforcing ‘decolonial’ personal convictions and institutional norms, risking the reproduction of the very illiteracy they seek to remedy. The article concludes that decolonial transformation requires cultivating dialogic practices that position racial illiteracy as a collective site of learning within the university’s civic mission. Full article
(This article belongs to the Special Issue Multiraciality and Social Mobility: Challenges Faced by Racialised Citizenship Across Europe)
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26 pages, 521 KB  
Article
The Crisis and Turning Point of Cultivation Deviations in Daoist Neidan: A Study on the Phenomenon of Zouhuo Rumo (走火入魔) and Its Contemporary Therapeutic Implications
by Ruoyi Wang and Changchun Ding
Religions 2025, 16(12), 1537; https://doi.org/10.3390/rel16121537 (registering DOI) - 6 Dec 2025
Abstract
Current research on Daoist neidan (內丹, Internal Alchemy) has primarily focused on its philosophical frameworks, practical methods, and therapeutic benefits; however, systematic inquiry into the mechanisms of failure during practice remains limited. This study investigates the long-neglected yet pivotal phenomenon of zouhuo rumo [...] Read more.
Current research on Daoist neidan (內丹, Internal Alchemy) has primarily focused on its philosophical frameworks, practical methods, and therapeutic benefits; however, systematic inquiry into the mechanisms of failure during practice remains limited. This study investigates the long-neglected yet pivotal phenomenon of zouhuo rumo (走火入魔, fire deviation and entry into demonic states) within Daoist cultivation, especially as it emerges in the context of dual cultivation of xing and ming (性命雙修). Through textual and hermeneutical analysis, this study traces the historical evolution, semantic transformation, and causal structure of the term, revealing its dual function as both a technical deviation and a religious warning. Findings indicate that zouhuo rumo arises from the interplay of impure self-refinement, loss of mental focus, improper fire phases (火候), and illusory disturbances, reflecting a profound psychosomatic imbalance rooted in the practitioner’s mind-nature (心性). Daoism interprets this state as mokao (魔考, demonic trials in Daoist cultivation), a transformative mechanism designed to refine inner alignment. On this basis, this study proposes a three-stage healing pathway—Spirit Preservation and Breath Stabilization (存神定息), Inner Vision and Self-Reflection (內觀返照), and Transformation of Form and Refinement of Essence (化形改質)—and constructs a Daoist cultural healing model that integrates moral cultivation, breath regulation, and introspection. This model provides a non-pathologizing cultural framework for enhancing psychological resilience, reconstructing meaning, and addressing contemporary spiritual and psychological crises. Full article
(This article belongs to the Special Issue Daoist Inner Alchemy Atlas: Practice and Related Medicine, Thunder Rites and Iconology)
36 pages, 777 KB  
Article
Integrated Artificial Intelligence Framework for Tuberculosis Treatment Abandonment Prediction: A Multi-Paradigm Approach
by Frederico Guilherme Santana Da Silva Filho, Igor Wenner Silva Falcão, Tobias Moraes de Souza, Saul Rassy Carneiro, Marcos César da Rocha Seruffo and Diego Lisboa Cardoso
J. Clin. Med. 2025, 14(24), 8646; https://doi.org/10.3390/jcm14248646 (registering DOI) - 6 Dec 2025
Abstract
Background/Objectives: Treatment adherence challenges affect 10–20% of tuberculosis patients globally, contributing to drug resistance and continued transmission. While artificial intelligence approaches show promise for identifying patients who may benefit from additional treatment support, most models lack the interpretability necessary for clinical implementation. We [...] Read more.
Background/Objectives: Treatment adherence challenges affect 10–20% of tuberculosis patients globally, contributing to drug resistance and continued transmission. While artificial intelligence approaches show promise for identifying patients who may benefit from additional treatment support, most models lack the interpretability necessary for clinical implementation. We aimed to develop and validate an integrated artificial intelligence framework combining traditional machine learning (interpretable algorithms like logistic regression and decision trees), explainable AI (methods showing which patient characteristics influence predictions), deep reinforcement learning (algorithms learning optimal intervention strategies), and natural language processing (clinical text analysis) to identify tuberculosis patients who would benefit from enhanced treatment support services. Methods: We analyzed 103,846 pulmonary tuberculosis cases from São Paulo state surveillance data (2006–2016). We evaluated models using precision (accuracy of positive predictions), recall (ability to identify all patients requiring support), F1-score (balanced performance measure), and AUC-ROC (overall discrimination ability) while maintaining interpretability scores above 0.90 for clinical transparency. Results: Our integrated framework demonstrated that explainable AI matched traditional machine learning performance (both F1-score: 0.77) while maintaining maximum interpretability (score: 0.95). The combined ensemble delivered superior results (F1-score: 0.82, 95% CI: 0.79–0.85), representing a 6.5% improvement over individual approaches (p < 0.001). Key predictors included substance use disorders, HIV co-infection, and treatment supervision factors rather than demographic characteristics. Conclusions: This multi-paradigm AI system provides a methodologically sound foundation for identifying tuberculosis patients who would benefit from enhanced treatment support services. The approach delivers excellent predictive accuracy while preserving full clinical transparency, demonstrating that the accuracy–interpretability trade-off in medical AI can be resolved through the systematic integration of complementary methodologies. Full article
(This article belongs to the Section Infectious Diseases)
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45 pages, 3087 KB  
Review
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 - 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  
Review
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 - 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  
Article
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 - 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  
Article
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 - 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  
Article
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 - 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  
Article
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 - 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  
Article
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 - 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  
Article
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 - 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  
Article
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 - 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  
Systematic 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 - 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  
Article
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 - 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  
Article
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 - 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  
Article
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 - 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  
Review
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  
Article
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 - 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  
Article
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  
Article
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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