Search Results (14,730)

Electrospun one-dimensional nanocomposites composed of polyvinylpyrrolidone (PVP) matrices reinforced with antimony sulphoiodide (SbSI) or antimony selenoiodide (SbSeI) nanowires were fabricated for the first time. Their properties were investigated for applications in piezoelectric sensors and nanogenerators. Precise control of the electrospinning parameters produced nanofibres with diameters comparable to the lateral dimensions of the nanowires, ensuring parallel alignment and a 1–3 composite structure. Structural analysis confirmed uniform nanowire distribution and stoichiometry retention. In both nanocomposites, the alignment of the nanowires enables clear observation of the anisotropy of their piezoelectric properties. PVP/SbSI nanocomposites exhibited a ferroelectric–paraelectric transition near 290 K. Under air-pressure excitation of 17.03 bar, they generated a maximum piezoelectric voltage of 2.09 V, with a sensitivity of 229 mV/bar and a surface power density of 12.0 µW/cm² for sandwich-type samples with nanowires aligned perpendicularly to the electrodes. PVP/SbSeI composites demonstrated stable semiconducting behaviour with a maximum piezoelectric voltage of 1.56 V, sensitivity of 130 mV/bar, and surface power density of 2.3 µW/cm² for the same type of sample and excitation. The high piezoelectric coefficients d₃₃ of 98 pC/N and 64 pC/N for PVP/SbSI and PVP/SbSeI, respectively, combined with mechanical flexibility, confirm the effectiveness of these nanocomposites as a practical solution for mechanical energy harvesting and pressure sensing in nanogenerators and sensors. Full article

Historic urban alleys encapsulate cultural identity and collective memory but are increasingly threatened by commercialization and context-insensitive redevelopment. Preserving their authenticity while enhancing environmental resilience requires design strategies that integrate both heritage and ecological values. This study explores the potential of generative artificial intelligence (AI) to support biophilic design in historic alleys, focusing on Daegu, South Korea. Four alley typologies—path, stairs, edge, and node—were identified through fieldwork and analyzed across cognitive, emotional, and physical dimensions of place identity. A Flux-based diffusion model was fine-tuned using low-rank adaptation (LoRA) with site-specific images, while a structured biophilic design prompt (BDP) framework was developed to embed ecological attributes into generative simulations. The outputs were evaluated through perceptual and statistical similarity indices and expert reviews (n = 8). Results showed that LoRA training significantly improved alignment with ground-truth images compared to prompt-only generation, capturing both material realism and symbolic cues. Expert evaluations confirmed the contextual authenticity and biophilic effectiveness of AI-generated designs, revealing typology-specific strengths: the path enhanced spatial legibility and continuity; the stairs supported immersive sequential experiences; the edge transformed rigid boundaries into ecological transitions; and the node reinforced communal symbolism. Emotional identity was more difficult to reproduce, highlighting the need for multimodal and interactive approaches. This study demonstrates that generative AI can serve not only as a visualization tool but also as a methodological platform for participatory design and heritage-sensitive urban regeneration. Future research will expand the dataset and adopt multimodal and dynamic simulation approaches to further generalize and validate the framework across diverse urban contexts. Full article

Addressing earthquake risk remains a significant global challenge, requiring rapid assessment of evacuation shelters for effective disaster response. Existing frameworks, such as FEMA’s Hazus, Copernicus EMS, and UNOSAT, offer valuable insights but are typically regional, static, and event-focused, lacking mechanisms for continuous shelter-level updates. This study introduces the Shelter Hazard Impact and Lifeline Outage Estimation (SHILOE) framework. SHILOE is a stepwise estimation approach integrating multisensor datasets for time-scaled assessments of shelter functionality and operability. These datasets include seismic intensity, liquefaction probability, tsunami inundation, IoT-derived power outage data, communication network disruptions, and social media. Application to the 2024 Noto Peninsula earthquake showed that ≥93.6% of designated and activated shelters were impacted by at least one hazard, with all experiencing at least one lifeline outage. The framework delivers estimates through three phases: immediate (within tens of minutes, e.g., simulation-based hazard models and lifeline data), intermediate (days, e.g., observation-based datasets), and refinement (ongoing, e.g., Social Networking Service and detailed field surveys). By progressively incorporating new data across these phases, SHILOE generates dynamic, facility-level insights that capture evolving hazard exposure and lifeline status. These outputs provide actionable information for emergency managers to prioritize resources, reinforce shelters, and sustain critical services, thereby advancing disaster resilience. Full article

Cyst nematodes of the genus Heterodera are important plant-parasitic nematodes that cause significant crop losses worldwide but are often overlooked due to their non-specific symptoms and complex biology. This study assessed Heterodera diversity in Portugal using an integrative molecular approach based on four genetic markers (mtCOI, 18S rDNA, ITS, and 28S rDNA). Five valid species were identified: Heterodera cruciferae, H. mani, H. schachtii, H. trifolii, and H. zeae, with H. mani reported for the first time in the country. A distinct taxon from Coimbra (central Portugal) may represent a new or unsequenced species, highlighting gaps in reference datasets. Among the markers, mtCOI was the most effective, though some taxa remained unresolved. These results reinforce the value of multi-marker approaches, contribute with new sequences, and improve diagnostic capability for nematode management. Full article

This research investigated the influence of volume fraction (30 vol.% and 40 vol.%) and particle size α-Al₂O₃ on the physical and mechanical properties of AlMg7 composites manufactured by the squeeze casting technique. The aim of the study was to characterize the microstructure, hardness, density, tensile strength (σmax), compressive strength (σcmax), and impact strength, with a discussion of the mechanisms of destruction. The obtained materials exhibited very low porosity (below 2%), confirming the high efficiency of the ceramic preforms infiltration process. It was found that both hardness and tensile strength increase with decreasing size of the reinforcing particles. The highest growth in hardness at 113% was observed for the composite with 40 vol.% of F1200 particles, while the highest tensile strength, 341 MPa, was noted for materials with 30 vol.% of the same fraction of α-Al₂O₃ particles. In the case of compressive strength, the opposite relationship was observed, where an increase in volume fraction to 40% resulted in a significant rise in σcmax to 522 MPa. The tests also indicated that an increase in the proportion of the brittle ceramic phase radically reduces the impact strength of composites compared to the matrix, which is typical for composite materials with a metallic matrix. Microstructure analysis of the fractures revealed that the mechanism of destruction depends on the type of load and the size and proportion of particles, which is reflected in the transition from transcrystalline cracking to delamination at the phase boundary. The results confirm that the strengthening processes of composites depend on the effective transfer of stresses at the microscopic level. Full article

Wire Arc Additive Manufacturing (WAAM) has been recognized as an efficient and cost-effective metal additive manufacturing technique due to its high deposition rate and scalability for large components. However, the quality and repeatability of WAAM parts are highly sensitive to process parameters such as arc voltage, current, wire feed rate, and torch travel speed, requiring advanced monitoring and adaptive control strategies. In this study, a vision-based monitoring system integrated with a reinforcement learning framework was developed to enable intelligent in situ control of WAAM. A custom optical assembly employing mirrors and a bandpass filter allowed simultaneous top and side views of the melt pool, enabling real-time measurement of layer height and width. These geometric features provide feedback to a tabular Q-learning algorithm, which adaptively adjusts voltage and wire feed rate through direct hardware-level control of stepper motors. Experimental validation across multiple builds with varying initial conditions demonstrated that the RL controller stabilized layer geometry, autonomously recovered from process disturbances, and maintained bounded oscillations around target values. While systematic offsets between digital measurements and physical dimensions highlight calibration challenges inherent to vision-based systems, the controller consistently prevented uncontrolled drift and corrected large deviations in deposition quality. The computational efficiency of tabular Q-learning enabled real-time operation on standard hardware without specialized equipment, demonstrating an accessible approach to intelligent process control. These results establish the feasibility of reinforcement learning as a robust, data-efficient control technique for WAAM, capable of real-time adaptation with minimal prior process knowledge. With improved calibration methods and expanded multi-physics sensing, this framework can advance toward precise geometric accuracy and support broader adoption of machine learning-based process monitoring and control in metal additive manufacturing. Full article

Originating from the practical demands of digital irrigation district construction, this study aims to provide support for precise irrigation management. This study developed a reinforcement learning-based intelligent irrigation decision-making model for districts employing traditional surface flood irrigation methods. Grounded in the theoretical framework of water cycle processes within the Soil–Crop–Atmosphere Continuum (SPAC) system and incorporating district-specific irrigation management experience, the model achieves intelligent and precise irrigation decision-making through agent–environment interactive learning. Simulation results show that in the selected typical area of the irrigation district, during the 10-year validation period from 2014 to 2023, the model triggered a total of 22 irrigation events with an average annual irrigation volume of 251 mm. Among these, the model triggered irrigation 18 times during the winter wheat growing season and 4 times during the corn growing season. The intelligent irrigation decision-making model effectively captures the coupling relationship between crop water requirements during critical periods and the temporal distribution of precipitation, and achieves preset objectives through adaptive decisions such as peak-shifting preemptive irrigation in spring, limited irrigation under low-temperature conditions, no irrigation during non-irrigation periods, delayed irrigation during the rainy season, and timely irrigation during crop planting periods. These outcomes validate the model’s scientific rigor and operational adaptability, providing both a scientific water management tool for irrigation districts and a new technical pathway for the intelligent development of irrigation decision-making systems. Full article

Using platform self-operation, customer reviews, and compensation commitments as traditional benchmarks, this study foregrounds blockchain traceability as a technology-enabled authenticity signal in cross-border cosmetic e-commerce (CBEC). Using an 8-scenario orthogonal experiment, we test a model in which perceived risk mediates the effects of authenticity signals on purchase intention. We probe blockchain boundary conditions by examining their interactions with traditional signals. Our results show that blockchain is the only signal with a significant direct effect on purchase intention and that it also exerts an indirect effect by reducing perceived risk. While customer reviews show no consistent effect, self-operation and compensation influence purchase intention indirectly via risk reduction. Moderation tests indicate that blockchain is most effective in low-trust settings—i.e., when self-operation, reviews, or compensation safeguards are absent or weak—while this marginal impact declines when such safeguards are strong. These findings refine signaling theory by distinguishing a technology-backed signal from institutional and social signals and by positioning perceived risk as the central mechanism in CBEC cosmetics. Managerially speaking, blockchain should serve as the anchor signal in high-risk contexts and as a reinforcing signal where traditional assurances already exist. Future work should extend to field/transactional data and additional signals (e.g., brand reputation, third-party certifications). Full article

Temperature control plays a critical role in mitigating the lifespan degradation mechanisms and ensuring thermal safety of lithium-ion batteries (LIBs) and proton exchange membrane fuel cells (PEMFCs). However, current energy management strategies (EMS) for fuel cell hybrid electric vehicles (FCHEVs) generally lack comprehensive thermal effect modeling and thermal runaway fault diagnosis, leading to irreversible aging and thermal runaway risks for LIBs and PEMFCs stacks under complex operating conditions. To address this challenge, this paper proposes a thermo-electrical co-optimization EMS incorporating thermal runaway fault diagnosis actuators, with the following innovations: firstly, a dual-layer framework integrates a temperature fault diagnosis-based penalty into the EMS and a real-time power regulator to suppress heat generation and constrain LIBs/PEMFCs output, achieving hierarchical thermal management and improved safety; secondly, the distributional soft actor–critic (DSAC)-based EMS incorporates energy consumption, state-of-health (SoH) degradation, and temperature fault diagnosis-based constraints into a composite penalty function, which regularizes the reward shaping and guides the policy toward efficient and safe operation; finally, a thermal safe constriction controller (TSCC) is designed to continuously monitor the temperature of power sources and automatically activate when temperatures exceed the optimal operating range. It intelligently identifies optimized actions that not only meet target power demands but also comply with safety constraints. Simulation results demonstrate that compared to DDPG, TD3, and SAC baseline strategies, DSAC-EMS achieves maximum reductions of 39.91% in energy consumption and 29.38% in SoH degradation. With the TSCC implementation, enhanced thermal safety is achieved, while the maximum energy-saving improvement reaches 25.29% and the maximum reduction in SoH degradation attains 20.32%. Full article

Thermoplastic starch (TPS) is a biodegradable polymer from renewable sources, but its limited mechanical and thermal properties restrict wider industrial use compared to petroleum-based plastics. In this study, TPS-based biocomposites were developed and optimized by incorporating agricultural and mineral Residues: coffee husks (CH), potassium feldspar (PF), and Bahia Beige marble (BB) as reinforcements. Mechanical, thermal, and morphological characterizations were carried out, and a simplex–lattice mixture design was applied to optimize the formulations. The 70/20/5/5 (TPS/CH/PF/BB, wt.%) composition achieved the highest tensile strength (2.0 MPa) and elastic modulus (70.2 MPa), while the 90/0/5/5 formulation showed superior impact resistance. FTIR and SEM analyses confirmed effective filler dispersion and strong matrix–filler interactions. Scheffé polynomial models (R² > 87%) accurately predicted performance, highlighting the reliability of the statistical approach. From a sustainability perspective, this work demonstrates that upcycling coffee husks and mineral residues into TPS-based biocomposites contributes to waste reduction, landfill diversion, and the development of cost-effective biodegradable materials. The proposed systems offer potential for eco-friendly packaging and agricultural applications, reducing dependence on fossil-based plastics and mitigating the environmental footprint of polymer industries. Statistical optimization further enhances efficiency by minimizing experimental waste. Moreover, this research supports circular economy strategies and provides scalable, sustainable solutions for waste valorization. Full article

Although the strain hardening phenomenon has been studied in different types of materials, there are only a few such reports regarding flexible food packaging. To address this issue, nanoclay-reinforced and control starch-based films were subjected to sequential and weekly tension and the rheological index, defined as the ratio of the tensile strength observed under weekly to that under consecutive elongation, was measured. The results showed that the values of the rheological index were >1, implying a strain hardening effect that was more notable when nanoclay was added and when the stress duration was increased. Additionally, a cumulative damage test was conducted, involving the gradual increase of two factors in each step: the percentage of the elongation level and the duration of each step. The data were fitted to a linear model, describing the correlation between the ln failure time (μ) and the tensile strength (X), μ = 6.021 − 0.478 X. This model enabled the prediction of the failure probability and the hazard rate of the films that were studied. In addition, from the survival of the units in the initial steps of the cumulative damage experiment, it can be hypothesized that the elongation of the units under low stress levels, for prolonged periods of time, exhibits rheological memory properties, which leads to an increase in their mechanical strength. Full article

The rapid development of the Internet of Things (IoT) poses significant problems in securing heterogeneous, massive, and high-volume network traffic against cyber threats. Traditional intrusion detection systems (IDSs) are often found to be poorly scalable, or are ineffective computationally, because of the presence of redundant or irrelevant features, and they suffer from high false positive rates. Addressing these limitations, this study proposes a hybrid intelligent model that combines quantum computing, chaos theory, and deep learning to achieve efficient feature selection and effective intrusion classification. The proposed system offers four novel modules for feature optimization: chaotic swarm intelligence, quantum diffusion modeling, transformer-guided ranking, and multi-agent reinforcement learning, all of which work with a graph-based classifier enhanced with quantum attention mechanisms. This architecture allows as much as 75% feature reduction, while achieving 4% better classification accuracy and reducing computational overhead by 40% compared to the best-performing models. When evaluated on benchmark datasets (NSL-KDD, CICIDS2017, and UNSW-NB15), it shows superior performance in intrusion detection tasks, thereby marking it as a viable candidate for scalable and real-time IoT security analytics. Full article

Reactive jamming remains a critical threat to low-latency telemetry of Unmanned Aerial Vehicles (UAVs) using Orthogonal Frequency Division Multiplexing (OFDM). In this paper, a Bi-scale Mahalanobis approach is proposed to detect and classify reactive jamming attacks on UAVs; it jointly exploits window-level energy and the Sevcik fractal dimension and employs self-adapting thresholds to detect any drift in additive white Gaussian noise (AWGN), fading effects, or Radio Frequency (RF) gain. The simulations were conducted on 5000 frames of OFDM signals, which were distorted by Rayleigh fading, a ±10 kHz frequency drift, and log-normal power shadowing. The simulation results achieved a precision of 99.4%, a recall of 100%, an F1 score of 99.7%, an area under the receiver operating characteristic curve (AUC) of 0.9997, and a mean alarm latency of 80 μs. The method used reinforces jam resilience in low-power commercial UAVs, yet it needs no extra RF hardware and avoids heavy deep learning computation. Full article

To investigate the influence of laser cladding overlap rate on the microstructure and corrosion resistance of cladded layers, Ni60-WC composite coatings with different overlap rates (30%, 50%, and 70%) were prepared on E690 offshore steel in this study. The relationship between the corrosion resistance and microstructure of the cladded layers fabricated at different overlap rates was analyzed using an electrochemical workstation, scanning electron microscope, X-ray diffractometer, and energy dispersive spectrometer. The results demonstrate that the overlap rate exerts a significant impact on the corrosion resistance of the cladded layers, and the corrosion resistance of the cladded layers gradually improves with the increase in overlap rate. The cladded layer prepared with a 70% overlap rate exhibits excellent corrosion resistance, featuring the highest open-circuit potential (−0.31 V vs. SCE), the lowest corrosion current density (3.35 μA/cm²), the largest capacitive arc radius in the electrochemical impedance spectroscopy (EIS), and a relatively flat surface after corrosion tests. Microstructural characterization results indicate that the increase in overlap rate promotes grain refinement and the formation of reinforcing phases (e.g., M₂₃C₆). The coating with a 70% overlap rate possesses the densest microstructure and abundant flocculent carbides, which act as an effective barrier against the penetration of corrosive media, thereby endowing it with optimal performance. Full article

Tunnel construction in weak and fractured strata often faces risks such as tunnel face instability and large deformation of surrounding rock, which are difficult to effectively control using conventional support methods. Based on the engineering background of the No. 8# TA Tunnel in the F3 section of Georgia’s E60 Highway, this study employed ADECO-RS and developed a 3D numerical model with finite difference software to simulate full-face tunnel excavation process. The influence of advanced reinforcement measures on the stability of the surrounding rock was systematically investigated. The control effectiveness of different advanced reinforcement schemes was evaluated by comparing the displacement field, stress field, and plastic zone distribution of the surrounding rock under three conditions: no support, advanced pipe roof support, and a combination of pipe roof and glass fiber bolts. A comprehensive quantitative analysis of the synergistic effect of the combined reinforcement was also performed. The results indicated that significant extrusion deformation of the tunnel face and vault settlement occurred after excavation. The pressure arch developed within a range of 17.5 to 22 m above the tunnel vault. The surrounding rock of this tunnel was classified as type B (short-term stable). Deformation primarily occurred within one tunnel diameter ahead of the face, with the deformation rate significantly reduced after support. Advanced pipe roof support effectively restrained surrounding rock deformation, while the combination of advanced pipe roof and glass fiber bolts delivered better performance: reducing final convergence by 73.10%, pre-convergence by 82.69%, and face extrusion by 87.66%. The combined support also contracted the pressure arch boundaries from 17.5 to 22 m to 6–12.5 m, reduced the extent of major principal stress deflection, and significantly shrinks the plastic zone. Glass fiber bolts played a key role in controlling plastic zone expansion and ensuring stability. This study provides theoretical and numerical references for safe construction and advanced support design in tunnels under complex geological conditions. Full article