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Search Results (3,943)

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25 pages, 2285 KiB  
Article
Modeling Critical Rework Factors in the Construction Industry: Insights and Solutions
by Gulden Gumusburun Ayalp and Fatma Arslan
Buildings 2025, 15(4), 606; https://doi.org/10.3390/buildings15040606 (registering DOI) - 15 Feb 2025
Abstract
Construction professionals recognize rework’s negative impact on project performance, yet a comprehensive understanding of its critical factors still needs to be provided. Consequently, this study sought to inquire deeply into the causes of construction rework. A systematic framework was employed to achieve the [...] Read more.
Construction professionals recognize rework’s negative impact on project performance, yet a comprehensive understanding of its critical factors still needs to be provided. Consequently, this study sought to inquire deeply into the causes of construction rework. A systematic framework was employed to achieve the research objectives. Initially, potential causes of rework were identified through a systematic literature review. Subsequently, a survey was developed and emailed to the sample group. Exploratory factor analysis was used to extract critical rework factors (CRFs) and normalized mean value analysis was used to evaluate the criticality of the obtained causes. Structural equation modeling was used to quantify and simulate the effect sizes of the components that were collected. Out of 43 possible causes, this study found 21 critical causes why rework occurs in the Turkish construction sector. Additionally, it uncovered four original CRFs, namely “management and planning deficiencies”, “design and time constraints”, “labor quality and compliance issues”, and “project dynamics and communication challenges”. While numerous studies have explored rework causes using different approaches and methodologies, there remains a lack of insight into the key factors leading to rework. Unlike earlier research, this study offers a thorough and quantitative identification of four distinct critical rework factors in Turkey. Full article
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20 pages, 6721 KiB  
Article
RPS-YOLO: A Recursive Pyramid Structure-Based YOLO Network for Small Object Detection in Unmanned Aerial Vehicle Scenarios
by Penghui Lei, Chenkang Wang and Peigang Liu
Appl. Sci. 2025, 15(4), 2039; https://doi.org/10.3390/app15042039 (registering DOI) - 15 Feb 2025
Abstract
The fast advancement of unmanned aerial vehicle (UAV) technology has facilitated its use across a wide range of scenarios. Due to the high mobility and flexibility of drones, the images they capture often exhibit significant scale variations and severe object occlusions, leading to [...] Read more.
The fast advancement of unmanned aerial vehicle (UAV) technology has facilitated its use across a wide range of scenarios. Due to the high mobility and flexibility of drones, the images they capture often exhibit significant scale variations and severe object occlusions, leading to a high density of small objects. However, the existing object detection algorithms struggle with detecting small objects effectively in cross-scale detection scenarios. To overcome these difficulties, we introduce a new object detection model, RPS-YOLO, based on the YOLOv8 architecture. Unlike the existing methods that rely on traditional feature pyramids, our approach introduces a recursive feature pyramid (RFP) structure. This structure performs two rounds of feature extraction, and we reduce one downsampling step in the first round to enhance attention to small objects during cross-scale detection. Additionally, we design a novel attention mechanism that improves feature representation and mitigates feature degradation during convolution by capturing spatial- and channel-specific details. Another key innovation is the proposed Localization IOU (LIOU) loss function for bounding box regression, which accelerates the regression process by incorporating angular constraints. Experiments conducted on the VisDrone-DET2021 and UAVDT datasets show that RPS-YOLO surpasses YOLOv8s, with an mAP50 improvement of 8.2% and 3.4%, respectively. Our approach demonstrates that incorporating recursive feature extraction and exploiting detailed information for multi-scale detection significantly improves detection performance, particularly for small objects in UAV images. Full article
(This article belongs to the Special Issue Multimodal Information-Assisted Visual Recognition or Generation)
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30 pages, 2258 KiB  
Article
A Conceptual Approach for the Knowledge-Based Computational Design of Prefabricated Façade Panels Using Constructability Features
by Puyan A. Zadeh, Santiago Diaz, Sheryl Staub-French and Devarsh Bhonde
Appl. Sci. 2025, 15(4), 2035; https://doi.org/10.3390/app15042035 (registering DOI) - 15 Feb 2025
Abstract
The use of parametric models in the architecture, engineering, and construction (AEC) industry has made it possible to create complex and creative building designs. However, this design complexity creates major constructability issues, especially in projects that incorporate prefabricated façade panels. Computational design methods [...] Read more.
The use of parametric models in the architecture, engineering, and construction (AEC) industry has made it possible to create complex and creative building designs. However, this design complexity creates major constructability issues, especially in projects that incorporate prefabricated façade panels. Computational design methods can solve some of these issues; however, such methods do not necessarily include the systematic approach to integrating domain knowledge, which results in inefficiencies in the design and construction processes. This paper introduces how constructability knowledge can be incorporated into computational design process using feature-based modeling (FBM). An ethnographic case study of a high-rise building with complex façade design is presented in this paper. The research identifies the critical geometric constraints that affect constructability and introduces a new three-level taxonomy (Micro, Meso, Macro) for classifying these constraints. The suggested taxonomy is then applied to inform developing a conceptual knowledge-based computational design approach that enables incorporating the insights of domain experts into the design process. Moreover, the research provides a range of external examples to validate the proposed taxonomy. The findings demonstrate the potential of FBM to streamline the design and fabrication of prefabricated façade panels, improving constructability without compromising architectural intent. This study provides a structured methodology that can be applied to enhance design efficiency and reduce construction risks in similar projects. Full article
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21 pages, 443 KiB  
Article
Consumer Perceptions and Sustainability Challenges in Game Meat Production and Marketing: A Comparative Study of Slovakia and the Czech Republic
by Martin Němec, Marcel Riedl, Jaroslav Šálka, Vilém Jarský, Zuzana Dobšinská, Milan Sarvaš, Zuzana Sarvašová, Jozef Bučko and Martina Hustinová
Foods 2025, 14(4), 653; https://doi.org/10.3390/foods14040653 (registering DOI) - 14 Feb 2025
Abstract
Game meat production represents a unique opportunity to align ecological sustainability with the growing consumer demand for sustainable agri-food products. This study focuses on the perspectives of processors and landowners in Slovakia and the Czech Republic, examining their views on market trends, customer [...] Read more.
Game meat production represents a unique opportunity to align ecological sustainability with the growing consumer demand for sustainable agri-food products. This study focuses on the perspectives of processors and landowners in Slovakia and the Czech Republic, examining their views on market trends, customer behaviours, barriers, and sustainability challenges. Focusing on these key stakeholders, the study highlights their central role as key drivers in shaping and sustaining the game meat value chain. This research combines secondary data analysis and in-depth interviews with key stakeholders to provide a comprehensive understanding of the game meat sector. Findings highlight that, while game meat is valued for its organic and sustainable qualities, barriers such as limited consumer awareness, high costs, and regulatory constraints hinder its market potential. The study reveals the vital role of consumer education, branding, and the development of value-added products in bridging the gap between ecological management and sustainable market growth. Moreover, the research underscores the need for tailored policies to address structural inefficiencies, promote collaboration across the value chain, and enhance accessibility to sustainable game meat products. By aligning production and marketing strategies with consumer preferences, the sector can contribute significantly to sustainable agri-food systems while supporting rural economies and biodiversity conservation. This study provides actionable recommendations for industry stakeholders and policymakers aiming to foster sustainable practices and consumer engagement in the game meat market. Full article
24 pages, 1367 KiB  
Article
Evaluating Agricultural Resource Pressure and Food Security in China and “Belt and Road” Partner Countries with Virtual Water Trade
by Chengyu Li, Jiayi Sun, Xin Wen, Zuhui Xia, Shuchang Ren and Jiaxin Wu
Sustainability 2025, 17(4), 1599; https://doi.org/10.3390/su17041599 - 14 Feb 2025
Abstract
Water scarcity has emerged as a critical constraint on agricultural development and food security worldwide, particularly in arid and semi-arid regions such as Central Asia, Western Asia, and North Africa, which are part of the “Belt and Road” Initiative. This study, based on [...] Read more.
Water scarcity has emerged as a critical constraint on agricultural development and food security worldwide, particularly in arid and semi-arid regions such as Central Asia, Western Asia, and North Africa, which are part of the “Belt and Road” Initiative. This study, based on a global multi-regional input–output model, quantitatively analyzes the virtual water flows between China and countries along the “Belt and Road”. It focuses on water-scarce regions, examining the impact of virtual water trade on agricultural resource pressures and food security, as well as the transfer of water resources in trade patterns. The findings indicate that virtual water trade, as an innovative water resource management strategy, can redistribute water resources through international trade, thereby alleviating water stress and enhancing food security in water-scarce areas. Despite China’s status as a net importer in virtual water trade with “Belt and Road” countries, the majority of virtual water flows toward nations with relatively abundant water resources, rather than to the most water-deficient areas. This discovery reveals imbalances in virtual water trade patterns, suggesting that current trade models do not effectively alleviate water and food security pressures in water-scarce regions. The “Belt and Road” mechanism should provide new ideas for solving the huge gap between virtual water theory and reality. In response, this paper proposes optimizing trade structures, strengthening agricultural water resource management, promoting green virtual water trade, fostering regional cooperation, improving data quality and transparency, encouraging agricultural diversification, and increasing investment in water-saving agricultural technologies. Full article
18 pages, 3989 KiB  
Article
Product and Process Data Structure for Automated Battery Disassembly
by Domenic Klohs, Moritz Frieges, Jonas Gorsch, Philip Ellmann, Heiner Hans Heimes and Achim Kampker
Recycling 2025, 10(1), 25; https://doi.org/10.3390/recycling10010025 - 14 Feb 2025
Abstract
Battery disassembly forms a central jumping-off point for recycling in the context of a sustainable closure of the battery loop. The main objective for economic realization in line with European recycling regulations is therefore a transformation of the battery disassembly from a manual [...] Read more.
Battery disassembly forms a central jumping-off point for recycling in the context of a sustainable closure of the battery loop. The main objective for economic realization in line with European recycling regulations is therefore a transformation of the battery disassembly from a manual to an automated process. Product-related influences such as design variations and process-side constraints including the selection of disassembly technologies require large amounts of data for implementation in an automated system. This article examines accessible data sources in the literature and the upcoming battery passport to build a basis for a multi-layered methodical analysis of the data required for the automation of battery disassembly. For this purpose, the disassembly sequence and depth of an Audi e-tron battery pack are first identified using a priority matrix and converted into a product and process structure. Definitions for product- and process-related elements are established, and a generalized process model is developed, which is finally converted into a data structure model approach. The result shows that much of the required data to automate the disassembly of used batteries are currently not yet available. Further efforts must be made to establish data structures and standards regarding product- and process-related disassembly data. Full article
(This article belongs to the Special Issue Lithium-Ion and Next-Generation Batteries Recycling)
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24 pages, 10147 KiB  
Article
A Siamese Network via Cross-Domain Robust Feature Decoupling for Multi-Source Remote Sensing Image Registration
by Qichao Han, Xiyang Zhi, Shikai Jiang, Wenbin Chen, Yuanxin Huang, Lijian Yu and Wei Zhang
Remote Sens. 2025, 17(4), 646; https://doi.org/10.3390/rs17040646 - 13 Feb 2025
Abstract
Image registration is a prerequisite for many multi-source remote sensing image fusion applications. However, due to differences in imaging factors such as sensor type, imaging time, resolution, and viewing angle, multi-source image registration faces challenges of multidimensional coupling such as radiation, scale, and [...] Read more.
Image registration is a prerequisite for many multi-source remote sensing image fusion applications. However, due to differences in imaging factors such as sensor type, imaging time, resolution, and viewing angle, multi-source image registration faces challenges of multidimensional coupling such as radiation, scale, and directional differences. To address this issue, this paper proposes a Siamese network based on cross-domain robust feature decoupling as an image registration framework (CRS-Net), aiming to improve the robustness of multi-source image features across domains, scales, and rotations. Firstly, we design Siamese multiscale encoders and introduce a rotation-invariant convolutional layer without additional training parameters, achieving natural invariance to any rotation. Secondly, we propose a modality-independent decoder that utilizes the self-similarity of feature neighborhoods to excavate stable high-order structural information. Thirdly, we introduce cluster-aware contrastive constraints to learn discriminative and stable keypoint pairs. Finally, we design three multi-source remote sensing datasets and conduct sufficient experiments. Numerous experimental results show that our proposed method outperforms other SOTA methods and achieves more accurate registration in complex multi-source remote sensing scenes. Full article
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26 pages, 27528 KiB  
Article
A Stereo Visual-Inertial SLAM Algorithm with Point-Line Fusion and Semantic Optimization for Forest Environments
by Bo Liu, Hongwei Liu, Yanqiu Xing, Weishu Gong, Shuhang Yang, Hong Yang, Kai Pan, Yuanxin Li, Yifei Hou and Shiqing Jia
Forests 2025, 16(2), 335; https://doi.org/10.3390/f16020335 - 13 Feb 2025
Abstract
Accurately localizing individual trees and identifying species distribution are critical tasks in forestry remote sensing. Visual Simultaneous Localization and Mapping (visual SLAM) algorithms serve as important tools for outdoor spatial positioning and mapping, mitigating signal loss caused by tree canopy obstructions. To address [...] Read more.
Accurately localizing individual trees and identifying species distribution are critical tasks in forestry remote sensing. Visual Simultaneous Localization and Mapping (visual SLAM) algorithms serve as important tools for outdoor spatial positioning and mapping, mitigating signal loss caused by tree canopy obstructions. To address these challenges, a semantic SLAM algorithm called LPD-SLAM (Line-Point-Distance Semantic SLAM) is proposed, which integrates stereo cameras with an inertial measurement unit (IMU), with contributions including dynamic feature removal, an individual tree data structure, and semantic point distance constraints. LPD-SLAM is capable of performing individual tree localization and tree species discrimination tasks in forest environments. In mapping, LPD-SLAM reduces false species detection and filters dynamic objects by leveraging a deep learning model and a novel individual tree data structure. In optimization, LPD-SLAM incorporates point and line feature reprojection error constraints along with semantic point distance constraints, which improve robustness and accuracy by introducing additional geometric constraints. Due to the lack of publicly available forest datasets, we choose to validate the proposed algorithm on eight experimental plots, which are selected to cover different seasons, various tree species, and different data collection paths, ensuring the dataset’s diversity and representativeness. The experimental results indicate that the average root mean square error (RMSE) of the trajectories of LPD-SLAM is reduced by up to 81.2% compared with leading algorithms. Meanwhile, the mean absolute error (MAE) of LPD-SLAM in tree localization is 0.24 m, which verifies its excellent performance in forest environments. Full article
(This article belongs to the Section Forest Inventory, Modeling and Remote Sensing)
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22 pages, 2839 KiB  
Article
Narrowband Radar Micromotion Targets Recognition Strategy Based on Graph Fusion Network Constructed by Cross-Modal Attention Mechanism
by Yuanjie Zhang, Ting Gao, Hongtu Xie, Haozong Liu, Mengfan Ge, Bin Xu, Nannan Zhu and Zheng Lu
Remote Sens. 2025, 17(4), 641; https://doi.org/10.3390/rs17040641 - 13 Feb 2025
Abstract
In the domain of micromotion target recognition, target characteristics can be extracted through either broadband or narrowband radar echoes. However, due to technical limitations and cost constraints in acquiring broadband radar waveform data, recognition can often only be performed through narrowband radar waveforms. [...] Read more.
In the domain of micromotion target recognition, target characteristics can be extracted through either broadband or narrowband radar echoes. However, due to technical limitations and cost constraints in acquiring broadband radar waveform data, recognition can often only be performed through narrowband radar waveforms. To fully utilize the information embedded within narrowband radar waveforms, it is necessary to conduct in-depth research on multi-dimensional features of micromotion targets, including radar cross-sections (RCSs), time frequency (TF) images, and cadence velocity diagrams (CVDs). To address the limitations of existing identification methodologies in achieving accurate recognition with narrowband echoes, this paper proposes a graph fusion network based on a cross-modal attention mechanism, termed GF-AM Net. The network first adopts convolutional neural networks (CNNs) to extract unimodal features from RCSs, TF images, and CVDs independently. Subsequently, a cross-modal attention mechanism integrates these extracted features into a graph structure, achieving multi-level interactions among unimodal, bimodal, and trimodal features. Finally, the fused features are input into a classification module to accomplish narrowband radar micromotion target identification. Experimental results demonstrate that the proposed methodology successfully captures potential correlations between modal features by incorporating cross-modal multi-level information interactions across different processing stages, exhibiting exceptional accuracy and robustness in narrowband radar micromotion target identification tasks. Full article
(This article belongs to the Special Issue Ocean Remote Sensing Based on Radar, Sonar and Optical Techniques)
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18 pages, 3346 KiB  
Article
Shallow Subsurface Wavefield Data Interpolation Method Based on Transfer Learning
by Danfeng Zang, Jian Li, Chuankun Li, Hengran Zhang, Zhipeng Pei and Yixiang Ma
Appl. Sci. 2025, 15(4), 1964; https://doi.org/10.3390/app15041964 - 13 Feb 2025
Abstract
The deployment density of surface sensors can significantly impact the accuracy of subsurface shallow seismic field energy inversion. With finite budget constraints, it is often not feasible to deploy a large number of sensors, resulting in limited seismic signal acquisition that hinders accurate [...] Read more.
The deployment density of surface sensors can significantly impact the accuracy of subsurface shallow seismic field energy inversion. With finite budget constraints, it is often not feasible to deploy a large number of sensors, resulting in limited seismic signal acquisition that hinders accurate inversion of the shallow subsurface explosions. To address the challenge of insufficient sensor signals needed for inversion, we conducted a study on a subsurface shallow wavefield data interpolation method based on transfer learning. This method is designed to increase the overall signal acquisition by interpolating signals at target locations from limited measurement points. Our research employs neural networks to interpolate real seismic data, supplementing the sampled signals. Given the lack of extensive samples from actual data collection, we devised a training approach that combines numerically simulated signals with real collected signals. Initially, we performed conventional interpolation training using a deep interpolation network with complete synthetic gather images obtained from numerical simulations. Subsequently, the feature extraction part was frozen, and the interpolation network was transferred to real datasets, where it was trained using incomplete gather images. Finally, these incomplete gather images were re-input into the trained network to obtain interpolated results at the target locations. Our study demonstrates the superiority of our method by comparing it with two other interpolation networks and validating the effectiveness of transfer learning through four sets of ablation experiments in the actual test. This method can also be applied to other shallow geological structures to generate a large number of seismic signals for energy inversion. Full article
29 pages, 1732 KiB  
Article
Integrating Participatory Approaches and Fuzzy Analytic Hierarchy Process (FAHP) for Barrier Analysis and Ranking in Urban Mobility Planning
by Uroš Kramar and Marjan Sternad
Sustainability 2025, 17(4), 1558; https://doi.org/10.3390/su17041558 - 13 Feb 2025
Abstract
This study examines the barriers to implementing sustainable mobility strategies in small municipalities by integrating participatory and multi-criteria decision-making methods. A triangulated approach combines the nominal group technique (NGT), focus groups (FGs), and the fuzzy analytic hierarchy process (FAHP) to systematically identify, refine, [...] Read more.
This study examines the barriers to implementing sustainable mobility strategies in small municipalities by integrating participatory and multi-criteria decision-making methods. A triangulated approach combines the nominal group technique (NGT), focus groups (FGs), and the fuzzy analytic hierarchy process (FAHP) to systematically identify, refine, and rank key barriers. The NGT enables stakeholders to list and prioritize barriers individually, ensuring balanced participation. FG discussions then refine and contextualize these barriers, addressing qualitative depth. Finally, the FAHP quantitatively ranks the barriers while accounting for uncertainty in stakeholder judgments. The results highlight systemic constraints, such as financial limitations and regulatory inefficiencies, alongside local challenges like inadequate infrastructure and public resistance. Integrating the NGT, FGs, and the FAHP enhances the analytical rigor by merging structured decision-making with participatory engagement. This methodological innovation strengthens the reliability of barrier assessment and offers a replicable framework for urban mobility planning. The findings underscore the need for locally tailored strategies that balance stakeholder inclusion with structured prioritization, contributing to improved governance in sustainable transport planning. Full article
(This article belongs to the Section Sustainable Transportation)
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27 pages, 1050 KiB  
Review
A Review of Biochar from Biomass and Its Interaction with Microbes: Enhancing Soil Quality and Crop Yield in Brassica Cultivation
by Kritsana Jatuwong, Worawoot Aiduang, Tanongkiat Kiatsiriroat, Wassana Kamopas and Saisamorn Lumyong
Life 2025, 15(2), 284; https://doi.org/10.3390/life15020284 - 12 Feb 2025
Abstract
Biochar, produced from biomass, has become recognized as a sustainable soil amendment that has the potential to improve soil quality and agricultural production. This review focuses on production processes and properties of biochar derived from different types of biomass, including the synergistic interactions [...] Read more.
Biochar, produced from biomass, has become recognized as a sustainable soil amendment that has the potential to improve soil quality and agricultural production. This review focuses on production processes and properties of biochar derived from different types of biomass, including the synergistic interactions between biochar and soil microorganisms, emphasizing their influence on overall soil quality and crop production, particularly in cultivation of Brassica crops. It additionally addresses the potential benefits and limitations of biochar and microbial application. Biomass is a renewable and abundant resource and can be converted through pyrolysis into biochar, which has high porosity, abundant surface functionalities, and the capacity to retain nutrients. These characteristics provide optimal conditions for beneficial microbial communities that increase nutrient cycling, reduce pathogens, and improve soil structure. The information indicates that the use of biochar in Brassica crops can result in improved plant growth, yield, nutrient uptake, and stress mitigation. This review includes information about biochar properties such as pH, elemental composition, ash content, and yield, which can be affected by the different types of biomass used as well as pyrolysis conditions like temperature. Understanding these variables is essential for optimizing biochar for agricultural use. Moreover, the information on the limitations of biochar and microbes emphasizes the importance of their benefits with potential constraints. Therefore, sustainable agriculture methods can possibly be achieved by integrating biochar with microbial management measurements, resulting in higher productivity and adaptability in Brassica or other plant crop cultivation systems. This review aims to provide a comprehensive understanding of biochar’s role in supporting sustainable Brassica farming and its potential to address contemporary agricultural challenges. Full article
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14 pages, 4154 KiB  
Article
Molecular Evolution of the Fusion (F) Genes in Human Parainfluenza Virus Type 2
by Tatsuya Shirai, Fuminori Mizukoshi, Ryusuke Kimura, Rina Matsuoka, Mitsuru Sada, Kazuya Shirato, Haruyuki Ishii, Akihide Ryo and Hirokazu Kimura
Microorganisms 2025, 13(2), 399; https://doi.org/10.3390/microorganisms13020399 - 12 Feb 2025
Abstract
Human parainfluenza virus type 2 (HPIV2) is a clinically significant respiratory pathogen, which highlights the necessity of studies on its molecular evolution. This study investigated the evolutionary dynamics, phylodynamics, and structural characteristics of the HPIV2 fusion (F) gene using a comprehensive [...] Read more.
Human parainfluenza virus type 2 (HPIV2) is a clinically significant respiratory pathogen, which highlights the necessity of studies on its molecular evolution. This study investigated the evolutionary dynamics, phylodynamics, and structural characteristics of the HPIV2 fusion (F) gene using a comprehensive dataset spanning multiple decades and geographic regions. Phylogenetic analyses revealed two distinct clusters of HPIV2 F gene sequences, which were estimated to have diverged from a common ancestor approximately a century ago. Cluster 1 demonstrated a higher evolutionary rate and genetic diversity compared to the more stable cluster 2. Bayesian Skyline Plot analyses indicated a significant increase in the effective population size of the F gene between 2005 and 2015; potentially linked to enhanced diagnostic and surveillance capabilities. Structural modeling identified conserved conformational epitopes predominantly in the apex and stalk regions of the F protein. These findings underscore the evolutionary constraints and antigenic landscape of the HPIV2 F protein. Full article
(This article belongs to the Section Public Health Microbiology)
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23 pages, 7897 KiB  
Article
Prestressed Concrete T-Beams Strengthened with Near-Surface Mounted Carbon-Fiber-Reinforced Polymer Rods Under Monotonic Loading: A Finite Element Analysis
by Laurencius Nugroho, Yanuar Haryanto, Hsuan-Teh Hu, Fu-Pei Hsiao, Gandjar Pamudji, Bagus Hario Setiadji, Chiao-Ning Hsu, Pu-Wen Weng and Chia-Chen Lin
Abstract
Prestressed concrete structures, designed to enhance the compressive strength of concrete through internal pretension, are increasingly susceptible to serviceability issues caused by rising live loads, material degradation, and environmental impacts. Strengthening or retrofitting offers a practical and cost-effective alternative to full replacement. This [...] Read more.
Prestressed concrete structures, designed to enhance the compressive strength of concrete through internal pretension, are increasingly susceptible to serviceability issues caused by rising live loads, material degradation, and environmental impacts. Strengthening or retrofitting offers a practical and cost-effective alternative to full replacement. This study investigated the flexural strengthening of prestressed concrete T-beams in the negative moment region using near-surface mounted (NSM) carbon-fiber-reinforced polymer (CFRP) rods. Validation against experimental results from the literature demonstrated high accuracy, with an average numerical-to-experimental ultimate load ratio of 0.97 for reinforced concrete T-beams strengthened with NSM-CFRP rods, a negligible difference of 0.49% for prestressed concrete I-beams, and a minimal error of 1.30% for prestressed concrete slabs strengthened with CFRP laminates. Parametric studies examined the effects of CFRP rod embedment depths and initial prestressing levels. In certain cases, achieving the minimum embedment depth is not feasible due to design or construction constraints. The results showed that fully embedded CFRP rods increased the ultimate load by up to 14.02% for low prestressing levels and 16.36% for high levels, while half-embedded rods provided comparable improvements of 11.20% and 15.76%, respectively. These findings confirm the effectiveness of NSM-CFRP systems and highlight the potential of partial embedment as a practical solution in design-constrained scenarios. Full article
(This article belongs to the Special Issue Emerging Trends in Inorganic Composites for Structural Enhancement)
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13 pages, 555 KiB  
Article
Exercise Habits and Preferences of Community-Dwelling Older Adults with Chronic Pain: An Exploratory Study
by Ziji Chen, Mimi Mun Yee Tse and Bonny Yee Man Wong
Healthcare 2025, 13(4), 384; https://doi.org/10.3390/healthcare13040384 - 11 Feb 2025
Abstract
Introduction: This study explored the exercise habits of community-dwelling older adults with chronic pain, examining the relationship between pain, physical activity, daily life impacts, and psychological effects. Method: The study was conducted through a cross-sectional approach and semi-structured interviews with five participants aged [...] Read more.
Introduction: This study explored the exercise habits of community-dwelling older adults with chronic pain, examining the relationship between pain, physical activity, daily life impacts, and psychological effects. Method: The study was conducted through a cross-sectional approach and semi-structured interviews with five participants aged fifty and above. Result: The findings revealed that exercise participation among those with chronic pain was significantly lower than in the non-pain participants, particularly for those exercising more than three times weekly (p = 0.012). Hypertension (59.64%) and arthritis (39.32%) were common among the respondents. Pain was predominantly reported in the lower back, legs, shoulders, and arms, severely affecting quality of life. Additionally, anxiety and depression were increasingly prevalent in this population, presenting greater challenges than financial constraints or lack of motivation. Lower impact exercises like walking were more doable, and social support and a good environment increased exercise engagement. Conclusions: We determined that interventions for older adults with chronic pain should address both physiological and psychological factors to boost exercise participation. This research emphasizes feasible exercise types and key factors to enhance engagement. Future research should focus on developing targeted intervention programs that incorporate these findings to improve the quality of life for this population. Full article
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