Search Results (2,196)

Colmation phenomena play a critical role in long-term gas flow through porous media, significantly influencing methane migration, mine ventilation efficiency, and emission control in both active and abandoned coal mines. In colmation modeling, three fundamental kinetic types are commonly distinguished, with the third kinetic providing a generalized nonlinear formulation capable of describing state-dependent and spatially variable permeability degradation. However, the strong nonlinearity of the coupled transport–colmation equations prevents the derivation of closed-form solutions, which necessitates the application of linearization techniques. In this study, gas flow with colmation governed by third-kinetics is analyzed with particular emphasis on methane migration in underground mining environments. Linearization of nonlinear kinetic terms is applied at the level of the coupled mass balance and colmation equations, resulting in an approximate form of Darcy’s law and an explicit analytical solution describing the evolution of the porous medium state. The primary objective of the study is to quantify the error introduced by the adopted linearization and to analyze its spatial and temporal propagation with respect to the nonlinear reference solution. A rigorous error estimation based on Taylor series truncation is developed, yielding an explicit criterion that defines the validity range of the linearized solution. The results demonstrate that the approximation remains reliable within the regime of weak colmation, while the associated error is locally generated and propagates through transport mechanisms without exhibiting uncontrolled growth. Full article

Background and Objectives: While the effectiveness of video modeling (VM) in teaching academic, daily living, and social skills to individuals with Autism Spectrum Disorder (ASD) is frequently investigated, studies examining the use of VM in teaching gymnastics-based motor skills are limited. This study aimed to examine the effects of VM on the acquisition and maintenance of a gymnastics-based motor skills in preschool children with ASD. Methods: The study employed a multiple-probe method across participants in a single-subject research design. Three preschool children diagnosed with mild ASD participated in this study. Baseline, intervention, and follow-up data were systematically collected and analyzed. Social validity data were obtained through semi-structured interviews with parents and special education teachers. Results: The percentage of correct responses increased throughout the VM intervention sessions, and all participants reached the proficiency criterion. Follow-up data collected after the intervention showed that the acquired skill was maintained, and the percentages of correct responses ranged from 80% to 100%. Social validity findings revealed that both teachers and parents perceived VM as an effective and feasible teaching approach for teaching motor skills to children with ASD. Conclusions: The research findings demonstrate that VM is an effective and socially valid teaching method for teaching and maintaining gymnastics-based motor skills in preschool children with ASD. These results contribute to the existing literature by demonstrating the applicability of video modeling in the context of gymnastics-based training. Full article

Industrial electrical houses are engineered systems that transform and control electrical power to supply industrial loads. Preparing technical proposals for these rooms requires consistent engineering choices across multiple artifacts while drawing from heterogeneous client documents, historical projects, and supplier catalogs. This paper reports an industrial prototype that integrates generative AI, system modeling, and mathematical decision methods to support that workflow. We represent requested outputs as ordered sequences of functions and link those functions to candidate equipment blocks through functional and physical graphs that enable traceable retrieval and reuse. Using this representation, we compute a minimal internal-cost baseline by solving a mixed-integer assignment model with sizing constraints, and we rank technically feasible alternatives using fuzzy DEMATEL to derive criterion weights and TOPSIS to obtain an overall ordering under multiple criteria. The workflow is illustrated with an example and the prototype tool used in a company operating in Chile, Peru, Ecuador, and Bolivia, where document ingestion and equipment-list extraction are integrated with human validation. The results illustrate how structured representations, optimization, and multi-criteria ranking can support auditable configurations for engineering review and commercial selection. Full article

This study proposes an innovative structural wall system and evaluates its compressive performance. The wall consists of GLPB manufactured using laminated bonding (along the grain direction) and assembled using a staggered interlocking masonry method. Two key geometric parameters controlling the mechanical response of the GLPB wall—the slenderness ratio (β) and the eccentricity (e)—were selected as the primary design variables. Using a combined experimental and numerical approach, the study systematically investigated the compressive mechanical behavior and performance evolution of the wall, including compressive strength and deformation behavior. Through axial and eccentric compression tests, six sets of specimens with varying geometric parameters β and e were analyzed, yielding relevant data and characteristics regarding failure modes, ultimate load-carrying capacity, load–displacement response, crack resistance, and wall deformation. To further characterize the compressive mechanical performance of GLPB walls, a refined nonlinear finite element model was developed in ABAQUS (version 2020). This model incorporates the anisotropic constitutive behavior of wood, the Hill yield criterion, and the mechanical interactions at the interlocking and bonding interfaces. The study indicates that the average compressive strength of GLPB walls is 2.63 MPa, with a crack-to-failure load ratio ranging from 0.68 to 0.83. GLPB walls demonstrate comparable load-bearing capacity. The total axial vertical strain ranges from 0.033 to 0.041, indicating that the walls possess good deformation capacity. Based on Chinese masonry design standards and experimental evidence, a preliminary predictive formula for the load-bearing capacity of this wall was derived. A comparison of the aforementioned experimental measurements with simulation results showed errors of less than 10%, verifying the model’s validity and accuracy. Numerical simulation can, to a certain extent, compensate for the limitations of experimental methods in capturing internal mechanical states. Full article

The integration of automated learning and video analysis enables the development of intelligent systems that can operate effectively in uncertain scenarios. These systems can autonomously identify dominant motion dynamics, depending on the theoretical framework used for representation and the learning process used for pattern identification. Current literature offers a state-based approach to describe the key temporal and spatial relationships required to understand motion dynamics. An important aspect of this approach is determining when the number of positively learned rules from a given information source is sufficient to detect dominant motion in automatic surveillance scenarios. This is crucial, as it affects both the variability of movements that monitored subjects can exhibit within the camera’s field of view and the resources needed for effective implementation. This study addresses these gaps through a grammar-based sufficiency criterion, which posits that learning is complete when production rule growth stabilizes, under the assumption of system stationarity. The stability criterion evaluates whether the most probable rules are learned over time, and whenever a high-growth rule is added, it is used to update the criterion. We outline several benefits of having a formal criterion for determining when a symbolic surveillance system has a robust model that explains the observed motion dynamics. Our hypothesis is that a correct model can consistently account for the majority of motion dynamics over time in an automated learning process. The proposed approach is evaluated by modeling motion dynamics in several scenarios using the SEQUITUR algorithm as input and computing the probability of stability along the learning curve, which indicates when the model reaches a steady state of consistent learning. Experimental validation was conducted in real-world scenarios under varying acquisition conditions. The results show that the proposed method achieves robust modeling performance, with accuracy values ranging from 83.56% to 95.92% in dynamic environments. Full article

The objective is to evaluate the validity and reliability of the Household Water Insecurity Experiences (HWISE) Scale as a tool to assess the experiences of households and the Argentine population regarding insecurity of access to water. Addressing water insecurity is critical for advancing several Sustainable Development Goals (SDGs), particularly SDG 6 (Clean Water and Sanitation), SDG 3 (Good Health and Well-Being), and SDG 1 (No Poverty), given the strong links between access to safe water, health, and poverty reduction. The scale was administered as part of the Argentine Social Debt Survey (EDSA), on a probabilistic sample of 5799 households. The HWISE Scale demonstrated high reliability both overall and at the item level (Cronbach’s alpha of 0.95 at a total level and greater than 0.94 for each of the items) and criterion validity in terms of correlation with a broad set of indicators: social deprivations, sanitary infrastructure, food insecurity, and psychological health. Finally, the scale showed internal consistency, with a total omega coefficient value of 0.96, suggesting that all scale indicators refer to the same concept of deprivation in water access. In sum, the HWISE Scale applied to the case of Argentina is deemed appropriate for estimating household water insecurity. Full article

This paper proposes a novel task assignment strategy for multiple fixed-wing loitering munitions, focusing on the kinematic capturability of maneuvering ground targets. Compared to rotary-wing UAVs, fixed-wing munitions are subject to significant turning radius constraints and limited maneuverability. Consequently, conventional assignment metrics based on relative distance or estimated time-to-go are insufficient to guarantee successful interception. To address this, we adopt a data-driven capturability prediction framework based on Gaussian Process Regression (GPR) and propose a novel task assignment strategy that leverages the predicted capture region as a decision-making criterion. Furthermore, a robustness-centric task assignment algorithm is proposed, which prioritizes interceptors based on the radius of the Maximum Inscribed Circle (MIC) within the predicted capture region. This metric quantifies the safety margin against target maneuvers and environmental uncertainties. Numerical simulations demonstrate that the proposed method significantly outperforms conventional distance-based and time-to-go-based approaches, achieving the highest interception success rate across all tested scenarios including maneuvering target conditions. The results validate that incorporating geometric capturability constraints is essential for the efficient operation of fixed-wing loitering munitions. Full article

Sub-15 nm line structures are key building blocks for advanced device prototyping, nanoscale electrodes, and lithography templates such as etch/deposition masks. Although ultrahigh-voltage (≥100 kV) electron-beam lithography (EBL) can more readily achieve extremely small critical dimensions, its tool and infrastructure requirements limit widespread adoption in many laboratories. In contrast, 30 kV field-emission SEM platforms are far more accessible; however, resolution-limit patterning at 30 kV is more sensitive to beam current, exposure dose, and development conditions, motivating the establishment of a reproducible process flow and a well-defined process window. Here, we investigate the resolution limit of isolated lines using a Zeiss Gemini 460 system operated at 30 kV and an in-house pattern generator with 950 k PMMA C2 resist. To demonstrate device-level applicability, we develop a stable lift-off process, and all critical dimensions are evaluated on metal lines after e-beam evaporation and lift-off. By screening beam current and scanning dose to build the dose-to-size relationship, we show that reducing beam current significantly improves the achievable minimum line width. Under 35 pA, using CD ≤ 15 nm as the criterion for sub-15 nm window extraction, the usable dose range is [700, 804.3] µC/cm², corresponding to a dose latitude of ~14.9%. The best performance is obtained at 700 µC/cm², yielding a transferred metal line width of 13.85 nm after lift-off. This work provides a practical resolution-limit process flow and a quantitative process window for performing sub-15 nm patterning on accessible 30 kV platforms, supported by product-level lift-off validation. Full article

This study offers a computational framework that analyzes the escape characteristics of transcendental complex maps by utilizing the AK iteration scheme. The well-known polynomial map of the form $z^n+c$ is generalized to the form $z^n+\sin \left(z\right)+\log \left(c^m\right)$, with $m\ge 1$ and $c\in \mathbb{C}\backslash \left\{0\right\}$, allowing the creation of complex fractal structures. A precise escape criterion is developed for the AK iteration scheme, ensuring the numerical stability of the scheme when applied to the construction of the Mandelbrot set and the Julia set. In order to validate the effectiveness of the developed framework, a comparative analysis is performed between the AK iteration scheme and the CR iteration scheme, focusing on the first parametric case of the Mandelbrot set and the Julia set. The average escape time, average number of iterations, non-escaping area index, and fractal dimension are analyzed with respect to the two iteration schemes. The numerical results indicate that the fractal structure obtained by the AK iteration scheme is different from the fractal structure obtained by the CR iteration scheme, showing the effectiveness of the AK iteration scheme as a powerful tool in the study of complex systems. Full article

This paper presents a distributed hierarchical model predictive control (MPC) framework designed to ensure dynamic consensus and stability in nonlinear vehicle platoons, addressing challenges posed by mixed communication topologies and hard constraints. By directed graph modeling of the mixed communication topologies, the dynamic consensus goal for the platoon is defined by the inter-vehicle distances between the host and its neighbors, whereas the stability criterion for an individual vehicle is expressed as a positive definite function of its position and velocity deviations. Then, a contractive constraint is elegantly designed to correlate these two objectives in a hierarchical model predictive control framework, where the lower layer optimizes the stability objective and the upper layer optimizes the dynamic consensus objective. The conditions ensuring stability and string stability for the vehicle platoon are shown to be only dependent on the deviations of the host vehicle, which achieves dynamic consensus and string stability simultaneously for nonlinear vehicle platoons. Several representative scenarios are used to validated the performance of the proposed strategy. Full article

This study carries out the research on event-triggered output feedback control tailored for discrete-time switched linear systems. A dynamic event-triggered mechanism (DETM) is utilized to mitigate the triggering frequency. To ensure stability and control performance, it is assumed that an event is triggered whenever the system undergoes a switch. First, the closed-loop stability of the underlying switched system with DETM is analyzed via the switched Lyapunov function method, followed by the establishment of a stability criterion for the system under arbitrary switching. Based on this criterion, a dynamic event-triggered output feedback control strategy is devised. The viability and application potential of our proposed control strategy is validated through simulation trials using a morphing aircraft model. Furthermore, when we pit dynamic event-triggered control (DETC) against its static (SETC) version, the proposed DETM reduces the trigger events and prolongs the inter-event intervals versus the SETM, while retaining nearly identical control accuracy and energy consumption, thus providing an efficient solution for resource-constrained networked control systems. Full article

The assessment of risk of bias in systematic reviews and meta-analyses is crucial, as it indicates the accuracy of the synthesized and evaluated data and the validity of the presented results and conclusions. Until now, standardized tools for this purpose have been available only for clinical and animal studies, while adapted forms of these tools and novel ones have been proposed for in vitro and laboratory studies. However, none of them have been universally standardized so far. The apparent lack of a risk of bias assessment tool for systematic reviews of in vitro antimicrobial susceptibility testing studies constitutes a methodological flaw in these studies. To this end, we developed a risk of bias assessment tool for in vitro antimicrobial susceptibility testing studies. Our tool assesses the risk of bias across six domains: methodological bias, selection bias, preparation bias (including contamination/cross-contamination bias), measurement/observer bias, reporting and publication bias, and bias related to unreported funding and conflicts of interest. The tool evaluates a total of 16 specific criteria. The risk of bias is graded as low, moderate, or high for each evaluated criterion. The proposed risk of bias assessment tool was tested in a pilot validation study of ten relevant studies by two reviewers independently. We believe that the use of the proposed risk of bias assessment tool will increase the methodological strength of systematic reviews and meta-analyses of in vitro antimicrobial susceptibility testing studies. Full article

The Sparrow Search Algorithm (SSA) is a novel optimization method inspired by sparrows’ foraging and anti-predator behavior. It mimics their exploration and exploitation strategies to find near-optimal solutions for various optimization problems. This paper presents the first application of SSA to the widely recognized Permutation Flowshop Scheduling Problem (PFSP) with the makespan criterion as the optimization target. Our study aims to assess the effectiveness and robustness of this cutting-edge metaheuristic through computational experiments and statistical analysis. The proposed SSA is a hybrid variant that incorporates the Variable Neighborhood Search (VNS) algorithm along with a Path Relinking Strategy. The effectiveness of the proposed method is evaluated through computational experiments on PFSP benchmark instances. The performance of the hybrid SSA is compared against several well-established swarm-intelligence metaheuristics, namely Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), Tuna Swarm Optimization Algorithm (TSO), Particle Swarm Optimization Algorithm (PSO), Firefly Algorithm (FA), Bat Algorithm (BA), and the Artificial Bee Colony (ABC). To ensure fair comparison, all methods are implemented within the same computational framework as the hybrid SSA. The experimental results show that the proposed hybrid SSA achieves the lowest average mean error compared with the competing methods in solving the PFSP. The results were further validated through a comprehensive non-parametric statistical analysis using Friedman, Aligned Friedman, and Quade tests, followed by post-hoc analysis with p-adjusted values, as well as Kruskal–Wallis and Wilcoxon post-hoc tests. Full article

Omnidirectional mobile robots are increasingly used in industrial and service applications due to their high maneuverability and ability to perform combined translational and rotational motions in confined spaces. However, these locomotion advantages are often accompanied by additional wheel–ground interaction losses, making power consumption an important design criterion in the design of efficient mobile platforms. This study presents a dynamic modeling and experimental-power benchmarking framework for a modular mobile robot equipped with three omnidirectional wheels, using a four-omni-wheel configuration as a baseline reference for comparison. A CAD-consistent multibody dynamic model of the three-wheel architecture is developed in the MATLAB/Simulink–Simscape Multibody R2024benvironment to estimate motor currents and electrical-power demand during motion. Experimental validation is carried out on the physical prototype using Hall-effect current sensors integrated into the drive modules, enabling real-time current acquisition for each motor. Both the simulation and experiments are performed on a standardized 1 m square-path benchmark at a constant 12 V supply. The results show that the proposed three-omni-wheel configuration reaches a total measured power of 14.43 W and a simulated power of 12.72 W, corresponding to a robot-level deviation of 11.85%. By comparison, the four-omni-wheel baseline exhibits a total measured power of 25.75 W and a simulated power of 24.92 W. Therefore, the proposed three-wheel architecture reduces the measured power demand by approximately 43.96% relative to the baseline, while the four-wheel configuration provides higher model fidelity. The proposed methodology supports power-oriented evaluation and informed design selection of omnidirectional locomotion architectures for modular mobile robots intended for industrial applications. Full article