Search Results (3,781)

The present study explores the relationship between the systemic approach, educational innovation, and the use of digital technologies in higher education, with an emphasis on military academies. The aim of the research is to shed light on how systemic thinking can support strategic planning, the quality of education, and the effective integration of innovative practices, such as artificial intelligence, information and communication technologies, and virtual reality. The methodology was based on quantitative research using a questionnaire, which was distributed to 452 members of the Hellenic Non-Commissioned Officers Academy educational community (teaching staff, cadets, and recent graduates). Data analysis showed that the adoption of a systemic approach is positively associated with the readiness of trainers, including both instructors and future professionals (cadets), to support and implement educational innovations. Furthermore, it was found that the clarity of educational objectives and the alignment of critical elements of the educational system (resources, technology, instructors, trainees, and processes) significantly reinforce the intention to adopt innovative practices. The findings also show that educators’ positive perceptions of artificial intelligence and virtual/augmented reality are associated with a higher appreciation of learning benefits, such as improved performance, trainee satisfaction, and collaboration. In contrast, demographic and professional factors have a limited effect on attitudes toward innovation. Overall, findings indicated that innovation in military academies is not limited to the technological dimension, but requires a holistic, systemic approach that integrates organizational, pedagogical, and strategic parameters. The study contributes both theoretically and practically, providing empirical evidence for the role of systemic thinking in the design and implementation of innovative educational policies in military and broader academic education. Full article

There is little evidence of the implementation of the Principles for Responsible Investment in Agriculture and Food Systems between universities and businesses, and there is even less research that prioritizes people and implements sustainable development with a territorial focus. In this article, we address a form of collaborative work that integrates academia with business, where the Principles for Responsible Investment in Agriculture and Food Systems (CFS-RIA) are seen as an opportunity to promote and strengthen the management of a business in the communities where it operates, and determine a new way of working from its links with the university. The experience is developed in the provinces of Santiago Rodríguez, Valverde (Mao), and Dajabón in the Dominican Republic, with the aim of contributing, using this new approach, to economic, social, environmental, and governance development in the territory. The conceptual and methodological basis for the university–business link is Working With People, a model that integrates key elements of planning such as social learning, collaborative participation, and project management models. The main catalysts of the experience are the business values and the stakeholders who insert the principles into their programs and projects. Among these is an innovative Family Social Responsibility Program with female entrepreneurs and organic banana production. It is concluded that the implementation of the CFS-RIA Principles has a significant impact on the sustainable development of the region and that the university–business link reinforces the social responsibility of companies, providing an opportunity for the entry of new actors. Full article

In magnetic separation processes, the capture radius R_c of magnetic particles achieved by the magnetic matrix constitutes a critical parameter governing the separation efficiency and operational performance of magnetic separation equipment. Through a systematic study of the characteristics of R_c and the factors influencing it, the application capability of separation systems can be notably improved. To address the lack of systematic research on R_c under low magnetic field intensities (<0.6 T), a key gap compared to conventional high gradient magnetic separation (HGMS) operating at ≥0.6 T, the motion trajectories of magnetic particles adjacent to a rod-shaped matrix, as well as their final capture or repulsion behaviors, were observed via a high-speed camera. Concurrently, these processes were accurately reproduced using the finite element method (FEM). This study innovatively integrates experimental validation and FEM simulation, achieving mutual verification that single-method studies cannot provide. Based on the experimentally validated FEM model, the effects of magnetic field intensity H, rod-shaped matrix diameter Φ, magnetic particle diameter d, and fluid viscosity η on the motion of magnetic particles were methodically investigated. The velocity characteristics of particles at critical positions between the capture and repulsion zones were analyzed to determine the capture radius of the rod-shaped matrix under specified conditions. Drawing on the identified parametric effects, the developed capture radius prediction model fills the research gap in low-intensity HGMS and serves as a theoretical reference for optimizing both the spacing design of industrial-scale rod-shaped matrix arrays and their matching with relevant operating parameters, and the development of energy-efficient magnetic separation equipment. Full article

In the context of Poland’s commitments under the European Union’s climate policy, including the European Green Deal and the Fit for 55 package, as well as the decision to ban imports of hard coal from Russia and Belarus, ensuring the stability of the domestic market for energy commodities is becoming a key challenge. The response to these needs is the Coal Platform concept developed by the KOMAG Institute of Mining Technology (KOMAG), which aims to integrate data on hard coal resources, production, and demand. The most important problem is not the just-in-time (JIT) strategy itself, but the lack of accurate, up-to-date data and the high technological and organizational inertia on the production side. The JIT strategy assumes an ability to predict future demand well in advance, which requires advanced analytical tools. Therefore, the Coal Platform project analyses the use of artificial intelligence algorithms to forecast demand and adjust production to actual market needs. The developed mathematical model (2024–2030) takes into account 12 variables, and the tested forecasting methods (including ARX and FLNN) exhibit high accuracy, which together make it possible to reduce overproduction, imports, and CO₂ emissions, supporting the country’s responsible energy transition. This article describes approaches to issues related to the development of the Coal Platform and, above all, describes the concept, preliminary architecture, and data model. As an additional element, a mathematical model and preliminary results of research on forecasting methods in the context of historical data on hard coal production and consumption are presented. The core innovation lies in integrating the just-in-time (JIT) philosophy with AI-driven forecasting and scenario-based planning within a cloud-ready Coal Platform architecture, enabling dynamic resource management and compliance with decarbonization targets. Full article

The Chinese government is committed to promoting the deep integration of artificial intelligence (AI) technology with rural development, advocating for the construction of a new “rural revitalization” model supported by information technology. However, its actual effectiveness and economic mechanisms require more thorough empirical verification. Based on Chinese sample data from 2011 to 2023, this study empirically examines the impact of AI on rural revitalization. Findings reveal that AI exerts a significant and sustained supportive role in comprehensive rural revitalization. Deeper analysis reveals that the AI-driven process of comprehensive rural revitalization is not linear but exhibits an inverted “U” pattern—initially promoting then inhibiting development. Mechanism tests indicate that AI empowers comprehensive rural revitalization by enhancing innovation activity and economic dynamism. In the samples where there is a data trading center, a high population density, and a large proportion of rural population, the enabling effect of artificial intelligence is more pronounced, indicating that data elements, population size, and location are important external influencing factors. The paper argues that policy optimization should focus on building a strategic ecosystem for “digital-intelligent villages,” advancing institutional development alongside technological implementation, and devising tiered implementation strategies to fully unleash AI’s enabling potential. Full article

Composite laminates possess complex anisotropic behavior, motivating the development of simplified yet accurate modeling approaches. In this paper, we present a study that introduces a stiffness-invariants-based constitutive model for symmetric, balanced composite laminates, highlighting a novel “quasi-Poisson’s ratio” parameter as a key innovation. The proposed method reconstructs the laminate stiffness matrices using invariant theory (trace of stiffness tensor) and a Master Ply concept, thereby reducing the number of independent material constants. The methods and assumptions (e.g., neglecting minor bending-twisting couplings) are outlined, and the model’s predictions of critical buckling loads are compared to classical laminate theory (CLT) results. Good agreement is observed in most cases, with a consistent conservative bias of CLT. The results confirm that the invariant-based model captures the dominant stiffness characteristics of the laminates and can slightly overestimate stability margins due to its idealizations. In conclusion, this work provides an efficient constitutive modeling framework that can be integrated with finite element analysis and extended to more general laminates in future studies. Full article

Aiming at analyzing the load characteristics and friction torque of triple-row hub bearings for new energy vehicles, this work established a comprehensive theoretical and experimental methodology for predicting the internal load distribution and friction torque. Firstly, considering the preload effect via an initial negative clearance, deformation coordination and force balance equations for the triple-row bearing under axial load were formulated, to analyze the external loads under various driving conditions. Based on contact deformation theory, a quasi-static model was developed to combine radial, axial, and moment loads. The Newton–Raphson iterative algorithm was employed to solve the ball load distribution equations, and the correctness was verified by using the finite element method. Furthermore, accounting for the elastic hysteresis, differential sliding, and spin sliding, the theoretical models for friction torque components were established, to investigate the influence of structural parameters and the total friction torque under different driving conditions. Finally, to confirm the effectiveness and the precision of the model, a finite element simulation and experimental measurements of friction torque were conducted, respectively, which showed good agreement with theoretical calculations. The main innovations include proposing a mechanical modeling method for triple-row hub bearings that accounts for preload effects, and establishing an integrated friction torque analysis model applicable to multiple driving conditions. This work provides theoretical support and a methodological foundation for the design of next-generation hub bearings for new energy vehicles. Full article

The construction of underground reservoirs in coal goafs is an innovative technology to alleviate the coal–water conflict in arid mining areas of northwest China. However, its widespread application is constrained by the challenge of accurately predicting water inflow, which fluctuates significantly due to the dynamic “expansion–closure” behavior of mining-induced fractures. This study focuses on the Shendong mining area, where repeated multi-seam mining occurs, and employs a coupled Finite Discrete Element Method (FDEM) and Computational Fluid Dynamics (CFD) numerical model, combined with in situ tests such as drilling fluid loss and groundwater level monitoring, to quantify the evolution of overburden fractures and their impact on reservoir water inflow during mining, 8 months post-mining, and after 7 years. The results demonstrate that the height of the water-conducting fracture zone decreased from 152 m during mining to 130 m after 7 years, while fracture openings in the key aquifer and aquitard were reduced by over 50%. This closure process caused a dramatic decline in water inflow from 78.3 m³/h to 2.6 m³/h—a reduction of 96.7%. The CFD-FDEM simulations showed a deviation of only 10.6% from field measurements, confirming fracture closure as the dominant mechanism driving inflow attenuation. This study reveals how fracture closure shifts water flow patterns from vertical to lateral recharge, providing a theoretical basis for optimizing the design and sustainable operation of underground reservoirs. Full article

To address the significant vibration and noise problems caused by the zero-order radial electromagnetic force (REF) in integer-slot permanent magnet synchronous motors (PMSMs), while simultaneously improving the motor’s overall electromagnetic performance, this paper proposes a hierarchical iterative optimization strategy integrating Taguchi methods and genetic algorithms. The optimization objectives include minimizing the zero-order REF amplitude, cogging torque, and torque ripple, while maximizing the average torque, with efficiency and back electromotive force total harmonic distortion (back-EMF THD) treated as constraints. First, an 8-pole 48-slot double-layer embedded PMSM model is constructed. An innovative parameter selection strategy, combining theoretical analysis with finite-element analysis, is employed to investigate the spatial order and frequency characteristics of the electromagnetic force. Subsequently, a sensitivity analysis is performed to stratify parameters: highly sensitive parameters undergo first-round optimization via the Taguchi method, followed by second-round optimization using a multi-objective genetic algorithm. The results demonstrate significant reductions in both the zero-order REF amplitude and cogging torque. Specifically, the motor’s peak vibration acceleration is reduced by 32.96%, and the peak sound pressure level (SPL) drops by 9.036 dB. Vibration acceleration and sound pressure across all frequency bands are significantly reduced to varying extents, validating the effectiveness of the proposed optimization approach. Full article

This study aims to gather a sustainable understanding of additive manufacturing and other Manufacturing 4.0 approaches like horizontal and vertical integration and cloud computing techniques with a focus on industrial applications. The DfAM will apply 4.0 tools to gather product feasibility and execution, with CAE—FEM analysis and CAM. This publication focuses on the redesign of a vehicle suspension arm. The main objective is to apply innovative design techniques that optimize component performance while minimizing cost and time. The IDeS method and TRIZ methodology were used, resulting in a composite element, aiming to make the FDM-sourced process a viable option, with a weight reduction of more than 80%, with less material consumption and, hence, less vehicle energy consumption. The part obtained is holistically sustainable as it was obtained by reducing the overall labor used and material/scrap generated, and the IDES data sharing minimized rework and optimized the overall production time. Full article

The impacts of land use on stormwater runoff quality and Best Management Practices to mitigate these impacts have been investigated since the 1970s, yet challenges remain in providing a modeling approach that concomitantly considers contributions from different land use types. In densely developed urban areas, a buildup/washoff approach is often applied, while in rural areas, some type of erosion modeling is employed, as the processes of detachment, entrainment, and transport are fundamentally different. This study presents a coupled modeling approach within PCSWMM, integrating exponential buildup/washoff for impervious surfaces with the Modified Universal Soil Loss Equation (MUSLE) for pervious areas, including construction sites, to characterize water quality in the large mixed urban–rural Sparrovale catchment in Geelong, Australia. The watershed includes an innovative cascading system of 12 online NbS wetlands along one of the main tributaries, Armstrong Creek, to manage runoff quantity and quality, as well as 16 offline NbS wetlands that are tributary to the online system. A total of 78 samples for Total Suspended Solids (TSS), Total Phosphorus (TP), and Total Nitrogen (TN) were collected from six monitoring sites along Armstrong Creek during wet- and dry-weather events between May and July 2024 for model validation. The data were supplemented with six other catchment stormwater quality datasets collected during earlier studies, which provided an understanding of water quality status for the broader Geelong region. Results showed that average nutrient concentrations across all the sites ranged from 0.44 to 2.66 mg/L for TP and 0.69 to 5.7 mg/L for TN, spanning from within to above the ecological threshold ranges for eutrophication risk (TP: 0.042 to 1 mg/L, TN: 0.3 to 1.5 mg/L). In the study catchment, upstream wetlands reduced pollutant levels; however, downstream wetlands that received runoff from agriculture, residential areas, and, importantly, construction sites, showed a substantial increase in sediment and nutrient concentration. Water quality modeling revealed washoff parameters primarily influenced concentrations from established urban neighborhoods, whereas erosion parameters substantially impacted total pollutant loads for the larger system, demonstrating the importance of integrated modeling for capturing pollutant dynamics in heterogeneous, urbanizing catchments. The study results emphasize the need for spatially targeted management strategies to improve stormwater runoff quality and also show the potential for cascading wetlands to be an important element of the Nature-based Solution (NbS) runoff management system. Full article

Carbon capture is pivotal for achieving carbon neutrality; however, its high energy consumption severely limits the operational flexibility of power plants and remains a key challenge. This study, targeting a full flue gas carbon capture scenario for a 1000 MW coal-fired power plant, identified the dual-element (“steam” and “power generation”) coupling convergence mechanism. Based on this mechanism, a comprehensive set of mathematical model equations for the “carbon–electricity–heat” coupling process is established. This model quantifies the dynamic relationship between key operational parameters (such as unit load, capture rate, and thermal consumption level) and system performance metrics (such as power output and specific power penalty). To address the challenge of flexible operation, this paper further proposes two innovative coupled modes: steam thermal storage and chemical solvent storage. Model-based quantitative analysis indicated the following: (1) The power generation impact rate under full THA conditions (25.7%) is lower than that under 30% THA conditions (27.7%), with the specific power penalty for carbon capture decreasing from 420.7 kW·h/tCO₂ to 366.7 kW·h/tCO₂. (2) Thermal consumption levels of the capture system are a critical influencing factor; each 0.1 GJ/tCO₂ increase in thermal consumption leads to an approximate 2.83% rise in unit electricity consumption. (3) Steam thermal storage mode effectively reduces peak-period capture energy consumption, while the chemical solvent storage mode almost fully eliminates the impact on peak power generation and provides optimal deep peak-shaving capability and operational safety. Furthermore, these modeling results provide a basis for decision-making in plant operations. Full article

As an innovative foundation type specifically developed for seabed conditions characterized by shallow overburden overlying bedrock, driven embedded rock-socketed jacket offshore wind turbines achieve high bearing capacity by embedding the pile tips into the bedrock. However, the mechanical behavior of this foundation system has not yet been fully clarified. In this study, based on the engineering conditions of an offshore wind power project in Fujian, a 1:100 scaled physical model test is conducted to validate Plaxis 3D finite-element model. On this basis, a parametric sensitivity analysis is conducted to investigate the influences of key geotechnical properties, pile rock-socketed depth, and geometric parameters, with the aim of elucidating the mechanisms governing the lateral loading behavior of the jacket foundation. The results show that the numerical simulations are in good agreement with the experimental measurements. Among all piles, the front-row pile exhibits the most significant displacement at the pile top at the mudline, reflecting the asymmetry in load transfer and deformation of the pile foundation system. The ultimate bearing capacity varies by about 91.7% among different bedrock types, while the influence of rock weathering degree on the lateral bearing performance of the foundation is about 4.7%. The effects of Pile rock-socketed depth and geometric parameters on the lateral bearing capacity of the foundation are approximately 15.2% and 80.8%, respectively. A critical threshold for rock-socket depth exists at about 6D (where D is the pile diameter), beyond which further improvements in embedment depth result in diminishing improvements in lateral bearing capacity. Full article

Langasite (LGS)-based surface acoustic wave (SAW) sensors are promising for high-temperature pressure detection. However, their performance is limited by the low pressure sensitivity of conventional sealed-cavity packaging and temperature-induced measurement drift. To address these issues, this study introduces a novel LGS SAW pressure sensor featuring two key innovations: a Kovar alloy point-force packaging structure to amplify pressure-induced LGS substrate deformation, enhancing sensitivity compared to traditional designs, and SAW resonators fabricated on an LGS (0°, 138.5°, 26.7°) cut, selected based on electromechanical simulations for its superior intrinsic pressure sensitivity and monotonic frequency–temperature response, effectively mitigating temperature interference on pressure measurements. Experimental characterizations show the resonator achieves a high Q-value of ~3000 at ~357 MHz. Tested under conditions of 250 °C and 0–0.4 MPa, the sensor exhibits a pressure sensitivity of 0.1866 MHz/MPa with a relative error of only 4.8% versus the finite element method (FEM)-simulated 0.196 MHz/MPa, demonstrating the proposed design’s effectiveness for accurate, stable pressure monitoring in harsh high-temperature environments such as turbine engines and high-temperature manufacturing lines. Full article

This work presents the design, modeling, and experimental validation of an innovative, highly autonomous, and economically viable photovoltaic solar cooker, integrating a robust battery storage system. The system combines 1200 Wp photovoltaic panels, a control block with DC/DC power converters and digital control for intelligent energy management, and a thermally insulated heating plate equipped with two resistors. The objective of the system is to reduce dependence on conventional fuels while overcoming the limitations of existing solar cookers, particularly insufficient cooking temperatures, the need for continuous solar orientation, and significant thermal losses. The optimization of thermal insulation using a ceramic fiber and glass wool configuration significantly reduces heat losses and increases the thermal efficiency to 64%, nearly double that of the non-insulated case (34%). This improvement enables cooking temperatures of 100–122 °C, heating element surface temperatures of 185–464 °C, and fast cooking times ranging from 20 to 58 min, depending on the prepared dish. Thermal modeling takes into account sheet metal, strengths, and food. The experimental results show excellent agreement between simulation and measurements (deviation < 5%), and high converter efficiencies (84–97%). The integration of the batteries guarantees an autonomy of 6 to 12 days and a very low depth of discharge (1–3%), allowing continuous cooking even without direct solar radiation. Crucially, the techno-economic analysis confirmed the system’s strong market competitiveness. Despite an Initial Investment Cost (CAPEX) of USD 1141.2, the high performance and low operational expenditure lead to a highly favorable Return on Investment (ROI) of only 4.31 years. Compared to existing conventional and solar cookers, the developed system offers superior energy efficiency and optimized cooking times, and demonstrates rapid profitability. This makes it a sustainable, reliable, and energy-efficient home solution, representing a major technological leap for domestic cooking in rural areas. Full article