Search Results (239)

This study presents a rigorous experimental investigation of in-cabin acoustic comfort across a heterogeneous set of road and special-purpose vehicles. Interior noise measurements were conducted on a total of 35 vehicles, comprising five vehicles from each of seven operational categories, grouped according to RNTR-2 regulations into three distinct vehicle classes: N1, N2, and N2G. The adopted research methodology ensures a unified, phenomenological, and experimental approach to the assessment of interior vehicle acoustics, enabling consistent data acquisition and comparative analysis across vehicle classes. Measurements were performed under both stationary and dynamic operating conditions using Class 1 precision instrumentation. The experimental results reveal systematic differences in acoustic performance between vehicle classes. While N1 and N2 vehicles generally comply with recommended comfort thresholds, N2G special-purpose vehicles exhibit significantly elevated interior noise levels, reaching up to 90 dBA during dynamic operation, together with increased variability at higher engine regimes. These findings highlight the influence of vehicle architecture, operational conditions, and mission-oriented design constraints on vibro-acoustic behavior. Passive noise control solutions based on advanced sound-absorbing and sound-insulating materials were further evaluated, demonstrating interior noise reductions of up to 10 dBA. The scientific contribution of this work lies in the establishment of a unified, reproducible methodology that enables direct cross-category comparison of in-cabin acoustic comfort while explicitly integrating special-purpose vehicles into a comfort-oriented analytical paradigm. By moving beyond regulatory compliance toward a multidimensional interpretation of acoustic comfort, the study provides a robust foundation for vehicle design optimization and supports the future development of dedicated comfort assessment standards. Full article

This work investigates the nonlinear flexural dynamics of a macroscale cantilever beam by combining analytical modeling, symbolic solution techniques, numerical simulation, and vision-based experiments. Starting from the Euler–Bernoulli equation with geometric and inertial nonlinearities, a reduced-order model is derived via a single-mode Galerkin projection, justified by the experimentally confirmed dominance of the fundamental bending mode. The resulting nonlinear ordinary differential equation is solved analytically using two symbolic methods rarely applied in structural vibration studies: the Extended Direct Algebraic Method (EDAM) and the Sardar Sub-Equation Method (SSEM). Comparison with high-accuracy numerical integration shows that EDAM reproduces the nonlinear waveform with high fidelity, including the characteristic non-sinusoidal distortion induced by mid-plane stretching. High-speed vision-based measurements provide displacement data for a physical cantilever beam undergoing free vibration. After calibrating the linear stiffness, analytical and experimental responses are compared in terms of the dominant oscillation frequency. The analytical model predicts the classical hardening-type amplitude–frequency dependence of an ideal Euler–Bernoulli cantilever, whereas the experiment exhibits a clear softening trend. This contrast reveals the influence of real-world effects, such as initial curvature, boundary compliance, or micro-slip at the clamp, which are absent from the idealized formulation. The combined analytical–experimental framework thus acts as a diagnostic tool for identifying competing nonlinear mechanisms in flexible structures and provides a compact physics-based reference for reduced-order modeling and structural health monitoring. Full article

While BioTRIZ is widely employed in biomimetic design to facilitate creative ideation and standardize workflows, accurately formulating domain conflicts and assessing design schemes during critical stages—such as initial concept development and scheme evaluation—remains a significant challenge. To address these issues, this study proposes an advanced BioTRIZ method. Firstly, the theory of technological evolution is integrated into the domain conflict identification stage, resulting in the development of a prompt framework based on patent analysis to guide large language models (LLMs) in verifying the laws of technological evolution (LTE). Building on these insights, domain conflicts encountered throughout the design process are formulated, and inventive principles with heuristic value, alongside standardized biological knowledge, are derived to generate conceptual solutions. Subsequently, a main parameter of value (MPV) model is constructed through mining user review data, and the evaluation of conceptual designs is systematically performed via the integration of orthogonal design and the fuzzy analytic hierarchy process to identify the optimal combination of component solutions. The optimization case study of a floor scrubber, along with the corresponding experimental results, demonstrates the efficacy and advancement of the proposed method. This study aims to reduce the operational difficulty associated with implementing BioTRIZ in product development processes, while simultaneously enhancing its accuracy. Full article

The embedded through-section (ETS) technique is a promising method for fiber-reinforced polymer (FRP)-strengthening reinforced concrete (RC) structures, offering higher bond resistance and reduced surface preparation compared to externally bonded or near-surface mounted FRP systems. A common failure in ETS applications is debonding at the FRP bar-to-concrete interface. However, current design standards often assume uniform bond stress and lack predictive models that account for debonding propagation and its effect on load capacity. Furthermore, a detailed analysis of interfacial stress development, including debonding initiation and progression along varying bond lengths, remains limited. To address these gaps, this study introduces an analytical model that describes the complete debonding process in ETS FRP bar-to-concrete joints, incorporating both long and short bond lengths and frictional effects. Based on a trilinear cohesive material law (CML), closed-form expressions are deduced for the load–slip response, maximum load, interfacial shear stress and strain distribution along the FRP bar. The proposed model is validated experimentally through pull-out tests on glass FRP (GFRP) bars adhesively bonded to concrete with different strength grades. The results show that the analytical predictions agree well with both the self-conducted experimental data for short joints and existing test results for long joints given in the literature. Therefore, the developed design-oriented solution enables accurate evaluation of the actual contribution of ETS FRP reinforcement to RC members by explicitly modeling debonding behavior. This provides a rigorous and mechanics-based tool for performance-based design of ETS FRP-to-concrete joints, addressing a critical gap in the future refinement of current design standards. Full article

Microfluidic cell culture systems and organ-on-a-chip platforms provide powerful tools for modeling physiological processes, disease progression, and drug responses under controlled microenvironmental conditions. These technologies rely on diverse cell culture methodologies, including 2D and 3D culture formats, spheroids, scaffold-based systems, hydrogels, and organoid models, to recapitulate tissue-level functions and generate rich, multiparametric datasets through high-resolution imaging, integrated sensors, and biochemical assays. The heterogeneity and volume of these data introduce substantial challenges in pre-processing, feature extraction, multimodal integration, and biological interpretation. Artificial intelligence (AI), particularly machine learning and deep learning, offers solutions to these analytical bottlenecks by enabling automated phenotyping, predictive modeling, and real-time control of microfluidic environments. Recent advances also highlight the importance of technical frameworks such as dimensionality reduction, explainable feature selection, spectral pre-processing, lightweight on-chip inference models, and privacy-preserving approaches that support robust and deployable AI–microfluidic workflows. AI-enabled microfluidic and organ-on-a-chip systems now span a broad application spectrum, including cancer biology, drug screening, toxicity testing, microbial and environmental monitoring, pathogen detection, angiogenesis studies, nerve-on-a-chip models, and exosome-based diagnostics. These platforms also hold increasing potential for precision medicine, where AI can support individualized therapeutic prediction using patient-derived cells and organoids. As the field moves toward more interpretable and autonomous systems, explainable AI will be essential for ensuring transparency, regulatory acceptance, and biological insight. Recent AI-enabled applications in cancer modeling, drug screening, etc., highlight how deep learning can enable precise detection of phenotypic shifts, classify therapeutic responses with high accuracy, and support closed-loop regulation of microfluidic environments. These studies demonstrate that AI can transform microfluidic systems from static culture platforms into adaptive, data-driven experimental tools capable of enhancing assay reproducibility, accelerating drug discovery, and supporting personalized therapeutic decision-making. This narrative review synthesizes current progress, technical challenges, and future opportunities at the intersection of AI, microfluidic cell culture platforms, and advanced organ-on-a-chip systems, highlighting their emerging role in precision health and next-generation biomedical research. Full article

This study presents an analysis of adhesively bonded reinforced concrete composite beams. Experimental results are compared with two computational approaches—an iterative algorithm based on an analytical solution and finite element analysis (FEA)—for simply supported composite beams subjected to four-point bending. The cross-section of the beam consists of two reinforced concrete beams bonded together with different adhesive layers: either flexible polyurethane or a stiff epoxy resin layer. This article begins by outlining the process used to determine the parameters for the flexible materials. The linear analytical model, based on the hypothesis of planar cross-sections for bent components and approximating the behavior of the adhesive layer by the pure shear state, leads to closed-form formulas for deflections and stresses in individual components of the system. These formulas are employed in an iterative procedure to evaluate the post-cracking behavior of composite beams. Conversely, the FEA model accounts for material non-linearity in both the adhesive and concrete, as well as the possibility of decohesion of the adhesive layer, providing a more detailed and accurate representation of the structure. The allowable loads, deflections, and stresses derived from both methods are evaluated and compared across various stages of structural performance: prior to cracking, and two serviceability limit states. The obtained results are validated through comparison with experimental data. The aim of this study is to evaluate the effectiveness of the analytical method for rapid assessment of the capacity of composite concrete structures in different work phases. The iterative procedure based on the analytical solution is found to provide reasonable approximations in terms of the deflection, stress distribution, and crack depth. Full article

The fractal extension of the time-dependent Ginzburg–Landau (TDGL) equation, formulated within the framework of Scale Relativity, generalizes superconducting dynamics to non-differentiable space–time. Although analytically well established, its numerical solution remains difficult because of the strong coupling between amplitude and phase curvature. Here we develop two complementary deep learning solvers for the fractal TDGL (FTDGL) system. The Fractal Physics-Informed Neural Network (F-PINN) embeds the Scale-Relativity covariant derivative through automatic differentiation on continuous fields, whereas the Fractal Graph Neural Network (F-GNN) represents the same dynamics on a sparse spatial graph and learns local gauge-covariant interactions via message passing. Both models are trained against finite-difference reference data, and a parametric study over the dimensionless fractality parameter $\mathcal{D}$ quantifies its influence on the coherence length, penetration depth, and peak magnetic field. Across multivortex benchmarks, the F-GNN reduces the relative L₂ error on ${\left|\psi \right|}^2$ from 0.190 to 0.046 and on B_z from approximately 0.62 to 0.36 (averaged over three seeds). This ≈4× improvement in condensate-density accuracy corresponds to a substantial enhancement in vortex-core localization—from tens of pixels of uncertainty to sub-pixel precision—and yields a cleaner reconstruction of the 2π phase winding around each vortex, improving the extraction of experimentally relevant observables such as ξ_eff, λ_eff, and local B_z peaks. The model also preserves flux quantization and remains robust under 2–5% Gaussian noise, demonstrating stable learning under experimentally realistic perturbations. The $\mathcal{D}$—scan reveals broader vortex cores, a non-monotonic variation in the penetration depth, and moderate modulation of the peak magnetic field, while preserving topological structure. These results show that graph-based learning provides a superior inductive bias for modeling non-differentiable, gauge-coupled systems. The proposed F-PINN and F-GNN architectures therefore offer accurate, data-efficient solvers for fractal superconductivity and open pathways toward data-driven inference of fractal parameters from magneto-optical or Hall-probe imaging experiments. Full article

The rapid accumulation of space debris poses a serious threat to operational spacecraft, with the capture and removal of rapidly tumbling non-cooperative targets being a primary challenge. Non-contact electromagnetic de-tumbling technology is a promising solution due to its enhanced safety. This paper addresses the issue of torque modeling and validation in the electromagnetic de-tumbling process for a specific configuration involving a magnetic dipole and a spherical shell under a symmetrically distributed magnetic field. Based on the principles of electromagnetic induction, an approximate analytical expression for the electromagnetic eddy current torque on a rotating spherical shell within a dipole magnetic field is first derived. A high-fidelity finite element model is then established, which reveals a systematic discrepancy between the initial theoretical model and numerical simulation results. A distance-dependent power-law correction factor is introduced to calibrate the theoretical model, significantly improving its accuracy and reducing the average error to 1.5 percent. Finally, a ground-based experimental platform is designed and implemented. The experimental results demonstrate that the corrected approximate analytical model agrees well with the empirical data, verifying its validity and accuracy under the given conditions and providing a reliable theoretical basis for the design of future space debris de-tumbling controllers. Full article

This work investigates the influence of ground effect on the performance of a UAV propeller through a combined experimental, analytical, and numerical approach. A dedicated test bench was designed and constructed to enable controlled measurements of thrust and power under static conditions. During experimental campaigns, it was observed that the measured thrust significantly exceeded theoretical free-air predictions, suggesting the presence of a ground-like amplification effect. To quantify and validate this phenomenon, complementary methods were employed: blade element momentum-based analytical modeling corrected for ground proximity and high-fidelity CFD simulations performed using ANSYS CFX. Three configurations were analyzed numerically—an isolated propeller, a propeller with a motor, and a propeller–motor–mounting plate configuration—highlighting the progressive impact of structural elements on the flow field. The results showed close agreement between corrected analytical predictions, CFD solutions, and experimental data, with deviations below 8%. The presence of the mounting plate induced pressure redistribution and jet reflection, analogous to the helicopter ground effect, leading to thrust amplification of up to 30% relative to free-air conditions. This study confirms the critical role of the experimental setup and mounting configuration in propeller characterization and establishes a validated methodology for capturing ground effect phenomena relevant to UAV propulsion systems. Full article

Medical waste management is a growing concern in Kazakhstan. Despite the presence of a regulatory framework, the current medical waste disposal system suffers from fragmentation, lack of transparency, and inefficient communication between stakeholders. These limitations result in illegal dumping, environmental pollution, and increased health risks. This paper presents the development and validation of an integrated Internet of Things (IoT)-based system designed to optimize and automate the monitoring, collection, and disposal of medical waste. The proposed architecture includes Global Positioning System (GPS) tracking, real-time sensor monitoring, cloud data analytics, and predictive routing algorithms, enabling efficient logistics and regulatory compliance. Utilizing a microcontroller and sensors, the system continuously transmits data to a centralized server for monitoring. Experimental deployments across urban and suburban routes in the Zhambyl region demonstrate that the system achieves a Circular Error Probable (CEP₅₀) of 11 m and a 95% positioning accuracy within 23 m, which aligns acceptably with the requirements for city-level route optimization. Statistical analysis confirms that the observed positioning accuracy is consistent with an urban propagation model and adequate for municipal dispatching, though it remains below automotive-grade precision. The system is further supported by a robust power supply solution, allowing up to 49 h of autonomous operation. Full article

This paper presents FRESCO (Fiber REinforced Strengthening COmposite Database), a comprehensive open-source database designed to systematically organize experimental data on infilled RC frame systems that can be strengthened with advanced composite materials, such as Fiber-Reinforced Polymers (FRP), Textile-Reinforced Mortars (TRM), and other fiber-based solutions. The database employs open source practices while providing high-quality output that is fully compatible with leading commercial software packages such as ANSYS 2022R2. It uses Python3 as the main programming language and FreeCAD v1.0 as the model generation engine, with a systematic 13-section structure that ensures complete documentation of all parameters necessary for numerical modeling and validation of analytical methods. Two types of databases are provided: in comma-separated format (.csv) for common everyday interaction and in JSON format (.json) for easy programmatic access. The database features automated 3D modeling capabilities, converting experimental data into detailed finite element models with solid RC frame geometry, reinforcement details, and infill configurations. Validation through three comprehensive examples demonstrates that numerical models generated from the database closely match experimental results, with response curves that closely match the initial stiffness, the peak loading and the post-peak stiffness degradation phase across different loading conditions. The database focuses on RC frame systems with unreinforced brick infill. Reflecting the term FRESCO, which in Greek (φρέσκο) means “fresh”, the database is designed as a dynamic, evolving resource, with future versions planned to include RC walls and full buildings. Full article

This paper presents the design and implementation of a custom-built LiFePO₄ battery monitoring system that offers real-time visibility into the status of individual battery cells. The system is based on a Battery Management System (BMS) architecture and is implemented by measuring the voltage, current, and temperature of each cell in a multi-cell pack. These key parameters are essential for ensuring safe operation, prolonging battery life, and optimizing energy usage in off-grid or mobile power systems. The system architecture is based on an ESP32 microcontroller that interfaces with INA219 and DS18B20 sensors to continuously measure individual cell voltage, current, and temperature. Data are transmitted wirelessly via Wi-Fi to a remote time-series database for centralized storage, analysis, and visualization. Experimental validation, conducted over a 15-day period, demonstrated stable system performance and reliable data transmission. Analytically, the findings indicate that utilizing an advanced smart charger for precise cell balancing and improving the physical layout for cooling led to superior thermal performance. Even when load current nearly tripled to 110 mA, the system maintained a stable cell operating temperature range of 29.8 °C to 30.3 °C. This result confirms significantly reduced cell stress compared to previous iterations, which is critical for enhancing battery health and lifespan. The application of this project aimed to demonstrate how a combination of open hardware components and lightweight network protocols can be used to create a robust, cost-effective battery monitoring solution suitable for integration into smart energy systems or remote IoT infrastructures. Full article

The increasing global demand for electricity has heightened the need for stable and reliable power distribution systems. Disruptions in power distribution can cause substantial economic losses and societal impact, underscoring the importance of accurate, timely, and scalable monitoring. The integration of Internet of Things (IoT) technologies into smart grids offers promising capabilities for real-time data collection and intelligent control. However, the application of IoT has created new challenges such as high communication overhead and insufficient user privacy protection due to the continuous exchange of sensitive data. In this paper, we propose a method for power distribution analytics in smart grids based on IoT called PSDA. PSDA collects real-time power usage data from IoT sensor nodes distributed across different grid regions. The collected data is spatially organized using Hilbert curves to preserve locality and enable efficient encoding for subsequent processing. Meanwhile, we adopt a dual-server architecture and distributed point functions (DPF) to ensure efficient data transmission and privacy protection for power usage data. Experimental results indicate that the proposed approach is capable of accurately analyzing power distribution, thereby facilitating prompt responses within smart grid management systems. Compared with traditional methods, our scheme offers significant advantages in privacy protection and real-time processing, providing an innovative IoT-integrated solution for the secure and efficient operation of smart grids. Full article

The heavy environmental impact of the construction industry—responsible for 39% of world CO₂ emissions and consuming over 40% of natural resources—supports the need for evidence-based decision-making tools for sustainable material selection balancing environmental, economic, and social considerations. This research develops and evaluates an integrated decision support system that couples cradle-to-grave lifecycle assessment (LCA) with various multi-criteria decision-making (MCDM) methods to optimize climate-resilient material selection for schools. The methodology is an integration of hybrid Analytic Hierarchy Process–Technique for Order of Preference by Similarity to Ideal Solution (AHP-TOPSIS) and VIKOR techniques validated with eight case studies in hot-arid, hot-humid, and temperate climates. Environmental, economic, social, and technical performance indices were evaluated from primary experimental data and with the input from 22 international experts with climate change assessment expertise. Ten material options were examined, from traditional, recycled, and bio-based to advanced composite systems throughout full building lifecycles. The results indicate geopolymer–biofiber composite systems achieve 42% reduced lifecycle carbon emissions, 28% lower cost of ownership, and 35% improved overall sustainability performance compared to traditional equivalents. Three MCDM techniques’ cross-validation demonstrated a satisfactory ranking correlation (Kendall’s τ = 0.87), while Monte Carlo uncertainty analysis ensured framework stability across 95% confidence ranges. Climate-adaptive weighting detected dramatic regional optimization contrasts: thermal performance maximization in tropical climates and embodied impact emphasis in temperate climates. Three case studies on educational building projects demonstrated 95.8% accuracy in validation of environmental performance and economic payback periods between 4.2 and 6.8 years in real-world practice. Full article

Graduate education has entered the era of big data, and systematic analysis of dissertation evaluations has become crucial for quality monitoring. However, the complexity and subjectivity inherent in peer-review texts pose significant challenges for automated analysis. While natural language processing (NLP) offers potential solutions, most existing methods fail to adequately capture nuanced disciplinary criteria or provide interpretable inferences for educators. Inspired by soft-sensor, this study employs a BERT-based model enhanced with additional attention mechanisms to quantify latent evaluation dimensions from dissertation reviews. The framework integrates Shapley Additive exPlanations (SHAP) to ensure the interpretability of model predictions, combining deep semantic modeling with SHAP to quantify characteristic importance in academic evaluation. The experimental results demonstrate that the implemented model outperforms baseline methods in accuracy, precision, recall, and F1-score. Furthermore, its interpretability mechanism reveals key evaluation dimensions experts prioritize during the paper assessment. This analytical framework establishes an interpretable soft-sensor paradigm that bridges NLP with substantive review principles, providing actionable insights for enhancing dissertation improvement strategies. Full article