Search Results (33,024)

The gas desorption behavior of coal is a key basis for guiding gas parameter determination, optimizing gas extraction, and preventing gas-related disasters. Coal in mine working faces typically exhibits a mixed particle size distribution. However, research on the gas desorption behavior of mixed-size coal samples and comparative studies with single-sized samples remains insufficient. This study employed a self-developed experimental system for the multi-field coupled seepage desorption of gas-bearing coal to conduct comparative experiments on gas desorption behavior between single-sized and mixed-size coal samples. Systematic analysis revealed significant differences in their desorption and diffusion patterns: smaller particle sizes and higher proportions of small particles correlate with greater total gas desorption amounts and higher desorption rates. The desorption process exhibits distinct stages: the initial desorption amount is primarily influenced by the particle size, while the later stage is affected by the proportion of coal samples with different particle sizes. The desorption intensity for both single-sized and mixed-size samples decays exponentially over time, with the decay rate weakening as the proportion of small particles decreases. The gas diffusion coefficient decays over time during desorption, eventually approaching zero, and increases as the proportion of small particles rises. Conversely, the gas desorption attenuation coefficient increases with a higher proportion of fine particles. Based on the desorption laws of coal samples with single and mixed particle sizes, this study can be applied to coalbed gas content measurements, emission prediction, and extraction design, thereby providing a theoretical foundation and technical support for coal mine operations. Full article

Canvases and preparation layers consist of diverse materials that respond differently to mechanical stress. In a canvas painting, elongations and shrinkages can cause deformations—either recoverable or permanent—as well as shear stresses and potential cracks, which may weaken the overall structure. This study aims to better understand the interaction between the canvas and preparatory strata in terms of mechanical behavior. To achieve this, a set of canvases and the same types of canvases with preparation layers were selected. Two types of linen and two types of polycotton were chosen to represent contemporary materials currently available in fine-art stores. Additionally, an accelerated aging process was applied to the samples to compare their mechanical response before and after aging. By examining the mechanical behavior of both primed and unprimed canvases through dynamometric tests, a method to evaluate the mechanical degradation attributable to the ground layer has been developed and explained in detail. This method is applicable to cases with similar characteristics. Analysis of the force/elongation graphs for the ground layer allows for the calculation of how this layer evolves with increasing elongation and how the mechanical degradation worsens. The results highlight the differing mechanical behaviors among the analyzed canvas types in both the warp and weft directions, as well as the degradation values resulting from both the aging process and the dynamometric testing of the canvases and ground layers. Full article

Background: The human visual system is capable of processing large quantities of visual objects with varying levels of abstraction. The brain also exhibits hierarchical integration and learning capabilities that combine various attributes of visual objects (e.g., color, shape, local features, and categories) into coherent representations. However, prevailing theories in visual neuroscience employ simple stimuli or natural images with uncontrolled feature correlations, which constrains the systematic investigation of multidimensional representation dynamics. Methods: In this study, we aimed to bridge this methodological gap by developing a novel large tangram paradigm in visual cognition research and proposing cognitive-associative encoding as a mathematical basis. Critical representation dimensions—including animacy, abstraction level, and local feature density—were computed across a public dataset of over 900 tangrams, enabling the construction of a hierarchical model of visual representation. Results: Neural responses to 85 representative images were recorded using Electroencephalography (n = 24), and subsequent behavioral analyses and neural decoding revealed that distinct representational dimensions are independently encoded and dynamically expressed at different stages of cognitive processing. Furthermore, representational similarity analysis and temporal generalization analysis indicated that higher-order cognitive processes, such as “change of mind,” reflect the selective activation or suppression of local feature processing. Conclusions: These findings demonstrate that tangram stimuli, structured through cognitive-associative encoding, provide a generalizable computational framework for investigating the dynamic stages of human visual object cognition. Full article

This study examines the relationship between attitudes toward artificial intelligence (AI) and AI dependency among Ecuadorian university students. A cross-sectional design was used, applying two validated instruments: the Artificial Intelligence Dependence Scale (DAI) and the General Attitudes Toward Artificial Intelligence Scale (GAAIS), with a sample of 540 students. Structural equation modeling (SEM) assessed how both positive and negative attitudes predict dependency levels. Results indicate a moderate level of AI dependency and an ambivalent attitudinal profile. Both attitudinal dimensions significantly predicted dependency, suggesting dual-use behaviors shaped by perceived utility and ethical concerns. Urban students reported higher dependency and greater sensitivity to AI-related risks, highlighting digital inequalities. Although the SEM model showed adequate comparative fit (CFI = 0.976; TLI = 0.973), residual indicators (RMSEA = 0.075) suggest further refinement is needed. This study contributes to underexplored Latin American contexts and emphasizes the need for equity-driven digital literacy strategies in higher education. Findings support pedagogical frameworks promoting critical thinking, ethical reasoning, and responsible AI use. The study aligns with Sustainable Development Goals 4 (Quality Education) and 10 (Reduced Inequalities), reinforcing the importance of inclusive, learner-centered approaches to AI integration. Full article

This study focused on the thermal stability and ablative behavior assessment of five newly developed composite TPS configurations. All ten test samples were 3D printed via FDM using various fire-retardant thermoplastic materials, with and without reinforcement. Eight samples integrated a new thermal management internal air chamber conceptualized architecture. A prompt feasible approach for the flame resistance preliminary assessment of ablative TPS samples was developed, using an in-house oxy-acetylene torch test bench. Experimental OTB ablation tests involved exposing the front surface samples to direct flame at 1450 ± 50 °C at 100 mm distance. For each configuration, two samples were tested: one subjected to 30 s of flame exposure and the other to 60 s. During testing, internal temperatures were measured at two backside sample contact points. Recorded temperatures remained below 46 °C, significantly under the maximum allowable back face temperature of 180 °C set for TPSs. The highest mass losses were measured for PC and PETG FR materials, achieving around 19% (30 s) and, respectively, 36% (60 s), while the reinforced configurations showed overall only a third of this reduction. The study’s major outcomes were the internal air chamber concept validation and identifying two reinforced configurations as strong candidates for the further development of 3D-printed ablative TPSs. Full article

This study employs a sequential mediation model to investigate the cognitive mechanisms linking Earth science education to sustainable behavior. Grounded in construal level theory and temporal cognition research, we hypothesize that geological time perception mediates the relationship between Earth science education and temporal construal level, which in turn affects sustainable behavior. Structural equation modeling, based on data from 280 participants, validated the proposed model. It confirmed geological time perception as a second-order construct with four dimensions: time span perception, understanding of geological processes, time depth perception, and continuity of geological change. The results indicated significant indirect pathways. Earth science education influenced the temporal construal level via geological time perception (β = 0.325), and the temporal construal level mediated the relationship between geological time perception and sustainable behavior (β = 0.306). The sequential mediation path (β = 0.215) suggests that Earth science education promotes sustainable behavior by recalibrating temporal cognition and construal processes. This finding illuminates how educational interventions can address the temporal asymmetry in environmental decision-making by developing specific cognitive capacities rather than simply imparting knowledge. Full article

Considering the necessity of food safety testing, various biosensors have been developed based on biological elements (e.g., antibodies, aptamers), chemical elements (e.g., molecularly imprinted polymers), physical elements (e.g., nanopores) as recognition substances. According to the sensing patterns of signal transduction, the biosensors could be classified into optical and electrochemical biosensing, including fluorescence sensing, Raman sensing, colorimetric sensing, electrochemical sensing, etc. To enhance the sensing sensitivity, kinds of nanomaterials have been applied for signal amplification. With merits of high selectivity, sensitivity, and accuracy, the sensing strategies have been widely applied for food safety testing. This review highlights their signal output behavior, (e.g., fluorescence intensity shifts, Raman peak alterations, colorimetric changes, electrochemical current/voltage/impedance variations), nanostructure-mediated amplification mechanisms, and the fundamental recognition principles. Future efforts should prioritize multiplexed assay platforms, integration with microfluidics and smart devices, novel biorecognition elements, and sustainable manufacturing. Emerging synergies between biosensors and AI-driven data analytics promise intelligent monitoring systems for predictive food safety management, addressing challenges in food matrix compatibility and real-time hazard identification. Full article

Grain storage is one of the important means of national macro-control, significantly impacting people’s livelihood and social stability. In emergencies, grain storage enhances disaster relief efficiency and victim resettlement. Currently, developing countries primarily use government storage and government–enterprise joint storage. In response to the speculative behavior caused by the profit-seeking tendencies of agent storage enterprises in the process of joint government–enterprise grain storage, this study considers the current status of digital governance reform by the government and takes the government–enterprise emergency joint grain storage mechanism as its research object. We construct an evolutionary game model between the government and agent storage enterprises, analyze the evolutionary stability of the strategy choices of the two parties, explore the impact of various factors on the strategy choices of both parties, and discuss different stable strategy combinations. Through simulation analysis of the cost–benefit systems of both sides, initial strategy probabilities, key factor sensitivity, and the impact of digital governance levels, we propose a number of management recommendations that can effectively reduce speculative behavior and provide guidance for the government to improve its emergency grain storage system. Full article

Background/Objectives: Attention Deficit Hyperactivity Disorder (ADHD) is a common neurodevelopmental disorder frequently associated with other neuropsychiatric conditions, characterized by high clinical heterogeneity and a complex genetic background. Recent studies suggest that copy number variations (CNVs) may contribute to ADHD susceptibility, particularly when involving genes related to brain development, attention regulation, and impulse control. This study investigated the association between CNVs and ADHD phenotype by identifying patients with and without potential pathogenic CNVs. Methods: We evaluated 152 well-characterized ADHD pediatric patients through comprehensive clinical assessments, including dysmorphic features, brain MRI, EEG patterns, and cognitive testing. CNVs were identified using array Comparative Genomic Hybridization (array-CGH). Participants were classified as carrying potentially causative CNVs (PC-CNVs), non-causative CNVs (NC-CNVs), or without CNVs (W-CNVs) and statistically compared across clinical and neurodevelopmental measures. Results: CNVs were identified in 81 participants (53%), comprising 13 with PC-CNVs (8.5%) and 68 with NC-CNVs (44.7%). ADHD symptoms were pronounced across all groups, but PC-CNVs showed a higher burden of comorbidities, suggesting a stronger genetic contribution to ADHD complexity. Significant differences were observed in oppositional behavior, inattentive symptoms, brain MRI findings, and developmental language anomalies. Several CNVs involved genes previously implicated in neurodevelopmental disorders, supporting a potential genetic contribution to the clinical complexity of ADHD. Conclusions: This exploratory study supports the role of CNVs in ADHD susceptibility and highlights the value of genetic screening for understanding clinical variability. Larger studies are needed to clarify genotype–phenotype correlations in ADHD and to guide personalized clinical management. Full article

A methane-air premixed gas explosion is one of the most destructive disasters in the process of coal mining, and the dynamic coupling between the shock wave triggered by the explosion and the surrounding rock of the roadway can lead to the destabilization of the surrounding rock structure, the destruction of equipment, and casualties. The aim of this study is to systematically reveal the propagation characteristics of the blast wave, the spatial and temporal evolution of the wall load, and the damage mechanism of the surrounding rock by establishing a two-way fluid-solid coupling numerical model. Based on the Ansys Fluent fluid solver and Transient Structure module, a framework for the co-simulation of the fluid and solid domains has been constructed by adopting the standard $k-\epsilon$ turbulence model, finite-rate/eddy-dissipation (FR/ED) reaction model, and nonlinear finite-element theory, and by introducing a dynamic damage threshold criterion based on the Drucker–Prager and Mohr–Coulomb criteria. It is shown that methane concentration significantly affects the kinetic behavior of explosive shock wave propagation. Under chemical equivalence ratio conditions (9.5% methane), an ideal Chapman–Jouguet blast wave structure was formed, exhibiting the highest energy release efficiency. In contrast, lean ignition (7%) and rich ignition (12%) conditions resulted in lower efficiencies due to incomplete combustion or complex combustion patterns. In addition, the pressure time-history evolution of the tunnel enclosure wall after ignition triggering exhibits significant nonlinear dynamics, which can be divided into three phases: the initiation and turbulence development phase, the quasi-steady propagation phase, and the expansion and dissipation phase. Further analysis reveals that the closed end produces significant stress aggregation due to the interference of multiple reflected waves, while the open end increases the stress fluctuation due to turbulence effects. The spatial and temporal evolution of the strain field also follows a three-stage dynamic pattern: an initial strain-induced stage, a strain accumulation propagation stage, and a residual strain stabilization stage and the displacement is characterized by an initial phase of concentration followed by gradual expansion. This study not only deepens the understanding of methane-air premixed gas explosion and its interaction with the roadway’s surrounding rock, but also provides an important scientific basis and technical support for coal mine safety production. Full article

To promote the sustainable development of urban smart transportation systems, this study constructs a structural equation model (SEM) based on the Technology Acceptance Model (TAM), incorporating extended variables including social influence, environmental awareness, privacy concerns, and service similarity to investigate users’ behavioral intentions toward Mobility as a Service (MaaS). The research systematically examines key factors influencing user adoption behavior and their underlying mechanisms, providing theoretical foundations and practical guidance for optimizing MaaS system design and policy making. Using SEM as the core analytical framework, this study employs mediation analysis, moderation analysis, and multigroup comparison to empirically examine the direct and indirect effects among variables, as well as group heterogeneity. Data were collected through an online questionnaire survey, with Analysis of Variance (ANOVA) applied to identify the differential impacts of demographic and travel behavior characteristics on users’ intentions and related psychological constructs, thereby supporting precise user segmentation and evidence-based policy interventions. Key findings include the following: (1) Social influence, ease of use, and environmental awareness boost MaaS adoption, while privacy concerns hinder it. (2) Freelancers/self-employed weaken the positive effects of usefulness, ease of use, and social influence on adoption. (3) Service similarity and ease of use effects vary significantly between single-mode and multimodal commuters. The findings extend the theoretical boundaries of TAM and provide both theoretical and practical support for the development of sustainable urban transportation systems. Full article

To mitigate the risks posed by uncertainties in renewable energy output and Electric Vehicle (EV) travel patterns on the scheduling of Virtual Power Plants (VPPs), this paper proposes an optimal scheduling model for a VPP incorporating EVs based on Information Gap Decision Theory (IGDT). First, a Monte Carlo load forecasting model is established based on the behavioral characteristics of EV users, and a Sigmoid function is introduced to quantify the dynamic relationship between user response willingness and VPP incentive prices. Second, within the VPP framework, an economic optimal scheduling model considering multi-source collaboration is developed by integrating wind power, photovoltaics, gas turbines, energy storage systems, and EV clusters with Vehicle-to-Grid (V2G) capabilities. Subsequently, to address the uncertain parameters within the model, IGDT is employed to construct a bi-level decision-making mechanism that encompasses both risk-averse and opportunity-seeking strategies. Finally, a case study on a VPP is conducted to verify the correctness and effectiveness of the proposed model and algorithm. The results demonstrate that the proposed method can effectively achieve a 7.94% reduction in the VPP’s comprehensive dispatch cost under typical scenarios, exhibiting superiority in terms of both economy and stability. Full article

In many scientific and engineering fields, such as hydrogeology, petroleum engineering, geotechnical research, and developing renewable energy solutions, fluid flow modeling in porous media is essential. In these areas, optimizing extraction techniques, forecasting environmental effects, and guaranteeing structural safety all depend on an understanding of the behavior of single-phase flows—fluids passing through connected pore spaces in rocks or soils. Darcy’s law, which results in an elliptic partial differential equation controlling the pressure field, is usually the mathematical basis for such modeling. Analytical solutions to these partial differential equations are seldom accessible due to the complexity and variability in natural porous formations, which makes the employment of numerical techniques necessary. To approximate subsurface flow solutions, traditional methods like the finite difference method, two-point flux approximation, and multi-point flux approximation have been employed extensively. Accuracy, stability, and computing economy are trade-offs for each, though. Deep learning techniques, in particular convolutional neural networks, physics-informed neural networks, and neural operators such as the Fourier neural operator, have become strong substitutes or enhancers of conventional solvers in recent years. These models have the potential to generalize across various permeability configurations and greatly speed up simulations. The purpose of this study is to examine and contrast the mentioned deep learning and numerical approaches to the problem of pressure distribution in single-phase Darcy flow, considering a 2D domain with mixed boundary conditions, localized sources, and sinks, and both homogeneous and heterogeneous permeability fields. The result of this study shows that the two-point flux approximation method is one of the best regarding computational speed and accuracy and the Fourier neural operator has potential to speed up more accurate methods like multi-point flux approximation. Different permeability field types only impacted each methods’ accuracy while computational time remained unchanged. This work aims to illustrate the advantages and disadvantages of each method and support the continuous development of effective solutions for porous medium flow problems by assessing solution accuracy and computing performance over a range of permeability situations. Full article

Background/Objectives: Single-domain immunoglobulins are small protein modules with specific affinities. Among them, the variable domains of heavy chains of heavy-chain-only antibodies (VHH) as the antigen-binding fragment of heavy-chain-only antibodies (also termed nanobodies) have been widely investigated for their applicability, e.g., therapeutics and immunodiagnostics. However, despite their advantageous biochemical and biophysical characteristics, protein aggregation throughout recombinant synthesis is a serious drawback in the development of nanobodies with application perspectives. Therefore, we aimed to develop a computational method to predict the aggregation propensity of VHH antibodies for the selection of promising candidates in early discovery. Methods: We employed a deep learning-based structure prediction for VHHs and derived from it likely biophysical and biochemical properties of the framework region 2 with relevance for aggregation. A total of 106 nanobody variants were produced by recombinant expression and characterized for their aggregation behavior using size exclusion chromatography (SEC). Results: Quantitative characteristics of framework region 2 patches were combined into a function that defines an aggregation score (AS) predicting the aggregation propensities of VHH variants. AS was evaluated for its capability to forecast recombinant VHH aggregation by experimentally studying VHH Fc-fusion proteins for their aggregation. We observed a clear correlation between the calculated aggregation score and the actual aggregation propensities of biochemically characterized VHHs Fc-fusion proteins. Moreover, we implemented an easily accessible pipeline of software modules to design nanobodies with desired solubility properties. Conclusions: AI-based prediction of VHH structures, followed by analysis of framework region 2 properties, can be used to predict the aggregation propensities of VHHs, providing a convenient and efficient tool for selecting stable recombinant nanobodies. Full article

Inspired by the self-organizing optimization mechanisms in nature, the leaf venation of the royal water lily exhibits a hierarchically branched fractal network that combines excellent mechanical performance with lightweight characteristics. In this study, a structural bionic approach was adopted to systematically investigate the venation architecture through macroscopic morphological observation, experimental testing, 3D scanning-based reverse reconstruction, and finite element simulation. The influence of key fractal geometric parameters under vertical loading on the mechanical behavior and energy absorption capacity was analyzed. The results demonstrate that the leaf venation of the royal water lily exhibits a core-to-margin gradient fractal pattern, with vein thickness linearly decreasing along the radial direction. At each hierarchical bifurcation, the vein width is reduced to 65–75% of the preceding level, while the bifurcation angle progressively increases with branching order. During leaf development, the fractal dimension initially decreases and then increases, indicating a coordinated functional adaptation between the stiff central trunk and the compliant peripheral branches. The veins primarily follow curved trajectories and form a multidirectional interwoven network, effectively extending the energy dissipation path. Finite element simulations reveal that the fractal venation structure of the royal water lily exhibits pronounced nonlinear stiffness behavior. A smaller bifurcation angle and higher fractal branching level contribute to enhanced specific energy absorption and average load-bearing capacity. Moreover, a moderate branching length ratio enables a favorable balance between yield stiffness, ultimate strength, and energy dissipation. These findings highlight the synergistic optimization between energy absorption characteristics and fractal geometry, offering both theoretical insights and bioinspired strategies for the design of impact-resistant structures. Full article