Saved Queries

by Jiamin Mo, Ling Zhu, Wei Liu, Ping Wen, Zhiqiang Xie, Rong Li, Chunhou Ji, Wei Cheng, Yangbin Zhang, Chaoya Chen, Qijia Yang and Junxiao Wang

Land 2025, 14(3), 551; https://doi.org/10.3390/land14030551 (registering DOI) - 5 Mar 2025

Abstract

Assessing the geological suitability of urban underground space development is crucial for mitigating geological risks. Traditional 2D evaluation methods fail to capture complex vertical variations in underground space, hindering precise planning. This paper presents an innovative 3D-CWC framework, combining a weighted cloud model with three-dimensional geological modeling, to address vertical complexity and uncertainty in geological assessments. The study area, located in the northern part of Kunming’s Second Ring Road, is divided into 22 million 25 m × 25 m × 1 m 3D units for evaluation. The framework uses the improved AHP and CRITIC methods to assign weights to key geological indicators, addressing both subjective and objective uncertainty, and employs a cloud model to determine geological suitability levels. The results are visualized using 3D geological modeling. The key findings include the following: (1) approximately 71% of the area within a −50 m depth range is suitable or more suitable for underground space development; (2) active fractures and groundwater are the main unfavorable factors; and (3) the geological suitability varies significantly with depth, with shallow areas being less suitable due to soft soil and complex hydrogeological conditions. The framework is further applied to assess the geological suitability of Kunming Metro Line 10, providing valuable decision support for infrastructure development. Compared to existing methods, this framework integrates cloud modeling and 3D geological modeling, offering a more comprehensive approach to handling underground space complexity. It is adaptable and holds potential for global applications, supporting urban underground space development in diverse geological conditions. Full article

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20 pages, 4192 KiB

Open AccessArticle

Employing Fuzzy Adaptive and Event-Triggered Approaches to Achieve Formation Control of Nonholonomic Mobile Robots Under Complete State Constraints

by Kai Wang, Jinnan Lu and Haodong Zhou

Appl. Sci. 2025, 15(5), 2827; https://doi.org/10.3390/app15052827 (registering DOI) - 5 Mar 2025

Abstract

This article delves into the problem of fuzzy adaptive event-triggered (ET) formation control for nonholonomic mobile robots (NMRs) subject to full-state constraints. Fuzzy logic systems (FLSs) are employed to identify the unknown nonlinear functions within the system. To guarantee that all system states remain within their constraint boundaries, barrier Lyapunov functions (BLFs) are meticulously constructed. Subsequently, within the framework of the backstepping control design algorithm, we propose a novel fuzzy adaptive ET formation controller. Our ET mechanism can achieve an overall resource-saving rate of 88.17% for the four robots. Rigorous theoretical analysis demonstrates that the designed strategy not only ensures the stability of the controlled NMRs but also enables the formation tracking errors to converge to a small neighborhood around zero. Notably, the BLFs-based control approach presented herein endows the system with the capacity to avoid collisions to a certain degree, enhancing the overall safety and reliability of the robot formation. Finally, a simulation example is provided. The results vividly illustrate the effectiveness and practicality of the proposed theory, validating its potential for real-world applications in the field of nonholonomic mobile robot formation control. Full article

(This article belongs to the Special Issue Motion Control for Robots and Automation)

18 pages, 2844 KiB

Open AccessArticle

A Regional Approach to Strengthening the Implementation of Sustainable Antimicrobial Stewardship Programs in Five Countries in East, Central, and Southern Africa

by Evelyn Wesangula, Joseph Yamweka Chizimu, Siana Mapunjo, Steward Mudenda, Jeremiah Seni, Collins Mitambo, Kaunda Yamba, Misbah Gashegu, Aquino Nhantumbo, Emiliana Francis, Nyambura Moremi, Henry Athiany and Martin Matu

Antibiotics 2025, 14(3), 266; https://doi.org/10.3390/antibiotics14030266 (registering DOI) - 5 Mar 2025

Abstract

Background: Antimicrobial stewardship (AMS) programs optimize the use of antimicrobials and reduce antimicrobial resistance (AMR). This study evaluated the implementation of AMS programs in Africa using a harmonized regional approach. Methods: This was an exploratory cross-sectional study across five countries involving 32 hospitals using an adapted Periodic National and Hospitals Assessment Tool from the World Health Organization (WHO) policy guidance on integrated AMS activities in human health. Results: This study found baseline scores for AMS core elements ranging from 34% to 79% at the baseline which improved to 58% to 92% at the endline. At baseline, Drugs and Therapeutics Committee (DTC) functionality in updating facility-specific medicines and medical devices ranged from 58% to 100%, and this ranged from 79 to 100% at endline. Classifying antibiotics by WHO AWaRe, classification ranged from 33% to 83% at baseline and 64% to 100% at endline. Leadership commitment scores were 47% at baseline and 66% at endline. Education and training scores were 42% and 63% at baseline and endline, respectively. Reporting and feedback scores were 34% at baseline and 58% at endline. Conclusions: Our study showed that understanding context and standardizing regional stewardship approaches enhanced cross-country learning and improved AMS implementation. Although the challenges in Low- and Middle-Income Countries (LMICs) are similar, they vary by country and can be addressed by strengthening AMS regulatory frameworks and surveillance systems. Full article

(This article belongs to the Special Issue Antibiotics Stewardship in Low and Middle-Income Countries)

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18 pages, 6149 KiB

Open AccessArticle

Identification of Aldehyde Dehydrogenase Gene Family in Glycyrrhiza uralensis and Analysis of Expression Pattern Under Drought Stress

by Mengyuan He, Xu Ouyang, Linyuan Cheng, Yuetao Li, Nana Shi, Hongxia Ma, Yu Sun, Hua Yao and Haitao Shen

Int. J. Mol. Sci. 2025, 26(5), 2333; https://doi.org/10.3390/ijms26052333 - 5 Mar 2025

Abstract

Aldehyde dehydrogenases (ALDHs) are a gene family that relies on NAD +/NADP + proteins to oxidize toxic aldehydes to non-toxic carboxylic acids, and they play a crucial role in the growth and development of plants, as well as in their ability to withstand stress. This study identified 26 ALDH genes from six Glycyrrhiza uralensis gene families distributed on six chromosomes. By analyzing the phylogeny, gene structure, conserved motifs, cis-regulatory elements, collinearity of homologs, evolutionary patterns, differentiation patterns, and expression variations under drought stress, we found that the ALDH gene is involved in phytohormones and exhibits responsiveness to various environmental stressors by modulating multiple cis-regulatory elements. In addition, GuALDH3H1, GuALDH6B1, GuALDH12A2, and GuALDH12A1 have been identified as playing a crucial role in the response to drought stress. By analyzing the expression patterns of different tissues under drought stress, we discovered that GuALDH3I2 and GuALDH2B2 exhibited the most pronounced impact in relation to the drought stress response, which indicates that they play a positive role in the response to abiotic stress. These findings provide a comprehensive theoretical basis for the ALDH gene family in Glycyrrhiza uralensis and enhance our understanding of the molecular mechanisms underlying ALDH genes in licorice growth, development, and adaptation to drought stress. Full article

(This article belongs to the Section Molecular Plant Sciences)

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34 pages, 2523 KiB

Open AccessArticle

Scalable Container-Based Time Synchronization for Smart Grid Data Center Networks

by Kennedy Chinedu Okafor, Wisdom Onyema Okafor, Omowunmi Mary Longe, Ikechukwu Ignatius Ayogu, Kelvin Anoh and Bamidele Adebisi

Technologies 2025, 13(3), 105; https://doi.org/10.3390/technologies13030105 - 5 Mar 2025

Abstract

The integration of edge-to-cloud infrastructures in smart grid (SG) data center networks requires scalable, efficient, and secure architecture. Traditional server-based SG data center architectures face high computational loads and delays. To address this problem, a lightweight data center network (DCN) with low-cost, and fast-converging optimization is required. This paper introduces a container-based time synchronization model (CTSM) within a spine–leaf virtual private cloud (SL-VPC), deployed via AWS CloudFormation stack as a practical use case. The CTSM optimizes resource utilization, security, and traffic management while reducing computational overhead. The model was benchmarked against five DCN topologies—DCell, Mesh, Skywalk, Dahu, and Ficonn—using Mininet simulations and a software-defined CloudFormation stack on an Amazon EC2 HPC testbed under realistic SG traffic patterns. The results show that CTSM achieved near-100% reliability, with the highest received energy data (29.87%), lowest packetization delay (13.11%), and highest traffic availability (70.85%). Stateless container engines improved resource allocation, reducing administrative overhead and enhancing grid stability. Software-defined Network (SDN)-driven adaptive routing and load balancing further optimized performance under dynamic demand conditions. These findings position CTSM-SL-VPC as a secure, scalable, and efficient solution for next-generation smart grid automation. Full article

23 pages, 2653 KiB

Open AccessArticle

Integrated Lower Limb Robotic Orthosis with Embedded Highly Oriented Electrospinning Sensors by Fuzzy Logic-Based Gait Phase Detection and Motion Control

by Ming-Chan Lee, Cheng-Tang Pan, Jhih-Syuan Huang, Zheng-Yu Hoe and Yeong-Maw Hwang

Sensors 2025, 25(5), 1606; https://doi.org/10.3390/s25051606 - 5 Mar 2025

Abstract

This study introduces an integrated lower limb robotic orthosis with near-field electrospinning (NFES) piezoelectric sensors and a fuzzy logic-based gait phase detection system to enhance mobility assistance and rehabilitation. The exoskeleton incorporates embedded pressure sensors within the insoles to capture ground reaction forces (GRFs) in real-time. A fuzzy logic inference system processes these signals, classifying gait phases such as stance, initial contact, mid-stance, and pre-swing. The NFES technique enables the fabrication of highly oriented nanofibers, improving sensor sensitivity and reliability. The system employs a master–slave control framework. A Texas Instruments (TI) TMS320F28069 microcontroller (Texas Instruments, Dallas, TX, USA) processes gait data and transmits actuation commands to motors and harmonic drives at the hip and knee joints. The control strategy follows a three-loop methodology, ensuring stable operation. Experimental validation assesses the system’s accuracy under various conditions, including no-load and loaded scenarios. Results demonstrate that the exoskeleton accurately detects gait phases, achieving a maximum tracking error of 4.23% in an 8-s gait cycle under no-load conditions and 4.34% when tested with a 68 kg user. Faster motion cycles introduce a maximum error of 6.79% for a 3-s gait cycle, confirming the system’s adaptability to dynamic walking conditions. These findings highlight the effectiveness of the developed exoskeleton in interpreting human motion intentions, positioning it as a promising solution for wearable rehabilitation and mobility assistance. Full article

(This article belongs to the Special Issue Innovative Applications of Wearable Sensors in Musculoskeletal Biomechanics and Rehabilitation)

25 pages, 1039 KiB

Open AccessArticle

CEEMDAN-IHO-SVM: A Machine Learning Research Model for Valve Leak Diagnosis

by Ruixue Wang and Ning Zhao

Algorithms 2025, 18(3), 148; https://doi.org/10.3390/a18030148 - 5 Mar 2025

Abstract

Due to the complex operating environment of valves, when a fault occurs inside a valve, the vibration signal generated by the fault is easily affected by the environmental noise, making the extraction of fault features difficult. To address this problem, this paper proposes a feature extraction method based on the combination of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Fuzzy Entropy (FN). Due to the slow convergence speed and the tendency to fall into local optimal solutions of the Hippopotamus Optimization Algorithm (HO), an improved Hippopotamus Optimization (IHO) algorithm-optimized Support Vector Machine (SVM) model for valve leakage diagnosis is introduced to further enhance the accuracy of valve leakage diagnosis. The improved Hippopotamus Optimization algorithm initializes the hippopotamus population with Tent chaotic mapping, designs an adaptive weight factor, and incorporates adaptive variation perturbation. Moreover, the performance of IHO was proven to be optimal compared to HO, Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), Whale Optimization Algorithm (WOA), and Sparrow Search Algorithm (SSA) by calculating twelve test functions. Subsequently, the IHO-SVM classification model was established and applied to valve leakage diagnosis. The prediction effects of the seven models, IHO-SVM. HO-SVM, PSO-SVM, GWO-SVM, WOA-SVM, SSA-SVM, and SVM were compared and analyzed with actual data. As a result, the comparison indicated that IHO-SVM has desirable robustness and generalization, which successfully improves the classification efficiency and the recognition rate in fault diagnosis. Full article

(This article belongs to the Section Evolutionary Algorithms and Machine Learning)

19 pages, 2602 KiB

Open AccessArticle

Dynamic Optimization of Tramp Ship Routes for Carbon Intensity Compliance and Operational Efficiency

by Dequan Zhou, Yuhan Yang and Rui Cai

Sustainability 2025, 17(5), 2280; https://doi.org/10.3390/su17052280 - 5 Mar 2025

Abstract

To address the challenges of carbon emission reduction in the global shipping industry and the requirements of the International Maritime Organization (IMO)’s Carbon Intensity Indicator (CII) rating, this paper takes China’s commuter ships as an example to study the dynamic optimization of ship routes based on CII implementation requirements. In response to the existing research gap in the collaborative optimization of routes and carbon emissions under CII constraints, this paper constructs a mixed-integer programming model that comprehensively considers CII limits, port throughput capacity, channel capacity, and the stochastic demand for spot cargo. The objective is to minimize the operating costs of shipping companies, and an adaptive genetic algorithm is designed to solve the dynamic route scheduling problem. Numerical experiments demonstrate that the model can reasonably plan routes under different sequences of spot cargo arrivals, ensuring compliance with CII ratings while reducing total costs and carbon emissions. The results indicate that the proposed method provides efficient decision-making support for dynamic ship scheduling under CII constraints, contributing to the green transformation of the shipping industry. Future work will extend the model to scenarios involving multiple ship types and complex maritime conditions, further enhancing its applicability. Full article

(This article belongs to the Topic Carbon-Energy-Water Nexus in Global Energy Transition)

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Schematic diagram of a ship transportation plan. Full article ">Figure 2
Algorithm flowchart. Full article ">Figure 3
Illustration of optimization model encoding (a) Random assignment; (b) Sorting based on codes; (c) Allocation order; (d) Final solution. Full article ">Figure 4
Crossover operator design (1) Initial encoding; (2) Random exchange; (3) New code generated from code No. 1; (4) New code generated from code No. 2; (a) Randomly selected initial encoding; (b) Final encoding. Full article ">Figure 5
Comparison of GA, PSO, and GSA. Full article ">Figure 6
(a) Tramp Ship 1 transportation route A. (b) Tramp Ship 2 transportation route A. Full article ">Figure 7
(a) Tramp Ship 1 transportation route B. (b) Tramp Ship 2 transportation route B. Full article ">

17 pages, 769 KiB

Open AccessArticle

The Role of Foliar-Applied Silicon in Improving the Growth and Productivity of Early Potatoes

by Wanda Wadas and Tomasz Kondraciuk

Agriculture 2025, 15(5), 556; https://doi.org/10.3390/agriculture15050556 - 5 Mar 2025

Abstract

Climate change is leading to a decline in global potato production. To ensure food security, it is essential to adapt cultivation practices to the changing climate. The effects of foliar-applied silicon on potato growth and productivity under various hydrothermal conditions were investigated. Potato plants were treated with three Si-based biostimulants: Actisil (6 g of Si and 20 g of Ca per liter; choline-stabilized orthosilicic acid; Chol-sSa + Ca); Krzemix (6 g of Si per liter; choline-stabilized ammonium metasilicate; Chol-sNH₄-Sil); and Optysil (93 g of Si and 24 g of Fe per liter; sodium metasilicate and iron chelate Fe-EDTA; Na-Sil + Fe-EDTA). Biostimulants were foliar-applied twice, at the leaf development stage (BBCH 13–15) and two weeks after the first treatment, at 0.5 L/ha in each treatment. The plants treated with biostimulants were taller and produced greater above-ground biomass and a higher tuber weight than the control plants (without a biostimulant). As a result, the total tuber yield was higher, on average, by 10% to 13% and the marketable tuber yield by 11% to 15%. The plant-growth-promoting and yield-increasing effects of the Si-based biostimulants depended on the hydrothermal conditions during potato growth. Chol-sSA + Ca (Actisil) applications were the most effective. Na-Sil + Fe-EDTA (Optysil) produced better results during a warm and very dry year, while Chol-sNH₄-Sil (Krzemix) was effective during colder years with a periodic water deficit. Silicon foliar application can be a new method for increasing early crop potato yields under water shortage conditions. Full article

(This article belongs to the Special Issue The Role of Silicon in Improving Crop Growth Under Abiotic Stress)

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Mean air temperature (lines) and total precipitation (bars) during potato growth. Full article ">Figure 2
Plant height and above-ground biomass in relation to treatment and year (a) and treatment and cultivar (b). Mean and standard deviation. Means for each data type, indicated with the same letters, do not differ significantly according to Tukey’s post hoc test (p ≤ 0.05). Full article ">Figure 3
Leaf area and chlorophyll (ChlSPAD) content in relation to treatment and year (a) and treatment and cultivar (b). Mean and standard deviation. Means for each data type, indicated with the same letters, do not differ significantly according to Tukey’s post hoc test (p ≤ 0.05). Full article ">Figure 4
Plant growth indicators in relation to treatment and year (a) and treatment and cultivar (b). LAI—leaf area index, SLA—specific leaf area, LAR—leaf area ratio, LWR—leaf weight ratio. Mean and standard deviation. Means for each data type, indicated with the same letters, do not differ significantly according to Tukey’s post hoc test (p ≤ 0.05). Full article ">Figure 5
Tuber number and tuber weight per plant in relation to treatment and year (a) and treatment and cultivar (b). Mean and standard deviation. Means for each data type, indicated with the same letters, do not differ significantly according to Tukey’s post hoc test (p ≤ 0.05). Full article ">Figure 6
Tuber yield in relation to treatment and year (a) and treatment and cultivar (b). Mean and standard deviation. Means for each data type, indicated with the same letters, do not differ significantly according to Tukey’s post hoc test (p ≤ 0.05). Full article ">Figure 7
Tuber size in yield in relation to treatment and year (a) and treatment and cultivar (b). Percentage weight of tubers with diameter of <30 mm, 31–40 mm, 41–50 mm, and >50 mm. Full article ">

22 pages, 12118 KiB

Open AccessArticle

Modern Comprehensive Metabolomic Profiling of Pollen Using Various Analytical Techniques

by Petra Krejčí, Zbyněk Žingor, Jana Balarynová, Andrea Čevelová, Matěj Tesárek, Petr Smýkal and Petr Bednář

Molecules 2025, 30(5), 1172; https://doi.org/10.3390/molecules30051172 - 5 Mar 2025

Abstract

Pollen is a cornerstone of life for plants. Its durability, adaptability, and complex design are the key factors to successful plant reproduction, genetic diversity, and the maintenance of ecosystems. A detailed study of its chemical composition is important to understand the mechanism of pollen–pollinator interactions, pollination processes, and allergic reactions. In this study, a multimodal approach involving Fourier transform infrared spectrometry (FTIR), direct mass spectrometry with an atmospheric solids analysis probe (ASAP), matrix-assisted laser desorption/ionization (MALDI) and ultra-high-performance liquid chromatography–mass spectrometry (UHPLC-MS) was applied for metabolite profiling. ATR-FTIR provided an initial overview of the present metabolite classes. Phenylpropanoid, lipidic, and carbohydrate structures were revealed. The hydrophobic outer layer of pollen was characterized in detail by ASAP-MS profiling, and esters, phytosterols, and terpenoids were observed. Diacyl- and triacylglycerols and carbohydrate structures were identified in MALDI-MS spectra. The MALDI-MS imaging of lipids proved to be helpful during the microscopic characterization of pollen species in their mixture. Polyphenol profiling and the quantification of important secondary metabolites were performed by UHPLC-MS in context with pollen coloration and their antioxidant and antimicrobial properties. The obtained results revealed significant chemical differences among Magnoliophyta and Pinophyta pollen. Additionally, some variations within Magnoliophyta species were observed. The obtained metabolomics data were utilized for pollen differentiation at the taxonomic scale and provided valuable information in relation to pollen interactions during reproduction and its related applications. Full article

(This article belongs to the Special Issue Applied Analytical Chemistry: Second Edition)

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21 pages, 559 KiB

Open AccessArticle

Effectiveness of the Mindfulness-Based Social–Emotional Growth (MSEG) Program in Enhancing Mental Health of Elementary School Students in Korea

by Jongtae Kim, Seonyeop Kim, Misan W. D. Kim, Yong-Han Park, Kanguk Lee, Dong Sun Chung, Youn Hee Kim, Yong-Sil Kweon, Duk-Soo Mun, Hwa-Young Lee, June Sung Park, Yun Hyoung Kang, Seunghee Won and Young Sook Kwack

Behav. Sci. 2025, 15(3), 315; https://doi.org/10.3390/bs15030315 - 5 Mar 2025

Abstract

Since the COVID-19 pandemic, mental health challenges among children and adolescents in South Korea have intensified with rising rates of depression and suicide. Proactive interventions focusing on mental well-being are needed to address this critical issue. This study examines the effectiveness of the Mindfulness-based Social–Emotional Growth (MSEG) program for enhancing the mental health of elementary school students in Korea. This quasi-experimental study evaluated the impact of the MSEG program on students at an elementary school in Gyeonggi-do, South Korea. A total of 70 students (35 from lower grades [grades 1–3] and 35 from upper grades [grades 4–6]) participated in the 12-week intervention group, while 72 students were in the control group. Pre- and post-intervention assessments measured social–emotional, mindfulness, and clinical domains. The findings revealed significant improvements in emotional regulation across all grades. Lower-grade students showed substantially reduced anxiety and depression, while upper-grade students demonstrated enhanced resilience compared to the control group. These results indicate the potential of the MSEG program to promote early mental health when integrated into elementary school curricula. Further research is needed to adapt the program to diverse educational settings and optimize its effectiveness and impact. Full article

(This article belongs to the Section Health Psychology)

29 pages, 2128 KiB

Open AccessArticle

An Automated Repository for the Efficient Management of Complex Documentation

by José Frade and Mário Antunes

Information 2025, 16(3), 205; https://doi.org/10.3390/info16030205 - 5 Mar 2025

Abstract

The accelerating digitalization of the public and private sectors has made information technologies (IT) indispensable in modern life. As services shift to digital platforms and technologies expand across industries, the complexity of legal, regulatory, and technical requirement documentation is growing rapidly. This increase presents significant challenges in managing, gathering, and analyzing documents, as their dispersion across various repositories and formats hinders accessibility and efficient processing. This paper presents the development of an automated repository designed to streamline the collection, classification, and analysis of cybersecurity-related documents. By harnessing the capabilities of natural language processing (NLP) models—specifically Generative Pre-Trained Transformer (GPT) technologies—the system automates text ingestion, extraction, and summarization, providing users with visual tools and organized insights into large volumes of data. The repository facilitates the efficient management of evolving cybersecurity documentation, addressing issues of accessibility, complexity, and time constraints. This paper explores the potential applications of NLP in cybersecurity documentation management and highlights the advantages of integrating automated repositories equipped with visualization and search tools. By focusing on legal documents and technical guidelines from Portugal and the European Union (EU), this applied research seeks to enhance cybersecurity governance, streamline document retrieval, and deliver actionable insights to professionals. Ultimately, the goal is to develop a scalable, adaptable platform capable of extending beyond cybersecurity to serve other industries that rely on the effective management of complex documentation. Full article

(This article belongs to the Special Issue Applications of Information Extraction, Knowledge Graphs, and Large Language Models)

28 pages, 60546 KiB

Open AccessArticle

Adapting Cross-Sensor High-Resolution Remote Sensing Imagery for Land Use Classification

by Wangbin Li, Kaimin Sun and Jinjiang Wei

Remote Sens. 2025, 17(5), 927; https://doi.org/10.3390/rs17050927 - 5 Mar 2025

Abstract

High-resolution visible remote sensing imagery, as a fundamental contributor to Earth observation, has found extensive application in land use classification. However, the heterogeneous array of optical sensors, distinguished by their unique design architectures, exhibit disparate spectral responses and spatial distributions when observing ground objects. These discrepancies between multi-sensor data present a significant obstacle to the widespread application of intelligent methods. In this paper, we propose a method tailored to accommodate these disparities, with the aim of achieving a smooth transfer for the model across diverse sets of images captured by different sensors. Specifically, to address the discrepancies in spatial resolution, a novel positional encoding has been incorporated to capture the correlation between the spatial resolution details and the characteristics of ground objects. To tackle spectral disparities, random amplitude mixup augmentation is introduced to mitigate the impact of feature anisotropy resulting from discrepancies in low-level features between multi-sensor images. Additionally, we integrate convolutional neural networks and Transformers to enhance the model’s feature extraction capabilities, and employ a fine-tuning strategy with dynamic pseudo-labels to reduce the reliance on annotated data from the target domain. In the experimental section, the Gaofen-2 images (4 m) and the Sentinel-2 images (10 m) were selected as training and test datasets to simulate cross-sensor model transfer scenarios. Also, Google Earth images of Suzhou City, Jiangsu Province, were utilized for further validation. The results indicate that our approach effectively mitigates the degradation in model performance attributed to image source inconsistencies. Full article

(This article belongs to the Special Issue Semantic Segmentation of High-Resolution Remote Sensing Images with Advanced Deep Learning Techniques)

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17 pages, 700 KiB

Open AccessArticle

Context Matters: Extra-Personal Factors Underlying Concussion Reporting in University Athletes

by William Archambault and Dave Ellemberg

Sports 2025, 13(3), 77; https://doi.org/10.3390/sports13030077 - 5 Mar 2025

Abstract

Gaps remain in our understanding of which factors contribute to concussion disclosure and how they contribute to this process, thereby limiting our ability to improve disclosure. This study aimed to characterize the most relevant extra-personal determinants of SC disclosure and to describe their influence on the disclosure process. To that aim, the first author conducted substantive qualitative interviews with nine university student–athletes and analyzed their content via constant comparative analysis (guided by Straussian grounded theory). Eleven (11) extra-personal concepts influencing concussion reporting were identified and described across two categories: Contextual Incentives and Socio-Cultural Pressures. These findings suggest that each identified concept can individually shape the context around the injury, creating either higher-stakes conditions that deter disclosure or lower-stakes conditions that encourage it. Further, the results posit that these concepts interact and collectively influence athletes’ decision-making process by modulating the perceived stakes of disclosing a concussion. If these findings hold true in more diverse populations and contexts, they suggest that adapting concussion prevention efforts to consider these contextual variables could improve SC disclosure. This study also highlights the benefits of using qualitative methods in the investigation of concussion reporting. Full article

35 pages, 4865 KiB

Open AccessArticle

Indoor Air Quality and Thermal Comfort in University Classrooms in Southwestern Spain: A Longitudinal Analysis from Pandemic to Post-Pandemic

by Pilar Romero, Víctor Valero-Amaro, José Ignacio Arranz, Francisco José Sepúlveda and María Teresa Miranda

Buildings 2025, 15(5), 829; https://doi.org/10.3390/buildings15050829 - 5 Mar 2025

Abstract

After the COVID-19 lockdown, the health authorities established strict protocols for ventilating indoor spaces and reducing contagion. Although the control of the disease allowed these measures to be relaxed, indoor air quality (IAQ) and natural ventilation (NV) are still essential. However, in certain climatic conditions, this can affect the thermal comfort of the occupants. This situation is relevant in educational buildings, where thermal discomfort can influence students’ academic performance, especially during critical periods such as exams. In this context, this article explores how different NV strategies, both during and after the pandemic, affect the thermal comfort of students at a university in a Mediterranean climate zone. The analyses revealed that, despite the low temperatures and strict ventilation protocols due to COVID-19, thermal comfort during winter was higher than in spring and summer. These results led to an investigation into which variables could explain this phenomenon, detecting that the choice of clothing was crucial to achieving adequate comfort conditions. Regarding IAQ, ventilation was sufficient, even excessive, in some cases, especially during mandatory measures. In conclusion, it would be beneficial to establish ventilation protocols adapted to each environment and to advise students on individual strategies to improve their thermal comfort. Full article

(This article belongs to the Section Building Energy, Physics, Environment, and Systems)

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