Search Results (3,654)

Enhancing the energy storage properties of dielectric polymer capacitor films through composite materials has gained widespread recognition. Among the various strategies for improving dielectric materials, nanoscale coatings that create structurally controlled multiphase polymeric films have shown great promise. This approach has garnered considerable attention in recent years due to its effectiveness. This review examines surface-coated polymer composites used for dielectric energy storage, discussing their dielectric properties, behaviors, and the underlying physical mechanisms involved in energy storage. The review thoroughly examines the fabrication methods for nanoscale coatings and the selection of coating materials. It also explores the latest advancements in the rational design and control of interfaces in organic–inorganic, organic–organic, and heterogeneous multiphase structures. Additionally, the review delves into the structure–property relationships between different interfacial phases and various interface structures, analyzing how nanoscale coatings the impact dielectric constant, breakdown strength, conduction and charge transport mechanisms, energy density and efficiency, thermal stability, and electrothermal durability of polymeric capacitor films. Moreover, the review summarizes relevant simulation methods and offers computational insights. The potential practical applications and characteristics of such nanoscale coating techniques are discussed, along with the existing challenges and practical limitations. Finally, the review concludes with a summary and outlook, highlighting potential research directions in this rapidly evolving field. Full article

Enhancing the sustainability of the non-timber forest products industry has dual significance for both the management of local forest resources and socio-economic development. This paper adopts a systems theory perspective to construct an analytical model for the sustainable development of non-timber forest products, based on a “social-economic-natural” framework. By analyzing case studies of non-timber forest products industry sustainability from four underdeveloped counties in China, the paper derives the following main conclusions and insights: The sustainability of non-timber forest products development models is influenced by factors such as resource endowments and institutional environments and includes both single and composite models. Underdeveloped regions can achieve considerable sustainability in the development of non-timber forest products, but this requires a rational allocation of six key elements—policy, model, stakeholders, natural resources, funding, and technology—to stimulate industry growth. To promote the sustainable development of this industry, optimization should be pursued across five aspects: “policy leadership and top-level design to guide industry development”, “selection of appropriate development models based on local natural endowments and socio-economic foundations”, “large enterprise-driven mechanisms to form multi-stakeholder interest connections”, “focus on product technology research and development, and establishment of technical training mechanisms”, and “market-driven funding to develop product sales markets”. Full article

The correlation between metasurface structures and their corresponding absorption spectra is inherently complex due to intricate physical interactions. Additionally, the reliance on Maxwell’s equations for simulating these relationships leads to extensive computational demands, significantly hindering rapid development in this area. Numerous researchers have employed artificial intelligence (AI) models to predict absorption spectra. However, these models often act as black boxes. Despite training high-performance models, it remains challenging to verify if they are fitting to rational patterns or merely guessing outcomes. To address these challenges, we introduce the Explainable Encoder–Prediction–Reconstruction (EEPR) framework, which separates the prediction process into feature extraction and spectra generation, facilitating a deeper understanding of the physical relationships between metasurface structures and spectra and unveiling the model’s operations at the feature level. Our model achieves a 66.23% reduction in average Mean Square Error (MSE), with an MSE of 2.843 $\times {10}^{-4}$ compared to the average MSE of $8.421\times {10}^{-4}$ for mainstream networks. Additionally, our model operates approximately 500,000 times faster than traditional simulations based on Maxwell’s equations, with a time of $3\times {10}^{-3}$ seconds per sample, and demonstrates excellent generalization capabilities. By utilizing the EEPR framework, we achieve feature-level explainability and offer insights into the physical properties and their impact on metasurface structures, going beyond the pixel-level explanations provided by existing research. Additionally, we demonstrate the capability to adjust absorption by changing the metasurface at the feature level. These insights potentially empower designers to refine structures and enhance their trust in AI applications. Full article

This study aimed to compare calcium oxide (CaO) and calcium chloride (CaCl₂) as calcium sources for coconut fatty acid distillate (CFAD) calcium soap (Ca-soap) production and to evaluate the supplementation ratios of unprotected and protected CFAD in dairy rations to optimize rumen function. This research included two steps: (1) assessing the protection strength of Ca-soap made with CaO and CaCl₂ at mole ratios of Ca to CFAD of 1, 1.5, 2, and 2.5; (2) evaluating CFAD supplementation in an in vitro dairy ration study using a 5 × 4 randomized factorial block design. Factor A compared unprotected and protected CFAD ratios of A1 = 100:0, A2 = 75:25, A3 = 50:50, A4 = 25:75, and A5 = 0:100, and factor B compared supplementation levels of B1 = 0%, B2 = 1%, B3 = 2%, and B4 = 3%. CaCl₂ at a 2.5-mole ratio to CFAD produced the lowest acid value and the carboxylic acid (C=O) chemical bond. Complete protection (0:100) exhibited the highest densities of Bacteroides and nutrient digestibility (p < 0.05) without significantly affecting rumen fermentability (p > 0.05). Higher CFAD levels significantly reduced methanogens and protozoa (p < 0.05) without significantly affecting estimated methane production. In conclusion, CaCl₂ at a 2.5-mole ratio to CFAD provided the best protection, and its complete protection in CFAD supplementation optimized rumen function. Full article

The dimensions of tenons in solid wood furniture significantly influence the mechanical performance of mortise and tenon joints. While previous studies have primarily focused on tenon length, width, and thickness, they often overlooked the impact of clearance between the mortise and tenon. This study investigates the effects of tenon length, tenon width, and clearance on the mechanical performance of mortise and tenon joints, aiming to enhance their bending moment capacity (BMC) and stiffness. A three-factor, three-level orthogonal test was conducted, utilizing range analysis and variance analysis to assess the effects of each factor on BMC and stiffness. The LSD post hoc test was employed to identify significant differences between levels of the same factor, and nonlinear regression analysis was used to fit the experimental results. Based on orthogonal experiment outcomes, a grey relational theory-based evaluation system was developed to assess the comprehensive performance of joints, including both moment capacity and stiffness. Results indicate that tenon length has the most significant effect on BMC, followed by clearance and tenon width, while clearance has the greatest impact on stiffness, followed by tenon length and tenon width. These findings are consistent with those obtained from grey relational analysis. When considering both BMC and stiffness as a comprehensive evaluation, the optimal combination is a tenon length of 40 mm, a tenon width of 35 mm, and a clearance of −0.1 mm. This study offers valuable insights for the rational design of mortise and tenon joints, contributing to improved performance and reduced manufacturing costs. Full article

Frequent seismic events have demonstrated that building collapse is primarily caused by the loss of load-bearing capacity in vertical structural members. In response to this risk, various national design codes have been established. This study conducted field investigations at an earthquake site in Luding County, Sichuan Province, which was struck at a magnitude of 6.8 on 5 September 2022. In this case, the lower x-direction load-bearing wall of the Tianyi Hotel suffered severe shear damage, and the building was on the verge of collapse. However, no obvious damage was seen in the elementary school dormitory. Numerical simulation analysis revealed that during the earthquake, the buildings primarily experienced y-direction displacement in the x-direction, with significant differences in the stress state among different axes. In the model of Tianyi Hotel, the x-direction load-bearing walls suffered shear damage, while the frame columns were still in the elastic stage. At this point, the shear force of the walls was 6–9 times that of the frame columns. Comparing the damage characteristics of the two buildings during the earthquake, it was found that different structural forms lead to different internal force distributions. This phenomenon is further interpreted through the principle of “deformation saturation”, with core structural components being modeled and tested using quasi-static experiments. The results indicated substantial differences in material properties among different structural forms, including variations in lateral stiffness, ultimate load-bearing capacity, and maximum displacement. Moreover, at the same floor level, components with smaller ultimate displacements are decisive of the overall structural stability. To ensure seismic resilience and stability, it is essential to consider not only the load-bearing capacity but also the rational arrangement and cooperative interactions between different components to achieve a balanced distribution of overall stiffness. This approach significantly enhances the building’s resistance to collapse. Full article

Cellulose is one of the most abundant renewable resources in nature. However, its recalcitrant crystalline structure hinders efficient enzymatic depolymerization. Unlike cellulases, lytic polysaccharide monooxygenases (LPMOs) can oxidatively cleave glycosidic bonds in the crystalline regions of cellulose, playing a crucial role in its enzymatic depolymerization. An AA9 LPMO from Myceliophthora thermophila was previously identified and shown to exhibit a highly efficient catalytic performance. To further enhance its catalytic efficiency, consensus mutagenesis was applied. Compared with the wild-type enzyme, the oxidative activities of mutants A165S and P167N increased by 1.8-fold and 1.4-fold, respectively, and their catalytic efficiencies (k_cat/K_m) improved by 1.6-fold and 1.2-fold, respectively. The mutants also showed significantly enhanced activity in the synergistic degradation of cellulose with cellobiohydrolase. Additionally, the P167N mutant exhibited better H₂O₂ tolerance. A molecular dynamics analysis revealed that the increased activity of mutants A165S and P167N was due to the closer proximity of the active center to the substrate post-mutation. This study demonstrates that selecting appropriate mutation sites via a semi-rational design can significantly improve LPMO activity, providing valuable insights for the protein engineering of similar enzymes. Full article

Based on the Xiaolangdi North Bank Irrigation Area Project, this study combines numerical simulation and BP neural network methods to investigate the sensitivity of tunnel soil and its parameter inversion under continuous heavy rainfall. The research results indicate that changes in water-level and soil strength parameters have a significant impact on the deformation of tunnel surrounding rock. By comparing the sensitivity factors of different parameters, the main parameter sensitivities affecting the displacement of tunnel surrounding rock were determined to be water level, internal friction angle, and cohesion. The mechanical characteristics of the tunnel construction process were analyzed using finite difference method numerical analysis software FLAC3D, and the results were used as a sample dataset for inversion analysis. Through neural network inverse analysis based on orthogonal design method, the cohesion and internal friction angle of loess layer ④, loess layer ④-1, and loess layer ⑤ were determined, and the data of groundwater level elevation were obtained. Field applications proved the effectiveness and rationality of this method. Full article

Despite its medical relevance, there is no commercial vaccine that protects the population at risk from multidrug-resistant (MDR) Klebsiella pneumoniae infections. The availability of massive omic data and novel algorithms may improve antigen selection to develop effective prophylactic strategies. Up to 133 exposed proteins in the core proteomes, between 516 and 8666 genome samples, of the six most relevant MDR clonal groups (CGs) carried conserved B-cell epitopes, suggesting minimized future evasion if utilized for vaccination. Antigens showed a range of epitopicity, functional constraints, and potential side effects. Eleven antigens, including three sugar porins, were represented in all MDR-CGs, constitutively expressed, and showed limited reactivity with gut microbiota. Some of these antigens had important interactomic interactions and may elicit adhesion-neutralizing antibodies. Synergistic bivalent to pentavalent combinations that address expression conditions, interactome location, virulence activities, and clone-specific proteins may overcome the limiting protection of univalent vaccines. The combination of five central antigens accounted for 41% of all non-redundant interacting partners of the antigen dataset. Specific antigen mixtures represented in a few or just one MDR-CG further reduced the chance of microbiota interference. Rational antigen selection schemes facilitate the design of high-coverage and “magic bullet” multivalent vaccines against recalcitrant K. pneumoniae lineages. Full article

The presented article addresses the issue of the maintenance factor, which forms a part of the design variables in artificial lighting within engineering practices from a sustainability perspective. The maintenance factor was monitored using two simulation tools—Dialux, version 5.12.0.5527 and Relux, version 2024.2.8.0. In a production hall, inadequate lighting was identified with a value below 300 lx, prompting a redesign of the lighting system. The overall methodology of the Ergonomic Rationalization Sequence was expanded in the “Design of Lighting System” phase to include the determination of the maintenance factor as a necessary parameter for sustainability, which was subsequently verified in a virtual environment using two options in a practical study. According to the in situ measurements, the virtual environments of the production hall were created for both software, in which four alternatives for the lighting system were developed. The illuminance values met the normative requirements in each alternative; however, the first two (illuminance values 1000 lx–1200 lx) were predicted to have long-term high-energy consumption. In alternatives 3 and 4, the number of luminaires was therefore reduced from 6 pieces to 4, with a total illuminance in the range of 680 lx–780 lx. The determination of the variations in the methods for establishing the maintenance factor identified a deviation of 5%, which, indicating the changes in illuminance values, can be considered as the occurrence of a gross error in lighting design. Full article

By focusing on technical content, this study presents ‘two experimental building technologies courses’ connecting the conceptual and practical aspects of architectural object production. Built on the fundamental ‘concept of making’, these courses encourage students to explore their creative abilities by uniting material, form, and purpose. In the Building Technologies I course, exploration starts with the concept of ‘technique’, which involves the practical and theoretical knowledge necessary to shape architectural objects. This technique allows the production of architectural objects that encapsulate spaces carrying action and time, making a mere explanation of space creation insufficient. Thus, in the Building Technologies II course, the focus shifts to the ‘tectonic’ concept, which involves creating coherent spatial entities within a single structural system. The two courses aim to equip students with the ability to develop their unique knowledge and methods for construction before advancing to more theorised Building Technologies courses. Students are encouraged to engage with materials to uncover their potential, experiment with forms to achieve design goals, and personalise construction processes. This proposal advocates for foundational construction courses built on intuitive knowledge to replace traditional rational knowledge courses. Our study presents the methodologies and outputs of the proposed Building Technologies courses as a basis for ongoing construction courses. Full article

This paper proposes a mixed-mode (combining shear and squeeze working modes) vibration isolator using magnetorheological elastomer (MRE), which enables the isolator to have a larger working area and better isolation performance by combining the working modes of the MRE. Firstly, based on the magnetorheological effect working principle of the MRE, the material selection and dimensional parameters of each component are determined through structural design and magnetic circuit calculation. On this basis, magnetic field simulation is conducted using Maxwell 16.0 software to analyze the distribution of magnetic field lines and magnetic induction in the working area. Simultaneously, equivalent stiffness and equivalent damping models are established to explore the variation of vibration response with external current and excitation frequency conditions. Finally, a vibration isolation experimental platform is built to test the mixed-mode MRE isolator. The experimental results are basically consistent with the simulation modeling results. The experimental results showed that when the external excitation is in the frequency range of 16 Hz, effective semi-active vibration isolation control could be achieved by applying different current inputs. The isolation effect of the system is difficult to effectively control using current input when the external excitation is at high frequency. These results validate the rationality and feasibility of the mixed-mode MRE isolator structure, which provides a good reference for the design of MRE isolators. Full article

A smart grid is designed to enable the massive deployment and efficient use of distributed energy resources, including distributed photovoltaics (DPV). Due to the large number, wide distribution, and insufficient monitoring information of DPV stations, the pressure to maintain them has increased rapidly. Furthermore, based on reports in the relevant literature, there is still a lack of efficient large-scale maintenance strategies for DPV stations at present, leading to the high maintenance costs and overall low efficiency of DPV stations. Therefore, this paper proposes a maintenance period decision model and an areal maintenance strategy. The implementation steps of the method are as follows: firstly, based on the reliability model and dust accumulation model of the DPV components, the maintenance period decision model is established for different numbers of DPV stations and different driving distances; secondly, the optimal maintenance period is determined by using the Monte Carlo method to calculate the average economic benefits of daily maintenance during different periods; then, an areal maintenance strategy is proposed to classify all the DPV stations into different areas optimally, where each area is maintained to reach the overall economic optimum for the DPV stations; finally, the validity and rationality of this strategy are verified with the case study of the DPV poverty alleviation project in Badong County, Hubei Province. The results indicate that compared with an independent maintenance strategy, the proposed strategy can decrease the maintenance cost by 10.38% per year, which will help promote the construction of the smart grid and the development of sustainable cities. The results prove that the method proposed in this paper can effectively reduce maintenance costs and improve maintenance efficiency. Full article

Cell-free protein synthesis (CFPS) has emerged as a powerful tool for protein production, with applications ranging from basic research to biotechnology and pharmaceutical development. However, enhancing the efficiency of CFPS systems remains a crucial challenge for realizing their full potential. Computational strategies offer promising avenues for optimizing CFPS efficiency by providing insights into complex biological processes and enabling rational design approaches. This review provides a comprehensive overview of the computational approaches aimed at enhancing CFPS efficiency. The introduction outlines the significance of CFPS and the role of computational methods in addressing efficiency limitations. It discusses mathematical modeling and simulation-based approaches for predicting protein synthesis kinetics and optimizing CFPS reactions. The review also delves into the design of DNA templates, including codon optimization strategies and mRNA secondary structure prediction tools, to improve protein synthesis efficiency. Furthermore, it explores computational techniques for engineering cell-free transcription and translation machinery, such as the rational design of expression systems and the predictive modeling of ribosome dynamics. The predictive modeling of metabolic pathways and the energy utilization in CFPS systems is also discussed, highlighting metabolic flux analysis and resource allocation strategies. Machine learning and artificial intelligence approaches are being increasingly employed for CFPS optimization, including neural network models, deep learning algorithms, and reinforcement learning for adaptive control. This review presents case studies showcasing successful CFPS optimization using computational methods and discusses applications in synthetic biology, biotechnology, and pharmaceuticals. The challenges and limitations of current computational approaches are addressed, along with future perspectives and emerging trends, such as the integration of multi-omics data and advances in high-throughput screening. The conclusion summarizes key findings, discusses implications for future research directions and applications, and emphasizes opportunities for interdisciplinary collaboration. This review offers valuable insights and prospects regarding computational strategies to enhance CFPS efficiency. It serves as a comprehensive resource, consolidating current knowledge in the field and guiding further advancements. Full article