Engineering reports : open access最新文献

This paper investigates the fixed-time (FXT) synchronization issue of fuzzy memristive neural networks (MNNs) via using incomplete Beta functions from the view of improving the estimate accuracy of settling time (ST). First, the parameter mismatching issue brought by the switching characteristics of the memristor is handled through the convex analysis method. Then, a new FXT stability theorem that provides a more accurate ST estimation is derived by using incomplete Beta functions. Furthermore, based on this result, some new sufficient conditions are obtained to ensure the FXT synchronization of considered fuzzy MNNs via designing a class of control schemes by introducing a new saturation function as well as using some inequality techniques. Significantly, the introduced FXT controller can achieve synchronization aim at bounded ST and it is not affected by the system's initial values. Finally, a numerical example is provided to verify the affectivity of introduced results.

Feeding phased array antennas are crucial components of this type of antenna system. To change the angle of the beam in phased array antennas, the antenna arrays need to have a phase difference. A method for applying phase shifts in the power divider is presented, which can be integrated into a single antenna feed system structure. Using Substrate Integrated Waveguide (SIW) technology, which has been widely used in the manufacturing of microwave devices recently, makes it possible to design and manufacture all elements such as antennas, power dividers, filters, and phase shifters on a single substrate. This approach reduces manufacturing costs, size, and weight of microwave circuits; more importantly, it eliminates the losses associated with non-coplanar connections and circuits. A three-port phase shifter is proposed, illustrating how to control the phase difference between output ports. After designing, simulating, and optimizing the relevant parameters at a frequency of 10 GHz, the phase shifter was manufactured and measured on the Rogers RO4003 substrate. The measurement results matched well with the simulation results, and all findings are presented.

Under the dual influence of power system transition to integrated energy and the evolution of cyberattack technology, a correlation feature-multilabel cascade boosted forest based false data injection attack localization and detection method is proposed for the new energy power system to accurately locate the attacked position of the power grid in response to the stealthy false data injection attack (FDIA). Considering the FDIA principle and characteristics of the new energy power system, as well as the fact that the new energy power system contains a large amount of measurement data and variable operation states, the proposed method enhances the fitting ability of multi-label cascade forests to complex power measurement data by incorporating the extreme gradient boosting algorithm to identify the anomalies of state quantities of each node of the system, and introduces the “correlation feature” algorithm to detect the original power measurement data. The “correlation feature” algorithm is introduced to extract highly informative features from the original power measurement data to enhance the generalization ability of the multi-label cascade forest, so as to obtain more accurate localization detection. Simulation tests are conducted in the IEEE-57 node system to verify the effectiveness of the proposed method, and compared with other methods, the proposed method has better accuracy, detection rate, sensitivity, and F1 score.

The field of protein engineering has witnessed transformative advancements, with computational tools and databases driving novel innovations in de novo protein design. This review consolidates and critiques a comprehensive range of modern computational resources, offering a unique focus on their applications across diverse domains, including protein stability prediction, posttranslational modification analysis, and mutation effect evaluation. Key contributions include a detailed examination of tools integrating machine learning and artificial intelligence to enhance predictive accuracy and streamline protein engineering workflows. By highlighting underexplored tools and novel methodologies, such as advanced protein–ligand interaction predictors and neural network–based stability assessment models, this study establishes itself as a unique reference for researchers aiming to develop tailored proteins for therapeutic, industrial, and biomedical applications.

Buildings are responsible for 37% of CO₂ emissions and 36% of energy use worldwide, making them significant contributors to both energy use and carbon emissions, due to which building energy efficiency is currently a top priority for regional, national, and global energy policy. This study evaluates building design features, such as window wall ratio (WWR), orientation, and shading coefficient (SC) for its single-, low-E double-, and low-E triple-glazed windows. The building analyzed in this study is a hypothetical commercial building located in Lahore, Pakistan (ASHRAE zone 1B). The results show that compared to unglazed windows with a SC of 0.2 and 0.3, peak cooling total load (PCTL) and CO₂ emissions are reduced by 17.84% and 17%, respectively, for single-glazed windows. Similarly, low-E double-glazed windows reduce 21.3% and 20.9% in PCTL and CO₂ emissions, while low-E triple-glazed windows result in reductions of 21.8% and 21.1%, respectively. Reducing the WWR from 15.14% to 4.94% results in a 5.35% reduction in PCTL and CO₂ emissions. Moreover, Using the optimized orientation of the building (180° clockwise from north) further decreases PCTL and CO₂ emissions by 8.63%. This analysis concludes that significant energy and environmental gains can be achieved by higher-quality windows, utilizing optimized orientation, and reducing the WWR. In addition to ensuring long-term cost savings, this strategic approach promotes a more sustainable and environmentally friendly future for future generations.

The motivation for this study stems from the global demand for clean energy solutions and the limitations of conventional fluids in hydrogen production systems. By exploring hybrid nanofluids, this research aims to enhance efficiency and sustainability in solar-thermal energy applications. An evacuated tube solar collector (ETSC) with a polymer electrolyte membrane (PEM) electrolyzer efficiently harnesses solar energy for hydrogen production. The ETSC's vacuum design minimizes heat loss, providing consistent thermal performance. This system enables clean hydrogen generation, reducing emissions. This study investigated the integration of an ETSC with a PEM electrolyzer and organic Rankine cycle (ORC) for efficient hydrogen production. Water as the working fluid in the ETSC circuit resulted in lower hydrogen production rates, prompting the introduction of Al₂O₃ and SiO₂ hybrid nanoparticles at a 50:50 ratio to form an enhanced hybrid nanofluid. The resulting various volume concentrations (0.5%, 1%, 1.5%, and 2%) of the hybrid nanofluid were tested, yielding energy gains of 13.22%, 21.37%, 30.38%, and 48.52%, respectively, compared to water. The ORC efficiency enhanced by 12.29% at 0.5 vol.%, 23.10% at 1 vol.%, 34.15% at 1.5 vol.%, and 48.40% at 2 vol.%. The PEM electrolyzer produced a maximum hydrogen yield of 3105.6 g, with an overall system efficiency of 71.3% and hydrogen production of 2156.7 g at 2 vol.%, demonstrating the significant performance enhancements achieved with hybrid nanofluids. The results demonstrated the effectiveness of hybrid nanofluids in enhancing system efficiency and hydrogen output, underscoring their importance in promoting sustainable hydrogen production technologies.

Precision improvement in mechanical manufacturing faces challenges due to nonlinear effects impacting assembly accuracy analysis models. An assembly accuracy analysis method based on multi-stage linearized contact is proposed to address this issue. A model of part surface asperities considering morphological errors is established using the linear superposition of discrete cosine transform (DCT) kernel functions and the assembly interface is simplified. By employing homogeneous coordinate transformation (HCT), the prediction of the part's pose during the assembly process with the rigid body assumption is achieved. The elastic contact process is divided into multiple stages according to the order of the asperity participating in the contact and further subdivided into several linear processes by adding nodes at each stage. The relationship between the assembly load and the deformation amount is established based on the related theories of contact mechanics and the geometric relationship between the assembly interfaces, thus enabling the prediction of the part's pose during the assembly process. Taking a multi-way hydraulic valve as an object, by comparing the accuracy of pose prediction of the algorithm before and after adding nodes, it is proved that the proposed method can significantly improve the precision of assembly accuracy analysis.

New way of formalizing hybrid systems in models for managing the process of vessel unloading is caused by the significant increase in container transportation around the world over the past decade. The growth of container traffic is the main reason for the constant modernization of port container terminals and the improvement of cargo unloading technology is most promising when using those methods, that allow obtaining maximum results in terms of cargo processing speed. This problem is the subject of an article in which an analytical review of container handling technologies existing in world ports is carried out, their key performance indicators are formulated and it is shown how the use of a centralized hybrid control system based on the dynamics of discrete events can lead to increased profitability of the port. Developed concept of a hybrid control system makes possible to consider such features of the vessels unload process that have not been considered until now.

To address the challenge of dynamically assessing overhead transmission lines under varying meteorological conditions through manual inspections and drone monitoring, this study proposes an evaluation method for the operating status of overhead transmission lines based on the Analytic Hierarchy Process (AHP). A mathematical model based on AHP is first established to perform weighted processing of five meteorological factors, generating a comprehensive meteorological dataset. A simulation model for the LGJ-300/70 type conductor is then developed under the temperature field, where temperature distribution during operation is determined based on different meteorological data. Stress variations are observed, and evaluation criteria are established by calculating displacement deviations in the two-dimensional transmission line model. The transmission line is considered to be in a normal operating state when the maximum displacement deviation is ≤ ±0.63 mm. This demonstrates that the AHP-based evaluation method can effectively enable dynamic assessment of the operating status of overhead transmission lines.

Concrete is one of the most widely used construction materials worldwide; its primary component, cement, contributes substantially to natural resource depletion and greenhouse gas emissions. Alternative materials are being explored to mitigate these impacts and reduce concrete's environmental footprint. This review focuses on the potential of waste glass powder (GP) as a partial substitute for cement in concrete, examining its influence on mechanical properties, durability, and microstructural performance. Drawing from a wide range of studies published in reputable peer-reviewed journals (e.g., Wiley, ACI, MDPI, Elsevier), the analysis reveals that an optimal GP substitution level of 10%–20%, offers significant improvements in concrete durability, particularly in resistance to chloride permeability, sulfuric acid attack, and performance under high temperatures. GP contributes through its micro-filling ability, which reduces porosity and pozzolanic reaction, forming a secondary calcium silicate hydrate (C-S-H) gel that enhances binding strength. However, substituting GP above 25%–30% may reduce compressive strength due to decreased flowability and increased porosity. Overall, GP demonstrates considerable potential as an eco-friendly, cost-effective additive that improves concrete resilience and supports sustainable construction practices. This review not only consolidates existing research but also highlights GP's dual effects on concrete's microstructure and pozzolanic reactions, suggesting further optimization of GP content and synergies with other materials to enhance resilience across diverse applications. Therefore, future research should optimize GP content and investigate synergies with other materials for broader concrete applications.