Engineering reports : open access最新文献

In logistics systems, the use of methods, approaches, and management procedures with the aim of optimizing all activities has increasingly been promoted. In the case studies, the logistics of the concrete construction work during the implementation of concrete frame structures of residential building projects is analyzed. The examination of the logistics of concrete construction work was carried out using a combination of monitoring, measurement, comparison, and modeling methods. In addition to obtaining real data on the mentioned logistics of concrete construction work, by applying the Just in Time method, as one of the methods of lean logistics, the optimal logistics of ready-mix concrete transport to construction sites was simulated. In order to reveal the most significant wastages during concrete construction work, the essential parameters of real logistics were compared with the parameters of simulated optimal logistics of ready-mix concrete transport. The goal of the research is to evaluate the intensity of the application of logistics and its elements in the implementation of construction and to point out the importance of the involvement of lean production methods and thus also lean logistics in the construction industry. Based on this study results from construction projects in Slovakia and also based on the experience of the authors of this work, it can be concluded that the use of logistic management in the supply of the main construction material in the production of monolithic concrete structures is poor, and this causes a lot of unnecessary waste, having an adverse effect not only on the construction time, but also on the management of individual construction resources. It has been proven that investment in planning and management of logistics can lead to increased overall efficiency of the construction and can bring profit in the form of system, synchronization of construction process resources.

This research investigated the combined effect of nano-silicon dioxide (NSD) and steel fibers (SF) on the structural behavior of reinforced concrete beams. Eight specimens were tested: four without SF and four with SF, with varying NSD percentages (1%, 2%, and 3%) replacing cement. The study examined the mechanical properties of concrete and structural behavior outputs, including load-deflection, ductility, and damage patterns. Results indicate that NSD significantly enhances compressive strength, with increases of 8.7%, 25.2%, and 32.5% for 1%, 2%, and 3% NSD, respectively. Combined with SF, there are additional improvements in tensile and flexural strengths, leading to higher load capacity and reduced deflection. Beams with 1% steel fibers showed a 30% higher load capacity compared with those without fibers. The ACI code was found to underestimate the load capacity of beams with NSD and SF, indicating the need for updated equations. While NSD increased concrete brittleness, SF enhanced ductility and energy absorption. Future research should focus on optimizing NSD and SF dosages, studying long-term durability, and validating findings through large-scale tests.

Machine learning (ML) enables computers to learn from experience by identifying patterns and trends. Despite ML's advancements in extracting valuable data, there are instances necessitating the removal or deletion of certain data, as ML models can inadvertently memorize training data. In many cases, ML models may memorize sensitive or personal data, raising concerns about data privacy and security. Machine unlearning (MU) techniques offer a solution to these concerns by selectively removing sensitive data from trained models without significantly compromising their performance. Similarly, we can analyze and evaluate whether MU can successfully achieve the “right to be forgotten.” In this paper, we investigate various MU approaches regarding their accuracy and potential applications. Experiments have shown that the data-driven approach emerged as the most efficient method in terms of both time and accuracy, achieving a high level of precision with a minimal number of training epochs. When fine-tuning, the full test error rises somewhat to 14.57% from the baseline model's 14.28%. One approach shows a high forget error of 99.90% with a full test error of 20.68%, while retraining yields a 100% forget error and a test error of 21.37%. While error-minimizing noise preserves performance, the SCRUB technique results in a 21.08% test error and an 81.05% forget error, in contrast to the degradation brought on by error-maximizing noise. On the other hand, the agnostic approach displayed sluggishness and generated less accurate results compared to the data-driven approach. Furthermore, the choice of approach may depend on the unique requirements of the task and the available training resources.

Due to the long transmission distance of the constellation network represented by the low-orbit satellite system, the satellite-to-ground link is subject to large path loss and limited communication performance. This paper uses multi-satellite and multi-beam joint transmission technology to form a virtual multiplex network with ground multi-antenna terminals (Multiple-Input Multiple-Output [MIMO] system). However, due to the high-speed movement of satellites, the topology of the MIMO system is unstable, which in turn affects the stability of the system throughput. This article proposes two inter-satellite cooperation modes to improve the stability of MIMO system capacity. The first method is to optimize the channel capacity by rationally allocating the beam power of the gateway station so that the gateway station can achieve transparent forwarding through satellites. The second method rotates the angle of the user's receiving antenna to combat channel changes caused by changes in satellite position. Simulation results show that both methods can effectively improve the minimum channel capacity. Transparent forwarding can increase the capacity by about 22.7%, while the rotating antenna can still improve the performance gain by at least 36.3% even with a limited rotation angle. This research contributes to the development of stable and efficient communication systems in satellite-ground cooperative networks.

Although significant advancements have been made in the quality of machine translation by large-scale language models, their high computational costs and resource consumption have hindered their widespread adoption in practical applications. So this research introduces an English corpus-based machine translation algorithm that leverages knowledge distillation from large language model, with the goal of enhancing translation quality and reducing the computational demands of the model. Initially, we conducted a thorough analysis of the English corpus to identify prevalent language patterns and structures. Following this, we developed a knowledge distillation approach that transfers the translation expertise of a large teacher model to a smaller student model, thereby achieving increased translation accuracy and efficiency. We designed a dynamic temperature hyperparameter distillation strategy that effectively enhances the precision of translations. In the experimental phase, we utilized several standard English corpora to train and assess our algorithm. The findings indicate that, compared to current machine translation systems, our method significantly reduces the need for computational resources while preserving translation quality.

This paper deals with micromagnetic measurements for online detection of strain-induced α′-martensite during plastic deformation of metastable austenitic steel AISI 304L. The operating principles of the sensors are magnetic Barkhausen noise (MBN) and eddy currents (EC), which are suitable for detection of microstructure evolution due to formation of ferromagnetic phases. The focus of this study was put on the qualification of different micromagnetic techniques and different measurement systems under conditions similar to the real ones during production, which is crucial for implementation of a property-controlled flow forming process. The investigation was carried out on tubular specimens produced by flow forming, which have different content of α′-martensite. To characterize the sensitivity of the sensors, different contact conditions between sensors and workpieces were reproduced. MBN sensors are suitable for detecting amount of α′-martensite, but the measurements are affected by the surface roughness. This entails that the calibration models for MBN sensors must take account of these effects. EC sensors show a closer match with the amount of α′-martensite without having major affectation by other effects.

Coastal mudflats are vital land resources. In recent years, coastal reclamation has emerged as a vital strategy for alleviating coastal land supply shortages and for developing marine resources in coastal areas. However, large-scale coastal reclamation changes the natural landscape properties of sea areas, triggering alterations in the surrounding hydrodynamic and ecological environments. Despite the significant impact of coastal geomorphic features on tidal energy loss and disaster prevention, few studies have explored the intrinsic connection between the complexity of coastal landscapes and the rate of tidal reduction. This relationship is crucial for predicting potential tidal energy loss responses to large-scale changes in geomorphologic features caused by accelerating coastal land reclamation. This study focuses on Hangzhou Bay as the research area and develops a model that combines the correlation between the rate of tidal reduction and the complexity of coastal landscapes. The model is constructed using fractal geometry theory and quantitative methods, while also incorporating principles from ocean dynamics theory. The model is applied to assess the impacts of reclaimed land on the geomorphic complexity and tidal energy dissipation in the Zhoushan Islands. Results indicate a high correlation between differential tide reduction rates and coastal geomorphic complexity. Application of this model reveals that traditional reclamation planning and design substantially alter coastal morphology. Consequently, the significant reduction in the fractal dimension (D) and shape index (S) leads to a tidal reduction rate decrease of over 88%, posing substantial threats to coastal and estuarine bay disaster prevention and ecosystem services. The study proposes effective control indicators and scientific reclamation planning measures, providing a theoretical basis and novel methods for the rational utilization and sustainable development of coastal mudflat resources.

Research into hybrid renewable energy systems (HRESs) fulfills the need for the development of sustainable and environmentally friendly energy systems to supply house-holds. The design of HRESs is a challenging endeavor requiring the optimization of multiple objectives considered over multiple criteria. This paper presents a new multi-criteria decision-making framework (MCDM) to automate the design. The proposed framework initially uses a metaheuristic multi-objective (MO) optimization algorithm to generate optimal candidate configurations and then objectively evaluates candidates to select the best configuration. A combination of the MO particle swarm optimization and a newly developed MO leaders-and-follower algorithms (MO-LaF/PSO) is used to generate optimal configurations based on minimal levelized cost of energy (LCOE), renewable energy (RE) power abandonment, and CO2 emissions, while maintaining an acceptable level of reliability. The evaluation phase applies the VIKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje) ranking method that uses objective criteria weights calculated using MEREC (MEthod based on the Removal Effects of Criteria). This method is applied to a case-study of an off-grid Wind/PV/Diesel/Battery HRES. The results reveal that this newly proposed framework generates a unique top-ranking configuration with an LCOE of 0.199 $/kWh, 0% wastage of RE, and 982 tons of CO₂.

5-Methylcytosine (m⁵C) is a widely recognized epigenetic modification in ribonucleic acid (RNA), catalyzed by methyltransferases. This modification is crucial for various biological functions. While the role of m⁵C in deoxyribonucleic acid (DNA) has been extensively studied, its role in RNA is still in its early stages of exploration. Accurate and systematic detection and classification of m⁵C sites in RNA remain challenging tasks. Machine learning techniques offer an efficient alternative to traditional laboratory methods for identifying m⁵C sites in Homo sapiens. This study introduces a novel computational model m⁵C-TNKmer, which utilizes k-mer feature extraction to enhance the identification of m⁵C sites in RNA sequences. Four sub-datasets derived from the primary dataset Di-nucleotide (DNC), Tri-nucleotide (TNC), Tetra-nucleotide (Tetra-NC), and Penta-nucleotide (Penta-NC) were used to train the model. The results demonstrated that m⁵C-TNKmer achieved an impressive accuracy of 96.15%. This model provides a powerful tool for scientists to accurately identify RNA m⁵C sites, contributing to a deeper understanding of genetic functions and regulatory mechanisms.

Friction stir extrusion (FSE) is a versatile technique that plays a dual role in sustainable recycling and shaping of materials. This method involves a rotating mandrel and a fixed matrix within a mold, where compressed waste metal chips or primary bulk materials are introduced. The rotating mandrel exerts continuous axial pressure, generating frictional heat that softens and bonds the materials together. As the mandrel advances, the materials are reshaped and extruded through the cavity inside the mandrel or the space between the mandrel and the matrix, resulting in the desired product, such as wires or pipes. FSE finds applications in recycling machining wastes, improving powder metallurgy products, producing wire raw materials, creating structures with fine microstructures, and developing new alloys and composites. The resulting materials exhibit refined grains, leading to enhanced mechanical and metallurgical properties. This review article compiles experimental studies exploring the mechanical and microstructural characteristics of samples manufactured using FSE for recycling, reshaping, alloying, or bilayer production. Additionally, it discusses various tool, mold, and machine designs proposed by researchers. Beyond its unique properties, FSE is highlighted as an energy-efficient, sustainable, and eco-friendly process.