Engineering reports : open access最新文献

Accurate geometrical representation of stenosis is essential for stent design, surgical planning, and computational fluid dynamics (CFD) simulations, as even minor shape variations significantly alter hemodynamic predictions. This review systematically compiles and classifies the diverse stenosis geometries proposed in prior studies, creating a foundational reference for researchers. We catalog models ranging from idealized analytical shapes (e.g., axisymmetric cosine, Gaussian, and asymmetric profiles), patient-specific reconstructions to parametric frameworks, highlighting their mathematical formulations, hemodynamic implications, and clinical applications. By consolidating these geometries, this article enables researchers to: (1) identify the most suitable existing model for their specific study, (2) understand inconsistencies in results across studies due to geometrical differences, and (3) develop new models informed by prior morphological variations. Crucially, we emphasize how stenosis geometry governs key hemodynamic parameters such as wall shear stress and pressure gradients and influences stent performance, underscoring why shape selection cannot be overlooked. This structured classification is intended to guide model selection and study design by aligning geometric fidelity with specific research objectives and practical constraints, rather than to imply predictive accuracy or clinical superiority.

Hybrid energy storage systems (HES) based on the combination of lithium-ion battery (LII-BAT) and supercapacitor (SUP-CAP) are considered for hybrid electric vehicles (HEVs) with extended life cycle (LO-LT), decreased battery stress, and enhanced energy efficiency. Nevertheless, current studies are mainly based on traditional optimizers and do not consider the potential of hybrid approaches that can capture the complex dynamics of HESS. In addition, current methods rely on simulations that have not been experimentally validated and thus do not have real-world applicability. A common limitation of benchmarking techniques is that they are often limited to single-source or simple control-based techniques and do not benchmark to the state of the art. This paper fills in these gaps by introducing a new intelligent energy management system (IEMS) optimized using hybrid Marine Predator Algorithm (MPA) and Dynamic Floating Mechanism (DFM). The proposed MPA-DFM is a combination of adaptive refinement and global exploration to extend the system life and reduce battery power (BAT-POW) stress. System evaluation with an LII-BAT/SUP-CAP HESS prototype shows an increase of 21% of the LO-LT value compared to the previously used methods, which demonstrates a highly sustainable and efficient approach of next-generation HEVs.

Hybrid energy storage systems (HES) based on the combination of lithium-ion battery (LII-BAT) and supercapacitor (SUP-CAP) are considered for hybrid electric vehicles (HEVs) with extended life cycle (LO-LT), decreased battery stress, and enhanced energy efficiency. Nevertheless, current studies are mainly based on traditional optimizers and do not consider the potential of hybrid approaches that can capture the complex dynamics of HESS. In addition, current methods rely on simulations that have not been experimentally validated and thus do not have real-world applicability. A common limitation of benchmarking techniques is that they are often limited to single-source or simple control-based techniques and do not benchmark to the state of the art. This paper fills in these gaps by introducing a new intelligent energy management system (IEMS) optimized using hybrid Marine Predator Algorithm (MPA) and Dynamic Floating Mechanism (DFM). The proposed MPA-DFM is a combination of adaptive refinement and global exploration to extend the system life and reduce battery power (BAT-POW) stress. System evaluation with an LII-BAT/SUP-CAP HESS prototype shows an increase of 21% of the LO-LT value compared to the previously used methods, which demonstrates a highly sustainable and efficient approach of next-generation HEVs.

As technological nodes are scaled down to the nanoscale, power consumption emerges as a critical challenge in complementary metal-oxide-semiconductor (CMOS) technology. Emerging nanotechnologies and logic-in-memory (LIM) have been explored as promising solutions. The magnetic tunnel junction (MTJ), a spintronic device, consumes lower static power than the CMOS and FinFET technologies. Compared to the existing LIM-based MTJ/CMOS designs, the proposed MTJ/FinFET systems consume less energy, have a shorter delay, and utilize less static power. These improvements are due to the FinFETs' improved gate control and its lowest 7 nm technology, and the precharge sense amplifier's (PCSA) charge sharing. A HSPICE circuit simulator simulates the design utilizing the 7 nm FinFET and perpendicular MTJ (PMTJ) models. According to the simulation results, the suggested MTJ/FinFET-based OR/NOR, AND/NAND, and XOR/XNOR circuits perform noticeably better in energy consumption, delay, and power than LIM1 (a PCSA-based design) and LIM2 (a modified PCSA-based design). The results indicate that the proposed MTJ/FinFET designs improve overall efficiency compared with LIM2, which is the second-best performing MTJ/CMOS in terms of static power and energy. The OR/NOR gates reduce static power, delay, and energy by 29.42%, 54.23%, and 68.42%; the AND/NAND design lowers them by 7.69%, 55.32%, and 58.18%; and the XOR/XNOR gates achieve reductions of 13.12%, 65.52%, and 70.27%, respectively. This study elucidates the superior performance of the MTJ/FinFET designs over existing LIM structures when logic circuits are implemented.

Currently, the digital transformation of education and the professional development of teachers are at a crucial crossroads. The purpose of this research is to gain a profound understanding of the core contradictions in traditional education and to propose a systematic and engineering-oriented technical solution. This study takes the forecasting of teacher talent demand as the research object, constructs the corresponding teacher talent demand forecasting model based on high-resolution neural network in the edge computing environment, speculates and budgets the number of elementary school teachers in urban and rural schools based on the demand of student-teacher ratio in 2025–2035, and proposes the optimization strategy for the cultivation of teaching skills of teachers on the basis of this. The model data show that between 2025 and 2027, the demand for elementary school teachers gradually increases, reaching a peak of 5,961,500 in 2027, and then shows a decreasing trend after 2027, reaching 4,471,800 by 2035. The supply of basic education teachers for high-quality development is expected to undergo structural changes in the proportion of supply from different channels. Among them, the supply of undergraduate teacher training graduates will drop to 449,700, while the supply of master's degree graduates will increase to 211,500. Overall, the size of teacher training undergraduate graduates tends to stabilize, and the size of master's degree graduates gradually rises, and eventually the total number of teacher training graduates rises slightly, from an annual average of 604,700 in 2025–2030 to an annual average of 661,200 in 2031–2035 gradually. Taken together, this study provides a new method for further research on the problem of forecasting the demand for teacher personnel, which is conducive to improving the scientific nature of the research work on the development of teacher teaching skills.

In the field of civil engineering, the analysis of the composition of alkali-sprayed concrete is of great significance for material research. However, there are currently no existing machine learning (ML) methods suitable for analyzing the composition of alkali-sprayed concrete. Therefore, we developed an ensemble learning (EL) algorithm combining a multilayer perceptron (MLP) and a random forest (RF) to infer the composition of crushed test blocks. The results show that this EL method improves the training algorithm's ability by 30% compared to single-stage ML algorithms and reduces the prediction error by 70% compared to traditional ML algorithms. This proves the feasibility of the EL algorithm in inferring the composition of alkali-activated concrete (AAC) materials. This provides a feasible method for using ML to analyze the composition of AAC.

The paper examines the mechanical properties of hybrid polymer composites reinforced with jute, kenaf and glass fibers with silicon carbide (SiC) nanoparticles as a filler. Three important processing parameters, including fiber orientation (0°, 45°, 90°), fiber stacking arrangement (1–3 layers), and SiC content (3–5 wt%) were evaluated in a systematic manner. The response surface methodology (RSM) was used to design and analyze experimental trials to determine important interactions between the parameters that affected flexural strength and hardness. Meanwhile, predictive modeling was performed with artificial neural networks (ANN), and predictive accuracy was higher than that of RSM with a high correlation between forecasted and experimental outcomes. Optimized flow (90° fiber orientation, three layers stacked, 5 wt% SiC) led to an 18% flexural strength and 32% hardness enhancement over the starting composite. The results illustrate the possibility of using experimental design and machine learning models in order to make robust predictions and optimization of composite properties, and using them to support their application in structural and load-bearing engineering sustainable components.

This study examines the incorporation of digital twin (DT) technology into project life cycle management (PLM) systems, highlighting its contribution to improving project execution across several industries. The study investigates how digital transformation enables real-time data integration, predictive maintenance, and operational optimization, thereby enhancing efficiency and lowering expenses. This study elucidates the evolution of DT technology from its industrial origins to contemporary applications in areas such as aerospace, automotive, and construction, emphasizing its transformational potential. The report examines the obstacles and potential of incorporating digital transformation into intricate sectors, emphasizing interoperability, data quality, and substantial initial investment. The study examines the synergy between DT and PLM, providing optimal methods for effective integration and presenting case examples that illustrate the advantages in practical applications. The combination of DT and PLM within Industry 4.0 paradigms will enhance project management, promoting innovation and sustainability.

The paper examines the mechanical properties of hybrid polymer composites reinforced with jute, kenaf and glass fibers with silicon carbide (SiC) nanoparticles as a filler. Three important processing parameters, including fiber orientation (0°, 45°, 90°), fiber stacking arrangement (1–3 layers), and SiC content (3–5 wt%) were evaluated in a systematic manner. The response surface methodology (RSM) was used to design and analyze experimental trials to determine important interactions between the parameters that affected flexural strength and hardness. Meanwhile, predictive modeling was performed with artificial neural networks (ANN), and predictive accuracy was higher than that of RSM with a high correlation between forecasted and experimental outcomes. Optimized flow (90° fiber orientation, three layers stacked, 5 wt% SiC) led to an 18% flexural strength and 32% hardness enhancement over the starting composite. The results illustrate the possibility of using experimental design and machine learning models in order to make robust predictions and optimization of composite properties, and using them to support their application in structural and load-bearing engineering sustainable components.

This study examined efficiency optimization and thermophysics characteristics of three new polygonal collectors composed of chaotic TiO₂ and LiBr nanoparticles. The innovations of this study are (1) introduction of novel polygonal collectors with unconventional geometries, (2) utilization of chaotic TiO₂ and LiBr nanoparticles to enhance thermophysical properties, (3) application of Response Surface Methodology (RSM) for thermal performance optimization and analysis, and (4) identification of the octagonal collector as the optimal configuration in terms of radiation absorption and heat transfer performance. The optimization process of thermal efficiency is considered using response surface methodology. So, total temperature of octagonal collector influence of TiO₂ Nano particles is 63.27% and 24.32% more than the triangular and cubical collectors. Also, incident radiation of octagonal collector influence of TiO₂ Nano particles is 3.56% and 4.52% more than the triangular and cubical collectors. Moreover, the surface radiation absorption of the octagonal collector influence of LiBr Nano particles is 2.93% and 4.67% more than the triangular and cubical collectors. The results show that TiO₂ performs better than LiBr in terms of total thermal energy. For enthalpy, the maximum value at y = 0.028 m is 19,013.523 J/kg (22%) in the triangular collector, while the minimum is 2677.9698 J/kg (3%) in the octagonal collector, both for TiO₂. The total ground heat flux of the octagonal collector influence of LiBr Nano particles is 74.26% and 74.63% more than the triangular and cubical collectors.