Search Results (1,004)

The ongoing climate crisis has highlighted the need for sustainability and resilience in the development and maintenance of urban areas regarding climate comfort. Weather simulation tools can aid researchers in understanding the effects that weather has on the microclimate in urban areas. While simulations are handled autonomously by computers once set up, the creation of the requisite input urban models is still a highly manual process. In this study, a novel method for the automated generation of urban models using land and cadastral remote sensing data is presented. By analyzing grass, trees, buildings, and roads algorithmically, data can be extracted and configured into spatial models compatible with microclimate simulation software such as ENVI-Met. Comparison to a baseline model shows that our method enables the creation of models fit for use for exploring microclimate scenarios in the urban environment, saving time by eliminating the need for manual processing. Full article

In addressing the increasing global energy demand, this manuscript compares four distinct processes for hydrogen production from natural gas (NG): steam methane reforming (SMR), dry methane reforming (DMR), autothermal reforming (ATR), and catalytic methane decomposition (CMD). The comparison emphasizes their respective efficiencies and environmental impacts. Simulations were conducted using the Peng–Robinson model, implemented in the DWSIM 8.8.3 software package, considering commercially available Colombian natural gas. Technical and environmental impacts were taken into account for the evaluation of the most practical hydrogen production plant by employing, for the first time, the TOPSIS method of comparison. Reaching 0.36 kg H₂ per kg of NG, ATR stands out as the top TOPSIS solution. However, SMR is not far behind, producing more hydrogen than any of its competing alternatives (0.56 kg H₂ per kg of NG) but at a significantly larger environmental cost. DMR demonstrates promising potential for utilizing CO₂. Finally, CMD proves to be advantageous in terms of cleanliness and reduced CO emissions but is limited by the high temperature requirements and the constant need for catalyst regeneration. This paper aims to raise awareness about Colombia’s abundant natural resources and its potential to play a significant role in the future hydrogen economy. Full article

The integration of Software-Defined Networking (SDN), blockchain (BC), and machine learning (ML) has emerged as a promising approach to securing Internet of Things (IoT) and Industrial IoT (IIoT) networks. This paper conducted a comprehensive review of recent studies focusing on multi-layered security across device, control, network, and application layers. The analysis reveals that BC technology ensures decentralised trust, immutability, and secure access validation, while SDN enables programmability, load balancing, and real-time monitoring. In addition, ML/deep learning (DL) techniques, including federated and hybrid learning, strengthen anomaly detection, predictive security, and adaptive mitigation. Reported evaluations show similar gains in detection accuracy, latency, throughput, and energy efficiency, with effective defence against threats, though differing experimental contexts limit direct comparison. It also shows that the solutions’ effectiveness depends on ecosystem factors such as SDN controllers, BC platforms, cryptographic protocols, and ML frameworks. However, most studies rely on simulations or small-scale testbeds, leaving large-scale and heterogeneous deployments unverified. Significant challenges include scalability, computational and energy overhead, dataset dependency, limited adversarial resilience, and the explainability of ML-driven decisions. Based on the findings, future research should focus on lightweight consensus mechanisms for constrained devices, privacy-preserving ML/DL, and cross-layer adversarial-resilient frameworks. Advancing these directions will be important in achieving scalable, interoperable, and trustworthy SDN-IoT/IIoT security solutions. Full article

The unique material properties of Cyperus esculentus L. seeds present challenges for precision seeding, as no specialized seed-metering device is currently available. In practice, general-purpose planters such as peanut seeders are often adapted for this crop. However, the dry seeds of C. esculentus exhibit an irregular shape, uneven surface texture, significant size variation, and poor flowability, leading to inadequate seed pickup and suboptimal seeding performance in conventional metering devices. To address these issues, two types of seed pickup devices—one with a V-shaped scoop and the other with an arc-shaped scoop—were designed to improve the seed-filling process and enhance seed agitation within the seed pool. A comparative analysis of the material properties of seeds before and after soaking was conducted, and key structural parameters of the scoops were determined based on the post-soaking characteristics. A mechanistic analysis was performed to clarify the operational principles and influencing factors of the scoop-based pickup mechanism. Using EDEM software (2022 version), the motion characteristics of seeds inside the metering device were observed, and the agitating speed of the seed population was compared with and without the scoop devices. Performance comparison experiments were carried out with two scoop types under varying conditions, including metering disc rotation speed, seed size grade (large, medium, and small), and seed moisture state (dry vs. soaked). Simulation results of seed disturbance indicated that the V-shaped scoop significantly enhanced agitation intensity, with a maximum movement velocity 15.8% higher than that of the arc-shaped scoop. The V-shaped scoop demonstrated superior stability and adaptability across different seed sizes, rotation speeds, and moisture conditions. Seed pickup success rates reached 96%, 96%, and 85% for large, medium, and small seeds, respectively. Under high-speed operation (40 r/min), the V-shaped scoop showed a 9% lower miss-seeding rate compared to the arc-shaped scoop. Full article

The chemical composition, mineral composition, and mineral distribution characteristics of steel slag were characterized through petrographic analysis, X-ray diffraction (XRD), and particle size analysis. Limestone, silica, and silicomanganese slag were blended with converter steel slag to fabricate a reconstructed steel slag. Through burden calculation, the chemical composition ratio of this reconstructed steel slag approximated the silicate phase region. The high-temperature reconstruction process outside the furnace was simulated through reheating. The composition, structure, and cementitious characteristics of the reconstructed steel slag were investigated through X-ray diffraction (XRD), FactSage software (FactSage version 7.0 (GTT-Technologies, Aachen, Germany, 2015))analysis, scanning electron microscopy–energy dispersive spectroscopy (SEM–EDS) analysis, setting time determination, compressive strength measurement, and thermodynamic computation. The findings indicated that the primary mineral compositions of the reconstructed steel slag were predominantly silicates, such as Ca₃Al₂O₆, Ca₂SiO₄, Ca₂MgSi₂O₇, Ca₂Al(AlSiO₇), Ca₂(SiO₄), and FeAlMgO₄. In comparison with the original steel slag, these compositions underwent substantial alterations. The α′-C₂S phase appears at 1100 K and gradually transforms into α-C₂S at 1650 K. The liquid phase begins to precipitate at approximately 1550 K. Spinel exists in the temperature range from 1300 to 1700 K, and Ca₃MgSi₂O₈ melts into the liquid phase at 1400 K. As the temperature increases to 1600 K, the minerals C₂AF, Ca₂Fe₂O₅, and Ca₂Al₂O₅ gradually melt into the liquid phase. Melilite melts into the liquid phase at 1700 K. It was observed that the initial and final setting times of the reconstructed steel slag exhibited reductions of 7 and 43 min, respectively, in comparison to those of the original steel slag. In comparison with steel slag, the compressive strength of the reconstructed steel slag exhibited an increase of 0.6 MPa at the 3-day strength stage, 1.6 MPa at the 7-day strength stage, and 3.4 MPa at the 28-day strength stage. The reduction in setting time and the enhancement in compressive strength verified the improved cementitious activity of the reconstructed steel slag. Thermodynamic calculations of the principal reactions of the reconstructed steel slag at elevated temperatures verified that the primary reaction at 1748 K is thermodynamically favorable. Full article

Floods are among the most catastrophic natural disasters, causing severe impact on human lives and ecosystems. The proposed methodology integrates Geographic Information Systems, 2D hydraulic modeling, and deep learning techniques to develop a computational simulation approach for flood extent prediction and was implemented in the Enipeas River basin, located within the Thessalia River Basin District, Greece. Hydrological analysis was performed using the HEC-HMS software (version 4.12), while hydraulic simulations were conducted with HEC-RAS 2D. The hydraulic modeling produced synthetic flood scenarios for a 1000-year return period, generating spatially distributed outputs of flood extents. The deep learning algorithm was based on a U-Net (CNN) architecture. The model was trained using multi-channel raster tiles, including open access geospatial data such as Digital Elevation Model, slope, flow direction, stream centerline, land use, and simulated flood extents. Model validation was carried out in two independent domains (TS1 and TS2) located within the same river basin. Model outputs are adequately compared with both 2D hydraulic simulations and official Flood Risk Management Plan maps, and the comparison indicates close spatial and quantitative agreement, with flood extent area differences below 8%. Based on the results, the proposed methodology presents a potential and efficient tool for rapid flood risk mapping. Full article

This study comprehensively analyzes the free vibration and transient response for a sandwich piezoelectric laminated beam with elastic boundaries in a thermal environment. Quasi-3D shear deformation beam theory (Q3DBT) and Hamilton’s principle are used to obtain the thermo-electro-mechanical coupling equations, and the method of reverberation-ray matrix (MRRM) is utilized to integrate the phase and scattering relationship of the structure in a unified approach. Specifically, the scattering relationship established by the Mixed Rigid-Rod Model (MRRM) via dual coordinate systems describes the general dynamic model of the beam using generalized displacements and generalized forces at the two endpoints. This analytical solution is compared with the finite element numerical results based on Solid5 and Solid45 elements. The similarity of this approach lies in the fact that solid elements can account for the Poisson effect of thick beams, while the difference is that solid elements have a certain width; here, the error is minimized by adopting a single-element division in the width direction. Comparison of the numerical results under different geometric parameters and boundary conditions with the simulation software proves that MRRM has good accuracy and stability in analyzing the dynamic performance of sandwich piezoelectric laminated beams. On this basis, a spring-supported boundary technology is introduced to expand the flexibility of classical boundary conditions, and a detailed parameterization study is conducted on the material properties of the base layer, including the material parameters, geometric property, and the external temperature. The study in this article provides many new results for sandwich-type piezoelectric laminated structures to help further research. Full article

Junction temperature is considered a critical parameter that can directly affect the reliability and power handling capabilities of semiconductor devices. Effective thermal management, particularly under high-surge-current conditions, is therefore essential to maintain a lower junction temperature in order to enhance device performance and prevent device failure. Among various thermal management strategies, packaging technology plays an important role in optimizing junction temperature and enhancing the robustness of the device. In this article, a comparative analysis of high-power diodes is performed by investigating their junction temperature behavior and surge current handling capability. Moreover, an insulated module diode and a press-pack diode with pressure contact technology (PCT), both with identical specifications and power ratings, are selected for analysis. A 10 ms half-sine surge current waveform generator is developed both experimentally and in simulations to replicate realistic surge events. Experimental measurements of the forward voltage drop across varying surge levels are used to analyze device failure behavior. In addition, electro-thermal simulations are also employed in PSIM 2025.0 software to estimate and compare the temperature. Furthermore, this study enables practical insights into device thermal performance, robustness, and surge current handling capabilities, enabling a performance comparison between the two packaging technologies. Full article

An MMC is widely applied to the HVDC power transmission system. With a large number of insulated gate bipolar transistors (IGBTs) utilized in MMC-HVDC converter stations, an extremely complicated EM environment is generated due to the dv/dt and di/dt during the IGBT switching process. A magnetic field simulation model is proposed to calculate the magnetic field generated by a 4.5 kV/5 kA IGBT-based MMC submodule under the DPT, with the dynamic switching behavior of IGBTs considered. Firstly, a behavior model of 4.5 kV/5 kA IGBTs is built with the help of commercial software. To validate its effectiveness, a DPT simulation model is built. A comparison between the simulation result and the measured data is performed. Finally, a quasi-static Maxwell model is utilized to approximate the near field caused by the current Ic of the DPT. The simulation result of the magnetic field strength at the point near the gate driver PCB is verified by the measurement data. The proposed magnetic field simulation model can help to analyze the EMI behavior and EMI design for MMC-HVDC submodules under DPT. Full article

Buried pipelines represent critical lifeline infrastructure whose seismic performance is governed by complex soil–structure interaction mechanisms. In this study, a process-based numerical framework is developed to evaluate the pseudo-static seismic response of buried steel pipelines installed within a trench. A comprehensive parametric analysis is conducted using the finite-element software Rocscience RS2 (version 11.027) to examine the influence of burial depth, pipeline diameter, slope angle, groundwater level, soil type, and permanent ground deformation. The seismic loading was represented using a pseudo-static horizontal acceleration, which approximates permanent ground deformation rather than full dynamic wave propagation. Therefore, the results represent simplified lateral seismic demand and not the complete dynamic soil–structure interaction response. To verify the reliability of the 2D plane–strain formulation, a representative configuration is re-simulated using the fully three-dimensional platform Rocscience RS3. The comparison demonstrates excellent agreement in shear forces, horizontal displacements, and cross-sectional distortion patterns, confirming that RS2 accurately reproduces the dominant load-transfer and deformation mechanisms observed in three-dimensional (3D) models. Results show that deeper burial and stiffer soils increase shear demand, while higher groundwater levels and larger permanent ground deformation intensify lateral displacement and cross-sectional distortion. The combined 2D–3D evaluation establishes a validated computational process for predicting the behavior of buried pipelines under a pseudo-static lateral load and provides a robust basis for engineering design and hazard mitigation. The findings contribute to improving the seismic resilience of lifeline infrastructure and offer a validated framework for future numerical investigations of soil–pipeline interaction. Full article

Physics-based simulations are now widely employed in mechanical engineering. Flexible Multibody dynamic Simulations (FMBSs) have proven to be effective in representing the behavior of complex structures with local damping and stiffness nonlinearities. However, due to the broad range of component flexibilities as well as contact behavior between structural elements, time integration analyses can result in high computational burden. The challenge addressed in this article concerns the implementation of an efficient model reduction procedure in order to provide an acceptable tradeoff between calculation time and loss of accuracy in the prediction of system responses and dynamic loads. In most FMBS commercial software, the behavior of linear elastodynamic components is taken into account via imported Craig–Bampton superelements. In this context, dynamic mode selection techniques have been shown to provide a better order reduction than the standard low-frequency truncation. This article provides a review of dynamic mode selection methods that can be found in the literature, followed by a comparison based on simulations of an aircraft engine stator integrated in the full industrial engine model and tested on a speed ramp-up with unbalance. Full article

The stable operation of a rolling mill is crucial for the extremely thin strip rolling process. Moreover, the performance of the rolling mill directly dictates the quality of the extremely thin strip products. In view of the lack of research on the digital twin model and condition monitoring of twenty-high rolling mills, this paper takes the Sendzimir 280 mm twenty-high reversible rolling mill, an extremely thin strip rolling equipment, as the research object, and conducts digital twin modeling and visualization design for it. First and foremost, finite element analysis and vibration analysis were conducted on the rolling mill, based on which the finite element model and dynamics model of the twenty-high rolling mill were established. Secondly, through a comparison between the vibration data of the rolling mill obtained from simulation and those of the physical rolling mill, the accuracy of the simulation model was validated. Finally, a digital twin model of the rolling mill was constructed based on the finite element model and the dynamics model, and the digital twin model of the rolling mill was built using Unity (version 2022.3.57, Unity Technologies, San Francisco, CA, USA) software to complete the visualization design of the digital twin model. The results show that the digital twin platform of the rolling mill established in this paper achieves a high degree of similarity between the virtual rolling mill and the physical one, which proves the effectiveness of the platform and can meet the actual engineering requirements. Full article

This study presents an intelligent modeling framework for predicting fabric nesting efficiency (NE) based on geometric descriptors of garment patterns, offering a rapid alternative to conventional nesting software. A synthetic dataset of 1000 layouts was generated using a custom Python algorithm that simulates realistic garment-like shapes within a fixed fabric size. Each layout was characterized by five geometric descriptors: number of pieces (NP), average piece area (APA), average aspect ratio (AAR), average compactness (AC), and average convexity (CVX). The relationship between these descriptors and NE was modeled using a Sugeno-type Adaptive Neuro-Fuzzy Inference System (ANFIS). Various membership function (MF) structures were examined, and the configuration 3-3-2-2-2 was identified as optimal, yielding a mean relative error of −0.1%, with high coefficient of determination (R² > 0.98). The model was validated through comparison between predicted NE values and results obtained from an actual nesting process performed with Deepnest.io, demonstrating strong agreement. The proposed method enables efficient estimation of NE directly from CAD-based parameters, without requiring computationally intensive nesting simulations. This approach provides a valuable decision-support tool for fabric and apparel designers, facilitating rapid assessment of material utilization and supporting design optimization toward reduced fabric waste. Full article

This systematic review examines mathematics teachers’ knowledge for teaching using digital technologies (DTs), understood as the intersection of disciplinary, pedagogical, and technological domains that teachers mobilize when designing, implementing, and assessing mathematics lessons. In this study, DTs refer to the digital hardware, software, and online environments used to represent, simulate, or analyze mathematical ideas (e.g., GeoGebra, Tinkerplots, spreadsheets, CAS tools, and learning management systems). We analyzed 50 peer-reviewed journal articles published between January 2010 and April 2025, retrieved from Web of Science, Scopus, ERIC, and Scielo. ResearchGate was consulted only as a supplementary repository to access the full texts already identified in the indexed databases. These articles were analyzed according to predefined analytical categories, including research themes, country of origin, and the digital technologies addressed in each study, allowing for cross-comparisons across theoretical frameworks and methodological approaches. The results reveal a strong interest in this topic in countries such as Turkey, the United States, Mexico, Indonesia, and Spain, with the participation of in-service mathematics teachers at the primary, secondary, and university levels, as well as preservice teachers. The most frequently studied themes in the past five years regarding teacher knowledge include teacher education through digital technologies, the analysis of lesson planning and tasks designed by teachers using DTs, and the assessment of their knowledge through self-perception questionnaires. The review concludes that only a few of the analyzed studies qualitatively examined teacher knowledge when using digital technologies, particularly those that employed non-participant observation, audio and/or video recordings, and semi-structured interviews. Full article

This study integrates dynamic energy simulation with lifecycle assessment (LCA) to evaluate the energy and carbon performance of advanced glazing systems suitable for hot–arid climates. Using Design Builder software coupled with OpenLCA, six glazing configurations were analyzed under identical building and climatic conditions. The configurations included a conventional single 3 mm float glass pane (C0) as the reference case, a single 3 mm polycarbonate sheet (C1) representing common local construction practice, and four advanced multi-layer systems (C2–C5) incorporating air, argon, and nanogel insulation layers. The inclusion of C0 enabled direct comparison between typical glass construction and emerging polycarbonate-based systems, thereby enhancing the contextual relevance of the analysis. Results demonstrated that thermal and optical properties of glazing systems strongly influence both operational and embodied carbon outcomes. Relative to the conventional glass reference (C0), the nanogel–argon composite (C5) achieved a 32.4% reduction in annual cooling energy and a 28.9% decrease in total lifecycle carbon emissions, with a carbon payback period of approximately 1.1 years. The operational phase dominated total emissions (>97%), confirming that improvements in glazing thermal performance yield substantial long-term benefits even when embodied impacts are considered. While argon filling provided marginal benefit over air cavities, the nanogel insulation contributed the largest performance enhancement. However, the relatively low visible light transmittance (VLT = 0.27) of the C5 system suggests a potential daylight–comfort trade-off that warrants further investigation. The study demonstrates the importance of integrating energy simulation with lifecycle assessment to identify glazing systems that balance energy efficiency, embodied carbon, and indoor environmental quality in hot–arid regions. Full article