Search Results (18,848)

This study examines the forced vibration behavior of a hydroelastic system composed of a functionally graded material (FGM) plate, a barotropic compressible Newtonian viscous fluid, and an adjacent rigid wall. The fluid occupies the gap between the plate and the wall. A time-harmonic force, applied in and along the free surface of the FGM plate, excites vibrations within the system. The plate’s motion is modeled using the exact equations of elastodynamics, while the fluid dynamics are described by the linearized Navier–Stokes equations for compressible viscous flow. The governing equations, which feature variable coefficients, are solved using a discrete analytical approach. Boundary conditions enforce impermeability at the rigid wall and continuity of both forces and velocities at the fluid–plate interface. The investigation focuses on the plane strain state of the plate coupled with the corresponding two-dimensional fluid flow. Numerical analyses are conducted to evaluate normal stresses and velocity distributions along the interface. The primary objective is to assess how the graded material properties of the plate influence the frequency-dependent responses of stresses and velocities at the plate–fluid boundary. Full article

This study explores older passengers’ attitudes, behavior, and evaluations of digital air travel, as well as the impact of digital technologies on this demographic, using China as a case study. The findings of this study offer valuable insights for air transport companies to develop sustainable operational strategies, increase passenger satisfaction, and potentially achieve long-term viability. A structured questionnaire survey was conducted targeting this subgroup, applying the Technology Acceptance Model (TAM) and the Theory of Planned Behavior (TPB) as the primary analytical frameworks. While the study’s sample is skewed towards digitally literate individuals, this subgroup remains highly relevant for analyzing digital impact trends, as they are the most likely to interact with and be influenced by digital air travel tools. This study suggests that older passengers, particularly young-old passengers, in China have a generally positive attitude towards the use of digital air travel tools, with time saving, convenience, and cost saving identified as the top three perceived benefits. Over 80% of participants indicated that digital technology influenced their decision to continue choosing air travel, highlighting a link between digital engagement and sustainable passenger behavior. However, as this study is limited to digitally literate “young-old” passengers in China, the findings should be interpreted as exploratory and context-specific rather than globally generalizable. Future studies are needed with broader age groups and mixed methods to verify these results. Full article

An exact analytical model based on three-dimensional (3D) thermo-elasticity theory is developed to investigate the bending behavior of fiber metal laminate (FML) plates under thermo-mechanical load. The temperature-dependent properties and the orthotropy of the component materials are considered in this model. The analytical model is based on the heat conduction theory and thermoelasticity theory, and the solutions are determined by employing the Fourier series expansion, the state space approach and the transfer matrix method. Comparison study shows that the FE results are generally in good agreement with the present analytical solutions, exhibiting relative errors of less than 2%, except in the regions near the upper and lower surfaces. The present solution is close to the experimental values for the laminated plate within the linear range, with errors less than 10%. The decoupling analysis indicates that the thermo-mechanical performance of FML plates no longer strictly adheres to the traditional superposition principle, with errors reaching 30.39%. A modified principle accounting for modulus degradation is introduced to address this discrepancy. Furthermore, parametric studies reveal that the temperature and the lamina number have significant effect on the stresses and displacements of the FML plate. Full article

The emergence of Industry 5.0 brings a paradigm shift towards collaborative environments where humans and intelligent robots work side-by-side, enabling personalized, flexible, and resilient manufacturing. However, integrating humans and robots introduces new operational and safety risks that require proactive and adaptive control strategies. This study proposes an intelligent multi-criteria decision-making framework for smart risk assessment and the selection of optimal adaptive control strategies in human–robot collaborative manufacturing settings. The proposed framework integrates advanced risk analytics, real-time data processing, and expert knowledge to evaluate alternative control strategies, such as real-time wearable sensor integration, vision-based dynamic safety zones, AI-driven behavior prediction models, haptic feedback, and self-learning adaptive robot algorithms. A cross-disciplinary panel of ten experts structures six main and eighteen sub-criteria spanning safety, adaptability, ergonomics, reliability, performance, and cost, with response time and implementation/maintenance costs modeled as cost types. Safety receives the most significant weight; the most influential sub-criteria are collision avoidance efficiency, return on investment (ROI), and emergency response capability. The framework preserves linguistic semantics from elicitation to aggregation and provides a transparent, uncertainty-aware tool for selecting and phasing adaptive control strategies in Industry 5.0 collaborative cells. Full article

Reducing food loss and waste (FLW) is a global priority under UN SDG 12.3, yet Taiwan has lacked stage-specific FLW data and systematic valuation of its environmental and economic implications. This study addresses these gaps by integrating localized FLW estimates from the APEC-FLOWS database with an enhanced analytical framework—the Environmentally Extended Input–Output Valuation (EEIO-V) model. The EEIO-V extends conventional input–output analysis by monetizing multiple environmental burdens, including greenhouse gases, air pollutants, wastewater, and solid waste, thereby linking FLW reduction to tangible economic benefits and policy design. The simulations reveal substantial differences in environmental cost reductions across supply chain stages, with downstream interventions delivering the largest benefits, particularly in reducing air pollution and greenhouse gases. By contrast, upstream measures contribute relatively smaller improvements. These findings highlight the novelty of EEIO-V in bridging environmental valuation with system-level FLW analysis, and they provide actionable insights for designing cost-effective, stage-specific strategies that prioritize downstream interventions to advance Taiwan’s sustainability and policy goals. Full article

Currently, resin polymer anchoring agents are widely used for bolting support in coal mine roadways to anchor the bolts to the surrounding rock mass. However, due to the relatively low strength of the resin anchoring agent itself, the required anchoring length tends to be excessively long. Based on this, this paper proposes the use of resin concrete as a replacement for resin. Compared to resin anchoring agents, resin concrete offers greater mechanical interlocking force with anchor rods, which can reduce the theoretical anchoring length. To systematically investigate the influence of factors such as the diameter and anchorage length of Glass Fiber-Reinforced Polymer (GFRP) bolt on the bond behavior between GFRP bolts and resin concrete, 33 standard pull-out tests were designed and conducted in accordance with the CSA S807-19 standard. Taking the 18 mm-diameter bolt as an example, when the bond lengths were 2D, 3D, 4D, and 5D, the average bond strengths were 41.32 MPa, 39.18 MPa, 38.84 MPa, and 37.44 MPa, respectively. This represents a decrease of 5.18%, 6.00%, and 9.39% for each subsequent increase in bond length. The results indicate that the bond strength between GFRP anchors and resin decreases as the anchorage length increases. Due to the shear lag effect, the average bond strength also decreases with increasing anchor diameter. Taking a 5D (where D is the anchor diameter) anchorage length as a reference, the average bond strengths for anchor diameters of 18 mm, 20 mm, 22 mm, and 24 mm were 37.44 MPa, 33.97 MPa, 32.18 MPa, and 31.50 MPa, respectively. The corresponding reductions compared to the 18 mm diameter case were 9.27%, 14.05%, and 15.87%. Based on the experimental results, this paper proposes a bond–slip constitutive model between the bolt and resin concrete, which consists of a rising branch, a descending branch, and a residual branch. A differential equation relating shear stress to displacement was established, and the functions describing the variation in displacement, normal stress, and shear stress along the position were solved for the ascending branch. Although an analytical solution for the differential equation of the descending branch was not obtained, it will not affect the subsequent derivation of the theoretical anchorage length for the GFRP bolt–resin concrete system, as structural components in practical engineering are not permitted to undergo excessive bond-slip. Full article

In industrial equipment containing hydraulic lines for power transmission, the lines have boundary conditions defined by components such as pumps, valves, and actuators located at the ends of the lines. Sudden changes in any of the boundary conditions may result in significant pressure/flow dynamics (fluid transients) in the lines that may be detrimental or favorable to the performance of the equipment. Accurate models for line transients are defined by the exact solution to a set of simultaneous partial differential equations. In this paper, analytical solutions to the partial differential equations provide Laplace transform transfer functions applicable to any set of boundary conditions yet to be specified that satisfy the requirements of causality. Analytical solutions of these partial differential equations from previous publications are reviewed for cases of laminar and turbulent flow for Newtonian and a class of non-Newtonian fluids. This paper focuses on a method for obtaining total system analytical models and time domain solutions for cases in which the end-of-line components can be modeled with linear equations for perturbations relative to pre-transient flow conditions. Examples with pumps, valves, and actuators demonstrate the process of coupling equations for components at the ends of a line to obtain total system transfer functions and then obtain time domain solutions for outputs of interest associated with system inputs and load variations. Full article

Background: Recombinant monoclonal antibodies (mAbs) represent the fastest-growing sector of the biopharmaceutical industry, with their efficient expression being a key technological factor for scalability. Objectives: In this study we compared the performance of two bicistronic vectors, which alternate the positions of the light and heavy chain coding genes, employing a wild-type Encephalomyocarditis virus (EMCV) IRES functional element to drive expression of the second gene. Methods: Using two neutralizing anti-SARS-CoV-2 IgG1 antibodies as model molecules, we conducted transient transfections in the commercially available ExpiCHO^TM platform. Following protein A affinity purification and quantification, vectors positioning the light chain as the first cistron consistently yielded higher expression levels than those with the heavy chain upstream. To confirm the quality attributes of the mAbs, we applied a comprehensive analytical workflow, including SDS-PAGE and Western blot for molecular mass and purity, circular dichroism for secondary structure, intrinsic tryptophan fluorescence for tertiary structure, and SEC-HPLC for quaternary structure and aggregate detection. Additionally, we assessed binding affinity to the target using spot blot and surface plasmon resonance, analyzed N-glycosylation profiles by HILIC-HPLC and mass spectrometry, and examined molecular structure by transmission electron microscopy. Results and Conclusions: Together, these results provide insight into the impact of gene positioning within bicistronic vectors on mAb expression efficiency and quality, supporting optimization strategies for scalable recombinant antibody production. Full article

This study investigates the behaviour of dense silty sands with kaolinite clay under static drained/undrained conditions at low confining stress. Conventional laboratory tests assessed the mixtures’ physical properties, but standard void ratio methods proved inadequate for silty sands with kaolinite. Despite targeting 80% relative density, specimens exhibited loose sand behaviour in both drained and undrained tests. With increasing kaolinite content, conventionally reconstituted mixtures exhibit reduced peak stress ratios up to 10% fines, with little change beyond, while critical ratios generally rise at 25 kPa but remain unchanged or decrease slightly at 50 kPa. Analytical redefinition of minimum/maximum void ratios (based on sand–clay volumetric fractions) improved specimen reconstitution, yielding dense behaviour matching that of the host sand. The alternatively reconstituted mixtures display increasing drained peaks and minor changes in undrained peaks with increasing kaolinite content, with critical ratios increasing markedly at 25 kPa and only slightly at 50 kPa. However, this analytical void ratio determination method is limited to non-expansive, low-plasticity clays. Void ratios in silty sands with clay mineras are influenced by confining stress, drainage, saturation, clay content, and the sand skeleton structure. Unlike pure sands, these mixtures exhibit variable void ratios due to changes in the clay phase under different saturation levels. A new evaluation method is needed that accounts for clay composition, saturation-dependent consistency, and initial sand skeleton configuration to characterise these soils accurately. The findings highlight the limitations of conventional approaches and stress the need for advanced frameworks to model complex soil behaviour in geotechnical applications. Full article

The integration of AI-driven optimization into Electronic Design Automation (EDA) enables smarter and more adaptive circuit design, where symmetry and asymmetry play key roles in balancing performance, robustness, and manufacturability. This work presents a model-driven optimization methodology for sizing low-phase-noise LC voltage-controlled oscillators (VCOs) at 5 GHz, targeting Wi-Fi, 5G, and automotive radar applications. The approach uses the EKV transistor model for analytical CMOS device characterization and applies a diverse set of metaheuristic algorithms for both single-objective (phase noise minimization) and multi-objective (joint phase noise and power) optimization. A central focus is on how symmetry—embedded in the complementary cross-coupled LC-VCO topology—and asymmetry—introduced by parasitics, mismatch, and layout constraints—affect optimization outcomes. The methodology implicitly captures these effects during simulation-based optimization, enabling design-space exploration that is both symmetry-aware and robust to unavoidable asymmetries. Implemented in CMOS 180 nm technology, the approach delivers designs with improved phase noise and power efficiency while ensuring manufacturability. Yield analysis confirms that integrating symmetry considerations into metaheuristic-based optimization enhances performance predictability and resilience to process variations, offering a scalable, AI-aligned solution for high-performance analog circuit design within EDA workflows. Full article

Urban traffic congestion is a significant challenge faced by cities globally, resulting in delays, increased emissions, and diminished quality of life. This study introduces an innovative traffic load-balancing algorithm developed as part of the IN2CCAM Horizon 2020 project, which was specifically tested in the city of Vigo, Spain. The proposed method incorporates short-term traffic forecasting through machine learning models—primarily Long Short-Term Memory (LSTM) networks—alongside a dynamic routing algorithm designed to equalize travel times across alternative routes. Historical speed and volume data collected from Bluetooth sensors were analyzed and modeled to predict traffic conditions 15 min ahead. The algorithm was implemented within the PTV Vissim microsimulation environment to assess its effectiveness. Results from 20 distinct traffic scenarios demonstrated significant improvements: an increase in average speed of up to 3%, an 8% reduction in delays, and a 10% decrease in total standstill time during peak weekday hours. Furthermore, average emissions of CO₂, NO_x, HC, and CO were reduced by 4% to 11% across the scenarios. These findings highlight the potential of integrating predictive analytics with real-time load balancing to enhance traffic efficiency and promote environmental sustainability in urban areas. The proposed approach can further support policymakers and traffic operators in designing more sustainable mobility strategies and optimizing future urban traffic management systems. Full article

The Huangshui River Basin is located in the transition zone between the Loess Plateau and the Qinghai–Tibet Plateau, characterized by a fragile hydrological and ecological environment. Groundwater serves as a vital water source for local economic development and human livelihood. With the acceleration of urbanisation and climate change, groundwater resources face challenges such as pollution and over-exploitation. This study employs an improved DRASTIC model, tailored to the characteristics of the groundwater system in the Huangshui River Valley of the upper Yellow River, to integrate groundwater resources, groundwater environment, and ecological environment systems. Improving the DRASTIC model for groundwater vulnerability assessment. A two-tiered evaluation system with nine indicator parameters was proposed, including six groundwater quality vulnerability indicators and five groundwater quantity vulnerability indicators. Fuzzy analytic hierarchy process and entropy weight method were used to determine the weights, and Geographic Information System (GIS) spatial analysis was employed to evaluate groundwater vulnerability in the Huangshui River basin in 2006 and 2021. The results indicate that the proportion of areas with high groundwater quality vulnerability increased from 10.7% in 2006 to 31.57% in 2021, while the proportion of areas with high groundwater quantity vulnerability decreased from 22.33% to 14.02%. Overall, groundwater quality vulnerability in the Huangshui River basin is increasing, while groundwater quantity vulnerability is decreasing. Based on the evaluation results of water quality and quantity vulnerability, protection zoning maps for water quality and quantity were compiled, and preventive measures and recommendations for water quality and quantity protection zones were proposed. Human activities have a significant impact on groundwater vulnerability, with land use types and groundwater extraction coefficients having the highest weights. This study provides a scientific basis for the protection and sustainable use of groundwater in the Huangshui River basin. Full article

Despite the widespread adoption of network monitoring tools, Managed Service Providers (MSPs), specifically small- and medium-sized enterprises (SMEs), continue to face persistent challenges in achieving predictive, multi-tenant-aware visibility across distributed client networks. Existing monitoring systems lack integrated predictive analytics and edge intelligence. To address this, we propose an AI- and IoT-driven monitoring and visualisation framework that integrates edge IoT nodes (Raspberry Pi Prometheus modules) with machine learning models to enable predictive anomaly detection, proactive alerting, and reduced downtime. This system leverages Prometheus, Grafana, and Mimir for data collection, visualisation, and long-term storage, while incorporating Simple Linear Regression (SLR), K-Means clustering, and Long Short-Term Memory (LSTM) models for anomaly prediction and fault classification. These AI modules are containerised and deployed at the edge or centrally, depending on tenant topology, with predicted risk metrics seamlessly integrated back into Prometheus. A one-month deployment across five MSP clients (500 nodes) demonstrated significant operational benefits, including a 95% reduction in downtime and a 90% reduction in incident resolution time relative to historical baselines. The system ensures secure tenant isolation via VPN tunnels and token-based authentication, while providing GDPR-compliant data handling. Unlike prior monitoring platforms, this work introduces a fully edge-embedded AI inference pipeline, validated through live deployment and operational feedback. Full article

This study takes the environmental protection tax (EPT) implemented in China in 2018 as the policy background and systematically examines the impact mechanism and boundary conditions of EPT on the market value of listed companies in the polluting industries. The results indicate that EPT significantly inhibits Tobin’s Q of polluting companies. A one-unit increase in EPT leads to a 0.274-unit decrease in Tobin’s Q. The heterogeneity test reveals that the EPT shock exhibits a spatial gradient effect of “Eastern > Central > Western > Northeastern”. The rigidity of the tax system is stronger than that of the pollution discharge fee, and the effect on non-heavily polluting industries is stronger than that on heavily polluting industries. Mechanism analysis shows that while corporate financial flexibility can buffer against short-term EPT shocks, R&D investment and patent quality expose an “innovation trap” characterized by high investment but low conversion efficiency, largely determined by the type of innovation pursued. By elucidating the multiple moderating and mediating mechanisms at play, this study constructs an integrated “institutional pressure-resource constraints-market feedback” model, thereby providing a new analytical framework for environmental economics in emerging markets. Full article

Agroecological transition toward stronger sustainability demands systemic changes in various domains across farms, agroecosystem landscapes, and broader food systems. Business model innovation plays a critical enabling role, by aligning farming systems with agroecology. However, designing or transforming farming business models presents significant challenges, as it involves a radical rethinking of the foundational architecture of value creation, delivery, and capture. This study offers a structured and actionable approach to support this process, by developing a conceptual framework that systematically integrates the FAO’s 10 Elements of Agroecology into the Business Model Canvas, drawing on an exploratory literature review and following a five-stage process. The outcome is a prototype of an Agroecological Business Model Canvas (ABMC) that serves as both an analytical and strategic tool to support the design, evaluation, and improvement of agroecological business models. The proposed ABMC redefines conventional components and introduces additional ones to fully reflect agroecological principles and incorporate evaluation elements for assessing both the transition degree and multidimensional sustainability performance. By facilitating iterative reflection and co-design, the ABMC represents a practical device for advancing Agricultural Knowledge and Innovation Systems and supporting farmers in developing context-specific sustainable, resilient, and socially grounded agroecological business models. Full article