Search Results (67,497)

Fused Filament Fabrication (FFF) is a widely used additive manufacturing technology due to its versatility, low cost, and broad material compatibility. However, achieving high dimensional accuracy in FFF parts remains challenging because dimensional deviations are affected by material shrinkage, process parameters, and part geometry. This study analyses the dimensional deviations of PLA hollow cylindrical specimens manufactured by FFF, with particular attention to the different behaviour of outer and inner diameters. The methodology combines an iterative design-adjustment procedure with a neural-network-based compensation approach. First, specimens with different geometries were printed and measured to evaluate the evolution of dimensional error after successive design corrections. Then, the influence of print speed and layer thickness was analysed through the volumetric material flow rate, and the resulting data were used to train separate feedforward neural networks for the outer and inner diameters. The results showed that outer and inner diameters followed different deviation trends, confirming that they should be analysed independently. Print speed, layer thickness, and material flow affected dimensional accuracy in different ways depending on the measured diameter. The proposed neural network approach provided a practical means of estimating compensated design diameters within the experimental domain analysed, reducing the need for repeated trial and error adjustments. However, the results should be interpreted within the experimental limits of the study, particularly regarding the use of a single material, a single printer, and a limited validation dataset. Overall, the study provides a practical workflow for improving dimensional accuracy in FFF parts and highlights the importance of diameter-specific compensation strategies. Full article

In this paper, we investigate the relationships and singularities of three associated curves, T-dual curves, N-dual curves and evolutes, for mixed-type curves in the Minkowski plane. While T-dual curves and evolutes have been studied in previous works, the N-dual curve has remained unexplored in the mixed-type setting. To fill this gap, this paper makes three main contributions. Firstly, we provide a rigorous definition of the N-dual curve, explicitly resolving the technical difficulties that arise at lightlike points where the normal line is not well-defined. Secondly, we analyze its singularities and classify its point types. Thirdly, based on these results, we establish new geometric relations among the T-dual curve, N-dual curve, and evolute. In particular, we prove that at lightlike points, the T-dual and N-dual curves coincide when the fixed point lies on the tangent line, and that the T-dual curve of the evolute coincides with the N-dual curve of the original curve under suitable conditions. These results reveal a coherent geometric framework linking the three objects. All theoretical findings are supported and validated by a variety of examples throughout the paper. Full article

This study investigates the influence of curing conditions on mechanical performance, residual strength after high-temperature exposure, and microstructural evolution of geopolymer mortars based on ceramic sanitaryware waste (CSW). Direct and delayed thermal curing regimes were applied at 60 °C and 80 °C for 48 h and 72 h. The fresh mixtures exhibited adequate workability with a flow diameter of 21 cm, indicating suitable consistency for casting. Results show that direct curing consistently enhances compressive strength, reaching 30.97 MPa at 80 °C for 72 h, compared with 15.88 MPa under delayed curing. Increasing curing temperature and duration improved early-age mechanical performance, particularly under direct curing conditions. After exposure to 800 °C, directly cured specimens retained higher residual compressive strength, with an improvement of approximately 6.6% compared with delayed-cured specimens. Microstructural characterization using scanning electron microscopy coupled with energy-dispersive spectroscopy and X-ray diffraction supported the observed mechanical trends under different curing conditions. The findings highlight the role of curing strategy in optimizing CSW-based geopolymer mortars for construction applications where mechanical performance and high-temperature resistance are required. Full article

Photovoltaic desert control (PVDC), an innovative model integrating clean energy development and desertification control, faces complex coordination challenges among local governments, local communities, and photovoltaic enterprises. This study constructs a tripartite evolutionary game model to identify the conditions that drive PVDC toward coordinated governance. The model defines a three-dimensional strategy space: government regulatory intensity (Strong vs. Lax), community willingness to cooperate (Active Cooperation vs. Passive Resistance), and enterprise ecological integration (Active Ecological Integration vs. Passive Land Occupation). Replicator dynamic equations are derived to characterize nonlinear interactions, and the stability conditions of eight pure-strategy equilibrium points are identified through Jacobian matrix eigenvalue analysis. Numerical simulations are conducted using a baseline parameter set that satisfies the Evolutionary Stable Strategy conditions for the ideal equilibrium E8, namely Strong Regulation, Active Cooperation, and Active Ecological Integration. The results show that the system can converge to E8 when higher-level rewards cover government regulation, subsidy, and community-support costs; when community cooperation benefits exceed livelihood opportunity costs and compensation incentives from resistance; and when enterprises’ effective ecological integration costs are lower than the combined benefits of subsidies, avoided fines, and long-term returns. Sensitivity analysis further indicates that government subsidies, fines, community support, cooperation income, and enterprise long-term benefits are key drivers of system evolution, while excessive regulation costs, high opportunity costs, and high ecological integration costs may hinder coordination. Qualitative evidence from four PVDC-related cases in Xinjiang provides practical illustrations broadly consistent with the model mechanisms. This study offers a dynamic analytical framework for designing incentive-compatible governance mechanisms in PVDC and similar multi-stakeholder ecological restoration projects. Full article

Ammonia is a key chemical for fertilizers, industrial processes, and emerging energy applications, yet its conventional production via the Haber–Bosch process is associated with high energy demand and significant greenhouse gas emissions. In this context, electrochemical routes for ammonia synthesis have attracted increasing attention as a potential sustainable alternative, enabling nitrogen conversion under milder conditions and using renewable electricity. This review examines recent advances in electrochemical ammonia production, focusing on nitrogen reduction mechanisms, catalyst development, and electrochemical system design. The main reaction pathways for nitrogen activation are analyzed, together with the role of electrocatalysts in determining activity and selectivity. Progress in catalyst engineering, electrolyte optimization, and reactor configuration is discussed, with particular emphasis on strategies to mitigate competing reactions such as hydrogen evolution. In addition, alternative approaches based on nitrate reduction are considered due to their promising performance and potential integration with wastewater treatment. Unlike many recent reviews primarily focused on catalyst development or individual reaction pathways, this review provides an integrated perspective encompassing nitrogen reduction, nitrate reduction, electrolyte engineering, reactor architectures, and techno-economic considerations, thereby highlighting the interdependence between materials design, reaction environment, and system-level integration for scalable electrochemical ammonia synthesis. Full article

Driven by demanding global emission regulations and the urgent requirements for sustainable mobility, Electric Vehicles (EVs) have emerged as the primary alternative to Internal Combustion Engine (ICE) vehicles. Central to this transition is the electric propulsion system (EPS), a multidisciplinary integration of power electronics, advanced motor drives, and electrochemical energy storage. This paper provides a comprehensive overview of the current landscape of power electronic drives, focusing on the evolution of high-efficiency traction motors and next-generation energy storage systems (ESSs), and advancements in ultra-fast chargers. The analysis explores the vital impact of power converters, evaluating recent breakthroughs in wide-bandgap (WBG) semiconductors and advanced control topologies that enhance energy density and thermal management. Furthermore, the study identifies critical challenges in the design, modulation, and operational reliability of converters under dynamic automotive environments. By synthesizing current research trends and technical bottlenecks, this paper offers insights into the future trajectory of power electronics in achieving high-performance, cost-effective, and carbon-neutral transportation. Full article

China’s inland regions are vital for territorial spatial planning and sustainable development due to their abundant resources. However, the dynamic coordination between natural resource utilization (NRU) and ecological resilience (ER) remains poorly understood. Using panel data from 20 inland provinces in China (2009–2023), this study constructs NRU and ER evaluation systems, with ER assessed through the Pressure–State–Response (PSR) framework. Indicator weights are determined using an AHP–entropy method. Kernel density, panel vector autoregression (P-VAR), and coupling coordination models are applied to examine spatiotemporal evolution patterns, coordination levels, and interaction mechanisms between NRU and ER. The results show that: (1) The NRU index rises overall, peaking around 2020 (0.706), while the intensity of resource development continues to decline. Regional disparities widen, resulting in a spatial pattern of development intensity that was higher in the west and lower in the east. (2) The ER index continues to rise, accelerating at certain stages, and reaches a peak (0.723) between 2018 and 2020. Geographically, the eastern region led the way, with values decreasing in a stepwise manner, and regional disparities showed relatively gradual changes. (3) The degree of coordination between the two continues to improve, evolving from a “low level of dispersion” to a “medium-to-high level of concentration.” This has resulted in a pattern where the eastern region leads, followed by the central and southwestern regions in succession. Specifically, the EC index rose from 0.429 to 0.615, and the CC index rose from 0.384 to 0.533. Eastern and Central China have already reached a medium level of coordination, while Northwest and Southwest China remain primarily at a basic level of coordination. (4) Significant bidirectional dynamic interactions exist between the NRU and ER, with asymmetric pathways. By region, the NE, EC, and NC exhibit greater fluctuations and higher system sensitivity, while the CC experiences more concentrated short-term shocks; the SW and NW exhibit relatively smoother responses and converge more rapidly. Policy implications highlight the need for region-specific coordination strategies, better alignment between resource development and ecological protection, and enhanced cross-regional governance to support sustainable inland development. Full article

Actual evapotranspiration is a primary pathway for crop water consumption in irrigation districts, including the Shijin irrigation district, where understanding the impacts of irrigation is crucial for managing water resources in this large-scale, water-scarce region. However, existing studies on evapotranspiration driving mechanisms often overlook irrigation activities and lack an analysis of synergistic effects among different environmental factors, with such research remaining particularly limited for this area. This study investigates the synergistic impact mechanisms of multiple drivers on evapotranspiration. Using data from 2003 to 2017, a projection pursuit model was employed to quantitatively assess the contributions of meteorological factors, Leaf Area Index, and irrigation to evapotranspiration evolution. The results indicate a significant structural shift in evapotranspiration, and the reduction in soil evaporation plays an important role in driving the variation of total evapotranspiration. Among the various factors, Leaf Area Index and irrigation exhibited the highest contribution rates to evapotranspiration. Furthermore, irrigation primarily acts in synergy with crop growth to enhance evapotranspiration. This study provides critical scientific insights for evidence-based water resource management and policy optimization in the Shijin irrigation district. Full article

Against the backdrop of increasingly stringent natural forest protection and comprehensive logging bans, forest-dependent regions confront structural constraints between ecological conservation and economic development, necessitating the exploration of alternative livelihood pathways and collaborative governance mechanisms. As a cross-regional institutional synergy arrangement, the “Poverty-Relief Enclave” model integrates factor resources and industrial platforms, thereby offering a new trajectory for income source transformation and industrial succession in forest areas. However, its operational process entails multi-agent interactions and complex incentive and constraint relationships, and the stability of cooperation still warrants systematic investigation. In light of this, this paper constructs a quadripartite evolutionary game model encompassing the host government, the home government, the forest region industrial alliance, and the village collective. Within a bounded rationality and dynamic evolutionary framework, it analyzes the multi-agent strategic evolution process and its stability conditions. The findings reveal that the “Poverty-Relief Enclave” model in forest regions does not spontaneously converge to a high-level cooperative state; rather, three types of stable equilibria may emerge under varying cost–benefit structures and institutional incentives. An ideal state of multi-agent synergy is attainable only under conditions of incentive compatibility. Coordinated supervision by both governments, incentives for high-quality production by industrial entities, and guaranteed participation of village collectives are identified as pivotal factors shaping cooperation stability. The cross-regional institutional arrangement facilitating the “outward shift of income sources” helps alleviate pressure on direct forest resource utilization and fortifies the institutional enforcement foundation through grassroots participation mechanisms. From the perspectives of forest governance and multi-agent collaboration, this study unveils the intrinsic operating mechanism of the “Poverty-Relief Enclave” model in forest regions, thereby furnishing a theoretical underpinning for sustainable transformation and institutional innovation in forest-dependent areas. Full article

The optimisation design of agricultural machinery is shifting from offline, experience-driven engineering towards adaptive, data-driven, and closed-loop intelligent optimisation. Conventional approaches based on computer-aided engineering (CAE), empirical testing, mathematical modelling, and static multi-objective optimisation have provided an important engineering foundation, but they remain limited under unstructured field conditions involving soil heterogeneity, crop variability, climatic disturbance, and nonlinear machinery–environment interactions. This review systematically examines the evolution of intelligent optimisation design for agricultural machinery from conventional simulation-based methods to artificial intelligence (AI)- and digital twin (DT)-enabled paradigms. First, mathematical modelling, response surface methodology, discrete element method (DEM), computational fluid dynamics (CFD), multi-body dynamics (MBD), heuristic algorithms, and early AI-assisted surrogate optimisation are reviewed to clarify their contributions and limitations. Second, frontier enabling technologies are analysed, including agriculture-specific large models, generative AI, lightweight edge intelligence, deep reinforcement learning (DRL), embodied AI, federated learning (FL), and privacy-preserving computing. Third, system-level applications integrating DT and AI are discussed, with emphasis on full-lifecycle machinery optimisation, device–edge–cloud collaborative control, multi-agent fleet coordination, predictive maintenance, and Agriculture 5.0-oriented intelligent equipment systems. Key deployment bottlenecks are further identified, including sim-to-real inconsistency, virtual–physical mismatch in DTs, edge-side trade-offs among accuracy, latency, energy consumption, and cost, insufficient validation standards, and economic adoption barriers. Finally, a 2025–2030 roadmap is proposed, highlighting large-model–DT closed loops, control biomimetics, green low-carbon optimisation, and trustworthy human–machine symbiosis for sustainable Agriculture 5.0. Full article

Cyanobacterial blooms in lakes and reservoirs pose significant environmental and public health risks. This paper presents an effective exploration strategy to detect them from Autonomous Surface Vehicles (ASVs) equipped with probes, whose sensing trajectories are optimized by an AI-based planner that considers the 3D spatial-temporal evolution of the cyanobacteria concentration obtained by a multiphysics model. The planner, simultaneously working on the AI decision-making and robotic domains, optimizes the surface displacement of the ASV and the depth of its probe by solving a constrained multi-objective optimization problem that minimizes the mission duration and trajectory length, maximizes the possibilities of the probe to overpass areas with high concentration of cyanobacteria, and satisfies operational constraints (such as ASV velocity or acceleration limits, and obstacle avoidance). The optimization is supported by two well-known versions of the Non-Sorted Generic Algorithm (NSGA-II and NSGA-III) and by encoding the trajectories with spline curves whose number of control points can be fixed, progressively increased, or freely manipulated by the algorithm. The performance of different configurations of the planner is tested against six scenarios obtained from different simulations of the multiphysics model (which couples water dynamics and temperature, light transmission, daily vertical migration of the cyanobacteria and their growth). The statistical analysis of the planner results determines that NSGA-III working with variable-length chromosomes and NSGA-II with the progressive increment of spline points as the best configurations for maximizing cyanobacteria detection, and minimizing mission duration and trajectory length. Full article

Rice (Oryza sativa L.) is a staple food crop, feeding more than 3.5 billion people. With the increasing demand for food in the 21st century, weed infestation poses the most significant biotic threat to global food security, and herbicides remain the most effective and economic way to manage it in field. However, weeds can rapidly adapt under herbicide selection pressure due to their high competitiveness, rapid growth, and reproductive capacity. Hence, we collected Echinochloa crus-galli populations from Heilongjiang and Hebei provinces in China and investigated their resistance mechanisms to pyrazosulfuron-ethyl (PSE), a sulfonylurea herbicide that inhibits acetolactate synthase (ALS). Dose–response experiments confirm that the resistant (R) population exhibits 52.9-fold resistance to PSE compared with the susceptible (S) population. Inhibitor bioassays with malathion and NBD-Cl, together with ALS activity assays, ALS gene sequencing, and molecular docking, collectively suggest that resistance is strongly associated with the ALS Trp-574-Leu target-site substitution, with a possible additional contribution from enhanced herbicide metabolism. However, because the S and R populations originate from geographically distinct locations, some of the observed physiological and molecular differences may also reflect inherent population variation. Specifically, the ALS W574L substitution is predicted to reduce key interactions between ALS and PSE. This study provides valuable evidence for the risk of PSE resistance evolution in E. crus-galli and elucidates the molecular mechanism conferring resistance to ALS inhibitors. Full article

An optimum In_0.2Cd_0.8S composition was synthesized with graphene to enhance photocatalytic performance. Graphene incorporation altered the morphology from compact grains to a loosely aggregated structure without affecting the crystal phase, as confirmed by XRD. XPS analysis indicated surface-level interaction between graphene and the In–Cd–S matrix, rather than lattice integration. Mott–Schottky and Kubelka–Munk analyses revealed n-type semiconducting behavior and a slight band gap increase from 2.46 to 2.51 eV upon graphene blending. UV–Vis and IPCE measurements showed enhanced light absorption, with IPCE values of 9.33% and 5.01% at 380 nm and 480 nm, respectively. The 3.85 wt% graphene-modified photocatalyst achieved a hydrogen evolution rate of 4.97 μmolh⁻¹cm⁻², more than triple that of pristine In_0.2Cd_0.8S. These enhancements are attributed to improved charge transport and interfacial activity provided by the graphene. Full article

Electrochemical nitrate reduction (eNO₃⁻RR) to NH₃ is a sustainable solution. However, it faces challenges like poor selectivity and competitive hydrogen evolution (HER). We report a novel Ga@FeGa₃ catalyst for efficient eNO₃⁻RR. Its unique rough, flaky morphology provides abundant active sites. The optimized electron structure enhanced the nitrogen intermediate binding. The catalyst also shows exceptional hydrophilicity. This aids reactant access, rapid product desorption, and suppresses HER. These effects give Ga@FeGa₃ outstanding eNO₃⁻RR performance. It achieves an NH₃ Faradaic efficiency of 97.84% at −1.4 V (vs. Ag/AgCl) and a 3.87 mg h⁻¹ cm⁻² yield at −1.5 V. It also maintains high selectivity and stability for over 12 h. This work highlights rational intermetallic design. Such design optimizes active sites, electronic structure, and surface wettability. This is crucial for multi-electron transfer reactions. It offers a general strategy for high-performance electrocatalysts. Full article

Rural sustainable development is a core component of the global Sustainable Development Goals, and building digital villages and enhancing the resilience of rural economies are key pathways for underdeveloped regions to achieve rural sustainable development. The coordination and synergy between these two areas are central to rural revitalization. Taking 122 counties in Hunan Province as research units and using 2013–2023 spatial panel data, this study employs an improved coupling coordination model, spatial autocorrelation analysis and geographically weighted regression to explore their spatiotemporal evolution, clustering patterns and driving factors. The results show that both systems improved steadily: digital villages expanded from core areas, while economic resilience developed more balancedly. The coupling coordination evolved from near-disorder to a pattern characterized by regional equilibrium. The coupling coordination degree displayed significant positive spatial autocorrelation, forming an “High-High (H-H)” cluster in the Changsha-Zhuzhou-Xiangtan-Dongting Lake Plain and an “Low-Low (L-L)” cluster in western Hunan. Driving factors showed marked spatial heterogeneity. These findings provide empirical support for differentiated digital village policies in Hunan. Full article