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Owner-led self-medication for companion animals is a growing global practice; however, empirical data from Japan remain limited. Framing medication safety within a One Health perspective, this study aimed to characterize Japanese pet owners’ use of over-the-counter (OTC) drugs and identify possibilities for pharmacists to support rational self-medication. A cross-sectional 13-item online survey was administered to 500 owners in Japan between 30 May and 2 June 2025. Data on owner demographics, willingness to consult pharmacists, veterinary visit behavior, and OTC purchasing practices were summarized. Many owners were receptive to pharmacy support; 65% wished to consult a pharmacist, and 6.8% had already done so. Overall, 15.2% reported using OTCs drugs, primarily for treatment or prevention and prioritized perceived effectiveness and safety when selecting products. Some owners managed mild pet illnesses at home, citing perceived mildness and cost as reasons for not visiting a veterinary clinic. There is an unmet demand for accessible expert counseling at the point of purchase. Leveraging community pharmacies linked with pet specialty pharmacies as first-contact hubs could promote appropriate self-medication; doing so would require veterinary-specific training, establishing a formal credential for veterinary pharmacists, and defining pharmacist–veterinarian communication to ensure safe and effective use. Full article

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14 pages, 5360 KB

Open AccessArticle

Efficient Utilization Method of Motorway Lanes Based on YOLO-LSTM Model

by Xing Tong, Anxiang Huang, Yunxiao Pan, Yiwen Chen, Meng Zhou, Mengfei Liu and Yaohua Hu

Sensors 2025, 25(21), 6699; https://doi.org/10.3390/s25216699 (registering DOI) - 2 Nov 2025

Abstract

With the development of cities, traffic congestion has become a common problem, which seriously affects the efficiency of motorway transport. This study proposed an improved ML-YOLO video data extraction model based on You Only Look Once (YOLOv8n) combined with the Deep Simple Online and real-time tracking (DeepSORT) algorithm, to classify the obtained Traffic Performance Index (TPI) into different congestion levels by extracting traffic flow parameters in real-time and combining with the K-means clustering algorithm. The Long Short-Term Memory Dropout (LSTM-Dropout) model and the emergency lane opening model were used to implement the road congestion warning successfully. The practicality and stability of the model were also verified by calculating the relative error between the predicted traffic flow parameters and the extracted parameters through the LSTM time series model. According to the model results, emergency lanes are opened when the motorway traffic TPI exceeds 0.17 and closed when below 0.17. This study provided a reasonable theoretical basis for motorway traffic managers to decide whether or not to open the emergency lane, effectively relieved motorway road congestion, improved efficiency of road traffic, and had important practical value and significance in reality. Full article

(This article belongs to the Section Vehicular Sensing)

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17 pages, 2127 KB

Open AccessArticle

Leveraging Large Language Models for Real-Time UAV Control

by Kheireddine Choutri, Samiha Fadloun, Ayoub Khettabi, Mohand Lagha, Souham Meshoul and Raouf Fareh

Electronics 2025, 14(21), 4312; https://doi.org/10.3390/electronics14214312 (registering DOI) - 2 Nov 2025

Abstract

As drones become increasingly integrated into civilian and industrial domains, the demand for natural and accessible control interfaces continues to grow. Conventional manual controllers require technical expertise and impose cognitive overhead, limiting their usability in dynamic and time-critical scenarios. To address these limitations, this paper presents a multilingual voice-driven control framework for quadrotor drones, enabling real-time operation in both English and Arabic. The proposed architecture combines offline Speech-to-Text (STT) processing with large language models (LLMs) to interpret spoken commands and translate them into executable control code. Specifically, Vosk is employed for bilingual STT, while Google Gemini provides semantic disambiguation, contextual inference, and code generation. The system is designed for continuous, low-latency operation within an edge–cloud hybrid configuration, offering an intuitive and robust human–drone interface. While speech recognition and safety validation are processed entirely offline, high-level reasoning and code generation currently rely on cloud-based LLM inference. Experimental evaluation demonstrates an average speech recognition accuracy of 95% and end-to-end command execution latency between 300 and 500 ms, validating the feasibility of reliable, multilingual, voice-based UAV control. This research advances multimodal human–robot interaction by showcasing the integration of offline speech recognition and LLMs for adaptive, safe, and scalable aerial autonomy. Full article

(This article belongs to the Special Issue Innovations in NLP and Large Language Models: Shaping the Future of AI)

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27 pages, 5041 KB

Open AccessArticle

Transformative Art History, Empowering Geometry: STEAM-H Education and Critical–Visual Maker Culture Towards Sustainable Futures

by Elisa-Isabel Chaves-Guerrero and Silvia-Natividad Moral-Sánchez

Educ. Sci. 2025, 15(11), 1458; https://doi.org/10.3390/educsci15111458 (registering DOI) - 2 Nov 2025

Abstract

What if future teachers could learn to read the world like art historians, reason about it like mathematicians, and engage with it as sustainable change-makers? Through the lens of STEAM-H, this study examines their potential to become transformative educators fostering critical thinking and spatial–geometric competencies. The aim is to analyse how future teachers demonstrate Critical Spatial Literacy (CSL) skills—such as spatial literacy, critical thinking, and onto-semiotic dimensions—when carrying out hermeneutic readings of works of art and constructing models from AI-generated images within the framework of Critical–Visual Maker (CVM) Culture. This qualitative-descriptive study examines evidence from students’ analyses of pairs of classical and contemporary artworks, as well as models linked to the Sustainable Development Goals (SDGs), applying CSL categories in both cases. The findings reveal a transition from formal descriptions in mathematics and art history to more complex critical interpretations. Furthermore, the interrelationship among the three groups of categories proposed in the theoretical framework becomes evident. The study concludes that, by engaging in reflective and critical questioning, the interaction between STEAM-H, CSL, and CVM Culture can constitute an effective educational ecosystem for fostering geometric creativity, critical spatial literacy, and interdisciplinarity, thereby contributing to the development of a critical and egalitarian citizenship committed to global challenges and sustainable futures. Full article

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28 pages, 1343 KB

Open AccessArticle

Understanding Reverse Mortgage Acceptance in Spain with Explainable Machine Learning and Importance–Performance Map Analysis

by Jorge de Andrés-Sánchez and Laura González-Vila Puchades

Risks 2025, 13(11), 212; https://doi.org/10.3390/risks13110212 (registering DOI) - 2 Nov 2025

Abstract

In developed countries such as Spain, where the population is increasingly aging, retirement planning and longevity risk represent major societal challenges. In Spain, in particular, a significant proportion of household wealth is concentrated in real estate, primarily in the form of owner-occupied housing. For this reason, one emerging financial product in the retirement savings space is the reverse mortgage (RM). This study examines the determinants of acceptance of this financial product using survey data collected from Spanish individuals. The intention to take out an RM is explained through performance expectancy (PE), effort expectancy (EE), social influence (SI), bequest motive (BM), financial literacy (FL), and risk (RK). The analysis applies machine learning techniques: decision tree regression is used to visualize variable interactions that lead to acceptance; random forest to improve predictive capability; and Shapley Additive Explanations (SHAP) to estimate the relative importance of predictors. Finally, Importance–Performance Map Analysis (IPMA) is employed to identify the variables that merit greater attention in the acceptance of RMs. SHAP values indicate that PE and SI are the most influential predictors of intention to use RMs, followed by BM and EE with moderate importance, whereas the positive influence of RK and FL is more reduced. The IPMA highlights PE and SI as the most strategic drivers, and RK and BM act as relevant barriers to the widespread adoption of RMs. Full article

(This article belongs to the Special Issue Innovations in Annuities and Longevity Risk Management)

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17 pages, 8973 KB

Open AccessArticle

Experimental Research on Mechanical Behaviour of Precast Concrete Shear Walls with Horizontal Joint Quality Defects

by Mingjin Chu, Zhiqiang Zhang, Jiliang Liu, Shengtao Wu and Chao Dong

Buildings 2025, 15(21), 3951; https://doi.org/10.3390/buildings15213951 (registering DOI) - 2 Nov 2025

Abstract

In precast concrete shear wall structures, the joints formed during the vertical connection of precast units are referred to as the “horizontal joint”. Serving as vertical connection nodes in this structure system, the construction quality of theses horizontal joints significantly influences the structural integrity. To investigate the influence of horizontal joint quality defects on the mechanical behaviour of precast concrete shear walls, three precast concrete shear wall specimens with quality defects in different regions and three control specimens were designed. Quasi-static tests under a constant axial load were conducted to investigate the effects of defect area, location and other factors on the mechanical behaviour of the walls. Results demonstrate that the quality defects in horizontal joints significantly affect the mechanical behaviour of precast concrete shear walls. When the ratio of the quality defect area to the cross-sectional area of the boundary member reaches 100%, the yield load and peak load of the precast concrete shear wall decrease by 13% and 20%, respectively. Additionally, the structural stiffness exhibited a 13% degradation at a drift angle of 1/1000. Although the failure mode remains largely unchanged, yielding of longitudinal reinforcement in the boundary members is observed. Moreover, as the proportion of the quality defect area to the cross-sectional area decreases, its adverse effects on the mechanical behaviour of the precast concrete shear wall gradually diminish. The established numerical analysis model is shown to be reasonable and reliable. When the defective area of the horizontal joints is less than 25% of the total cross-sectional area, the quality defects essentially have no influence on the mechanical behaviour of the precast concrete shear walls. Full article

(This article belongs to the Special Issue Development and Application of Concrete Materials and Related Building Materials)

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25 pages, 3759 KB

Open AccessArticle

Mechanical Analysis and Prototype Testing of Prestressed Rock Anchors

by Xianzhi Xiao, Risheng Zhu, Zhi Huang, Fengying Xiao, Huajie Yin, Tengfei Zhao and Mojia Huang

Buildings 2025, 15(21), 3952; https://doi.org/10.3390/buildings15213952 (registering DOI) - 2 Nov 2025

Abstract

This study primarily investigates the mechanical performance of prestressed anchor foundations. Based on the assumptions of continuity, homogeneity, and isotropy of the anchor foundation and anchoring materials, a simplified elastic analysis model was developed. Using the superposition principle, the working stresses under vertical loads and bending moments were calculated, allowing for the determination of the maximum working stresses within the anchors and the foundation. Additionally, the distribution of bond strength of the prestressed tendons was analyzed, and the concept of effective anchorage length was introduced. The reliability of the model was validated through prototype testing, with the measured free segment strain values showing a high degree of consistency with theoretical calculations, with errors within 6.5%. Empirical data on ultimate bearing capacity and bond characteristics were also obtained. By integrating numerical calculations with experimental results, the performance of the anchoring system under extreme and specialized loading conditions was analyzed. The experimental results indicated that the failure modes of all anchor foundations were characterized by bond failure at the interface between the anchor and the surrounding rock mass. Based on the experimental data, a reasonable anchorage length satisfying design strength requirements was proposed. The findings provide a theoretical foundation and practical guidance for the design and application of prestressed anchor foundations in structures such as wind turbine towers. Full article

(This article belongs to the Section Building Structures)

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23 pages, 1737 KB

Open AccessArticle

Arc Flow Formulation for Efficient Uniform Parallel Machine Scheduling

by Khaled Bamatraf and Anis Gharbi

Symmetry 2025, 17(11), 1839; https://doi.org/10.3390/sym17111839 (registering DOI) - 2 Nov 2025

Abstract

This paper considers the scheduling problem of uniform parallel machines. The objective is to minimize the makespan. This problem holds practical significance and is inherently NP-hard. Therefore, solutions of the exact formulation are limited to small-sized instances. As the problem size increases, the exact formulation struggles to find optimal solutions within a reasonable time. To address this challenge, an arc flow formulation is proposed, aiming to solve larger instances. The arc flow formulation creates a pseudo-polynomial number of variables, with its size being significantly influenced by the problem’s bounds. Therefore, bounds from the literature are utilized, and symmetry-breaking rules are applied to reduce the size of the arc flow graph. To test the effectiveness of the proposed arc flow formulation, it was compared with a mathematical formulation from the literature on small instances with up to 30 jobs. Computational results showed that the arc flow formulation outperforms the mathematical formulation from the literature, solving all cases within a few seconds. Additionally, on larger benchmark instances, the arc flow formulation solved 84.27% of the cases to optimality. The maximum optimality gap does not exceed 0.072% for the instances not solved to optimality. Full article

(This article belongs to the Special Issue Symmetry/Asymmetry in Operations Research)

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28 pages, 9225 KB

Open AccessArticle

Cost-Factor Recognition and Recommendation in Open-Pit Coal Mining via BERT-BiLSTM-CRF and Knowledge Graphs

by Jiayi Sun, Pingfeng Li, Weiming Guan, Xuejiao Cui, Haosen Wang and Shoudong Xie

Symmetry 2025, 17(11), 1834; https://doi.org/10.3390/sym17111834 (registering DOI) - 2 Nov 2025

Abstract

Complex associations among production cost factors, multi-source cost information silos, and opaque transmission mechanisms of hidden costs in open-pit coal mining were addressed. The production process—including drilling, blasting, excavation, transportation, and dumping—was taken as the application context. A corpus of 103 open-pit coal mining standards and related research documents was constructed. Eleven entity types and twelve relationship types were defined. Dynamic word vectors were obtained through transformer (BERT) pre-training. The optimal entity tag sequence was labeled using a bidirectional long short-term memory–conditional random field (BiLSTM–CRF) 9 model. A total of 3995 entities and 6035 relationships were identified, forming a symmetry-aware knowledge graph for open-pit coal mining costs based on the BERT–BiLSTM–CRF model. The results showed that, among nine entity types, including Parameters, the F1-scores all exceeded 60%, indicating more accurate entity recognition compared to conventional methods. Knowledge embedding was performed using the TransH inference algorithm, which outperformed traditional models in all reasoning metrics, with a Hits@10 of 0.636. This verifies its strong capability in capturing complex causal paths among cost factors, making it suitable for practical cost optimization. On this basis, a symmetry-aware BERT–BiLSTM–CRF knowledge graph of open-pit coal mining costs was constructed. Knowledge embedding was then performed with the TransH inference algorithm, and latent relationships among cost factors were mined. Finally, a knowledge-graph-based cost factor identification system was developed. The system lists, for each cost item, the influencing factors and their importance ranking, analyzes variations in relevant factors, and provides decision support. Full article

(This article belongs to the Section Computer)

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17 pages, 448 KB

Open AccessArticle

Migration, Corruption, and Economic Drivers: Institutional Insights from the Balkan Route

by Bojan Baškot, Ognjen Erić, Dalibor Tomaš and Bogdan Ubiparipović

World 2025, 6(4), 147; https://doi.org/10.3390/world6040147 (registering DOI) - 1 Nov 2025

Abstract

This study investigates factors influencing migrants’ decisions to enter Europe via Bulgaria or Greece along the Balkan route, using logistic regression and machine learning models on data from the International Organization for Migration (IOM) Flow Monitoring Survey (August 2022–June 2025,

n = 5536

[...] Read more.

n = 5536

). We examine demographic variables (age), push factors (economic reasons, war/conflict, personal violence, limited access to services, and avoiding military service), and governance clusters derived from the World Bank’s Worldwide Governance Indicators (WGIs). An adapted migration gravity model incorporates corruption control as a key push–pull factor. Key findings indicate that younger migrants are significantly more likely to choose Bulgaria (

β \approx - 0.021

p < 0.001

), and governance clusters show that migrants from high-corruption origins (e.g., Syria and Afghanistan) prefer Bulgaria, likely due to governance similarities and facilitation costs. The Cluster Model achieves a slight improvement in fit (McFadden’s

R^{2} = 0.008

, AIC = 7367) compared to the Base (AIC = 7374) and Interaction (AIC = 7391) models. Machine learning extensions using LASSO and Random Forests on a subset of data (

n = 4429

) yield similar moderate performance (AUC: LASSO = 0.524, RF = 0.515). These insights highlight corruption’s role in route selection, offering policy recommendations for origin, transit, and destination phases. Full article

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61 pages, 15525 KB

Open AccessReview

Transesterification/Esterification Reaction Catalysed by Functional Hybrid MOFs for Efficient Biodiesel Production

by Luis P. Amador-Gómez, Delia Hernández-Romero, José M. Rivera-Villanueva, Sharon Rosete-Luna, Carlos A. Cruz-Cruz, Enrique Méndez-Bolaina, Elena de la C. Herrera-Cogco, Rafael Melo-González, Agileo Hernández-Gordillo and Raúl Colorado-Peralta

Reactions 2025, 6(4), 58; https://doi.org/10.3390/reactions6040058 (registering DOI) - 1 Nov 2025

Abstract

Biodiesel is an alternative, sustainable, renewable, and environmentally friendly energy source, which has generated interest from the scientific community due to its low toxicity, rapid biodegradability, and zero carbon footprint. Biodiesel is a biofuel produced by the transesterification of triglycerides or the esterification of free fatty acids (FFA). Both reactions require catalysts with numerous active sites (basic, acidic, bifunctional, or enzymatic) for efficient biodiesel production. On the other hand, since the late 1990s, metal–organic frameworks (MOFs) have emerged as a new class of porous materials and have been successfully used in various fields due to their multiple properties. For this reason, MOFs have been used as heterogeneous catalysts or as a platform for designing active sites, thus improving stability and reusability. This literature review presents a comprehensive analysis of using MOFs as heterogeneous catalysts or supports for biodiesel production. The optimal parameters for transesterification/esterification are detailed, such as the alcohol/feedstock molar ratio, catalyst amount, reaction time and temperature, conversion percentage, biodiesel yield, fatty acid and water content, etc. Additionally, novel methodologies such as ultrasound and microwave irradiation for obtaining MOF-based catalysts are described. It is important to note that most studies have shown biodiesel yields >90% and multiple reuse cycles with minimal activity loss. The bibliographic analysis was conducted using the American Chemical Society (ACS) Scifinder^® database, the Elsevier B.V. Scopus^® database, and the Clarivate Analytics Web of Science^® database, under the institutional license of the Universidad Veracruzana. Keywords were searched for each section, generally limiting the document type to “reviews” and “journals,” and the language to English, and published between 2000 and 2025. Full article

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18 pages, 4845 KB

Open AccessFeature PaperArticle

A Complexity-Aware Course–Speed Model Integrating Traffic Complexity Index for Nonlinear Crossing Waters

by Eui-Jong Lee, Hyun-Suk Kim and Yongung Yu

J. Mar. Sci. Eng. 2025, 13(11), 2086; https://doi.org/10.3390/jmse13112086 (registering DOI) - 1 Nov 2025

Abstract

We propose a complexity-aware extension of the Course–Speed (CS) model that integrates an AIS-derived Traffic Complexity Index (TCI) based on change in speed (ΔV) and course (Δθ) to quantify maneuvering complexity in nonlinear crossing waters. The framework consists of: (i) data preprocessing and gating to ensure navigationally valid AIS samples; (ii) CS index computation using distribution-aware statistics; (iii) TCI estimation from variability in speed and course along intersecting flows; and (iv) an integrated CS–TCI for interpretable mapping and ranking. Using one year of AIS data from a high-density crossing area near the Korean coast, we show that the integrated index reveals crossing hotspots and small-vessel maneuvering burdens that are not captured by spatial regularity metrics alone. The results remain robust across reasonable parameter ranges (e.g., speed filter and σ-based weighting), and they align with operational observations in vessel traffic services (VTS). The proposed CS–TCI offers actionable decision support for port and coastal operations by jointly reflecting traffic smoothness and complexity; it can complement collision-risk screening and efficiency-oriented planning (e.g., energy and emission considerations). The approach is readily transferable to other crossing waterways and can be integrated with real-time monitoring to prioritize control actions in complex marine traffic environments. Full article

(This article belongs to the Section Ocean Engineering)

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29 pages, 1308 KB

Open AccessArticle

Analysis of the Dual-Functional Broadband Properties of an Asymmetric Piezoelectric Metamaterial Beam for Simultaneous Vibration Reduction and Energy Harvesting

by Xingguo Wang, Qiuju Xie, Lan Wang, Haisheng Shu and Hongyan Wang

Materials 2025, 18(21), 5003; https://doi.org/10.3390/ma18215003 (registering DOI) - 1 Nov 2025

Abstract

This paper investigates the dual-functional broadband properties of an asymmetric piezoelectric metamaterial beam for simultaneous vibration reduction and energy harvesting. Firstly, a grading method is proposed, and an asymmetric piezoelectric metamaterial beam structure model with the gradient mode is established. The effects of various gradient modes on the grading parameters of each segment are examined. Subsequently, the band structure and group velocity of each segment are examined to elucidate the propagation and energy harvesting mechanisms for the bending-dominated wave. Furthermore, the evaluation criteria for dual-functional properties in the gradient mode are introduced, revealing the broadening law of the dual-functional band under various gradient modes. Finally, the theoretical results are analyzed and compared with the finite element method (FEM). The results show that in gradient mode, the bending-dominated wave in the asymmetric piezoelectric metamaterial beam generates the spatial frequency division and enhances wave field energy. Compared with the uniform mode, the gradient modes can simultaneously achieve dual-functional effects in both the low-frequency and broadband ranges, significantly improving performance. Parameters such as gradient modes and grading variation ranges significantly impact the dual-functional performance. By reasonably selecting the grading parameters, enhanced dual-functional performance can be achieved. Full article

(This article belongs to the Section Energy Materials)

20 pages, 3991 KB

Open AccessArticle

Tubing String Dynamics During Transient Start-Up and Shutdown in CO₂ Flooding

by Xiangyang Wu, Jianxun Li, Dong Chen, Yinping Cao, Yihua Dou and Xin Luo

Processes 2025, 13(11), 3514; https://doi.org/10.3390/pr13113514 (registering DOI) - 1 Nov 2025

Abstract

In CO₂ flooding technology, the injection tubing string is prone to intense fluid–structure interaction (FSI) vibrations and water hammer effects during transient start-up and shutdown processes, which seriously threaten injection safety. This study is based on a four-equation FSI model and employs the method of characteristics (MOC) and numerical simulations to analyze the dynamic responses of fluid velocity, pressure, axial vibration velocity, and additional stress in the tubing string during start-up and shutdown processes. The results indicate that the most severe vibrations occur within 12 s after pump start-up, with a significant increase in the amplitude of axial additional stress. Increasing the injection rate leads to a notable rise in the peak water hammer pressure. Extending the shutdown time effectively reduces impact loads. This research provides an important theoretical basis for the safe design and operational control of the CO₂ injection wells. It is recommended to adopt operational strategies such as low rate, slow start-up, and reasonably extended shutdown times to mitigate vibration hazards. Full article

(This article belongs to the Section Energy Systems)

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18 pages, 282 KB

Open AccessArticle

Clinical Characteristics and Associated Socio-Demographic Factors of Autistic Spectrum Disorder in Erbil City: A Cross-Sectional Study

by Hewa Zrar Jaff and Banaz Adnan Saeed

Psychiatry Int. 2025, 6(4), 132; https://doi.org/10.3390/psychiatryint6040132 (registering DOI) - 1 Nov 2025

Abstract

The increasing prevalence of Autism Spectrum Disorder (ASD) is a significant health concern influenced by both genetic and environmental factors. However, limited data exist on the socio-demographic and clinical characteristics associated with ASD in our region. This cross-sectional study assessed 200 children (155 boys and 45 girls) diagnosed with ASD at Hawler Psychiatric Hospital in Erbil city between January and December 2023. The Childhood Autism Rating Scale-Second Edition (CARS-2) was used for diagnosis and severity assessment. The mean age of participants was 4.6 ± 1.8 years, with males representing 77.5% of the sample. Cesarean section was the most common mode of delivery. The average parental ages were 34.8 years for mothers and 38.5 years for fathers. The first signs of autism were noticed at a mean age of 25.7 ± 9.7 months, with the first medical consultation at 34.6 ± 15.4 months and diagnosis at 42.4 ± 15.5 months. Delayed speech was the most common reason for seeking medical help. Statistically significant associations were found between severe autism symptoms and several factors, including older child age, younger age at first assessment, delayed speech, parental consanguinity, paternal age over 40, lower paternal education, and lower socioeconomic status. These findings emphasize the critical role of early detection and the influence of both socio-demographic and clinical factors on ASD symptom severity, highlighting the need for targeted early intervention strategies to improve outcomes in affected children. Full article

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