Search Results (36,932)

This study investigates the mechanical performance of two prosthetic forelimb designs for dogs—one with a solid structure and the other with a perforated structure—using Finite Element Analysis (FEA). Both models were analyzed under static loading conditions representing approximately 60% of a dog’s body weight, the typical load borne by the forelimbs. The prosthetics were modeled with ABS plastic, a widely used 3D printing material, and evaluated for Von Mises stress, total deformation, elastic strain, and factor of safety. The analysis showed that both models remained within the elastic limit of the material, indicating that no permanent deformation would occur under the applied loads. The Solid Model demonstrated a significantly higher factor of safety (14) and lower deformation, confirming its structural strength but also highlighting excessive rigidity, increased material use, and higher cost. In contrast, the Perforated Model exhibited slightly higher localized stresses and a lower factor of safety (3.01), yet it still met essential safety requirements while providing greater compliance, flexibility, and material efficiency. These attributes are desirable for comfort, adaptability, and practicality in veterinary applications. Although its long-term durability requires further evaluation, the Perforated Model strikes a more effective balance between safety, comfort, and sustainability. Based on these findings, the perforated design is considered the more suitable option for canine prosthetic development. Future work will extend the analysis to dynamic loading scenarios, such as walking and running, to better simulate real-world performance. Full article

The non-structural protein 1 (NS1) of dengue virus (DENV) plays a multifaceted role in viral pathogenesis and immune modulation. Although vaccine strategies have traditionally focused on neutralizing antibodies against the envelope (E) protein, recent evidence highlights the protective potential of anti-NS1 antibodies—particularly those that mediate Fc-dependent effector functions. These functions include antibody-dependent cellular cytotoxicity (ADCC), antibody-dependent cellular phagocytosis (ADCP), and complement-dependent cytotoxicity (CDC), which collectively bridge adaptive antibody responses with innate immune activation. However, the outcomes of anti-NS1 responses are context-dependent: certain antibody specificities confer protection, while others may contribute to immunopathology. In this review, I synthesize current evidence on the roles of anti-NS1 antibodies in modulating Fc receptor engagement, subclass-specific responses, glycosylation patterns, and their effector functions. Understanding these mechanisms is essential for guiding rational vaccine design and the development of antibody-based diagnostics and therapeutics. By integrating the findings from both innate and adaptive immunology, this review emphasizes the importance of NS1 as a multifunctional immune determinant in dengue virus infection. Full article

Mechanical ventilation is indispensable for patients with severe respiratory conditions, and high-fidelity lung simulators play a pivotal role in ventilator testing, clinical training, and respiratory research. However, most existing simulators are passive, single-lung models with limited and discrete control over respiratory mechanics, which constrains their ability to reproduce realistic breathing dynamics. To overcome these limitations, this study presents a dual-chamber lung simulator that can operate in both active and passive modes. The system integrates a sliding mode controller enhanced by a linear extended state observer, enabling the accurate replication of complex respiratory patterns. In active mode, the simulator allows for the precise tuning of respiratory muscle force profiles, lung compliance, and airway resistance to generate physiologically accurate flow and pressure waveforms. Notably, it can effectively simulate pathological conditions such as acute respiratory distress syndrome (ARDS) and chronic obstructive pulmonary disease (COPD) by adjusting key parameters to mimic the characteristic respiratory mechanics of these disorders. Experimental results show that the absolute flow error remains within ±3L/min, and the response time is under 200ms, ensuring rapid and reliable performance. In passive mode, the simulator emulates ventilator-dependent conditions, providing continuous adjustability of lung compliance from 30 to 100mL/cmH${}_2$O and airway resistance from 2.01 to 14.67$\sqrt{{\mathrm{cmH}}_2\mathrm{O}}/(\mathrm{L}/\mathrm{s})$, with compliance deviations limited to ±5%. This design facilitates fine, continuous modulation of key respiratory parameters, making the system well-suited for evaluating ventilator performance, conducting human–machine interaction studies, and simulating pathological respiratory states. Full article

The GaN-on-Si virtual substrate is now an indispensable platform for the application of GaN in the fields of power devices, radio frequency, light-emitting devices, etc. Such applications are still in need of more effective stress balancing techniques to achieve higher quality and stress balance in GaN-on-Si at a lower thickness. In this study, three promising practical prototypes of highly effective stress-balancing structures are proposed to realize the concept of an ideal AlN interlayer (AlN-IL) featuring a completely relaxed lower AlN/GaN interface and a fully strained upper GaN/AlN interface. The first is a single-layer AlN interlayer grown via precursor pulsed-injection (PI-AlN-IL). The second combines a low-temperature AlN (LT-AlN) underlayer with a PI-AlN-IL. The third integrates LT-AlN with a high-temperature AlN cap. Compared with optimal conventional single-layer AlN interlayer references, all these designs more effectively induced compressive stress and strain in overlying GaN layers. This study opens new technical paths to balancing stress in GaN-on-Si systems at a reduced thickness more efficiently. Full article

To address the limitations of traditional buckling-restrained braces (BRB), which feature a single-stage yielding and inadequate energy dissipation under small earthquakes, this study proposes a novel double-stage yielding buckling-restrained brace (DSY-BRB). The proposed design integrates a sliding friction damper with shape memory alloy (SMA) bolts and conventional BRB components, enabling effective energy dissipation at small deformations and adaptive performance across varying displacement amplitudes compared with traditional BRBs. Leveraging SMA superelasticity, the DSY-BRB also exhibits self-centering capability that distinguishes it from prior DSY-BRB configurations. Experimental investigations were conducted on DSY-BRB specimens with varying core plate widths under cyclic quasi-static loading to evaluate hysteresis behavior, energy dissipation capacity, and self-centering performance. Results demonstrate that DSY-BRBs exhibit symmetric flag-shaped hysteresis curves with enhanced energy dissipation and excellent self-centering capabilities, achieving minimal residual deformation compared to traditional BRBs. Complementary finite element modeling with parametric analysis was performed to establish design guidelines for optimal double-stage buckling behavior. The findings reveal critical stiffness ratio requirements between BRB and SMA bolt-based friction damper components, providing valuable design criteria for engineering applications. This hybrid approach offers significant advantages in seismic energy dissipation and structural resilience compared to existing DSY-BRB systems. Full article

This study examines the determinants of online customer engagement (OCE) and its role in influencing the repurchase intention of healthy food through social commerce (s-commerce) platforms. Using the Stimulus-Organism-Response (S-O-R) framework, 300 Indonesian urban shoppers were surveyed to explore the impact of customer internal and external buying stimuli through online content quality (OCQ) and customer experiences, encompassing hedonic and social value. PLS-SEM analysis highlights the significance of OCQ in enhancing customer trust and engagement while underscoring the importance of emotional gratification and perceived social benefits mediating customer engagement in building repurchase intentions. The analysis also reveals the insignificant direct effect between social value and repurchase intention, suggesting a more nuanced mechanism in consumer behavioural response. The findings provide theoretical insights into s-commerce research and practical implications for designing online food services to retain customers, emphasising the need for integrative strategies incorporating emotional, social, and informational elements. This research contributes to a deeper understanding of consumer behaviour in using social media for healthy food marketing. It offers sustainable and actionable recommendations for the digital era. Full article

This study presents a machine learning-enhanced optimization framework for proton exchange membrane fuel cell (PEMFC), designed to address critical challenges in dynamic load adaptation and thermal management for automotive applications. A high-fidelity model of a 65-cell stack (45 V, 133.5 A, 6 kW) is developed in MATLAB/Simulink, integrating four core subsystems: PID-controlled fuel delivery, humidity-regulated air supply, an electrochemical-thermal stack model (incorporating Nernst voltage and activation, ohmic, and concentration losses), and a 97.2–efficient SiC MOSFET-based DC/DC boost converter. The framework employs the NSGA-II algorithm to optimize key operational parameters—membrane hydration (λ = 12–14), cathode stoichiometry (λO₂ = 1.5–3.0), and cooling flow rate (0.5–2.0 L/min)—to balance efficiency, voltage stability, and dynamic performance. The optimized model achieves a 38% reduction in model-data discrepancies (RMSE < 5.3%) compared to experimental data from the Toyota Mirai, and demonstrates a 22% improvement in dynamic response, recovering from 0 to 100% load steps within 50 ms with a voltage deviation of less than 0.15 V. Peak performance includes 77.5% oxygen utilization at 250 L/min air flow (1.1236 V/cell) and 99.89% hydrogen utilization at a nominal voltage of 48.3 V, yielding a peak power of 8112 W at 55% stack efficiency. Furthermore, fuzzy-PID control of fuel ramping (50–85 L/min in 3.5 s) and thermal management (ΔT < 1.5 °C via 1.0–1.5 L/min cooling) reduces computational overhead by 29% in the resulting digital twin platform. The framework demonstrates compliance with ISO 14687-2 and SAE J2574 standards, offering a scalable and efficient solution for next-generation fuel cell electric vehicle (FCEV) aligned with global decarbonization targets, including the EU’s 2035 CO₂ neutrality mandate. Full article

Tactile sensation is a crucial sensory pathway for humans to acquire information from the environment, and vibration feedback is one form of tactile feedback, offering advantages such as low cost, ease of integration, and high comfort. Avoiding mechanical crosstalk without changing the spacing between vibration units is a significant challenge in the design of haptic interfaces. This work focuses on the joint optimization design of vibration source characteristics and packaging materials of vibration units. From a theoretical modeling perspective, we explore the correlation between material properties and the amplitude of vibrations generated on the skin surface. A three-layer vibration unit optimization design scheme using a pogo pin structure is thus proposed. Parameters are optimized through finite element analysis, and experimental results prove that the three-layer vibration unit with pogo pins has amplitude modulation capabilities, laying the foundation for the design of array-based vibration tactile feedback interfaces and human-inspired grasp control. Full article

To reduce the high computational cost and lengthy design cycles of traditional solid rocket motor (SRM) development, this paper proposes an efficient surrogate-assisted multi-objective optimization approach. A comprehensive performance model was first established, integrating internal ballistics, grain structural integrity, and cost estimation, to enable holistic assessment of the coupled effects of key motor components. A parametric analysis framework was then developed to automate the model, facilitating seamless data exchange and coordination among sub-models through chain coupling. Leveraging this framework, a large-scale, high-fidelity dataset was generated via uniform sampling of the design space. The Kriging surrogate model with the highest global fitting accuracy was subsequently employed to replicate the integrated model’s complex responses and reveal underlying design principles. Finally, an enhanced NSGA-III algorithm incorporating a phased hybrid crossover operator was applied to improve global search performance and guide solution evolution along the Pareto front. Applied to a specific SRM, the proposed method achieved a 4.72% increase in total impulse and a 6.73% reduction in cost compared with the initial design, while satisfying all constraints. Full article

Cloud imagery analysis from terrestrial observation points represents a fundamental capability within contemporary atmospheric monitoring infrastructure, serving essential functions in meteorological prediction, climatic surveillance, and hazard alert systems. However, traditional ground-based cloud image segmentation methods have fundamental limitations, particularly their inability to effectively model the graph structure and symmetry in cloud data. To address this, we propose G-CLIP, a ground-based cloud image segmentation method based on graph symmetry. G-CLIP synergistically integrates four innovative modules. First, the Prototype-Driven Asymmetric Attention (PDAA) module is designed to reduce complexity and enhance feature learning by leveraging permutation invariance and graph symmetry principles. Second, the Symmetry-Adaptive Graph Convolution Layer (SAGCL) is constructed, modeling pixels as graph nodes, using cosine similarity to build a sparse discriminative structure, and ensuring stability through symmetry and degree normalization. Third, the Multi-Scale Directional Edge Optimizer (MSDER) is developed to explicitly model complex symmetric relationships in cloud features from a graph theory perspective. Finally, the Uncertainty-Driven Loss Optimizer (UDLO) is proposed to dynamically adjust weights to address foreground–background imbalance and provide uncertainty quantification. Extensive experiments on four benchmark datasets demonstrate that our method achieves state-of-the-art performance across all evaluation metrics. Our work provides a novel theoretical framework and practical solution for applying graph neural networks (GNNs) to meteorology, particularly by integrating graph properties with uncertainty and leveraging symmetries from graph theory for complex spatial modeling. Full article

This study examines how platform-based artificial intelligence resources (PAIRs) influence sustainable performance in e-business ecosystems by shaping stakeholder cognition and behavior. Guided by the Resource-Based View (RBV), the Theory of Planned Behavior (TPB), and institutional theory, we examine the psychological mechanisms—particularly trust in AI and environmental identity—that mediate the relationship between PAIRs and green value co-creation (GVC), with sustainable development (SD) acting as a moderating factor. Drawing on survey data from 466 platform managers in China’s digital economy hubs (Yangtze River Delta, Pearl River Delta, Beijing-Tianjin), covering diverse industries (e-commerce, consumer goods, healthcare), our data suggest that PAIRs may influence firm performance via GVC, and that this association appears to be stronger under high-SD contexts. Our results underscore the importance of responsible and psychologically informed AI design—such as algorithmic transparency, cognitive load reduction, and ethical calibration—to facilitate stakeholder trust and pro-environmental engagement. This research contributes both theoretically and practically to elucidating how AI integration in e-business can be leveraged for responsible innovation and sustainable value creation. Full article

In this study, the key challenges faced by global navigation satellite systems (GNSSs) in the field of security are addressed, and an eye diagram-based deep learning framework for intelligent classification of interference types is proposed. GNSS signals are first transformed into two-dimensional eye diagrams, enabling a novel visual representation wherein interference types are distinguished through entropy-centric feature analysis. Specifically, the quantification of information entropy within these diagrams serves as a theoretical foundation for extracting salient discriminative features, reflecting the structural complexity and uncertainty of the underlying signal distortions. We designed a hybrid architecture that integrates spatial feature extraction, gradient stability enhancement, and time dynamics modeling capabilities and combines the advantages of a convolutional neural network, residual network, and bidirectional long short-term memory network. To further improve model performance, we propose an improved coati optimization algorithm (ICOA), which combines chaotic mapping, an elite perturbation mechanism, and an adaptive weighting strategy for hyperparameter optimization. Compared with mainstream optimization methods, this algorithm improves the convergence accuracy by more than 30%. Experimental results on jamming datasets (continuous wave interference, chirp interference, pulse interference, frequency-modulated interference, amplitude-modulated interference, and spoofing interference) demonstrate that our method achieved performance in terms of accuracy, precision, recall, F1 score, and specificity, with values of 98.02%, 97.09%, 97.24%, 97.14%, and 99.65%, respectively, which represent improvements of 1.98%, 2.80%, 6.10%, 4.59%, and 0.33% over the next-best model. This study provides an efficient, entropy-aware, intelligent, and practically feasible solution for GNSS interference identification. Full article

In recent years, the application of deep learning technology in the field of food engineering has developed rapidly. As an essential food raw material and processing target, the number of kernels per maize plant is a critical indicator for assessing crop growth and predicting yield. To address the challenges of frequent target ID switching, high falling speed, and the limited accuracy of traditional methods in practical production scenarios for maize kernel falling count, this study designs and implements a real-time kernel falling counting system based on a Convolutional Neural Network (CNN). The system captures dynamic video streams of kernel falling using a high-speed camera and innovatively integrates the YOLOv8 object detection framework with the ByteTrack multi-object tracking algorithm to establish an efficient and accurate kernel trajectory tracking and counting model. Experimental results demonstrate that the system achieves a tracking and counting accuracy of up to 99% under complex falling conditions, effectively overcoming counting errors caused by high-speed motion and object occlusion, and significantly enhancing robustness. This system combines high intelligence with precision, providing reliable technical support for automated quality monitoring and yield estimation in food processing production lines, and holds substantial application value and prospects for widespread adoption. Full article

Sparse Matrix–Vector Multiplication (SpMV) is a critical computational kernel in high-performance computing (HPC) and artificial intelligence (AI). However, its irregular memory access patterns lead to frequent cache misses on multi-level cache hierarchies, significantly degrading performance. Scratchpad memory (SPM), a software-managed, low-latency on-chip memory, offers improved data locality and control, making it a promising alternative for irregular workloads. To enhance SpMV performance, we propose a vectorized execution framework targeting SPM-augmented processors. Recognizing the limitations of traditional formats for vectorization, we introduce Sorted-Row-Block ELL (SRB-ELL), a new matrix storage format derived from ELLPACK (ELL). SRB-ELL stores only non-zero elements, partitions the matrix into row blocks, and sorts them by block size to improve load balance and SIMD efficiency. We implement and evaluate SRB-ELL on a custom processor architecture with integrated SPM using the gem5 simulator. Experimental results show that, compared to vectorized CSR-based SpMV, the SRB-ELL design achieves up to 1.48× speedup and an average of 1.19×. Full article

Methane steam reforming is the most widely adopted hydrogen production technology. To address the challenges associated with the large radial thermal resistance and low mass transfer rates inherent in the tubular packed bed reactors during the MSR process, this study proposes a structural design optimization that integrates annular metal foam gas channels along the inner wall of the reforming tubes. Utilizing multi-physics simulation methods and taking the conventional tubular reactor as a baseline, a comparative analysis was performed on physical parameters that characterize flow behavior, heat transfer, and reaction in the reforming process. The integration of the annular channels induces a radially non-uniform distribution of flow resistance in the tubes. Since the metal foam exhibits lower resistance, the fluid preferentially flows through the annular channels, leading to a diversion effect that enhances both convective heat transfer and mass transfer. The diversion effect redirects the central flow toward the near-wall region, where the higher reactant concentration promotes the reaction. Additionally, the higher thermal conductivity of the metal foam strengthens radial heat transfer, further accelerating the reaction. The effects of operating parameters on performance were also investigated. While a higher inlet velocity tends to hinder the reaction, in tubes integrated with annular channels, it enhances the diversion effect and convective heat transfer. This offsets the adverse impact, maintaining high methane conversion with lower pressure drop and thermal resistance than the conventional tubular reactor does. Full article