Search Results (21,995)

To address the problems of high unharvested rates and header loss rates in existing plot-breeding wheat harvesters, this study presents the design of a comb-type header for plot wheat harvesters. Based on the loss suppression mechanism during wheat harvesting, the key components of the comb-type header were designed. To address the issue in which some wheat ears escape combing during the harvesting process, a multi-stage comb-tooth structure was developed. For the problem of seed retention on the bottom plate of the screw conveyor, the telescopic tooth at the feeding port of the screw conveyor was replaced with a scraper, and a rubber plate was added. To determine the optimal combing time, wheat plant posture changes under the action of the nose (hereinafter referred to as the nose) were analyzed through theoretical analysis, simulation, and bench testing. It was determined that the optimal combing moment occurs when the plants begin to rebound to the maximum reverse bending. On this basis, a numerical simulation model of the header combing system was constructed using the discrete element method, with the header loss rate as the evaluation index to explore the influence of the nose height, the machine forward speed, and the combing drum rotation speed on the header performance. A regression model of header loss was constructed using the Box–Behnken response surface method, and the optimal working parameters were determined as follows: a nose height of 554 mm, a machine forward speed of 0.65 m/s, a combing drum rotation speed of 667 r/min, and the predicted loss rate of 8.59%. To verify the operational performance of the comb-type header, a field test of the wheat-harvesting prototype was conducted. The results showed that, under the optimal working parameters, the header loss rate was 7.24%, no wheat ears escaped combing, and no seed retention occurred in the header, which meets the requirements for plot wheat-breeding harvesting. This study provides a theoretical basis for the design and development of small-sized combing harvesters. Full article

This work aims to enhance the stability of the Mg/Al₃Y interface through first-principles investigations of low-cost dopants. Density functional theory calculations were employed to systematically examine the bulk properties of Mg and Al₃Y, as well as the structural stability, electronic characteristics, and alloying element effects at the Mg(0001)/Al₃Y(0001) interface. The calculated lattice parameters, elastic moduli, and phonon spectra demonstrate that both Mg and Al₃Y are dynamically stable. Owing to the similar hexagonal symmetry and a small lattice mismatch (~1.27%), a low-strain semi-coherent Mg(0001)/(2 × 2)Al₃Y(0001) interface can be constructed. Three representative interfacial stacking configurations (OT, MT, and HCP) were examined, among which the MT configuration exhibits significantly higher work of adhesion, indicating superior interfacial stability. Differential charge density and density of states analyses reveal pronounced charge transfer from Mg to Al/Y atoms and strong orbital hybridization, particularly involving Y-d states, which underpins the enhanced interfacial bonding. Furthermore, the segregation behavior and adhesion enhancement effects of typical alloying elements (Si, Ca, Ti, Mn, Cu, Zn, Zr, and Sn) were systematically evaluated. The results show that Mg-side interfacial sites, especially Mg₂ and Mg₃, are thermodynamically favored for segregation, with Zr and Ti exhibiting the strongest segregation tendency and the most significant improvement in interfacial adhesion. These findings provide fundamental insights into interfacial strengthening mechanisms and offer guidance for the alloy design of high-performance Mg-based composites. Full article

Maintenance of building fire protection facilities is crucial for preventing fires and safeguarding lives and property; the standardization and timeliness of these activities directly determine operational reliability. However, as fire-safety requirements escalate, manually drafting maintenance work orders remains inefficient and prone to omissions. Furthermore, regulatory documents in this domain are inherently complex, and annotated resources are scarce, hampering the digitalization of fire-safety management. To address these challenges, this paper presents an LLM-based method for automatically generating maintenance work orders for building fire protection facilities. The proposed approach integrates a domain-specific knowledge base and incorporates the FS-RAG (Fire Services–Retrieval-Augmented Generation) framework to enhance both the accuracy and practical usability of generated work orders. First, we construct a lightweight domain knowledge base, FSKB (Fire Services Knowledge Base), derived from extensive maintenance regulations, capturing key elements such as equipment types, components, maintenance actions, and frequencies. Second, we design an FS-RAG framework that leverages retrieval-augmented generation to extract critical information from regulations and fuse it with the knowledge base, ensuring high accuracy and operational feasibility. Multi-round evaluations across stages B0–B4 validate the effectiveness of our method. Results indicate significant improvements over traditional approaches: the line-level compliance rate reaches 97.3% (an increase of 5.7% over B1 and 30.4% over B0), and the F1 score achieves 90.42% (an increase of 12.62% over B1 and 29.87% over B0). Full article

Public participation is a central element of democratic decision-making processes, but it often faces challenges within planning approval procedures due to problems of understanding and accessibility. This paper aims to counteract these challenges through the conceptual development, prototypical implementation and validation of a chatbot. The chatbot is designed to facilitate access to planning documents and improve the participation process as a whole. After presenting the theoretical foundations of chatbots and large language models (LLMs), three central use cases are described. The main tasks of the chatbot are to simplify the language of complex planning documents, find documents and information, and answer frequently asked questions. The underlying architecture of the prototype is based on the concept of retrieval augmented generation (RAG) and uses a vector database in which the information is embedded and stored as vectors. To evaluate the developed prototype, four focus workshops were conducted with professionals affiliated with road and rail infrastructure administrations at both state and federal levels in Germany. During these workshops, participants tested the core functionalities and assessed the system using both quantitative and qualitative criteria. The results indicate a strong potential for improving the handling of standard inquiries. By improving access to complex planning documents, the system may also contribute to a reduction in objections. At the same time, the evaluation emphasizes the importance of limiting hallucinations through appropriate technical safeguards and clearly indicating the use of AI to users. The insights gained from this study will be incorporated into the prototype developed within the BIM4People research project, funded by the German Federal Ministry of Transport. The aim therefore is to implement additional use cases and continuously optimize the functionality of the system through an iterative development process. Full article

TwinArray Sort is a non-comparison integer sorting algorithm designed for non-negative integers with relatively dense key ranges, offering competitive runtime performance and reduced memory usage relative to other counting-based methods. The algorithm introduces a conditional distinct-array verification mechanism that adapts the reconstruction strategy based on data characteristics while maintaining worst-case time and space complexity of O(n + k). Comprehensive experimental evaluations were conducted on datasets containing up to 10⁸ elements across multiple data distributions, including random, reverse-sorted, nearly sorted, and their unique variants. The results demonstrate consistent performance improvements compared with established algorithms such as Counting Sort, Pigeonhole Sort, MSD Radix Sort, Spreadsort, Flash Sort, Bucket Sort, and Quicksort. TwinArray Sort achieved execution times up to 2.7 times faster and reduced memory usage by up to 50%, with particularly strong performance observed for unique and reverse-sorted datasets. The algorithm exhibits good scalability for large datasets and key ranges, with performance degradation occurring primarily in extreme cases where the key range significantly exceeds the input size due to auxiliary array requirements. These findings indicate that TwinArray Sort is a competitive solution for in-memory sorting in high-performance and distributed computing environments. Future work will focus on optimizing performance for wide key ranges and developing parallel implementations for multi-core and GPU architectures. Full article

Tetra-missing rib honeycombs (TMRHs), characterized by monoclinic geometry, exhibit high elastic stiffness but suffer from poor deformation stability and reduced axial load-bearing capacity, which limit their applicability in energy-absorbing and load-sensitive engineering structures. To address these inherent drawbacks, this study proposes two symmetry-enhanced tetra-missing rib honeycomb configurations through overall axisymmetric design and subunit-level symmetric optimization. A finite element model was established in Abaqus/Explicit and validated against quasi-static compression experiments, demonstrating good agreement in deformation modes and mechanical responses. Systematic numerical investigations were then conducted to compare the mechanical properties and deformation behaviors of three honeycomb layouts, including the conventional TMRH and the proposed symmetric designs. Furthermore, the effects of impact velocity on mechanical performance were examined to evaluate the dynamic response characteristics of the structures. Finally, the influence of subunit angle parameters on the stiffness, energy absorption, and deformation stability of the tetra-missing rib honeycombs was comprehensively analyzed. The results provide insight into the role of symmetry and geometric parameters in improving the mechanical performance of TMRH-based structures and offer guidance for the design of high-performance auxetic honeycombs. Full article

Amid growing environmental concerns, resource depletion, and the pressing challenges of industrial waste management, this study investigates the potential of magnesium slag (MS) as a sustainable alternative binder in the production of one-part geopolymer concretes (OPGCs). The objective is to reduce reliance on conventional cementitious materials while promoting the valorization of industrial by-products in construction practices. For this purpose, ten different mixtures were designed by replacing ground granulated blast furnace slag (GGBS), the conventional aluminosilicate precursor, with MS, an innovative aluminosilicate precursor, at replacement levels of 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, and 100% by weight, using a solid activator. The fresh and hardened properties of these mixtures were systematically evaluated through slump, setting time, density, ultrasonic pulse velocity (UPV), and strength tests, while microstructural characterization was also conducted using scanning electron microscopy (SEM) coupled with energy-dispersive X-ray spectroscopy (EDX) to further investigate the geopolymerization process, elemental distribution, and the role of MS in binder formation in OPGC. The results revealed that MS incorporation significantly influenced both workability and mechanical performance, and it was confirmed that MS actively participates in geopolymerization and can be effectively utilized up to a certain threshold. Replacement levels up to 30% were found to maintain acceptable mechanical performance, providing evidence that MS is a promising precursor for developing sustainable OPGC. Full article

Falling debris impact from damaged upper structures is a key cause of building progressive collapse, yet relevant research lags behind that on column removal scenarios. This study uses ANSYS/LS-DYNA 16.0 to simulate the dynamic responses of steel-framed subassemblies with five typical beam–column connections under debris impact, with the finite element model validated by drop hammer tests and showing good agreement with the experimental results. Parametric analyses are conducted to explore the effects of the impact velocity, impactor mass, impact energy, and horizontal restraint on structural responses. The results show that under the same impact energy, the velocity and mass significantly affect the maximum impact force but barely the stable-stage force and maximum displacement; horizontal restraint exerts negligible effects at a low impact energy while a single horizontal restraint markedly impairs impact resistance at high energy. These findings are clarified via energy conservation, momentum theorem, and anti-collapse mechanisms. The study’s originality lies in systematically investigating the dynamic responses of the five subassemblies, deriving quantitative relationships between the impact parameters and impact force, duration, and horizontal restraint. It provides theoretical and technical support for anti-progressive collapse building design. Full article

Rolling bearings are critical components in industrial machinery, and their failures can lead to equipment downtime or safety hazards, making accurate fault diagnosis vital. While data-driven intelligent methods perform well with sufficient labeled data, acquiring large-scale fault data in real-world scenarios remains challenging. To address this issue, this paper proposes a fault diagnosis method combining finite element simulation and deep domain adaptation transfer learning. First, a finite element model of rolling bearings under normal, outer race, inner race, and rolling element fault conditions is developed, and ANSYS/LS-DYNA simulates motion to generate labeled synthetic fault data. The model’s reliability is validated through time-domain, frequency-domain, and time-frequency analyses. A lightweight 1D convolutional neural network (1D CNN) is then designed for fault diagnosis. When trained solely on simulated data, the model achieves only 61.4% accuracy on real data due to domain discrepancies. To bridge this gap, a transfer learning approach integrating generative adversarial networks (GANs) and multi-kernel maximum mean discrepancy (MK-MMD) is proposed: GANs synthesize data resembling real distributions, while MK-MMD minimizes domain shifts between simulated and actual data. This improves the model’s accuracy to 93.8% on real fault datasets. Performance evaluation under variable working conditions and bearing types demonstrates the method’s robustness, providing a practical solution for fault diagnosis in industrial applications with limited data. Full article

With the increasingly prominent issues of energy shortage and environmental pollution, the development of clean energy materials has become a core topic in the academic community. SnSe, as a material with moderate bandgap, a high light absorption coefficient, and environmental friendliness, has shown broad application prospects in the fields of photovoltaics and thermoelectrics. However, pure SnSe thin films have inherent defects, low carrier concentration, and high recombination rates, which limit their photoelectric conversion efficiency. This article provides a detailed overview of the characteristics of band engineering control technology, defect control technology, and carrier concentration control technology, as well as the improvements in the characteristics of SnSe thin films that they bring. This article systematically reviews the research progress on doping control technology for SnSe thin films characteristics in recent years and analyzes and discusses the differences in typical doping elements on SnSe thin films characteristics, such as optical bandgap and absorption coefficient, and applicable application scenarios, such as photovoltaics, near-infrared/infrared detection, and thermoelectric and flexible optoelectronic devices. Furthermore, the interaction between the doping mechanism of dopants and natural defects, as well as the influence of the structural parameters of doped films on doping efficiency, were analyzed, and a predictive design route for the doping mechanism of SnSe films was proposed. Finally, the influence of different atomic fractions on the characteristics of SnSe thin films was discussed. Low atomic fractions are beneficial for bandgap tuning and absorption enhancement; high atomic fractions can easily introduce phase separation and non-radiative recombination. It is suggested that future researchers can continue to focus on the precise control of atomic fractions, exploration of new element co-doping, and industrial large-scale production applications, providing theoretical guidance for the design and application of SnSe thin films in photothermal devices. Full article

Growing pressures on water resources, exacerbated by climate change, resource depletion, and population growth, underline the need for sustainable and energy-efficient wastewater management. Wastewater treatment plants (WWTPs) are among the most energy-intensive elements of the Integrated Water Service, and their environmental performance depends on infrastructure design, resource availability, and treatment configuration. Improving resource efficiency while reducing energy demand and environmental impacts is therefore a priority for water utilities seeking innovative decision-support tools. Within the national project “WATERGY—Energy Efficiency of the Integrated Water Service”, this study proposes a life-cycle-based framework to assess the sustainability of technological interventions in WWTPs. A comparative gate-to-grave Life Cycle Assessment (LCA) was applied to the municipal WWTP of Potenza (Southern Italy). Three sludge End-of-Life Scenarios were assessed: the current landfill-based configuration, an enhanced oxygenation–nitrification setup, and anaerobic digestion with biogas-based cogeneration. Compared to the current scenario, anaerobic digestion with cogeneration reduces Global Warming Potential by 17% and decreases freshwater ecotoxicity by approximately 30%. Compost production shows the highest reduction in ecotoxicity (−51%) but increases fossil resource depletion and acidification due to higher energy demand. Overall, energy recovery pathways, particularly anaerobic digestion with cogeneration, provide the most balanced environmental benefits, supporting more sustainable WWTP operation within the Integrated Water Service. Full article

Ultra-high-molecular-weight polyethylene (UHMWPE) thin films are considered promising shielding materials against hypervelocity microparticle impacts in space environments. In this study, a finite element-smoothed particle hydrodynamics (FEM-SPH) adaptive coupling simulation method was developed to reveal the damage mechanisms of UHMWPE films impacted by alumina (Al₂O₃) particles with a diameter of 10 μm. A 100 μm thick single-layer UHMWPE film was subjected to normal impacts at velocities ranging from 1 to 30 km/s. The morphology and characteristics of craters formed on the film surface were analyzed, revealing the velocity-dependent transition from plastic deformation to complete perforation. At 10 km/s, additional oblique impact simulations at 30°, 45°, 60° and 75° were performed to assess the effect of impact angle on damage morphology. Furthermore, the damage evolution in double-layer UHMWPE films was examined under impact velocities of 5, 10, 15, 20 and 25 km/s, showing enhanced protective performance compared to single-layer films. Finally, the critical influence parameters for UHMWPE failure were discussed, providing criteria for evaluating the shielding limits. This work offers computational methods and predictive tools for assessing hypervelocity microparticle impact and contributes to the structural protection design of spacecraft operating in the harsh space environment. Full article

The mechanical behavior of assembled culverts under high rocky backfill presents significant challenges due to the complex interaction between the rigid structure and coarse-grained fill. This study investigates the full-process mechanical performance of an assembled culvert through comprehensive in situ monitoring and three-dimensional finite element numerical analysis. Key parameters, including earth pressure distribution, structural deformation, and joint strain, were continuously monitored throughout the backfilling process. A high-fidelity numerical model considering the soil-structure interaction was established and strictly validated against field data. The results indicate that the earth pressure growth rate gradually decreases with fill height, confirming the development of a soil arching effect within the rocky backfill. The numerical predictions show strong consistency with experimental measurements, verifying the model’s accuracy. Crucially, the culvert exhibited minimal deformation, with cumulative settlement less than 25 mm, fully meeting safety requirements. Furthermore, a distinct alternating tension-compression strain pattern was observed at the joints during early backfilling, highlighting the critical necessity of symmetrical layered compaction. These findings validate the safety of the proposed construction methodology and provide a theoretical basis for optimizing the design and quality control of high-fill infrastructure in mountainous terrain. Full article

Thermodynamic entropy generation quantifies irreversibility in energy conversion processes, providing rigorous thermodynamic foundations for optimizing efficiency and sustainability in thermal and energy systems. This critical review synthesizes advances in computational entropy modeling across numerical methods, optimization strategies, and sustainable energy applications. Computational fluid dynamics, finite element methods, and lattice Boltzmann methods enable spatially resolved entropy analysis in convective, conjugate, and microscale systems, but exhibit varying maturity levels and accuracy–cost trade-offs. The minimization of entropy generation and the integration of artificial intelligence demonstrate quantifiable performance improvements in heat exchangers, renewable energy systems, and smart grids, with reported efficiency gains of 15 to 39% in specific applications under controlled conditions. While overall performance depends critically on system scale, operating regime, and baseline configuration, persistent limitations still constrain practical deployment. Systematic conflation between thermodynamic entropy (quantifying physical irreversibility) and information entropy (measuring statistical uncertainty) leads to inappropriate method selection; validation challenges arise from entropy’s status as a non-directly-measurable state function; high-order maximum entropy models achieve superior uncertainty quantification but require prohibitive computational resources; and standardized benchmarking protocols remain absent. Research fragmentation across thermodynamics, information theory, and machine learning communities limits integrated frameworks capable of addressing multi-scale, transient, multiphysics systems. This review provides structured, cross-method, application-aware synthesis identifying where computational entropy modeling achieves industrial readiness versus research-stage development, offering forward-looking insights on physics-informed machine learning, unified theoretical frameworks, and real-time entropy-aware control as critical directions for advancing sustainable energy system design. Full article

Slope stability is a critical and extensively researched topic that is important in structural design, especially when slopes are located near residential or civil engineering structures, as human lives are at risk. This paper presents a detailed analysis and evaluation of slope stability, synthesizing current understanding of slope behaviour, soil shear strength parameters, and the methodologies applied in stability assessment. In the conducted parametric study, the stability of slopes composed of fine-grained soils was investigated using both the limit equilibrium method (LEM) and the finite element method (FEM). The principal objective of the research was to assess the influence of soil shear strength parameters on the resulting factor of safety (FoS), while also accounting for variations in slope height. The results of the study show that an increase in soil shear strength parameters leads to a linear increase in FoS, with this relationship being more pronounced for changes in soil cohesion than for changes in the angle of internal friction. The effect of shear strength variations on stability is more pronounced in slopes of smaller height. Furthermore, the comparative analysis indicates that LEM provides more conservative estimates of slope stability in comparison with FEM. Full article