Search Results (1,942)

Cooperation is an important influencing factor for individual morality and harmonious social development. Currently, most scholars select adult samples and adopt laboratory research methods. They have found that compared with asynchronous behavior, interpersonal synchronization (including both intentional and incidental synchronization) is significantly associated with higher levels of cooperative behavior. Does this conclusion apply to Chinese children? Childhood is a critical period for the development of cooperative abilities. Therefore, more effective educational approaches for fostering cooperation should be explored and adopted to promote children’s cooperative behaviors. This study randomly selected 193 students aged 8–11 (95 boys and 98 girls, M = 9.74, SD = 1.16) from 5 primary schools in a city. Based on a 2 (intentional synchronization, incidental synchronization) × 3 (measurement occasion) mixed design, a field experiment was conducted to explore the effects of different types of interpersonal synchronization on children’s cooperative behavior. However, the results showed that neither asynchronous behavior nor incidental synchronization significantly improved children’s cooperative behavior. However, the level of cooperative behavior under intentional synchronization conditions was significantly higher than that under incidental synchronization conditions. This characteristic may be related to China’s long-standing collectivistic education, which can help educators reflect on and optimize their cooperation education practices. This finding deserves attention from cooperation researchers. Full article

This paper presents the results of tests on concrete modified with waste powder from the production of epoxy powder coating, planned using design of experiment’s (DOE) experimental design methods. The scope of the investigation included detailed identification of the waste itself (TG/DTA, FTIR, SEM + EDS, laser diffraction), as well as evaluation of selected properties of concretes containing this waste, including compressive strength, density, and durability parameters such as frost resistance and chemical resistance. The scope of the experiment was defined by varying modifier content in the range of 4 to 11% of the cement mass and a water-cement ratio between 0.44 and 0.56. The concrete mixes obtained were characterized by good workability, fluidity, and consistency stability over time, despite the use of the modifier as an additional component in the concrete mix. No adverse effect of the waste used on the durability of the concrete was observed. Concretes modified with waste from the production of epoxy powder coating achieved a frost resistance class of F150 and showed good resistance to chemically aggressive environments (sulfates and chlorides). No products indicating adverse reactions between waste powder and reagents were found. The use of the DOE approach made it possible to determine, in the form of functional relationships, the influence of the modifier content depending on the water-cement ratio (w/c) of the concrete on its compressive strength and density. In general, a decrease in the compressive strength of concrete containing a waste powder modifier was observed, ranging from approximately 11% to 26% compared to unmodified concrete. However, the trend of decreasing compressive strength was reduced as the water-cement ratio of concrete decreased. At a water-cement ratio (w/c) of 0.443, no further decrease in compressive strength was observed. Concrete with 11% waste powder and a w/c ratio of 0.443 achieved 4.7% higher compressive strength than unmodified concrete with the same water-cement ratio. A beneficial interaction was found between a carboxylate-based plasticizer and the waste powder from the production of epoxy powder coatings. The proposed method of using waste as a concrete component is promising and may contribute to reducing the problem of waste management, as well as greenhouse gas emissions. Full article

Deep neural networks (DNNs) are traditionally analyzed as black-box function approximators, yet their internal structure exhibits phase transitions characteristic of complex physical systems. In this study, we investigate topological–functional decoupling—the phenomenon whereby a network retains full graph connectivity while losing computational function—in trained neural networks through the lens of percolation theory. By subjecting three distinct architectures (Shallow, Deep, and Wide MLPs) to a unified edge-pruning analysis on Fashion-MNIST, we uncover a fundamental divergence between structural integrity and computational capacity in this experimental setting. We report three key phenomena observed in these experiments: (1) the zombie network state under stochastic pruning, where the system retains global connectivity ($P_{\infty }\approx 1.0$) yet suffers a catastrophic functional collapse (accuracy falls below 50% of baseline at prunning ratio $p_f\approx 0.35$–$0.68$ depending on depth), proves that graph reachability does not imply computational capability; (2) depth fragility, where increased network depth triggers multiplicative signal decay (the avalanche effect), rendering deep architectures exponentially more vulnerable to random edge removal than shallow ones ($p_f^{\mathrm{deep}}\approx 0.35$ vs. $p_f^{\mathrm{shallow}}\approx 0.68$); and (3) scale-free universality, observed under magnitude-based pruning, where a robust functional skeleton maintains accuracy near the baseline (∼89%) up to extreme sparsity ($p_f\approx 0.85$–$0.95$) across all three architectures. Robustness stems not from holographic redundancy in the overall connection count but from the emergent heavy-tailed rich-club organization of weight magnitudes—a sparse set of high-magnitude synapses that form the functional backbone of the network, decoupled from the redundant topological mass. These findings offer new physical constraints for the design of resilient neuromorphic hardware. Full article

The limited self-starting capability of H-type Darrieus Vertical-Axis Wind Turbines (VAWT) remains one of the main obstacles to their deployment in low-power and urban applications, where wind conditions are typically weak and intermittent. Although several passive geometric modification strategies have been proposed to enhance initial torque generation, most available studies rely predominantly on numerical simulations, with limited systematic experimental validation under low tip-speed ratio (TSR) conditions. In this work, the influence of passive blade modifications on self-starting performance is assessed through a combined numerical–experimental approach. An integrated numerical–experimental framework was used to systematically compare passive blade configurations under equivalent low-wind conditions. Two modified configurations, a biomimetic profile incorporating passive trailing-edge devices and an asymmetric J-type geometry, were optimized using transient CFD simulations of the first rotation cycle and a Design of Experiments (DOE) framework. Additively manufactured full-rotor test blades were then manufactured via additive manufacturing and tested in a controlled wind tunnel at 3.0 m/s and 2.25 m/s. Start-up time, azimuthal robustness, tip-speed-ratio evolution, and static start-up torque (interpreted through its corresponding torque coefficient) were measured and compared against a baseline NACA0018 profile. The biomimetic configuration consistently produced higher start-up torque and shorter acceleration times, achieving self-starting in 66.7% of the evaluated azimuthal positions at 2.25 m/s, compared to 22.2% for the baseline profile. Within the investigated operating range, this configuration emerged as the most robust passive strategy. The agreement between CFD predictions and experimental measurements supports the use of first-cycle maximum torque as a representative indicator of self-starting performance. These findings highlight the comparative value of first-cycle maximum torque as a practical metric for passive self-starting design assessment in low-TSR Darrieus turbines. These findings provide direct experimental evidence to guide the rational design of Darrieus turbines intended for marginal wind conditions. Full article

In this study, we developed a chatbot-based system for detecting early signs of depression and verified its effectiveness through experimental evaluations and user surveys. Emphasizing that it does not rely on medical checklists, the system is designed to automatically extract three linguistic features associated with depression—frequent use of first-person pronouns, pessimistic expressions, and obsessive-compulsive writing styles—from natural user conversations. Multiple models were constructed for these features, and an ensemble layer integrates their outputs for a comprehensive judgment. The implemented system analyzes input sentences obtained through chat, extracts the three categories of features, calculates a final score through an ensemble layer, and visualizes potential signs of depression based on the total score. We performed an evaluation experiment with 20 participants. In the test data evaluation, the system demonstrated over 76% accuracy in each of the three classification categories: first-person usage, pessimistic tendency, and obsessive-compulsive tendency. Full article

Honeycomb composites are extensively utilized in critical applications where weight is a concern in a structure, due to their high efficiency in stiffness-to-weight ratio. In this study, the effective elastic orthotropic behavior of honeycomb composites is analytically expressed as a function of the elastic properties of the constituent sheet material and the geometric parameters of the representative unit cell. Closed-form expressions based on classical beam theory and plate theory are evaluated and systematically validated against a high-fidelity finite element analysis FE-based homogenization benchmark constructed from a representative unit cell with in-plane periodic kinematic constraints. The analytical predictions exhibit generally good agreement with the FE results, with plate-theory-based formulations capturing most elastic constants with higher accuracy. To further support the fidelity of the numerical benchmark, the predicted normalized in-plane moduli are additionally compared with published experimental measurements for aluminum honeycombs, demonstrating close agreement for representative specimens. To quantify the influence of the geometric parameters, a Taguchi-style design-of-experiments (DOE) study reveals that relative density and internal cell angle jointly govern the majority of elastic moduli and Poisson’s ratios, while cell height plays a minor role. Furthermore, dedicated parametric studies confirm the cubic thickness-scaling of in-plane moduli (E₁, E₂, G₁₂), demonstrating the dominant role of bending-controlled deformation. Together, these results establish a validated, high-fidelity FE homogenization benchmark for assessing analytical formulations and providing design-level constitutive data for optimizing honeycomb core sandwich structures. Full article

Tensile and compressive stresses generated during the exploitation of wheel rims can lead to significant failures, posing risks to safety and the environment. Among non-destructive evaluation (NDE) methods, ultrasonic velocity measurement has become widely used for assessing stress states in critical rail vehicle components such as wheel rims. In this study, the relationship between ultrasonic wave velocity and applied compressive stresses in aluminum (EN AW-2011) and austenitic stainless steel (1.4301) specimens is investigated. The methodology integrates ultrasonic time-of-flight (TOF) measurements with controlled mechanical loading up to the elastic limit. The results show that ultrasonic velocity increases with applied compressive stress, with an average change of approximately 40 m/s between unloaded and maximum loading conditions. The material type was identified as the dominant factor, with velocity differences of up to 800 m/s between aluminum and steel, while the applied load contributed changes of approximately 200 m/s. Statistical analysis using Design of Experiments (DOE) and ANOVA confirmed the significance of all main factors (p < 0.0001). The findings demonstrate the sensitivity of ultrasonic velocity to elastic stress states and provide a quantitative basis for the development of reliable in situ ultrasonic stress monitoring systems in rail applications. Full article

Hydrogen-enriched fuel injection in staged gas-turbine combustors is commonly achieved through jet-in-crossflow (JICF) configurations, where flame stabilization is governed by a local balance between flow-induced strain/mixing and chemical reaction rates. This work investigates turbulent reacting JICF relevant to staged combustion conditions using high-fidelity simulations with adaptive mesh refinement (AMR) and differential-diffusion effects together with Lagrangian particle statistics. Chemistry model uncertainties are incorporated by using a projection method that maps uncertainty estimates from detailed mechanisms into the model used in this work. Results show that the macroscopic flame topology remains in a stable two-branch regime (lee-stabilized and lifted) and is primarily controlled by the jet momentum–flux ratio J. Visualization of the normalized scalar dissipation rate reveals that the flame front resides on the low-dissipation side of intense mixing layers, occupying an intermediate region between over-strained and under-mixed regions. While hydrogen content does not significantly change the global stabilization mode for the cases studied, uncertainty analysis reveals composition-dependent differences that are not apparent in the mean behavior alone. In particular, visualization in Eulerian (χ, T) state-space analysis and particle statistics conditioned on the stoichiometric surface demonstrate that higher-hydrogen cases observe a lower scalar dissipation rate and exhibit substantially reduced variability in OH production under kinetic-parameter perturbations, whereas lower-hydrogen blends experience higher dissipation and amplified chemical sensitivity. These findings highlight that, even in globally similar JICF regimes, the hydrogen content can modify the local response of the flame to kinetic-parameter uncertainty, motivating uncertainty-aware interpretation and design for hydrogen-fueled staging systems. Full article

Increased experience with a second language (L2) can affect one’s speech perception and production. Some studies have suggested that experience does not affect the production of English bilabial stops by Arabic speakers. They produce the English bilabial stops /p/ and /b/ as the Arabic /b/, which differs in VOT. However, the effect of English experience on the perception of English bilabial stops remains underinvestigated. This study examines the effect of L2 immersion experience on the perception of the English stops /p/–/b/ to investigate whether the lack of /p/ in Arabic can affect the perception of the /p/–/b/ contrast and whether L2 experience shifts the category boundary toward that of native speakers. Sixtysix participants, comprising two groups of Arabic speakers with differing L2 experience and a control group of native English speakers, completed identification and discrimination tasks using the /p/–/b/ VOT continuum. The regression analysis showed that listeners with more L2 experience (i.e., ≥3 years in the UK) had a closer category boundary to that of native listeners than those with less L2 experience. However, category discrimination accuracy did not differ significantly between the Arabic groups. The results highlight the importance of L2 immersion experience in altering VOT perceptual strategies, which can help in designing future training studies that focus on VOT perception as an L2 phonetic cue. Full article

When ground transportation is disrupted by natural disasters, airdropped rescue vehicles require energy-absorbing cushioning devices to prevent landing impact damage. Thin-walled circular tubes are preferred for their high energy absorption capacity and structural efficiency. However, to reduce platform force fluctuations and decrease residual stroke after compression, thereby avoiding unbalanced loading and ensuring post-landing mobility, slots are introduced into the tube wall, which renders the mean crushing force (MCF) difficult to predict accurately using conventional methods. To address this issue, this paper proposes a physics–data-driven method for predicting the energy absorption characteristics of slotted thin-walled circular tubes. The engineering scenario is introduced, followed by comparative validation via drop weight tests and impact simulations to obtain a sample set via design of experiments (DOE). A multi-layer perceptron (MLP) neural network then augments the samples to generate a dataset. Dimensional analysis yields candidate MCF prediction equations, whose forms and coefficients are determined via a physics–data-driven approach. Weighted graph encoding transforms the equation-solving problem into a graph optimization problem to reduce the computational complexity, and an improved differential evolution (DE) algorithm with a dual-adaptive mutation operator (DSADE) adjusts the parameters and accelerates convergence. The resulting MCF prediction formula, combined with drop test requirements as the optimization objective, achieves a simulation relative error below 5%. These parameters also satisfy engineering requirements in actual airdrop tests, confirming the method’s effectiveness in predicting the energy absorption characteristics of slotted thin-walled tubes. Full article

Disparity estimation from stereo-endoscopic images is critical for 3D reconstruction in minimally invasive surgery (MIS). However, surgical environments have inherent interference factors including soft tissue deformation, motion blur, and photometric inconsistency. Currently, self-supervised generative networks such as StyleGAN offer an alternative method, but their reliance on iterative latent optimization leads to high computational latency and limits practical deployment. In this work, we propose a temporal latent prediction method to accelerate this optimization process. Instead of designing a brand new generator, our framework learns to predict an optimized initial latent vector, thereby reducing the number of optimization steps and per-frame inference time. Crucially, this prediction-guided mechanism does not alter the architecture or inference logic of the generator, ensuring the fidelity of reconstruction is comparable to that of the original method. Experiments on Phantom and In vivo datasets demonstrate that our method reduces average optimization steps by 16–59% and cuts per-frame latency by about 2.3×, compared to baseline predictors and initialization strategies. Importantly, the final photometric loss remains nearly identical across all methods, confirming that acceleration does not compromise reconstruction quality. These results position our approach as a practical step toward efficient, self-supervised stereo-endoscopic reconstruction in clinical settings. Full article

This study addresses the challenge of early-stage recognition of control chart patterns in statistical process control, which is critical for timely detection of process abnormalities in real-time manufacturing environments. Unlike most existing approaches that focus on fully developed patterns, this work targets partially developed patterns within a fixed observation window to enable proactive intervention. A multi-layer perceptron (MLP) classifier was developed using statistical features, and a structured design of experiments (DOE) approach was employed to optimize both the feature set and network parameters. Simulated X-bar chart data representing six pattern types were used, and candidate features were systematically evaluated using fractional factorial design. The results identified an effective feature subset consisting of autocorrelation, mean, mean square value, standard deviation, slope, and cumulative sum. The optimized MLP achieved an offline accuracy of approximately 86%, while online implementation yielded an overall accuracy of 70.6% with acceptable error rates and average run length performance (ARL₀ = 207.3, ARL_I = 10.9). The findings demonstrate that, despite greater difficulty in online recognition, the proposed approach provides a practical and interpretable solution for early detection in quality control systems. Full article

Objectives: This study aimed to demonstrate the feasibility of a novel wireless energy-transmitting implantable diaphragm pacemaker for restoring respiratory ventilation. Methods: The diaphragm pacing (DP) system was designed based on the principle of electromagnetic resonance coupling. The safety of device implantation was analyzed through finite-element simulations of multi-field coupling between electromagnetic heating and biological tissue. In vitro testing with coils embedded in pork demonstrated the system output characteristics. This device was used in miniature Bama pigs that underwent deep anesthesia and respiratory arrest (N = 8). Respiratory airflow, diaphragmatic displacement, and blood gases were used to evaluate the effectiveness of the designed DP system. Results: Thermal effect simulation results show that the temperature rise of the surrounding tissue does not exceed 2 °C during 1 h of transmission power (0.5–1.3 W) operation of the receiver. In vitro tests with two receivers embedded in pork showed that the DP system can effectively output stimulation waveforms over a certain transmission distance (5–35 mm). The stimulation waveform output by the receiver is consistent with the parameters set by the external controller. In phrenic nerve electrical stimulation experiments, the peak respiratory airflow and tidal volume remained stable over 50 consecutive respiratory cycles. The tidal volume (108.63 mL) and diaphragmatic displacement (0.883–2.15 cm) in a pig induced by DP demonstrate the effectiveness of respiratory ventilation. The arterial blood gas analysis results and temperature rise experiment during implantation further confirmed the effectiveness and safety of the ventilation. Conclusions: The implantable diaphragmatic pacemaker developed in this study exhibits good thermal safety, stable output, and effective respiratory ventilation. A control group with commercial diaphragmatic pacemakers and data from chronic implantation experiments are needed to further evaluate its effectiveness. Full article

Background/Objectives: A robust and scalable manufacturing framework for lipid-based nanocarriers remains a critical challenge, particularly for labile phytochemicals such as curcuminoids in turmeric. This study presents an integrated Quality by Design (QbD)-driven and Outcome-Based Design (ObD) strategy to establish a scalable, resource-efficient manufacturing process for curcuminoids-loaded nanostructured lipid carriers (NLCs). Methods: To overcome the limitations of conventional multivariate design of experiments (DOE), which require extensive experimental runs, a risk-based, knowledge-driven single-factor screening approach was employed. Guided by risk assessment tools, including Ishikawa diagrams and failure mode considerations, 12 representative processing conditions were selected to define the design space. Critical quality attributes (CQAs), namely, particle size, polydispersity index (PDI), and zeta potential, were predefined to establish a robust control strategy. A two-step homogenization process—high-shear homogenization (HSH) for pre-emulsification followed by high-pressure homogenization (HPH) for nanoscale refinement—was systematically optimized. Results: Multivariate data analysis using principal component analysis (PCA) and hierarchical cluster analysis (HCA) identified key critical process parameters (CPPs), particularly HSH speed, processing time, and HPH cycles, as dominant factors influencing nanoparticle characteristics. The optimized 1-h process enabled successful scale-up of NLCs from 100 g to 5000 g, demonstrating the capability to generate nanosized particles within 100–500 nm. The combined HSH–HPH approach produced smaller, more uniform nanoparticles with high encapsulation efficiency and physical stability, outperforming HSH alone. Conclusions: Overall, this study establishes a practical and industrially viable framework that integrates QbD principles with data-driven optimization tools, for enabling reliable translation from laboratories to semi-industrial production. Full article

Subgraph isomorphism counting is a fundamental yet computationally challenging task in graph analysis, with broad applications in bioinformatics and social network mining. With the tightening of data privacy regulations and the emergence of data silos, traditional centralized Graph Neural Network (GNN) approaches face significant deployment hurdles. Existing federated subgraph counting methods are primarily designed for database federation scenarios, focusing on exact queries and the privacy and security concerns of databases. However, this rigid focus on exactness and heavy cryptographic security severely limits their scalability and generalizability to complex, arbitrary query patterns. To bridge this gap, we propose a general Federated Edge-Centric Framework for Subgraph Isomorphism Counting (FedCount), shifting the paradigm from exact querying on federated databases to neural approximate counting under federated architectures. Rather than relying on heavy cryptographic techniques, we exclusively leverage the inherent structural isolation of federated learning as a lightweight empirical privacy measure. While this framework does not theoretically defend against advanced gradient-based inference attacks, it successfully prevents the direct leakage of raw graph topology and node features, achieving high-precision approximate counting without the prohibitive cryptographic overheads. Specifically, we introduce two key technical innovations to enhance local counting capability: (1) we integrate a provable edge encoding scheme into the interaction-based GNN architecture, explicitly modeling edge-to-edge interactions to break the expressiveness bottleneck of standard message passing; (2) we design a Residual Edge-Centric Readout mechanism that mitigates the gradient vanishing problem, enabling the effective training of deeper networks to capture high-order topological dependencies. Extensive experiments on multiple benchmark datasets demonstrate that our framework significantly outperforms existing distributed enumeration baselines in terms of generalization and efficiency, approaching the performance of centralized state-of-the-art models. Full article