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Keywords = local reinforcement

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13 pages, 849 KiB  
Article
Morphofunctional Profile Focusing on Strength and Ultrasound of the Upper Limbs in Female Breast Cancer Survivors: A Comparative Cross-Sectional Study Between Groups with and Without Lymphoedema and Between Ipsilateral and Contralateral Limbs
by Ana Rafaela Cardozo Da Silva, Juliana Netto Maia, Vanessa Maria Da Silva Alves Gomes, Naiany Tenório, Juliana Fernandes de Souza Barbosa, Ana Claudia Souza da Silva, Vanessa Patrícia Soares de Sousa, Leila Maria Alvares Barbosa, Armèle de Fátima Dornelas de Andrade and Diego Dantas
Biomedicines 2025, 13(8), 1884; https://doi.org/10.3390/biomedicines13081884 (registering DOI) - 2 Aug 2025
Abstract
Background: Breast cancer is the most common neoplasm in women. Despite effective treatments, sequelae such as decreased muscle strength, upper limb dysfunction, and tissue changes are common, highlighting the need for functional assessments during rehabilitation. This study analysed the morphofunctional profile of [...] Read more.
Background: Breast cancer is the most common neoplasm in women. Despite effective treatments, sequelae such as decreased muscle strength, upper limb dysfunction, and tissue changes are common, highlighting the need for functional assessments during rehabilitation. This study analysed the morphofunctional profile of the upper limbs in breast cancer survivors, comparing muscle strength and ultrasound findings between groups with and without lymphoedema, as well as between ipsilateral and contralateral limbs. Methods: This cross-sectional study included female breast cancer survivors treated at an oncology physical therapy clinic. Muscle strength was measured using dynamometry (handgrip and arm flexor strength), and ultrasound assessed the thickness of the dermal–epidermal complex (DEC), subcutaneous tissue (SUB), and muscle (MT). Results: The upper limbs of 41 women were evaluated. No significant differences were observed between those with and without breast cancer-related lymphoedema (BCRL). When comparing the ipsilateral and contralateral limbs, significant reductions were observed in arm flexor strength (p < 0.001; 95% CI: −9.77 to −2.50), handgrip strength (p < 0.001; 95% CI: −4.10 to −1.22), and tissue thickness, with increased DEC thickness on the forearm (0.20 mm; p = 0.022) and arm flexors (0.25 mm; p < 0.001) of the ipsilateral limb. Conclusion: Significant differences in muscle strength and tissue structure between ipsilateral and contralateral limbs may reflect surgical and local pathophysiological effects. A trend toward reduced values for these parameters was also noted in limbs with BCRL, reinforcing the importance of future research to elucidate underlying mechanisms and guide more effective therapeutic strategies. Full article
(This article belongs to the Section Molecular and Translational Medicine)
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23 pages, 2497 KiB  
Article
Biosphere Reserves in Spain: A Holistic Commitment to Environmental and Cultural Heritage Within the 2030 Agenda
by Juan José Maldonado-Briegas, María Isabel Sánchez-Hernández and José María Corrales-Vázquez
Heritage 2025, 8(8), 309; https://doi.org/10.3390/heritage8080309 (registering DOI) - 2 Aug 2025
Abstract
Biosphere Reserves (BRs), designated by UNESCO, are uniquely positioned to serve as model territories for sustainable development, as they aim to harmonize biodiversity conservation with the socio-economic vitality and cultural identity of local communities. This work examines the commitment of the Spanish Network [...] Read more.
Biosphere Reserves (BRs), designated by UNESCO, are uniquely positioned to serve as model territories for sustainable development, as they aim to harmonize biodiversity conservation with the socio-economic vitality and cultural identity of local communities. This work examines the commitment of the Spanish Network of Biosphere Reserves to the United Nations 2030 Agenda and the Sustainable Development Goals (SDGs). Using a survey-based research design, this study assesses the extent to which the reserves have integrated the SDGs into their strategic frameworks and operational practices. It also identifies and analyses successful initiatives and best practices implemented across Spain that exemplify this integration. The findings highlight the need for enhanced awareness and understanding of the 2030 Agenda among stakeholders, alongside stronger mechanisms for participation, cooperation, and governance. The conclusion emphasises the importance of equipping all reserves with strategic planning tools and robust systems for monitoring, evaluation, and accountability. Moreover, the analysis of exemplary cases reveals the transformative potential of sustainability-oriented projects—not only in advancing environmental goals but also in revitalizing local economies and reinforcing cultural heritage. These insights contribute to a broader understanding of how BRs can act as dynamic laboratories for sustainable development and heritage preservation. Full article
(This article belongs to the Section Biological and Natural Heritage)
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31 pages, 5203 KiB  
Article
Projecting Extinction Risk and Assessing Conservation Effectiveness for Three Threatened Relict Ferns in the Western Mediterranean Basin
by Ángel Enrique Salvo-Tierra, Jaime Francisco Pereña-Ortiz and Ángel Ruiz-Valero
Plants 2025, 14(15), 2380; https://doi.org/10.3390/plants14152380 (registering DOI) - 1 Aug 2025
Abstract
Relict fern species, confined to microhabitats with stable historical conditions, are especially vulnerable to climate change. The Alboran Arc hosts a unique relict fern flora, including Culcita macrocarpa, Diplazium caudatum, and Pteris incompleta, and functions as a major Pleistocene refuge. [...] Read more.
Relict fern species, confined to microhabitats with stable historical conditions, are especially vulnerable to climate change. The Alboran Arc hosts a unique relict fern flora, including Culcita macrocarpa, Diplazium caudatum, and Pteris incompleta, and functions as a major Pleistocene refuge. This study assesses the population trends and climate sensitivity of these species in Los Alcornocales Natural Park using annual abundance time series for a decade, empirical survival projections, and principal component analysis to identify key climatic drivers. Results reveal distinct climate response clusters among populations, though intra-specific variation highlights the importance of local conditions. Climate change is already impacting population viability, especially for P. incompleta, which shows high sensitivity to rising maximum temperatures and prolonged heatwaves. Climate-driven models forecast more severe declines than empirical ones, particularly for C. macrocarpa and P. incompleta, with the latter showing a projected collapse by the mid-century. In contrast, D. caudatum exhibits moderate vulnerability. Crucially, the divergence between models underscores the impact of conservation efforts: without reinforcement and reintroduction actions, projected declines would likely be more severe. These results project a decline in the populations of the studied ferns, highlighting the urgent need to continue implementing both in situ and ex situ conservation measures. Full article
(This article belongs to the Special Issue Plant Conservation Science and Practice)
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29 pages, 5505 KiB  
Article
Triaxial Response and Elastoplastic Constitutive Model for Artificially Cemented Granular Materials
by Xiaochun Yu, Yuchen Ye, Anyu Yang and Jie Yang
Buildings 2025, 15(15), 2721; https://doi.org/10.3390/buildings15152721 (registering DOI) - 1 Aug 2025
Abstract
Because artificially cemented granular (ACG) materials employ diverse combinations of aggregates and binders—including cemented soil, low-cement-content cemented sand and gravel (LCSG), and concrete—their stress–strain responses vary widely. In LCSG, the binder dosage is typically limited to 40–80 kg/m3 and the sand–gravel skeleton [...] Read more.
Because artificially cemented granular (ACG) materials employ diverse combinations of aggregates and binders—including cemented soil, low-cement-content cemented sand and gravel (LCSG), and concrete—their stress–strain responses vary widely. In LCSG, the binder dosage is typically limited to 40–80 kg/m3 and the sand–gravel skeleton is often obtained directly from on-site or nearby excavation spoil, endowing the material with a markedly lower embodied carbon footprint and strong alignment with current low-carbon, green-construction objectives. Yet, such heterogeneity makes a single material-specific constitutive model inadequate for predicting the mechanical behavior of other ACG variants, thereby constraining broader applications in dam construction and foundation reinforcement. This study systematically summarizes and analyzes the stress–strain and volumetric strain–axial strain characteristics of ACG materials under conventional triaxial conditions. Generalized hyperbolic and parabolic equations are employed to describe these two families of curves, and closed-form expressions are proposed for key mechanical indices—peak strength, elastic modulus, and shear dilation behavior. Building on generalized plasticity theory, we derive the plastic flow direction vector, loading direction vector, and plastic modulus, and develop a concise, transferable elastoplastic model suitable for the full spectrum of ACG materials. Validation against triaxial data for rock-fill materials, LCSG, and cemented coal–gangue backfill shows that the model reproduces the stress and deformation paths of each material class with high accuracy. Quantitative evaluation of the peak values indicates that the proposed constitutive model predicts peak deviatoric stress with an error of 1.36% and peak volumetric strain with an error of 3.78%. The corresponding coefficients of determination R2 between the predicted and measured values are 0.997 for peak stress and 0.987 for peak volumetric strain, demonstrating the excellent engineering accuracy of the proposed model. The results provide a unified theoretical basis for deploying ACG—particularly its low-cement, locally sourced variants—in low-carbon dam construction, foundation rehabilitation, and other sustainable civil engineering projects. Full article
(This article belongs to the Special Issue Low Carbon and Green Materials in Construction—3rd Edition)
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14 pages, 1714 KiB  
Article
A Kalman Filter-Based Localization Calibration Method Optimized by Reinforcement Learning and Information Matrix Fusion
by Zijia Huang, Qiushi Xu, Menghao Sun and Xuzhen Zhu
Entropy 2025, 27(8), 821; https://doi.org/10.3390/e27080821 (registering DOI) - 1 Aug 2025
Abstract
To address the degradation in localization accuracy caused by insufficient robustness of filter parameters and inefficient multi-trajectory data fusion in dynamic environments, this paper proposes a Kalman filter-based localization calibration method optimized by reinforcement learning and information matrix fusion (RL-IMKF). An actor–critic reinforcement [...] Read more.
To address the degradation in localization accuracy caused by insufficient robustness of filter parameters and inefficient multi-trajectory data fusion in dynamic environments, this paper proposes a Kalman filter-based localization calibration method optimized by reinforcement learning and information matrix fusion (RL-IMKF). An actor–critic reinforcement learning network is designed to adaptively adjust the state covariance matrix, enhancing the Kalman filter’s adaptability to environmental changes. Meanwhile, a multi-trajectory information matrix fusion strategy is introduced, which aggregates multiple trajectories in the information domain via weighted inverse covariance matrices to suppress error propagation and improve system consistency. Experiments using both simulated and real-world sensor data demonstrate that the proposed method outperforms traditional extended Kalman filter approaches in terms of localization accuracy and stability, providing a novel solution for cooperative localization calibration of unmanned aerial vehicle (UAV) swarms in dynamic environments. Full article
(This article belongs to the Special Issue Complexity, Entropy and the Physics of Information II)
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26 pages, 2081 KiB  
Article
Tariff-Sensitive Global Supply Chains: Semi-Markov Decision Approach with Reinforcement Learning
by Duygu Yilmaz Eroglu
Systems 2025, 13(8), 645; https://doi.org/10.3390/systems13080645 (registering DOI) - 1 Aug 2025
Abstract
Global supply chains often face uncertainties in production lead times, fluctuating exchange rates, and varying tariff regulations, all of which can significantly impact total profit. To address these challenges, this study formulates a multi-country supply chain problem as a Semi-Markov Decision Process (SMDP), [...] Read more.
Global supply chains often face uncertainties in production lead times, fluctuating exchange rates, and varying tariff regulations, all of which can significantly impact total profit. To address these challenges, this study formulates a multi-country supply chain problem as a Semi-Markov Decision Process (SMDP), integrating both currency variability and tariff levels. Using a Q-learning-based method (SMART), we explore three scenarios: (1) wide currency gaps under a uniform tariff, (2) narrowed currency gaps encouraging more local sourcing, and (3) distinct tariff structures that highlight how varying duties can reshape global fulfillment decisions. Beyond these baselines we analyze uncertainty-extended variants and targeted sensitivities (quantity discounts, tariff escalation, and the joint influence of inventory holding costs and tariff costs). Simulation results, accompanied by policy heatmaps and performance metrics, illustrate how small or large shifts in exchange rates and tariffs can alter sourcing strategies, transportation modes, and inventory management. A Deep Q-Network (DQN) is also applied to validate the Q-learning policy, demonstrating alignment with a more advanced neural model for moderate-scale problems. These findings underscore the adaptability of reinforcement learning in guiding practitioners and policymakers, especially under rapidly changing trade environments where exchange rate volatility and incremental tariff changes demand robust, data-driven decision-making. Full article
(This article belongs to the Special Issue Modelling and Simulation of Transportation Systems)
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15 pages, 1487 KiB  
Article
Protective Effects of a Bifidobacterium-Based Probiotic Mixture on Gut Inflammation and Barrier Function
by Yeji You, Tae-Rahk Kim, Minn Sohn, Dongmin Yoo and Jeseong Park
Microbiol. Res. 2025, 16(8), 168; https://doi.org/10.3390/microbiolres16080168 - 1 Aug 2025
Abstract
Disruption of the intestinal epithelial barrier is a key driver of gut-derived inflammation in various disorders, yet strategies to preserve or restore barrier integrity remain limited. To address this, we evaluated a four-strain Bifidobacterium mixture—selected for complementary anti-inflammatory potency and industrial scalability—in lipopolysaccharide [...] Read more.
Disruption of the intestinal epithelial barrier is a key driver of gut-derived inflammation in various disorders, yet strategies to preserve or restore barrier integrity remain limited. To address this, we evaluated a four-strain Bifidobacterium mixture—selected for complementary anti-inflammatory potency and industrial scalability—in lipopolysaccharide (LPS)-challenged RAW 264.7 macrophages and a Caco-2/THP-1 transwell co-culture model. Pretreatment with the probiotic blend reduced nitric oxide (NO) release in a dose-dependent manner by 25.9–48.3% and significantly down-regulated the pro-inflammatory markers in macrophages. In the co-culture system, the formulation decreased these markers, increased transepithelial electrical resistance (TEER) by up to 31% at 105 colony-forming unit (CFU)/mL after 48 h, and preserved the membrane localization of tight junction (TJ) proteins. Adhesion to Caco-2 cells (≈ 6%) matched that of the benchmark probiotic Lacticaseibacillus rhamnosus GG, suggesting direct epithelial engagement. These in vitro findings demonstrate that this probiotic mixture can attenuate LPS-driven inflammation and reinforce epithelial architecture, providing a mechanistic basis for its further evaluation in animal models and clinical studies of intestinal inflammatory disorders. Full article
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28 pages, 7472 KiB  
Article
Small but Mighty: A Lightweight Feature Enhancement Strategy for LiDAR Odometry in Challenging Environments
by Jiaping Chen, Kebin Jia and Zhihao Wei
Remote Sens. 2025, 17(15), 2656; https://doi.org/10.3390/rs17152656 (registering DOI) - 31 Jul 2025
Abstract
LiDAR-based Simultaneous Localization and Mapping (SLAM) serves as a fundamental technology for autonomous navigation. However, in complex environments, LiDAR odometry often experience degraded localization accuracy and robustness. This paper proposes a computationally efficient enhancement strategy for LiDAR odometry, which improves system performance by [...] Read more.
LiDAR-based Simultaneous Localization and Mapping (SLAM) serves as a fundamental technology for autonomous navigation. However, in complex environments, LiDAR odometry often experience degraded localization accuracy and robustness. This paper proposes a computationally efficient enhancement strategy for LiDAR odometry, which improves system performance by reinforcing high-quality features throughout the optimization process. For non-ground features, the method employs statistical geometric analysis to identify stable points and incorporates a contribution-weighted optimization scheme to strengthen their impact in point-to-plane and point-to-line constraints. In parallel, for ground features, locally stable planar surfaces are fitted to replace discrete point correspondences, enabling more consistent point-to-plane constraint formulation during ground registration. Experimental results on the KITTI and M2DGR datasets demonstrated that the proposed method significantly improves localization accuracy and system robustness, while preserving real-time performance with minimal computational overhead. The performance gains were particularly notable in scenarios dominated by unstructured environments. Full article
(This article belongs to the Special Issue Laser Scanning in Environmental and Engineering Applications)
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20 pages, 10604 KiB  
Article
A Safety-Based Approach for the Design of an Innovative Microvehicle
by Michelangelo-Santo Gulino, Susanna Papini, Giovanni Zonfrillo, Thomas Unger, Peter Miklis and Dario Vangi
Designs 2025, 9(4), 90; https://doi.org/10.3390/designs9040090 (registering DOI) - 31 Jul 2025
Abstract
The growing popularity of Personal Light Electric Vehicles (PLEVs), such as e-scooters, has revolutionized urban mobility by offering compact, cost-effective, and environmentally friendly transportation solutions. However, safety concerns, including inadequate infrastructure, poor protective measures, and high accident rates, remain critical challenges. This paper [...] Read more.
The growing popularity of Personal Light Electric Vehicles (PLEVs), such as e-scooters, has revolutionized urban mobility by offering compact, cost-effective, and environmentally friendly transportation solutions. However, safety concerns, including inadequate infrastructure, poor protective measures, and high accident rates, remain critical challenges. This paper presents the design and development of an innovative self-balancing microvehicle under the H2020 LEONARDO project, which aims to address these challenges through advanced engineering and user-centric design. The vehicle combines features of monowheels and e-scooters, integrating cutting-edge technologies to enhance safety, stability, and usability. The design adheres to European regulations, including Germany’s eKFV standards, and incorporates user preferences identified through representative online surveys of 1500 PLEV users. These preferences include improved handling on uneven surfaces, enhanced signaling capabilities, and reduced instability during maneuvers. The prototype features a lightweight composite structure reinforced with carbon fibers, a high-torque motorized front wheel, and multiple speed modes tailored to different conditions, such as travel in pedestrian areas, use by novice riders, and advanced users. Braking tests demonstrate deceleration values of up to 3.5 m/s2, comparable to PLEV market standards and exceeding regulatory minimums, while smooth acceleration ramps ensure rider stability and safety. Additional features, such as identification plates and weight-dependent motor control, enhance compliance with local traffic rules and prevent misuse. The vehicle’s design also addresses common safety concerns, such as curb navigation and signaling, by incorporating large-diameter wheels, increased ground clearance, and electrically operated direction indicators. Future upgrades include the addition of a second rear wheel for enhanced stability, skateboard-like rear axle modifications for improved maneuverability, and hybrid supercapacitors to minimize fire risks and extend battery life. With its focus on safety, regulatory compliance, and rider-friendly innovations, this microvehicle represents a significant advancement in promoting safe and sustainable urban mobility. Full article
(This article belongs to the Section Vehicle Engineering Design)
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20 pages, 2619 KiB  
Article
Fatigue Life Prediction of CFRP-FBG Sensor-Reinforced RC Beams Enabled by LSTM-Based Deep Learning
by Minrui Jia, Chenxia Zhou, Xiaoyuan Pei, Zhiwei Xu, Wen Xu and Zhenkai Wan
Polymers 2025, 17(15), 2112; https://doi.org/10.3390/polym17152112 - 31 Jul 2025
Viewed by 4
Abstract
Amidst the escalating demand for high-precision structural health monitoring in large-scale engineering applications, carbon fiber-reinforced polymer fiber Bragg grating (CFRP-FBG) sensors have emerged as a pivotal technology for fatigue life evaluation, owing to their exceptional sensitivity and intrinsic immunity to electromagnetic interference. A [...] Read more.
Amidst the escalating demand for high-precision structural health monitoring in large-scale engineering applications, carbon fiber-reinforced polymer fiber Bragg grating (CFRP-FBG) sensors have emerged as a pivotal technology for fatigue life evaluation, owing to their exceptional sensitivity and intrinsic immunity to electromagnetic interference. A time-series predictive architecture based on long short-term memory (LSTM) networks is developed in this work to facilitate intelligent fatigue life assessment of structures subjected to complex cyclic loading by capturing and modeling critical spectral characteristics of CFRP-FBG sensors, specifically the side-mode suppression ratio and main-lobe peak-to-valley ratio. To enhance model robustness and generalization, Principal Component Analysis (PCA) was employed to isolate the most salient spectral features, followed by data preprocessing via normalization and model optimization through the integration of the Adam optimizer and Dropout regularization strategy. Relative to conventional Backpropagation (BP) neural networks, the LSTM model demonstrated a substantial improvement in predicting the side-mode suppression ratio, achieving a 61.62% reduction in mean squared error (MSE) and a 34.99% decrease in root mean squared error (RMSE), thereby markedly enhancing robustness to outliers and ensuring greater overall prediction stability. In predicting the peak-to-valley ratio, the model attained a notable 24.9% decrease in mean absolute error (MAE) and a 21.2% reduction in root mean squared error (RMSE), thereby substantially curtailing localized inaccuracies. The forecasted confidence intervals were correspondingly narrower and exhibited diminished fluctuation, highlighting the LSTM architecture’s enhanced proficiency in capturing nonlinear dynamics and modeling temporal dependencies. The proposed method manifests considerable practical engineering relevance and delivers resilient intelligent assistance for the seamless implementation of CFRP-FBG sensor technology in structural health monitoring and fatigue life prognostics. Full article
(This article belongs to the Section Artificial Intelligence in Polymer Science)
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18 pages, 3506 KiB  
Review
A Review of Spatial Positioning Methods Applied to Magnetic Climbing Robots
by Haolei Ru, Meiping Sheng, Jiahui Qi, Zhanghao Li, Lei Cheng, Jiahao Zhang, Jiangjian Xiao, Fei Gao, Baolei Wang and Qingwei Jia
Electronics 2025, 14(15), 3069; https://doi.org/10.3390/electronics14153069 (registering DOI) - 31 Jul 2025
Abstract
Magnetic climbing robots hold significant value for operations in complex industrial environments, particularly for the inspection and maintenance of large-scale metal structures. High-precision spatial positioning is the foundation for enabling autonomous and intelligent operations in such environments. However, the existing literature lacks a [...] Read more.
Magnetic climbing robots hold significant value for operations in complex industrial environments, particularly for the inspection and maintenance of large-scale metal structures. High-precision spatial positioning is the foundation for enabling autonomous and intelligent operations in such environments. However, the existing literature lacks a systematic and comprehensive review of spatial positioning techniques tailored to magnetic climbing robots. This paper addresses this gap by categorizing and evaluating current spatial positioning approaches. Initially, single-sensor-based methods are analyzed with a focus on external sensor approaches. Then, multi-sensor fusion methods are explored to overcome the shortcomings of single-sensor-based approaches. Multi-sensor fusion methods include simultaneous localization and mapping (SLAM), integrated positioning systems, and multi-robot cooperative positioning. To address non-uniform noise and environmental interference, both analytical and learning-based reinforcement approaches are reviewed. Common analytical methods include Kalman-type filtering, particle filtering, and correlation filtering, while typical learning-based approaches involve deep reinforcement learning (DRL) and neural networks (NNs). Finally, challenges and future development trends are discussed. Multi-sensor fusion and lightweight design are the future trends in the advancement of spatial positioning technologies for magnetic climbing robots. Full article
(This article belongs to the Special Issue Advancements in Robotics: Perception, Manipulation, and Interaction)
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23 pages, 5688 KiB  
Article
Fragility Assessment and Reinforcement Strategies for Transmission Towers Under Extreme Wind Loads
by Lanxi Weng, Jiaren Yi, Fubin Chen and Zhenru Shu
Appl. Sci. 2025, 15(15), 8493; https://doi.org/10.3390/app15158493 (registering DOI) - 31 Jul 2025
Viewed by 36
Abstract
Transmission towers are particularly vulnerable to extreme wind events, which can lead to structural damage or collapse, thereby compromising the stability of power transmission systems. Enhancing the wind-resistant capacity of these towers is therefore critical for improving the reliability and resilience of electrical [...] Read more.
Transmission towers are particularly vulnerable to extreme wind events, which can lead to structural damage or collapse, thereby compromising the stability of power transmission systems. Enhancing the wind-resistant capacity of these towers is therefore critical for improving the reliability and resilience of electrical infrastructure. This study utilizes finite element analysis (FEA) to evaluate the structural response of a 220 kV transmission tower subjected to fluctuating wind loads, effectively capturing the dynamic characteristics of wind-induced forces. A comprehensive dynamic analysis is conducted to account for uncertainties in wind loading and variations in wind direction. Through this approach, this study identifies the most critical wind angle and local structural weaknesses, as well as determines the threshold wind speed that precipitates structural collapse. To improve structural resilience, a concurrent multi-scale modeling strategy is adopted. This allows for localized analysis of vulnerable components while maintaining a holistic understanding of the tower’s global behavior. To mitigate failure risks, the traditional perforated plate reinforcement technique is implemented. The reinforcement’s effectiveness is evaluated based on its impact on load-bearing capacity, displacement control, and stress redistribution. Results reveal that the critical wind direction is 45°, with failure predominantly initiating from instability in the third section of the tower leg. Post-reinforcement analysis demonstrates a marked improvement in structural performance, evidenced by a significant reduction in top displacement and stress intensity in the critical leg section. Overall, these findings contribute to a deeper understanding of the wind-induced fragility of transmission towers and offer practical reinforcement strategies that can be applied to enhance their structural integrity under extreme wind conditions. Full article
(This article belongs to the Section Civil Engineering)
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25 pages, 16276 KiB  
Article
Localized Compression Behavior of GFRP Grid Web–Concrete Composite Beams: Experimental, Numerical, and Analytical Studies
by Yunde Li, Hai Cao, Yang Zhou, Weibo Kong, Kun Yu, Haoting Jiang and Zhongya Zhang
Buildings 2025, 15(15), 2693; https://doi.org/10.3390/buildings15152693 - 30 Jul 2025
Viewed by 94
Abstract
Glass fiber-reinforced polymer (GFRP) composites exhibit significant advantages over conventional structural webbing materials, including lightweight and corrosion resistance. This study investigates the localized compression performance of the proposed GFRP grid web–concrete composite beam through experimental and numerical analyses. Three specimen groups with variable [...] Read more.
Glass fiber-reinforced polymer (GFRP) composites exhibit significant advantages over conventional structural webbing materials, including lightweight and corrosion resistance. This study investigates the localized compression performance of the proposed GFRP grid web–concrete composite beam through experimental and numerical analyses. Three specimen groups with variable shear-span ratios (λ = 1.43, 1.77) and local stiffener specimens were designed to assess their localized compressive behavior. Experimental results reveal that a 19.2% reduction in shear-span ratio enhances ultimate load capacity by 22.93% and improves stiffness by 66.85%, with additional performance gains of 77.53% in strength and 94.29% in stiffness achieved through local stiffener implementation. In addition, finite element (FE) analysis demonstrated a strong correlation with experimental results, showing less than 5% deviation in ultimate load predictions while accurately predicting stress distributions and failure modes. FE parametric analysis showed that increasing the grid thickness and decreasing the grid spacing within a reasonable range can considerably enhance the localized compression performance. The proposed analytical model, based on Winkler elastic foundation theory, predicts ultimate compression capacities within 10% of both the experimental and numerical results. However, the GFRP grid strength adjustment factor βg should be further refined through additional experiments and numerical analyses to improve reliability. Full article
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24 pages, 1508 KiB  
Article
Genomic Prediction of Adaptation in Common Bean (Phaseolus vulgaris L.) × Tepary Bean (P. acutifolius A. Gray) Hybrids
by Felipe López-Hernández, Diego F. Villanueva-Mejía, Adriana Patricia Tofiño-Rivera and Andrés J. Cortés
Int. J. Mol. Sci. 2025, 26(15), 7370; https://doi.org/10.3390/ijms26157370 - 30 Jul 2025
Viewed by 193
Abstract
Climate change is jeopardizing global food security, with at least 713 million people facing hunger. To face this challenge, legumes as common beans could offer a nature-based solution, sourcing nutrients and dietary fiber, especially for rural communities in Latin America and Africa. However, [...] Read more.
Climate change is jeopardizing global food security, with at least 713 million people facing hunger. To face this challenge, legumes as common beans could offer a nature-based solution, sourcing nutrients and dietary fiber, especially for rural communities in Latin America and Africa. However, since common beans are generally heat and drought susceptible, it is imperative to speed up their molecular introgressive adaptive breeding so that they can be cultivated in regions affected by extreme weather. Therefore, this study aimed to couple an advanced panel of common bean (Phaseolus vulgaris L.) × tolerant Tepary bean (P. acutifolius A. Gray) interspecific lines with Bayesian regression algorithms to forecast adaptation to the humid and dry sub-regions at the Caribbean coast of Colombia, where the common bean typically exhibits maladaptation to extreme heat waves. A total of 87 advanced lines with hybrid ancestries were successfully bred, surpassing the interspecific incompatibilities. This hybrid panel was genotyped by sequencing (GBS), leading to the discovery of 15,645 single-nucleotide polymorphism (SNP) markers. Three yield components (yield per plant, and number of seeds and pods) and two biomass variables (vegetative and seed biomass) were recorded for each genotype and inputted in several Bayesian regression models to identify the top genotypes with the best genetic breeding values across three localities on the Colombian coast. We comparatively analyzed several regression approaches, and the model with the best performance for all traits and localities was BayesC. Also, we compared the utilization of all markers and only those determined as associated by a priori genome-wide association studies (GWAS) models. Better prediction ability with the complete SNP set was indicative of missing heritability as part of GWAS reconstructions. Furthermore, optimal SNP sets per trait and locality were determined as per the top 500 most explicative markers according to their β regression effects. These 500 SNPs, on average, overlapped in 5.24% across localities, which reinforced the locality-dependent nature of polygenic adaptation. Finally, we retrieved the genomic estimated breeding values (GEBVs) and selected the top 10 genotypes for each trait and locality as part of a recommendation scheme targeting narrow adaption in the Caribbean. After validation in field conditions and for screening stability, candidate genotypes and SNPs may be used in further introgressive breeding cycles for adaptation. Full article
(This article belongs to the Special Issue Plant Breeding and Genetics: New Findings and Perspectives)
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18 pages, 5013 KiB  
Article
Enhancing Document Forgery Detection with Edge-Focused Deep Learning
by Yong-Yeol Bae, Dae-Jea Cho and Ki-Hyun Jung
Symmetry 2025, 17(8), 1208; https://doi.org/10.3390/sym17081208 - 30 Jul 2025
Viewed by 136
Abstract
Detecting manipulated document images is essential for verifying the authenticity of official records and preventing document forgery. However, forgery artifacts are often subtle and localized in fine-grained regions, such as text boundaries or character outlines, where visual symmetry and structural regularity are typically [...] Read more.
Detecting manipulated document images is essential for verifying the authenticity of official records and preventing document forgery. However, forgery artifacts are often subtle and localized in fine-grained regions, such as text boundaries or character outlines, where visual symmetry and structural regularity are typically expected. These manipulations can disrupt the inherent symmetry of document layouts, making the detection of such inconsistencies crucial for forgery identification. Conventional CNN-based models face limitations in capturing such edge-level asymmetric features, as edge-related information tends to weaken through repeated convolution and pooling operations. To address this issue, this study proposes an edge-focused method composed of two components: the Edge Attention (EA) layer and the Edge Concatenation (EC) layer. The EA layer dynamically identifies channels that are highly responsive to edge features in the input feature map and applies learnable weights to emphasize them, enhancing the representation of boundary-related information, thereby emphasizing structurally significant boundaries. Subsequently, the EC layer extracts edge maps from the input image using the Sobel filter and concatenates them with the original feature maps along the channel dimension, allowing the model to explicitly incorporate edge information. To evaluate the effectiveness and compatibility of the proposed method, it was initially applied to a simple CNN architecture to isolate its impact. Subsequently, it was integrated into various widely used models, including DenseNet121, ResNet50, Vision Transformer (ViT), and a CAE-SVM-based document forgery detection model. Experiments were conducted on the DocTamper, Receipt, and MIDV-2020 datasets to assess classification accuracy and F1-score using both original and forged text images. Across all model architectures and datasets, the proposed EA–EC method consistently improved model performance, particularly by increasing sensitivity to asymmetric manipulations around text boundaries. These results demonstrate that the proposed edge-focused approach is not only effective but also highly adaptable, serving as a lightweight and modular extension that can be easily incorporated into existing deep learning-based document forgery detection frameworks. By reinforcing attention to structural inconsistencies often missed by standard convolutional networks, the proposed method provides a practical solution for enhancing the robustness and generalizability of forgery detection systems. Full article
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