Search Results (4,261)

Global warming and increasing heat events necessitate long-term assessments of passive design strategies to ensure thermal resilience under future climatic conditions. Although machine-learning-based Surrogate Models (SMs) offer timely approximation of building performance compared to conventional simulation-based approaches, the lack of uncertainty quantification raises concerns about the reliability of their design optimization outcomes. This study aims to develop a robust surrogate-assisted optimization framework, based on a probabilistic Bayesian Neural Network (BNN) model and supported by an uncertainty-aware objective function. The framework is applied to an affordable housing case study in Surakarta, Indonesia, evaluating its generalizability under current and future climatic scenarios for 2050, 2070, and 2090. Thermal resilience is assessed through overheating hours exceeding acceptability limits in Southeast Asian context, using a parametric workflow implemented in Ladybug-tools and Grasshopper 3D. Compared to simulated test data, the BNN model demonstrates reliable predictive accuracy and probabilistic inference (R² = 0.99, MAE = 0.52%, CRPS = 0.38%). Furthermore, validation against re-evaluated optimal solutions shows low error ranges (RMSE = 0.43%, MAE = 0.33%), outperforming the deterministic SM optimization approach—using Artificial Neural Networks—by a factor of five. Overall, the uncertainty-aware framework provides a feasible, overconfidence-resistant, and reliable surrogate-assisted optimization method, identifying optimal solutions closely matching those from simulation-based optimization while reducing computational time by 96%. Full article

This study evaluated the ballistic performance and failure mechanisms of epoxy-based hybrid laminates reinforced with abaca/UHMWPE and pineapple leaf fiber (PALF)/UHMWPE fabrics fabricated by using vacuum-assisted hand lay-up. Ballistic tests utilized 9 mm full metal jacket (FMJ) rounds (~426 m/s impact velocity) under NIJ Standard Level IIIA conditions (44 mm maximum allowable BFS). This experimental test was complemented by finite element analysis (FEA) incorporating an energy-based bilinear fracture criterion to simulate matrix cracking and fiber pull-out. The results showed that abaca/UHMWPE composites exhibited lower backface signature (BFS) and depth of penetration (DOP) values (~23 mm vs. ~42 mm BFS; ~7 mm vs. ~9 mm DOP) than PALF/UHMWPE counterparts, reflecting superior interfacial adhesion and more ductile failure modes. Accelerated weathering produced matrix microcracking and delamination in both systems, reducing overall ballistic resistance. Scanning electron microscopy confirmed improved fiber–matrix bonding in abaca composites and interfacial voids in PALF laminates. The FEA results reproduced major failure modes, such as delamination, fiber–matrix debonding, and petaling, and identified stress concentration zones that agreed with experimental observations, though the extent of delamination was slightly underpredicted. Overall, the study demonstrated that abaca/UHMWPE hybridcomposites offer enhanced ballistic performance and durability compared with PALF/UHMWPE laminates, supporting their potential as sustainable alternatives for lightweight protective applications. Full article

This paper examines the growing field of AI-assisted urban planning within the context of sustainable urban development, with a particular focus on spatial optimization of urban green spaces under conditions of scarcity, density, and economic pressure. While the economic, ecological, and social values of UGS are widely acknowledged, urban planners lack a cohesive, data-driven framework to quantify and spatially optimize these often-conflicting values for effective land-use optimization. To address this gap, we propose a methodology that combines Geographic Information Systems (GISs), the Analytic Hierarchy Process (AHP), and an Artificial Intelligence-Based Genetic Algorithm (AI-GA). Vračar was chosen as the case study area. Our approach evaluates (1) the economic value of UGS through housing prices; (2) the ecological value through UGS density; and (3) the social value by measuring access to urban green pockets. The integrated method simulates environmental scenarios and optimizes UGS placement for resilient urban areas. Results demonstrate that properties in mixed-use green areas proximate to urban parks have the highest economic and social value. Additionally, higher densities of UGS correlate with higher housing prices, highlighting the economic impact of green space distribution. The methodology enables planners to make decisions based on evidence that integrates statistical modeling, expert judgment, and artificial intelligence into one cohesive platform. Full article

The containment of misinformation diffusion on social media is a critical challenge in computational social science. However, prevailing intervention strategies predominantly rely on static topological metrics or time-agnostic learning models, thereby overlooking the profound impact of temporal–demographic heterogeneity. This oversight frequently results in a “spatio-temporal mismatch”, where limited intervention resources are misallocated to structurally central but temporarily inactive nodes, particularly during non-stationary propagation bursts driven by exogenous triggers. To bridge this gap, we propose a Spatio-Temporal Deep Reinforcement Learning (ST-DRL) framework for proactive misinformation defense. By seamlessly integrating continuous trigonometric time encoding with demographic-aware Graph Attention Networks, our model explicitly captures the coupling dynamics between group-specific circadian rhythms and event-driven transmission surges. Extensive simulations on heterogeneous networks demonstrate that ST-DRL achieves a Peak Prevalence Reduction of 93.2%, significantly outperforming static heuristics and approaching the theoretical upper bound of oracle-assisted baselines. Crucially, interpretability analysis reveals that the agent autonomously evolves a “Preemptive Strike” strategy—prioritizing the sanitization of high-risk bridge nodes, such as bots, prior to event onsets—thus establishing a new paradigm for predictive rather than reactive network governance. Full article

Wind-assisted ship propulsion (WASP) has gained considerable interest as a means of reducing fuel consumption and Greenhouse Gas (GHG) emissions, with further benefits when combined with weather-optimized routing. This study employs and extends a National Technical University of Athens (NTUA) weather-routing optimization tool to more realistically assess WASP performance through integrated modeling. The original tool minimized fuel consumption using forecasted weather data and a physics-based performance model. A previous extension to account for the WASP effect introduced a 1-Degree Of Freedom (DOF) model that accounted only for longitudinal hydrodynamic and aerodynamic forces, estimating the reduced main-engine power required to maintain speed in given conditions. The current study incorporates a 3-DOF model that includes side forces and yaw moments, capturing resulting drift and rudder deflection effects. A Kamsarmax bulk carrier equipped with suction sails served as the case study. Initial simulations across various operating and weather conditions compared the two models. The 1-DOF model predicted fuel-saving potential up to 26% for the tested apparent wind speed and the range of possible headings, whereas the 3-DOF model indicated that transverse effects reduce WASP benefits by 2–7%. Differences in Main Engine (ME) power estimates between the two models reached up to 7% Maximum Continuous Rating (MCR) depending on the speed of wind. The study then applied both models within a weather-routing optimization framework to assess whether the optimal routes produced by each model differ and to quantify performance losses. It was found that the revised optimal route derived from the 3-DOF model improved total Fuel Oil Consumption (FOC) savings by 1.25% compared with the route optimized using the 1-DOF model when both were evaluated with the 3-DOF model. Full article

Pulse current-assisted flexible granular medium forming is a promising approach for manufacturing complex thin-walled components from difficult-to-deform Ti₂AlNb-based alloys. In this study, the electro-thermo-mechanical deformation behavior of Ti₂AlNb sheets is investigated through pulse current-assisted uniaxial tensile tests, microstructural characterization, and finite element simulations. The influences of pulse current intensity and strain rate on flow behavior, fracture characteristics, and phase evolution are clarified, and an effective forming window is identified. Numerical simulations are employed to analyze the role of granular medium friction in material flow and wall thickness distribution, providing guidance for forming box-shaped components. The results demonstrate that forming at approximately 950 °C with a strain rate of 0.001 s⁻¹ reduces deformation resistance, while enhanced tangential interaction between the granular medium and the sheet improves wall thickness uniformity. This study provides a feasible processing route and practical guidelines for the fabrication of complex Ti₂AlNb sheet components. Full article

Background: Effective communication is a fundamental competency in veterinary medicine that shapes the quality of veterinarian–client relationships, shared decision-making, and animal welfare. However, consistent and systematic integration of communication training across veterinary curricula remains uneven worldwide. Methods: This scoping review mapped and analyzed educational programs aimed at developing communication competencies in veterinary education and professional practices. A systematic search was conducted according to PRISMA-ScR guidelines, identifying 37 eligible studies published between 2005 and 2024. Results: Most publications were in English and originated from North America, particularly Canada (n = 15) and the United States (n = 8). Regarding target populations, 15 studies (40.5%) focused on veterinary students, 12 (32.4%) on practicing veterinarians, 8 (21.6%) on animal owners or clients, and 2 on veterinary educators. 18 studies (48.7%) described structured programs that used active learning strategies such as role-play, clinical simulations, peer-assisted learning, and formative feedback. The competencies frequently emphasized include empathy, active listening, nonverbal communication, conflict resolution, and rapport building. Notable best practices included the Calgary–Cambridge model, Objective Structured Clinical Examination (OSCE), and reflective video analysis. Conclusions: The available evidence indicates a growing emphasis on clinical communication within veterinary education, primarily implemented through experiential and practice-based approaches. However, substantial gaps persist in the representation of Latin American contexts and in the systematic, longitudinal integration of communication skills across veterinary curricula. Addressing these gaps may contribute to more coherent, equitable, and context-sensitive communication training in veterinary education. Full article

Carbon-reinforced epoxy laminated composite (CREC) structures are increasingly utilized in high-speed marine vehicles (HSMVs) due to their high specific strength and stiffness; however, they are frequently subjected to impact loads like slamming and aggressive environmental agents during operation. This study experimentally investigates the Compression After Impact (CAI) behavior of CREC plates with varying lamination sequences under both atmospheric and accelerated aging conditions. The samples were produced using the vacuum-assisted resin infusion method with three specific orientation types: quasi-isotropic, cross-ply, and angle-ply. To simulate the marine environment, specimens were subjected to accelerated aging in a salt fog and cyclic corrosion cabin for periods of 2, 4, and 6 weeks. Before and following the aging process, low-velocity impact tests were conducted at an energy level of 30 J, after which the residual compressive strength was measured by CAI tests. At the end of the aging process, after the sixth week, the performance of plates with different layer configuration characteristics can be summarized as follows: Plates 1 and 2, which are quasi-isotropic, exhibit opposite behavior. Plate 1, with an initial toughness of 23,000 mJ, increases its performance to 27,000 mJ as it ages, while these values are around 27,000 and 17,000 mJ, respectively, for Plate 2. It is thought that the difference in configurations creates this difference, and the presence of the 0° layer under the effect of compression load at the beginning and end of the configuration has a performance-enhancing effect. In Plates 3 and 4, which have a cross-ply configuration, almost the same performance is observed; the performance, which is initially 13,000 mJ, increases to around 23,000 mJ with the effect of aging. Among the options, angle-ply Plates 5 and 6 demonstrate the highest performance with values around 35,000 mJ, along with an undefined aging effect. Scanning Electron Microscopy (SEM) and Energy-Dispersive X-ray Spectroscopy (EDS) analyses confirmed the presence of matrix cracking, fiber breakage, and salt accumulation (Na and Ca compounds) on the aged surfaces. The study concludes that the impact of environmental aging on CRECs is not uniformly negative; while it degrades certain configurations, it can enhance the toughness and energy absorption of brittle, cross-ply structures through matrix plasticization. Full article

Background: Respiratory-induced target displacement remains a major challenge for achieving accurate and safe augmented-reality-guided thoracoabdominal percutaneous puncture. Existing approaches often suffer from system latency, dependence on intraoperative imaging, or the absence of intelligent timing assistance; Methods: We developed a mixed-reality (MR) surgical navigation system that incorporates Adaptive Kalman-filter-based respiratory prediction module and visual gating cues. The system was evaluated using a dynamic respiratory motion simulation platform. The Kalman filter performs real-time state estimation and short-term prediction of optically tracked respiratory motion, enabling simultaneous compensation for MR model drift and forecasting of the end-inhalation window to trigger visual guidance; Results: Compared with the uncompensated condition, the proposed system reduced dynamic registration error from (3.15 ± 1.23) mm to (2.11 ± 0.58) mm (p < 0.001). Moreover, the predicted guidance window occurred approximately 142 ms in advance with >92% accuracy, providing preparation time for needle insertion; Conclusions: The integrated MR system effectively suppresses respiratory-induced model drift and offers intelligent timing guidance for puncture execution. Full article

The accurate simulative reconstruction of blind spot accidents requires innovative simulation methods. The objective of this paper is to analyze the avoidability of a specific blind spot accident and assess the impact of various parameters as if an active turning assistant had been installed in the truck. Additionally, it proposes a novel adaptation of the turning assistant system, along with an adapted simulation model tailored for drawbar trailers. The analyses presented in this paper were performed using PC-Crash accident simulation software, applying the “Active Safety” module. After performing a simulation of an accident involving a right-turning truck with a center axle trailer and a pedestrian, the avoidability of the accident was examined by simulating the scenario as if the truck involved in the accident had been equipped with an active turning assistant system. Subsequently, a parameter analysis was conducted to analyze the effect of changes in the active turning assistant’s parameters and changes in the pedestrian’s direction of entry on the avoidability of the accident. In doing so, we determined the parameters for the worst-case (collision) and the best-case (no collision) scenarios. Finally, an adaptation and further development of the active turning assistant, along with a corresponding simulation method for drawbar trailers, are proposed. Full article

Control of elbow exoskeletons using muscular signals, although promising for the rehabilitation of millions of patients, has not yet been widely commercialized due to challenges in real-time intention estimation and management of dynamic uncertainties. From a practical perspective, millions of patients with stroke, spinal cord injury, or neuromuscular disorders annually require active rehabilitation, and elbow exoskeletons with precise and safe motion intention tracking capabilities can restore functional independence, reduce muscle atrophy, and lower treatment costs. In this research, an intelligent control framework was developed for an elbow joint exoskeleton, designed with the aim of precise and safe real-time tracking of the user’s motion intention. The proposed framework consists of two main stages: (a) real-time estimation of desired joint angle (as a proxy for movement intention) from High-Density Surface Electromyography (HD-sEMG) signals using an LSTM network and (b) implementation and comparison of three PID, impedance, and sliding mode controllers. A public EMG dataset including signals from 12 healthy individuals in four isometric tasks (flexion, extension, pronation, supination) and three effort levels (10, 30, 50 percent MVC) is utilized. After comprehensive preprocessing (Butterworth filter, 50 Hz notch, removal of faulty channels) and extraction of 13 time-domain features with 99 percent overlapping windows, the LSTM network with optimal architecture (128 units, Dropout, batch normalization) is trained. The model attained an RMSE of 0.630 Nm, R² of 0.965, and a Pearson correlation of 0.985 for the full dataset, indicating a 47% improvement in R² relative to traditional statistical approaches, where EMG is converted to desired angle via joint stiffness. An assessment of 12 motion–effort combinations reveals that the sliding mode controller consistently surpassed the alternatives, achieving the minimal tracking errors (average RMSE = 0.21 Nm, R² ≈ 0.96) and showing superior resilience across all tasks and effort levels. The impedance controller demonstrates superior performance in flexion/extension (average RMSE ≈ 0.22 Nm, R² > 0.94) but experiences moderate deterioration in pronation/supination under increased loads, while the classical PID controller shows significant errors (RMSE reaching 17.24 Nm, negative R² in multiple scenarios) and so it is inappropriate for direct myoelectric control. The proposed LSTM–sliding mode hybrid architecture shows exceptional accuracy, robustness, and transparency in real-time intention monitoring, demonstrating promising performance in offline simulation, with potential for real-time clinical applications pending hardware validation for advanced upper-limb exoskeletons in neurorehabilitation and assistive applications. Full article

This study presents a method for enriching oil-suspended particles within a rectangular microfluidic channel using acoustic standing waves. A modified Helmholtz equation is solved to establish the acoustic field model, and the equilibrium between acoustic radiation forces and viscous drag is described by combining Gor’kov potential theory with the Stokes drag model. Based on this force balance, the particle motion equation is derived, enabling the determination of the critical particle size necessary for efficient enrichment in oil-filled microchannels. A two-dimensional standing-wave microchannel model is subsequently developed, and the influences of acoustic, fluidic, and particle parameters on particle migration and aggregation are systematically investigated through theoretical analysis and numerical simulations. The results indicate that when the channel dimension and acoustic wavelength satisfy the half-wavelength resonance condition, a stable standing-wave field forms, effectively focusing suspended particles at the acoustic pressure nodes. Enrichment efficiency is found to be strongly dependent on inlet flow velocity, particle diameter, acoustic frequency, temperature, and particle density. Lower flow velocities and larger particle sizes result in higher enrichment efficiencies, with the most uniform and stable pressure distribution achieved when the acoustic frequency matches the resonant channel width. Increases in temperature and particle density enhance the acoustic radiation force, thereby accelerating the aggregation of particles. These findings offer theoretical foundations and practical insights for acoustically assisted online monitoring of wear particles in lubricating oils, contributing to advanced condition assessment and fault diagnosis in mechanical systems. Full article

Polymer-assisted hot water flooding (PAHWF) is an important enhanced oil recovery technique involving strongly coupled thermal, chemical, and multiphase flow processes. Accurate prediction of water saturation, polymer concentration, and temperature evolution in PAHWF is challenging due to the highly nonlinear and multiscale governing equations. In this study, a physics-informed neural network (PINN) framework is developed for one-dimensional PAHWF simulation as a controlled benchmark system to systematically investigate PINN behavior in multiphysics-coupled problems. The PAHWF governing equations incorporating temperature- and concentration-dependent viscosity are embedded into the PINN loss function. Three progressively designed numerical examples are conducted to examine the effects of temperature normalization, network architecture (PINN-1 versus PINN-2), and network depth on training stability and solution accuracy. The results demonstrate that temperature normalization effectively mitigates gradient-scale imbalance, significantly improving convergence stability and prediction accuracy. Furthermore, the PINN-2 architecture, which employs a dedicated network for temperature, exhibits enhanced robustness and accuracy compared with the unified PINN-1 structure. Variations in network depth show limited influence on solution quality, indicating the inherent robustness of PINNs under the proposed framework. Although conventional numerical methods remain more efficient for one-dimensional forward problems, this study establishes a methodological foundation for extending PINNs to higher-dimensional, strongly coupled PAHWF simulations and inverse reservoir problems. The proposed framework provides insights into improving PINN trainability and reliability for complex enhanced oil recovery processes. Full article

Wireless medical sensor networks (WMSNs) have evolved alongside the development of communication systems, and the integration of cloud computing has enabled scalable and efficient medical data management. However, since the messages in WMSNs are transmitted over open channels, they are vulnerable to eavesdropping, replay, impersonation, and other various attacks. In response to these security concerns, Keshta et al. suggested an authentication protocol to establish secure communication in the cloud-assisted WMSNs. However, our analysis reveals their protocol cannot prevent session key disclosure, impersonation of the user and sensor node, and denial of service (DoS) attacks. Moreover, Keshta et al.’s protocol cannot support user untraceability due to fixed hidden identity. To address these weaknesses, we propose a physical unclonable function (PUF) based secure authentication protocol for cloud-assisted WMSNs. The protocol uses lightweight operations, provides mutual authentication between user, cloud server, and sensor node, and supports user anonymity and untraceability. We validate the proposed protocol’s security through informal analysis on various security attacks and formal analysis including “Burrows–Abadi–Needham (BAN) logic”, “Real-or-Random (RoR) model” for session key security, and “Automated Validation of Internet Security Protocols and Application (AVISPA) simulations”. Performance evaluation demonstrates lower communication cost and computation overhead compared with existing protocols, making the proposed protocol suitable for WMSN environments. Full article

The objective of this research is to develop environmentally friendly, risk-free and effective adsorbent composite beads that remove Cu(II) ions from aqueous solutions using cost-effective biopolymers (Carboxymethylcellulose (CMC) and sodium alginate (AL)). The synthesized hydrogel beads (AL@CMC) were dried using two drying modes, namely air-drying and freeze-drying, and characterized using scanning electron microscopy (SEM), Fourier Transform Infrared Spectroscopy (FT-IR), and Brunauer–Emmett–Teller (BET) analysis. The study investigated factors such as pH, adsorbent dosage, reaction time, Cu(II) ions concentration, and temperature to elucidate the adsorption mechanisms involved in removing copper ions. The results indicated that the hydrogel exhibited a maximum adsorption capacity of 99.05 mg·g⁻¹, which is highly competitive compared to previous studies; the AL@CMC beads prepared in this work show a significantly higher adsorption capacity, improved stability due to the interpenetrated biopolymer network, and a clear enhancement from freeze-drying, which greatly increases porosity and active surface area. In addition, the pseudo-second-order nonlinear kinetic model best described the experimental data, implying the chemical nature of the adsorption process. Furthermore, the thermodynamic studies revealed that the adsorption process was endothermic, spontaneous, and homogenous. A Monte Carlo simulation model was utilized to ensure compatibility with the adsorption mechanism, in order to delve deeper into the intricacies of the adsorption process and gain a more comprehensive understanding of its underlying mechanisms and behavior. In conclusion, the prepared hydrogel beads proved to be an effective adsorbent for efficiently removing copper ions, making them a promising solution for addressing Cu(II) ion pollution. Full article