Search Results (8,945)

Driven by the growing demand for functional textiles featuring excellent waterproofness, moisture permeability and mechanical robustness in outdoor sportswear, medical protection and technical apparel, traditional pongee—despite its desirable softness, high wrinkle resistance and good stability as an ideal substrate fabric—is severely restricted in further application by its intrinsically poor hydrostatic pressure resistance in extremely wet environments. Accordingly, we developed a modified waterborne polyurethane (WPU) coating for pongee substrates to fabricate functional textiles that maintain high hydrostatic pressure resistance while possessing good mechanical properties and increased UV absorption. In this study, by using the sol–gel method, an amorphous silicon dioxide (SiO₂) coating layer was constructed on the surface of titanium dioxide (TiO₂) particles, forming silica-modified titania particles (SiO₂/TiO₂). These SiO₂-modified particles were subsequently physically blended with an anionic waterborne polyurethane system that had been previously modified with a polyester-type modifier A to enhance its hydrostatic pressure resistance. The resulting composite coating was designed to combine the high hydrostatic pressure resistance inherited from the modified WPU matrix, the mechanical reinforcement and increased UV absorption contributed by SiO₂/TiO₂, and satisfactory water repellency on fabric substrates. The results indicate that the incorporation of an appropriate amount of modifier A into the prepolymer system significantly enhances hydrostatic pressure resistance while maintaining high elongation at break. At a SiO₂/TiO₂ loading of 0.2 wt%, the composite film exhibits optimal comprehensive performance, characterized by superior mechanical properties, low water absorption, and static water contact angles exceeding 100° for coated fabrics. SiO₂/TiO₂ composite WPU coatings substantially improve hydrostatic pressure resistance across various fabrics, with 380T polyester taffeta demonstrating the best performance. This resistance remains remarkably stable after standard washing, indicating excellent wash fastness and practical applicability. Full article

As sustainable finance continues to advance, an important question is how scientifically designed and well-targeted policies can curb corporate ESG greenwashing and improve the quality of firms’ ESG and sustainability disclosure. From the perspective of fiscal–financial policy coordination, we exploit the green loan interest subsidy policy (GLIS) as a quasi-natural experiment and develop an analytical framework around four policy components: commercial banks’ information screening, local governments’ green screening, the subsidy instrument’s leverage and certification effects, and firms’ internal green governance. Within this framework, we examine whether the GLIS can restrain corporate ESG greenwashing. Using Chinese listed firms from 2009 to 2022 as the sample and identifying the effect through a multi-period difference-in-differences (DID) model, we find that the GLIS significantly curbs corporate ESG greenwashing. In exploring the underlying channels, we find that the GLIS curbs corporate ESG greenwashing by strengthening commercial banks’ information screening, enhancing local governments’ green screening, easing firms’ external financing constraints, and reinforcing firms’ internal green governance. Further analysis indicates that the inhibitory effect of the GLIS on corporate ESG greenwashing is more pronounced among non-state-owned firms, firms in the growth stage, firms in heavily polluting industries, and firms located in regions with weaker resource endowments. In addition, the stronger a firm’s digital technology R&D capability and corporate governance capability, the greater the restraining effect of the GLIS on its ESG greenwashing. By systematically evaluating the governance effect of fiscal–financial policy coordination on corporate ESG greenwashing, our study provides useful insights for governments seeking to improve green finance policies and optimize the coordination of green policy instruments. Full article

Aortic stenosis is predominantly treated through transcatheter bioprosthetic heart valve implantation. However, the materials used in these devices are prone to premature failure. Polymer heart valves provide an alternative to current commercial devices, offering materials with greater durability and customisation through fibre reinforcement. Given the wide range of available materials and structures, there is a need for a systematic and efficient approach to designing and optimising novel bioinspired polymeric leaflets. This work presents a framework that employs computational modelling and Design of Experiments (DOE) tools to optimise bioinspired, 3D-printed, fibre-reinforced polymer leaflets made using melt electrowriting (MEW). Here, finite element (FE) models are created to represent MEW fibre-reinforced polymer leaflets for application in a transcatheter aortic heart valve. The behaviour of this valve under physiological loading conditions is modelled to predict valve performance and leaflet material response. These models were first used to investigate the impact of fibre orientation on valve performance and leaflet response, thereby demonstrating the benefits of a bioinspired fibre reinforcement structure. Using a DOE approach, the structural combination of MEW fibre reinforcement and an elastomeric matrix was optimised to improve valve performance and reduce leaflet stress and strain. Overall, the framework offers an efficient and versatile methodology for optimising fibre-reinforced polymer leaflets using an in silico approach, thereby reducing the need for physical prototyping and testing of these next-generation devices during early product development. Full article

Rapid urbanization in tropical Asia has led to a critical loss of green cover, exacerbating urban environmental challenges. While green roofs offer a promising Nature-based solution, their implementation in Asian countries is hindered by the prevalence of sloped roofs and high structural conversion costs. This research addresses this gap by developing a novel, lightweight vegetative roof tile designed as a direct structural replacement for conventional roofing materials in Sri Lanka. Existing roofing systems were studied, followed by a laboriousness study to determine the optimum tile dimensions. To meet these requirements, a modular tile measuring 900 mm × 1200 mm with a wave-shaped corrugated profile (a 10 mm rise and a 200 mm pitch) was engineered using SolidWorks 2024 and ABAQUS 2024 to meet Eurocode standards. Field investigations into plant health helped to finalize the depth of the roof tile as 2.5 cm. Following root penetration testing, fiber-reinforced plastic was selected for the tile structure to ensure durability while maintaining a total saturated weight of 52.5 kg/m². Biological testing demonstrated robust greening performance, with Axonopus compressus and Zoysia matrella achieving 100% survival rates and over 80% canopy coverage. This design methodology can be adapted across tropical Asia, contributing significantly to regional green infrastructure development and sustainable building practices. Full article

Landslides are the most common geological hazards, and chemical reinforcement is an effective method for enhancing the stability of soil slopes. Based on the coupled Eulerian–Lagrangian method, finite element analyses were conducted to develop a simple design approach for soil slope reinforcement using the curing agent. First, the effects of internal friction angle, cohesion, soil unit weight, slope height and angle on the slope stability were systematically quantified through 93 numerical cases. On this basis, an empirical formula was established for the factor of safety (FOS) of soil slope, and a method for determining the failure mode was proposed using a dimensionless parameter and two critical values related to slope angle. Subsequently, the reinforcement performance of the SH curing agent was investigated by varying the reinforcement position and length. The results indicate that the reinforcement of Case I-II-III and Case I-II provide the best performance, and the optimum reinforcement length was determined for different slope conditions. For slope angles ranging from 25° to 65°, the FOS after reinforcement was found to increase by 12.1% to 18.8% compared with that before reinforcement. Based on the FE results, empirical formulae for predicting the FOS of reinforced slope were further developed. Finally, a simple design approach was proposed for soil slope reinforcement with curing agent. The proposed method provides a convenient and effective reference for engineering practice in soil slope reinforcement with curing agents. Full article

Non-ductile reinforced concrete frames with unconfined joints dominate the collapse hazard of the existing building stock. Their CFRP-retrofit margin at collapse demand is poorly quantified. Two one-third-scale portal sub-frames were tested under Froude similitude. Specimen 1 was bare. Specimen 2 carried a three-ply hoop CFRP jacket on columns, beams, and joints. Both received the Antakya 3141 record from the 2023 Kahramanmaraş $M_w$ 7.7 mainshock at design intensity 0.35 g and collapse intensity 1.0 g. Cyclic response was decomposed into flexural, shear, and slip energy. At design intensity, the retrofit cut peak roof drift by 54%, suppressed residual offset, and lowered the calibrated Park–Ang index from 0.89 to 0.32. Slip share dropped from 47% to 5%. At collapse intensity, the retrofitted frame transitioned to joint-panel debonding-controlled failure at 8% drift with 245 mm residual, and shear share rose to 64%. The dominant-half-cycle ratio $R_1$ ≈ 0.72 emerged as a candidate brittle-damage signature for collapse-level response. A Lam–Teng confinement check confirms that the failure migrates from the column ends to debonding fracture in the wrapped panel rather than being eliminated by the retrofit. Supplementary joint-corner anchorage is recommended for non-ductile joints at collapse demand. Full article

The transition towards sustainable manufacturing necessitates complex optimization that integrates economic goals with environmental factors, such as energy consumption and greenhouse gas emissions. This research addresses the critical challenge of optimizing the Incoming Quality Control (IQC) policy for raw material batches. The primary objective is formulated as a multi-criteria control problem that jointly minimizes the weekly final product cost, carbon footprint, and energy consumption. To handle sequential decision making under uncertainty, we adopt a scalarized reinforcement learning (RL) reward that combines these objectives into a single value function and explores different trade-offs through alternative weight configurations. To effectively handle the uncertainty in incoming quality and the sequential decision making required for dynamic control, the optimization problem is modeled as a Bayesian Adaptive Markov Decision Process (BAMDP). To maintain computational tractability despite the continuous belief space inherent in the BAMDP formulation, we employ a Deep Q-Network (DQN) architecture acting as an approximate dynamic programming solver. The Bayesian framework represents model uncertainty explicitly, updates beliefs as new inspection evidence becomes available, and allows prior domain knowledge on supplier quality to be incorporated into the learning process. The BAMDP formulation is used to learn a set of adaptive inspection policies that adjust the IQC strategy over time to achieve conflicting goals: reducing inspection costs while maintaining standard quality, minimizing energy consumption, and lowering CO₂-equivalent emissions. The goal is to find robust policies that balance these trade-offs under different quality and demand conditions. This methodology aligns with the principles of Industry 5.0 by leveraging advanced artificial intelligence (AI) methods, such as reinforcement learning (RL), coupled with a stochastic simulation of the production system, based on a geometric/physical model of the component’s tolerance chains, to support decision-makers in designing and assessing sustainable IQC strategies. Comparative simulations on the case study, including a benchmark against ISO 2859-1 sampling plans, confirm that this dynamic and risk-aware optimization paradigm can reduce overall cost, energy use, and environmental impact across various quality conditions, while preserving outgoing quality. Full article

Roof stability plays a crucial role in maintaining the overall stability of surrounding rocks to ensure safety of tunnel construction and operation. In this work, tension cut-off (TC) technique is introduced to modify the Hoek–Brown (HB) criterion to describe the tensile failure of rock strata. Thereafter, stability analysis of anchor bolt-reinforced tunnel roofs in rock strata subjected to a hybrid tensile-shear fracture is performed. The work balance equation is established by equating the external work rates of the falling block and the anchor bolts to the internal energy dissipation rate. Two stability indicators, that is the stability number (N) and the factor of safety (FoS) are proposed to quantitatively analyze the stability of tunnel roofs. Optimization algorithms combining genetic algorithm and particle swarm optimization are programmed to capture the optimal upper bound solutions. The influences of TC, strength criterion parameters, and anchor bolt-reinforcement strength on roof stability are explored in this work. It was found that increasing the anchor tension T improves the FoS of reinforced tunnel roofs, with an increase of up to 68% observed for rectangular tunnel roofs under the selected representative case, while the improvement is relatively less pronounced for circular tunnel roofs. Regarding anchor support, as ξ increases, the N for rectangular tunnels nearly doubles. This work provides a theoretical basis for preliminary designing of tunnels in reinforced rock strata. Full article

Particulate-reinforced polymer composites (PRPCs) are susceptible to cracking under tensile loading, severely limiting their service life. Here, we propose a pressure-gradient-driven infiltration method that rapidly repairs narrow (<10 μm) cracks in a highly filled PRPC (95 wt.% BaSO₄/5 wt.% fluororubber). Microstructural evidence confirms that the adhesive completely fills the tortuous crack and forms a continuous adhesive–matrix interface capable of supporting load transfer. Semi-circular bend (SCB) testing demonstrates a substantially higher peak load and increased apparent structural stiffness after repair under the present semi-circular bend configuration, indicating apparent mechanical enhancement beyond simple load-bearing recovery. Digital image correlation (DIC) and fracture morphology show that repair suppresses notch-tip strain localization, reduces the strain concentration factor, shifts the failure-controlling zone away from the original notch tip, and deflects the crack propagation path. Phase-field simulations further show that the post-repair load-bearing capacity is governed by the adhesive–matrix interfacial strength; once this strength approaches or exceeds the tensile strength of the intact PRPC (~8.3 MPa), the repaired crack path is stabilized, enabling peak-load enhancement while suppressing damage localization along the original crack path and shifting failure to adjacent weaker regions. Overall, this work establishes a promising crack repair approach for highly filled PRPCs, while the underlying interface-controlled mechanism provides guidance for adhesive selection and repair design. Full article

This study investigates the shear damage mechanisms in reinforced concrete (RC) beams through non-linear numerical modeling. Using the Finite Element Method (FEM) in ABAQUS, a Concrete Damaged Plasticity (CDP) framework was validated against experimental results and subsequently utilized for a 36-model parametric investigation. The study isolated the influence of stirrup spacing, diameter, and yield strength to evaluate their roles in ultimate shear capacity. The results indicated that while increasing stirrup diameter yielded modest capacity enhancements of approximately 7%, the impact of increasing yield strength was negligible, as the failure modes were primarily governed by concrete web crushing before reinforcement yielding could occur. These physical limit states were compared against the linear Truss Analogy adopted by major design standards—including ACI 318-19, Eurocode 2, and TS 500—to quantify discrepancies in heavily reinforced sections. The findings reveal that, strictly within the investigated parameter space (a/d = 2.67, f’_c = 28.5 MPa), current linear equations can significantly overestimate the physical capacity gains provided by reinforcement modifications. These observations are configuration-specific and highlight the need for cautious application of linear models in heavily reinforced scenarios. Furthermore, the study suggests that utilizing 3D beam elements for transverse reinforcement provides a more nuanced representation of shear transfer mechanisms, such as dowel action, compared to standard truss models. Full article

Long-term station-keeping of stratospheric airships is challenged by strong time-varying wind fields, pronounced vertical stratification of wind speed and direction, and limited onboard energy. Existing reinforcement-learning approaches typically rely on instantaneous observations to make reactive decisions and therefore struggle to deliver foresighted control in dynamic environments. This paper proposes a Spatio-Temporal Foresight Reinforcement-Learning framework (STF-RL) that explicitly incorporates future wind information. A Transformer is introduced to model multi-step, multi-altitude forecast wind sequences, and a time–height dual positional encoding is designed to characterize both the temporal evolution and the vertical structure of the wind field. A task-conditioned attention pooling mechanism then extracts the future-wind features most relevant to the current state, which are concatenated with the airship state and fed into an actor–critic network to enable foresighted policy learning. A continuous action space supporting three-dimensional maneuvering is constructed, together with a multi-objective reward that jointly accounts for station-keeping performance, energy consumption and safety. Experimental results show that the proposed method outperforms baseline approaches in station-keeping performance, trajectory stability and energy-utilization efficiency, while exhibiting strong robustness across different wind-field conditions. Full article

The study investigated the tensile strength and elongation properties of abaca fiber-reinforced polymer (AFRP) composites after varying durations of seawater soaking, with a focus on their potential for reinforcing fiber-optic cables. It aims to bridge industrial technology education, experiential learning, and green technology by evaluating abaca fiber as a sustainable alternative to synthetic aramid yarn. Conducted at Caraga State University, Cabadbaran Campus (CSUCC), the research utilized a quasi-experimental product development design involving industrial technology students and instructors. Tensile strength testing and comparative analysis were performed on abaca fiber samples (A, B, and C) subjected to different seawater soaking durations. Results show that soaking time significantly affects the fiber strength, with Sample A achieving the highest tensile strength (5631.5 MPa) and Sample C the lowest (1679.8 MPa). Findings indicate that prolonged exposure to seawater weakens abaca fiber, emphasizing the need for controlled treatment to optimize its industrial applications. This study emphasizes the importance of hands-on learning in industrial technology education, promoting critical thinking and technical skills while underscoring sustainability. The research advocates for eco-friendly materials in industrial applications and highlights the potential of abaca fiber composites. Future studies should investigate pre-treatment methods to enhance fiber durability, assess the long-term environmental performance, and conduct large-scale pilot testing to evaluate commercial viability. By integrating sustainable innovations into industrial technology education, this study contributes to advancing natural fiber composites for manufacturing and telecommunications infrastructure. Full article

This paper proposes a reinforcement learning-assisted active disturbance rejection control (ADRC) framework for a nonlinear quadrotor unmanned aerial vehicle (UAV). Conventional ADRC controllers are designed for the quadrotor altitude and attitude channels. To evaluate robustness under disturbance-intensive conditions, a composite external disturbance is injected into the roll-channel dynamics. A Deep Deterministic Policy Gradient (DDPG)-based adaptive tuning mechanism is integrated into the roll-channel ADRC for the nonlinear state error feedback (NLSEF) gain adaptation, while fixed-parameter ADRC is retained for the remaining three channels. Without requiring system linearization and prior knowledge of disturbance models, the reinforcement learning agent learns an optimal gain adaptation policy directly through interaction with the nonlinear roll subsystem. Quantitative simulations demonstrate superior roll-axis disturbance rejection, leading to 90% faster settling time, the root mean square (RMS) control effort being reduced by 5.1%, and a 7.6% peak input suppression compared to conventional ADRC. The learning-based adaptation maintains comparable tracking accuracy across all channels while significantly improving transient recovery and control smoothness in the most disturbance-sensitive axis, validating selective reinforcement learning integration for robust nonlinear quadrotor flight control. Full article

Urban traffic congestion remains a persistent global challenge, contributing to significant economic inefficiencies, elevated greenhouse gas emissions, and diminished quality of life. This paper presents a real-world video-based traffic monitoring study combined with a proposed adaptive signal control framework. In the monitoring component, YOLOv11 object detection was applied directly to footage recorded from an overhead bridge position on a 40 km/h road. The model successfully detected and tracked multiple road-user categories, including cars, trucks, buses, motorcycles, cyclists, and pedestrians, yielding 1041 vehicle detections across 25 unique tracked objects. Vehicle speeds were estimated from inter-frame centroid displacement, and a Region of Interest (ROI) occupancy model was used to classify congestion states as High, Medium, or Free Flow using thresholds grounded in Highway Capacity Manual (HCM) level-of-service criteria. The system detected 11 high-congestion frames (3.8%), 184 medium-congestion frames (63.9%), and 93 free-flow frames (32.3%), consistent with moderate congestion observed during the recording period. In the proposed control component, a Proximal Policy Optimisation (PPO)-based reinforcement learning signal controller is designed around the YOLOv11 detection outputs as its state representation. Based on comparable adaptive traffic signal control studies in the literature, the proposed framework is projected to achieve approximately 25% higher peak-hour throughput, 35% shorter queue lengths, and 32% lower average waiting times relative to a fixed-time signal baseline. The detection accuracy (mAP@0.5 = 93.2%) and inference speed (32 FPS) cited are published YOLOv11 benchmarks used as indicative performance references. This work bridges real-world perception and proposed intelligent control, providing a transparent and reproducible methodology for next-generation smart city traffic management. Full article

Carbon/inorganic hybrid composites have evolved from filler-reinforced materials into design platforms for coupled electromagnetic, thermal, sensing, environmental, protective and energy-related functions. Their distinctive value lies in the possibility of combining a conductive, polarizable or porous carbon phase with an inorganic phase that contributes dielectric, magnetic, catalytic, ionic, thermally conductive or barrier behavior. This review examines carbon/inorganic hybrid multifunctional composites from the viewpoint of structure–property relationships, with emphasis on interfacial design, percolation, anisotropy, hierarchical architecture, processing and metrology. Selected graphitic composite studies are discussed as case studies for broadband dielectric spectroscopy, microwave shielding, high-frequency contact metrology, thermal diffusivity analysis and impedance-monitored graphene filters; these case studies are integrated with the broader international literature on CNT and graphene polymer composites, MXene films and foams, graphene/metal oxide photocatalysts, boron nitride/carbon thermal networks, biochar–graphene adsorbents, smart coatings, sensors, supercapacitors and water remediation systems. The central argument is that credible multifunctionality requires more than measuring several properties on the same material. It requires simultaneous or service-relevant co-optimization under constraints of thickness, density, processability, aging, humidity, corrosive media, regeneration, toxicity, economic feasibility and scalable fabrication. The review concludes with design rules and reporting recommendations intended to help move the field from impressive property demonstrations toward application-ready hybrid material systems. Full article