Search Results (1,196)

Hydraulic actions may compromise dam foundation stability. Helical anchors have been used in dam foundation reinforcement projects because of the advantages of large uplift and compression bearing capacity, fast installation, and convenient recovery. However, the research on the anchor plate, which plays a key role in the bearing performance of helical anchors, is insufficient at present. Based on the finite element model of helical anchor, this study reveals the failure mode and influencing factors of the anchor plate and establishes the theoretical model of deformation calculation. The results showed that the helical anchor plate had obvious bending deformation when the dam foundation reinforced with a helical anchor reached large deformation. The helical anchor plate can be simplified to a flat circular disk. The stress distribution of the closed flat disk and the open flat disk was consistent with that of the helical disk. The maximum deformation of the closed flat disk was slightly smaller than that of the helical disk (less than 6%), and the deformation of the open flat disk was consistent with that of the helical disk. The results fill the blank of the design basis of helical anchor plate and provide a reference basis for the engineering design. Full article

In this study, steel fibers were used to improve the mechanical properties of high-strength self-compacting concrete (HSSCC), and its effect on the fracture mechanical properties was investigated by a three-point bending test with notched beams. Coupled with the digital image correlation (DIC) technique, the fracture process of steel-fiber-reinforced HSSCC was analyzed to elucidate the reinforcing and fracture-resisting mechanisms of steel fibers. The results indicate that the compressive strength and flexural strength of HSSCC cured for 28 days exhibited an initial decrease and then an enhancement as the volume fraction (V_f) of steel fibers increased, whereas the flexural-to-compressive ratio linearly increased. All of them reached their maximum of 110.5 MPa, 11.8 MPa, and 1/9 at 1.2 vol% steel fibers, respectively. Steel fibers significantly improved the peak load (F_P), peak opening displacement (CMOD_P), fracture toughness (K_IC), and fracture energy (G_F) of HSSCC. Compared with HSSCC without steel fibers (HSSCC-0), the F_P, K_IC, CMOD_P, and G_F of HSSCC with 1.2 vol% (HSSCC-1.2) increased by 23.5%, 45.4%, 11.1 times, and 20.1 times, respectively. The horizontal displacement and horizontal strain of steel-fiber-reinforced HSSCC both increased significantly with an increasing V_f. HSSCC-0 experienced unstable fracture without the occurrence of a fracture process zone during the whole fracture damage, whereas the fracture process zone formed at the notched beam tip of HSSCC-1.2 at its initial loading stage and further extended upward in the beams of high-strength self-compacting concrete with a 0.6% volume fraction of steel fibers and HSSCC-1.2 as the load approaches and reaches the peak. Full article

Two-wheeled self-balancing robots (TWSBRs) are underactuated, inherently nonlinear systems that exhibit unstable dynamics. Due to their structural simplicity and rich control challenges, TWSBRs have become a standard platform for validating and benchmarking various control algorithms. This paper presents a comprehensive and structured review of control strategies applied to TWSBRs, encompassing classical linear approaches such as PID and LQR, modern nonlinear methods including sliding mode control (SMC), model predictive control (MPC), and intelligent techniques such as fuzzy logic, neural networks, and reinforcement learning. Additionally, supporting techniques such as state estimation, observer design, and filtering are discussed in the context of their importance to control implementation. The evolution of control theory is analyzed, and a detailed taxonomy is proposed to classify existing works. Notably, a comparative analysis section is included, offering practical guidelines for selecting suitable control strategies based on system complexity, computational resources, and robustness requirements. This review aims to support both academic research and real-world applications by summarizing key methodologies, identifying open challenges, and highlighting promising directions for future development. Full article

To investigate the crack extension mechanism in CFRP-reinforced concrete, this paper derives analytical expressions for the external load and crack opening displacement in the fracture process of CFRP concrete beams based on the crack emergence toughness criterion and the Paris displacement formula as the theoretical basis. A numerical iterative method was used to computationally simulate the fracture process of CFRP-reinforced concrete beams and to analyze the effect of different initial crack lengths on the fracture process. The research results indicate that the numerical simulation results of the crack initiation load are in good agreement with the test results, and the crack propagation curves and the test results are basically consistent before the CFRP-concrete interface peels off. The numerical results of ultimate load are lower than the test results, but it is safe for fracture prediction in actual engineering. With the increase in the initial crack length, the effect of the initial crack length on the critical effective crack propagation length is more obvious. Full article

This comprehensive review maps the fast-evolving landscape in which artificial intelligence (AI) and deep-learning (DL) techniques converge with the Internet of Things (IoT) to manage energy, comfort, and sustainability across smart environments. A PRISMA-guided search of four databases retrieved 1358 records; after applying inclusion criteria, 143 peer-reviewed studies published between January 2019 and April 2025 were analyzed. This review shows that AI-driven controllers—especially deep-reinforcement-learning agents—deliver median energy savings of 18–35% for HVAC and other major loads, consistently outperforming rule-based and model-predictive baselines. The evidence further reveals a rapid diversification of methods: graph-neural-network models now capture spatial interdependencies in dense sensor grids, federated-learning pilots address data-privacy constraints, and early integrations of large language models hint at natural-language analytics and control interfaces for heterogeneous IoT devices. Yet large-scale deployment remains hindered by fragmented and proprietary datasets, unresolved privacy and cybersecurity risks associated with continuous IoT telemetry, the growing carbon and compute footprints of ever-larger models, and poor interoperability among legacy equipment and modern edge nodes. The authors of researches therefore converges on several priorities: open, high-fidelity benchmarks that marry multivariate IoT sensor data with standardized metadata and occupant feedback; energy-aware, edge-optimized architectures that lower latency and power draw; privacy-centric learning frameworks that satisfy tightening regulations; hybrid physics-informed and explainable models that shorten commissioning time; and digital-twin platforms enriched by language-model reasoning to translate raw telemetry into actionable insights for facility managers and end users. Addressing these gaps will be pivotal to transforming isolated pilots into ubiquitous, trustworthy, and human-centered IoT ecosystems capable of delivering measurable gains in efficiency, resilience, and occupant wellbeing at scale. Full article

Physical challenges such as illness, injury, and disability significantly alter women’s relationships with their bodies, disrupting established notions of functionality and self-worth. This study re-examines the Holistic Model of Positive Body Image and Outdoor Physical Activity through secondary analysis focusing on women with illness, injury, and disability. From the original sample of N = 553 female participants, open-ended survey responses were identified from n = 84 participants (15.2%) who self-disclosed as having illness, injury, or disability to examine how outdoor settings facilitate positive body image. Through reflexive thematic analysis, the study revealed three key mechanisms: (1) personalized redefinition of functionality transcending standardized metrics, (2) therapeutic engagement with natural environments fostering embodied acceptance, and (3) cyclical reinforcement between physical capability and psychological wellbeing. The findings confirm the model’s utility while indicating necessary adaptations to address the fluctuating nature of body functionality. The adapted model emphasizes how outdoor recreational activities create contexts for reimagining body-self relationships across the spectrum of physical experiences—from temporary recovery to ongoing adaptation of persistent conditions—with implications for rehabilitation professionals, outdoor educators, and healthcare providers. Full article

Ultra-high-performance concrete (UHPC) jackets present a promising solution for enhancing the seismic resilience of seismically deficient reinforced concrete (RC) bridge columns. This study investigates jacket thickness optimization through integrated experimental and numerical analyses. Quasi-static cyclic tests on a control column and a specimen retrofitted with a 30-mm UHPC jacket over the plastic hinge region demonstrated significant performance improvements: delayed damage initiation, controlled cracking, a 24.6% increase in lateral load capacity, 139.5% higher energy dissipation at 3% drift, and mitigated post-peak strength degradation. A validated OpenSees numerical model accurately replicated this behavior and enabled parametric studies of 15-mm, 30-mm, and 45-mm jackets. Results identified the 30-mm thickness as optimal, balancing substantial gains in lateral strength (~12% higher than other thicknesses), ductility, and energy dissipation while avoiding premature failure modes—insufficient confinement in the 15-mm jacket and strain incompatibility-induced brittle failure in the 45-mm jacket. These findings provide quantitative design guidance, establishing 30 mm as the recommended thickness for efficient seismic retrofitting of existing RC bridge columns. Full article

Accurate and timely forecasting of cross-regional tourist flows is essential for sustainable destination management, yet existing models struggle with sparse data, complex spatiotemporal interactions, and limited interpretability. This paper presents SE-TFF, a multi-scale tourism-flow forecasting framework that couples a Squeeze-and-Excitation (SE) network with reinforcement-driven optimization to adaptively re-weight environmental, economic, and social features. A benchmark dataset of 17.8 million records from 64 countries and 743 cities (2016–2024) is compiled from the Open Travel Data repository in github (OPTD) for training and validation. SE-TFF introduces (i) a multi-channel SE module for fine-grained feature selection under heterogeneous conditions, (ii) a Top-K attention filter to preserve salient context in highly sparse matrices, and (iii) a Double-DQN layer that dynamically balances prediction objectives. Experimental results show SE-TFF attains 56.5% MAE and 65.6% RMSE reductions over the best baseline (ARIMAX) at 20% sparsity, with 0.92 × 10³ average MAE across multi-task outputs. SHAP analysis ranks climate anomalies, tourism revenue, and employment as dominant predictors. These gains demonstrate SE-TFF’s ability to deliver real-time, interpretable forecasts for data-limited destinations. Future work will incorporate real-time social media signals and larger multimodal datasets to enhance generalizability. Full article

Aerated accumulation bioreactors represent a promising alternative for the aerobic bioremediation of solid contaminated substrates. However, achieving homogeneous mixing and effective air distribution remains a key design challenge in solid-phase systems. This study presents the design and construction of a novel pilot-scale aerated bioreactor equipped with an angled-paddle agitation system, specifically developed to improve solid mixing and aeration. To evaluate the geometric configuration, a series of simulations were performed using the Discrete Element Method (DEM), with particle dynamics analyzed through the Lacey Mixing Index ($LMI$). Four paddle angles (0°, 15°, 45°, and 55°) were compared, with the 45° configuration achieving optimal performance, reaching $LMI$ values above 0.95 in less than 15 s and maintaining high homogeneity at a filling volume of 70%. These results confirm that the paddle angle significantly influences mixing efficiency in granular media. While this work focuses on engineering design and DEM-based validation, future studies will include experimental trials to evaluate biodegradation kinetics. The proposed design offers a scalable and adaptable solution for ex situ bioremediation applications. This work reinforces the value of integrating DEM simulations early in the bioreactor development process and opens pathways for further optimization and implementation in real-world environmental remediation scenarios. Full article

The relationship between economic progress and environmental quality remains a central focus in global sustainability discourse. This study examines the link between per capita economic growth and CO₂ emissions across 128 countries from 1996 to 2022, controlling for energy consumption, trade volume, and foreign direct investment (FDI) inflows. It also evaluates the role of governance quality—measured by regulatory quality and its volatility—while considering the globalization index as a confounding factor influencing CO₂ emissions. We test the Environmental Kuznets Curve (EKC) hypothesis, which suggests that emissions initially rise with income but decline after reaching a certain economic threshold. Our findings confirm the global presence of the EKC. The analysis further shows that trade openness, governance, and globalization significantly influence FDI inflows, with FDI, in turn, reinforcing institutional quality through improved governance and globalization indicators. However, in countries with weaker governance and regulatory frameworks, FDI tends to promote pollution-intensive industrial growth, lending support to aspects of the Pollution Haven Hypothesis (PHH). We find a significant departure in EKC explained by post-COVID governance and globalization compromises, which induced the environment towards the PHH phenomenon. These results highlight the need for context-specific policy measures that align economic development with environmental constraints. Full article

REG3A, a prominent member of the human regenerating islet-derived (REG) lectin family, plays a pivotal and multifaceted role in immune defense, inflammation, and cancer biology. Primarily expressed in gastrointestinal epithelial cells, REG3A reinforces barrier integrity, orchestrates mucosal immune responses, and regulates host–microbiota interactions. It also functions as a potent non-enzymatic antioxidant, protecting tissues from oxidative stress. REG3A expression is tightly regulated by inflammatory stimuli and is robustly induced during immune activation, where it limits microbial invasion, dampens tissue injury, and promotes epithelial repair. Beyond its antimicrobial and immunomodulatory properties, REG3A contributes to the resolution of inflammation and the maintenance of tissue homeostasis. However, its role in cancer is highly context-dependent. In some tumor types, REG3A fosters malignant progression by enhancing cell survival, proliferation, and invasiveness. In others, it acts as a tumor suppressor, inhibiting growth and metastatic potential. These opposing effects are likely dictated by a combination of factors, including the tissue of origin, the composition and dynamics of the tumor microenvironment, and the stage of disease progression. Additionally, the secreted nature of REG3A implies both local and systemic effects, further modulated by organ-specific physiology. Experimental variability may also reflect differences in methodologies, analytical tools, and model systems used. This review synthesizes current knowledge on the pleiotropic functions of REG3A, emphasizing its roles in epithelial defense, immune regulation, redox homeostasis, and oncogenesis. A deeper understanding of REG3A’s pleiotropic effects could open up new therapeutic avenues in both inflammatory disorders and cancer. Full article

Lumped plasticity models available in commercial software offer a limited resolution of damage distribution along structural members. This study presents an open-source workflow that combines force-based fiber elements in OpenSeesPy with automated 3D post-processing for visualizing distributed plasticity in reinforced concrete piles. A 60 cm diameter pile subjected to monotonic lateral loading is analyzed using both SAP2000’s default plastic hinges and OpenSeesPy fiber sections (Concrete02/Steel02). Although the fiber model incurs a runtime approximately 2.5 times greater, it captures the gradual spread of yielding and deterioration with improved fidelity. The presented workflow includes Python routines for interactive stress–strain visualization, facilitating the identification of critical sections and verification of strain limits. This approach offers a computationally feasible alternative for performance-based analysis with enhanced insight into member-level behavior. Because the entire workflow—from model definition through post-processing—is fully scripted in Python, any change to geometry, materials, or loading can be re-run in seconds, dramatically reducing the time taken to execute sensitivity analyses. Full article

Loco-manipulation tasks using humanoid robots have great practical value in various scenarios. While reinforcement learning (RL) has become a powerful tool for versatile and robust whole-body humanoid control, visuomotor control in loco-manipulation tasks with RL remains a great challenge due to their high dimensionality and long-horizon exploration issues. In this paper, we propose a loco-manipulation control framework for humanoid robots that utilizes model-free RL upon model-based control in the robot’s tasks space. It implements a visuomotor policy with depth-image input, and uses mid-way initialization and prioritized experience sampling to accelerate policy convergence. The proposed method is validated on typical loco-manipulation tasks of load carrying and door opening resulting in an overall success rate of 83%, where our framework automatically adjusts the robot motion in reaction to changes in the environment. Full article

Reinforcement learning (RL), a branch of artificial intelligence (AI), is becoming more popular in a variety of application fields such as games, workplaces, and behavioral analysis, due to its ability to model complex decision-making through interaction and feedback. Traditional systems for personality and behavior assessment often rely on self-reported questionnaires, which are prone to bias and manipulation. RL offers a compelling alternative by generating diverse, objective behavioral data through agent–environment interactions. In this paper, we propose a Reinforcement Learning-based framework in a game environment, where agents simulate personality-driven behavior using context-aware policies and exhibit a wide range of realistic actions. Our method, which is based on the OCEAN Five personality model—openness, conscientiousness, extroversion, agreeableness, and neuroticism—relates psychological profiles to in-game decision-making patterns. The agents are allowed to operate in numerous environments, observe behaviors that were modeled using another simulation system (HiDAC) and develop the skills needed to navigate and complete tasks. As a result, we are able to identify the personality types and team configurations that have the greatest effects on task performance and collaboration effectiveness. Using interaction data derived from self-play, we investigate the relationships between behaviors motivated by the personalities of the agents, communication styles, and team outcomes. The results demonstrate that in addition to having an effect on performance, personality-aware agents provide a solid methodology for producing realistic behavioral data, developing adaptive NPCs, and evaluating team-based scenarios in challenging settings. Full article

Urban road traffic congestion poses significant challenges to sustainable mobility in smart cities. Traditional traffic light systems, reliant on static or semi-fixed timers, fail to adapt to dynamic traffic conditions, exacerbating congestion and limiting inclusivity. To address these limitations, this paper proposes H-ATLM (a hybrid adaptive traffic lights management), a system utilizing the deep deterministic policy gradient (DDPG) reinforcement learning algorithm to optimize traffic light timings dynamically based on real-time data. The system integrates advanced sensing technologies, such as cameras and inductive loops, to monitor traffic conditions and adaptively adjust signal phases. Experimental results demonstrate significant improvements, including reductions in congestion (up to 50%), increases in throughput (up to 149%), and decreases in clearance times (up to 84%). These findings open the door for integrating accessibility-focused features such as adaptive signaling for accessible vehicles, dedicated lanes for paratransit services, and prioritized traffic flows for inclusive mobility. Full article