Search Results (7,419)

To elucidate the response characteristics of radial growth and water use strategies in coniferous forests of cold-arid regions to drought stress, this study focused on Larix sibirica in different forestry areas of the Altai Mountains. Using dendrochronology and stable isotope techniques, we calculated the basal area increment (BAI) and intrinsic water-use efficiency (iWUE), and combined these with the standardized precipitation–evapotranspiration index (SPEI) to identify drought events, to investigate tree growth and water-use efficiency responses to climate variability. The results showed that drought years were characterized by reduced radial growth and increased iWUE in Larix sibirica across both forest regions, and tree-ring-derived intercellular CO₂ concentration (Ci) increased with rising atmospheric CO₂ concentrations, whereas the Ci/Ca ratio remained relatively stable throughout the study period. Scenario analysis revealed that, prior to 1980, the long-term trend in iWUE was more consistent with the constant Ci scenario, suggesting relatively strong stomatal regulation. After 1980, iWUE trends became more closely aligned with the constant Ci/Ca scenario, indicating that trees maintained a relatively stable Ci/Ca ratio to balance carbon assimilation and water loss. With increasing drought severity, drought resistance declined in both forest regions; however, substantial spatial differences were observed in drought responses. Under moderate drought conditions, trees in the Haba-River forest area exhibited higher resistance, whereas trees in the Hanaslin forest area showed greater recovery capacity and ecological resilience. Winter temperature, growing-season temperature, late-season temperature, and water availability were identified as key climatic factors influencing variations in the radial growth and iWUE of Larix sibirica. Overall, under the combined influences of rising atmospheric CO₂ concentrations and increasing water limitations, Larix sibirica exhibited adaptive adjustments in carbon–water regulation; however, enhanced iWUE did not fully compensate for the negative effects of drought on radial growth. These findings provide valuable insights into the responses and adaptive strategies of cold-arid forest ecosystems under ongoing climate change and offer scientific support for the conservation and sustainable management of Larix sibiric forests. Full article

Effective task allocation is fundamental to the success of heterogeneous multi-robot cooperative missions in smart manufacturing workshops, yet real-world operational uncertainties pose severe challenges to solution feasibility and mission robustness. Addressing these challenges, this paper focuses on the inspection and maintenance task allocation problem for heterogeneous mobile robot teams operating under fluctuating equipment maintenance time windows, variable task execution durations, and uncertain robot travel speeds caused by workshop congestion and payload variations. First, the aforementioned uncertain parameters are characterized using triangular fuzzy numbers, upon which a fuzzy chance-constrained programming model is constructed with the objective of minimizing total operational cost while ensuring constraint satisfaction under uncertainty. The proposed model simultaneously handles two types of critical constraints: the service time window constraint, which requires each task to be completed before its latest allowable service deadline, and the time sequence constraint, which enforces that each equipment inspection task must be completed prior to the corresponding maintenance task. Then, to tackle the inherent NP-hardness of this problem, a multi-strategy hybrid Harris Hawks Optimization algorithm incorporating differential evolution, termed MSHHODE, is proposed. In detail, three targeted enhancement mechanisms are introduced: a hunting enthusiasm factor that governs the dynamic balance between global exploration and local exploitation throughout the search process; an elite-assisted guidance strategy that stabilizes convergence by leveraging high-quality solutions to direct population evolution; and an adaptive differential evolution mechanism that reinforces global search diversity and mitigates premature convergence to local optima. Finally, simulation experiments conducted across multiple workshop-scale scenarios demonstrate that MSHHODE consistently outperforms benchmark algorithms across different key performance metrics under varied uncertain conditions, which validates the effectiveness and robustness of the proposed approach in solving complex, constrained allocation problems, offering a practical and reliable framework for real-world heterogeneous multi-robot task planning in smart manufacturing environments. Full article

Hydrogen, as a clean and renewable energy carrier, offers a promising solution to the global energy challenge, yet its safe and efficient storage remains a critical bottleneck. Metal–organic frameworks (MOFs), with their ultrahigh surface area, tunable porosity, and excellent stability, have emerged as leading candidates for physical hydrogen storage. This review systematically surveys recent progress in MOF-based hydrogen storage, organized by metal center type and examines the distinct adsorption mechanisms that govern hydrogen uptake. The regulatory effects of critical parameters including metal ion selection, pore architecture, and ligand functionalization on hydrogen storage capacity are analyzed in detail. Beyond material-level discussion, this review discusses the potential of MOFs for cryo-compressed hydrogen storage conditions. Key challenges facing practical deployment, including synthesis scalability, structural stability under cryogenic high-pressure cycling, and the knowledge gap in multi-cycle temperature-swing stability, are critically assessed. The roles of computational simulations and machine learning in accelerating MOF discovery and high-throughput screening are also reviewed. Finally, an application-oriented outlook is presented, mapping MOF performance to three specific industrial scenarios with reference to relevant economic analyses, thereby bridging fundamental materials chemistry with practical engineering requirements. Full article

Turfgrass phenotyping relies heavily on visual quality (VQ) ratings and RGB indices like DGCI, but these are limited by observer subjectivity, coarse ordinal scales, or ratio formulations that do not reflect perceptual color differences. Hyperspectral and machine-learning tools overcome some limitations but remain costly and difficult to generalize, motivating the need for scalable and interpretable RGB color metrics. We introduce ΔEg, a perceptually anchored CIELAB ΔE distance from an ideal green that provides a continuous and interpretable measure of canopy color evaluated alongside a panel of RGB-derived metrics. A 3 × 3 nitrogen × irrigation greenhouse experiment using hybrid bermudagrass (TifTuf, Cynodon dactylon × C. transvaalensis) quantified canopy responses with RGB imaging, spectral reflectance, CCM-300 fluorescence, and chlorophyll assays. ΔEg correlated strongly with chlorophyll (r = 0.72), similar to DGCI (r = 0.73), and both exceeded CCM-300 (r = 0.29). HSVi showed the strongest association with VQ (r = 0.84) and was most sensitive to irrigation (η_p² = 0.63). CIELUV v* explained the greatest model variation (R²m = 0.94) and responded most to fertilizer (η_p² = 0.84). The yellow fraction was significant across all main and interaction effects and captured canopy decline (r = −0.82 with VQ). An illustrative decision-support scenario using ΔEg indicated that moderate fertilizer combined with mild deficit irrigation optimized turf color and input efficiency. Conclusions apply to controlled conditions, with field-scale validation identified as future work. These results demonstrate that interpretable RGB color metrics, anchored by ΔEg, offer a scalable alternative to VQ scoring and spectral systems. Full article

Sustainable agriculture requires simultaneously increasing food production and mitigating climate change, yet the extent to which crop improvement strategies deliver co-benefits at regional scales remains poorly understood. Improving radiation-use efficiency (RUE) has been widely proposed as a pathway to increase crop productivity, but its potential benefits such as soil organic carbon (SOC) sequestration are not well understood. Here, we developed a hybrid modeling framework that integrates a process-based agricultural system model (APSIM) with machine learning to capture genetic × environment × management (G × E × M) interactions and their effects on crop yield and SOC dynamics across the North China Plain. The results show that improving RUE increases both crop yields and SOC, but the magnitude of these benefits is strongly modulated by nitrogen inputs and varies widely across the region. In the future period (2021–2060) under a moderate-emissions scenario SSP2-4.5, increasing RUE of current cultivars by 10% and 20% led to additional wheat yield gains of 1.1 (+16%) and 1.8 t ha⁻¹ (+26%) and maize gains of 0.8 (+11%) and 1.1 t ha⁻¹ (+14%), respectively. These productivity gains also translated into an increase in SOC sequestration (+10% and +26%, respectively), as a consequence of enhanced carbon inputs. Notably, the coupling between yield gains and SOC sequestration varied substantially across the region, indicating spatially differentiated benefits. Our results highlight that improving RUE can contribute to both productivity and soil carbon gains, but these co-benefits are not universal and depend on local environmental and management contexts. This study provides a scalable and feasible approach for evaluating crop improvement strategies and their environmental consequences represented by SOC dynamics, as well as demonstrate that RUE improvement offers great opportunities for sustainable agriculture. Full article

Background: Circular economy transitions challenge conventional supply chain design and require decision-support tools that remain valid across multiple plausible futures. However, futures thinking in circular supply chain research is still used mainly as a narrative device, with limited guidance on how to translate scenario insights into model-ready assumptions. Methods: This paper proposes a four-step framework that converts qualitative circular-futures narratives into modelling factors and parameterised assumptions for waste electrical and electronic equipment supply chain simulation and optimisation. Four plausible circular futures, defined along economic-orientation and governance axes, are translated into operational and circularity factors and then into numerical parameters or structural modelling choices. The framework is illustrated through three applications: circular Supply Chain Resilience, electric-vehicle battery remanufacturing, and regional collection and treatment optimisation. Results: Circular strategies create value only when reverse flows, capacity and allocation choices are coherently aligned. Higher return rates improve resilience and material recovery but can destabilise upstream inventories when remanufacturing capacity and return shares are unbalanced. Different scenario priorities lead to distinct trade-offs across cost, emissions, resilience and circularity. Conclusions: This paper offers a replicable pathway for embedding plausible futures into quantitative circular supply chain models and supports more transparent decision-making under uncertainty. Full article

High penetrations of renewable generation are increasing the need for long-duration energy storage capable of intertemporal balancing and reserve provision. However, the market value of heterogeneous storage portfolios under shared grid-connection constraints remains insufficiently quantified. This study develops a risk-averse day-ahead co-bidding model for a hybrid pumped-hydro and compressed-air energy storage (CAES) portfolio participating jointly in energy and spinning-reserve markets. Monte Carlo sampling and scenario reduction are used to represent price uncertainty, while conditional value-at-risk (CVaR) captures downside-profit risk. Shared point-of-common-coupling (PCC) constraints explicitly couple electricity sales, purchases, and reserve offers. Compared with homogeneous pumped-hydro expansion, replacing the equivalent incremental pumped-hydro capacity with CAES increases the cumulative reserve bid by 65.71%, while expected profit decreases by 1.17% and raw-scenario back-test CVaR remains nearly unchanged, decreasing by only 0.05%. Relative to the unconstrained hybrid-storage case, the shared PCC constraints reduce expected profit, raw-scenario back-test CVaR, and reserve bids by 1.01%, 1.34%, and 18.62%, respectively. Scenario-reduction sensitivity and synthetic price–spread analyses indicate that the main operating mechanisms remain stable within the assumed scenario-generation framework, while sensitivity analyses reveal diminishing returns from CAES expansion and saturation of PCC-related profit gains near 5000 MW. Because all price scenarios are synthetic and neither historical nor independent out-of-sample market data are used, these analyses constitute model-based robustness tests rather than seasonal or real-market validation. The findings support the coordinated configuration of heterogeneous storage, grid-interface capacity, and risk preferences, but should be interpreted as market-bidding-level comparative evidence under the adopted equivalent CAES representation rather than as market-specific profitability forecasts. Full article

Phenolic resin ablation is a complex multi-physics process characterized by intense pyrolysis, strong thermal–chemical coupling, and drastic evolution of material properties. Predicting the through-thickness thermal response is computationally challenging, primarily due to the intricate coupling and the non-linear evolution of properties with temperature and pyrolysis. This study proposes a variable-property integrated Physics-Informed Neural Network (PINN) framework to model the high-temperature ablation of phenolic resin. Specifically, by constructing a loss function that strictly embeds the physics of heat conduction and pyrolysis, the framework explicitly captures the non-linear evolution of five thermophysical parameters, including thermal conductivity, specific heat, and density, throughout the ablation process. Numerical verifications across multiple scenarios demonstrate that the proposed variable-property PINN framework can simultaneously predict the sharp transient temperature and complex volumetric pyrolysis fields with a maximum relative error of less than 8.0% compared to the high-fidelity FEM baseline solutions. This mesh-free, efficient approach offers a novel paradigm for solving strongly coupled, variable-property ablation problems. It effectively overcomes the computational stiffness of conventional techniques, thereby providing valuable insights for the precise optimization of thermal protection systems. Full article

The convex blazed grating is a key dispersive component in high-performance spectrometers, offering advantages such as a broad operating wavelength range, uniform dispersion, high diffraction efficiency, and the ability to achieve a large field of view. With the popularization of spectral detection technology and the ever-increasing demand for specialization, its design and fabrication technologies have drawn considerable attention in the field. This paper systematically reviews the development history of convex blazed grating design theory, from early scalar diffraction theory to the current mainstream rigorous vector methods, including rigorous coupled-wave analysis (RCWA), the finite-difference time-domain (FDTD) method, and commercial software such as Gsolver and PCGrate, and summarizes the applicable scenarios and limitations of each method. In terms of fabrication techniques, we comprehensively survey three typical technology routes—mechanical ruling, holographic ion beam etching, and electron beam lithography—covering their principles and progress, and analyze their respective merits and drawbacks in terms of precision, operating waveband, groove profile flexibility, and production capacity through comparative analysis. On this basis, we highlight recent breakthroughs achieved via electron beam lithography in blaze angle control and high-aspect-ratio etching for convex blazed gratings spanning from the ultraviolet to the very-long-wave infrared band; the diffraction efficiency has exceeded 80%, and such gratings have been successfully applied in aerospace engineering projects. Finally, this paper summarizes the current challenges facing convex blazed grating technology and provides an outlook on future development trends, including fabrication uniformity on curved substrates, large-area high-precision manufacturing, and design–process co-optimization, with the aim of offering a systematic reference for researchers and engineers in related fields. Full article

The industrial chain forms a network of interconnected enterprises spanning from raw material procurement to final product distribution, enabling cost reduction, operational efficiency improvement, and enhanced market competitiveness. However, business resource conversion among enterprises often leads to information asymmetry, which impedes effective interconnection and collaboration. To address this challenge, WFC-Net (Workflow-Control Petri Net with Resource Fusion), a model tailored for multi-collaboration scenarios among interconnected enterprises in the industrial chain, is proposed. The proposed approach integrates WF-Net workflow characteristics with C-Net dependency modeling and Petri net transitions to enable precise workflow control and effective business resource coordination. The resource attributes are incorporated as first-class net elements, and a receding-horizon optimization policy is employed to dynamically adapt to varying operational conditions. Comparative simulation experiments demonstrate that WFC-Net achieves superior performance across all metrics, with an 89.1% authorization success rate and 36.0% event completion rate, outperforming Petri Net, C-Net, and WF-Net baselines. The full-rank reachability matrix provides a necessary condition for linearized controllability, offering a theoretical foundation for stable authorization behavior under the receding-horizon policy. These results demonstrate that WFC-Net consistently achieves or approximates target business-side metrics while ensuring controllability and soundness. Full article

Coordinated management of drip irrigation water demand and lateral canal supply is a critical strategy for improving water use efficiency in arid irrigation districts; however, under water-deficit conditions, the efficient and equitable allocation of limited canal water among multiple drip irrigation systems remains largely unresolved. This study developed a bi-objective cooperative water allocation and scheduling model for a lateral canal serving 11 subordinate drip irrigation systems. Subject to canal diversion flow balance and total deficit constraints, the model simultaneously minimized (i) the mean coefficient of variation (CV) of water allocation duration within rotation irrigation groups, targeting temporal uniformity, and (ii) the sum of squared deviations of the water supply satisfaction rate across systems, targeting distributional equity. Water demand inputs were derived from a localized FAO-56 Penman–Monteith irrigation schedule for Jinghe County with stage-specific crop coefficients. A hybrid binary–continuous NSGA-II encoding with a dynamic intra-group flow allocation mechanism was employed. For the baseline deficit scenario (Early May, supply-to-demand ratio β = 67.76%), the model partitioned the 11 systems into four rotation groups with a mean CV of 3.15 × 10⁻³, while the sum of squared deviations of the satisfaction rate decreased from 4.366 under the empirical scheme to 1.50 × 10⁻⁴, confining all systems to 67.38–68.45% and eliminating the coexistence of over-supply and complete deprivation (Wilcoxon signed-rank test, p < 0.001; Cohen’s d = −1.698). The Pareto front revealed a significant efficiency–equity trade-off (Spearman’s ρ = −0.9999), and NSGA-II outperformed SPEA2 by approximately 29-fold and 22-fold in the two objectives. Robustness was confirmed across three deficit scenarios and algorithm parameter sensitivity analyses (CV < 2%). The study offers methodological support for refined water allocation management of terminal canal systems in arid regions. Full article

Lipoprotein(a) (Lp(a)) is increasingly recognized as a genetically determined and clinically relevant contributor to residual cardiovascular risk. Through pro-atherogenic, pro-inflammatory, pro-thrombotic, and pro-calcific mechanisms, Lp(a) appears to play a significant role in both coronary artery disease and aortic valve disease. In patients undergoing percutaneous coronary intervention, elevated Lp(a) has been associated with worse long-term outcomes, including recurrent ischemic events, repeat revascularization, and in-stent restenosis, even in the setting of controlled low-density lipoprotein cholesterol. In parallel, experimental, genetic, and clinical data support a role for Lp(a) in the initiation and progression of calcific aortic stenosis, while its prognostic significance after transcatheter aortic valve interventions remains less clearly defined. Current guidelines now recognize Lp(a) as a relevant risk-enhancing factor, and emerging targeted therapies are achieving substantial reductions in circulating levels. Overall, Lp(a) should be regarded as both a meaningful biomarker and a promising therapeutic target, although ongoing outcome trials are needed to determine whether selective Lp(a) lowering translates into clinical benefit across interventional cardiovascular settings. The aim of this narrative review is to provide a single, comprehensive account of lipoprotein(a) [Lp(a)] in interventional cardiology, following this lipoprotein from its biology and pathophysiology through to its clinical impact and to the therapies that are now reaching the clinic, with a specific focus on the two most frequent catheter-based procedures in which it may carry prognostic weight: percutaneous coronary intervention (PCI) and transcatheter aortic valve implantation (TAVI). PCI and TAVI are deliberately addressed within the same review since they share a common upstream biology: Lp(a) contributes both to the atherosclerotic process that underlies coronary disease and to the calcific process that underlies aortic valve disease, and the corresponding patient populations overlap considerably in everyday interventional practice. Covering them together offers the interventional cardiologist a single, practical reference on how a patient with elevated Lp(a) may be approached in both scenarios. Full article

This paper investigates frequency manoeuvring as a method to improve the resilience of unmanned aerial vehicle (UAV) networks operating in contested electromagnetic environments. The study considers scenarios in which the network initially operates on a single channel and is then exposed to intentional jamming or unintentional interference affecting the primary channel, adjacent channels or a wider frequency range. Several response policies are compared, including no channel change, immediate switching after quality degradation is detected, delayed switching after a defined loss-of-connectivity interval, and periodic frequency hopping. In addition to channel switching, the analysis also considers changes in channel bandwidth, comparing narrower channels with lower throughput but potentially higher resistance to interference against wider channels with greater capacity but increased susceptibility to disruption. The evaluation includes the switching cost, which is modelled as temporary packet loss, additional delay and jitter during reconfiguration. Performance is assessed using the packet delivery ratio, latency, jitter, packet loss and communication continuity. The main objective is to identify the interference conditions under which frequency manoeuvring becomes operationally beneficial and to determine which policy offers the best trade-off between resilience and communication performance. In quantitative terms, immediate switching under environmental interference achieved a PDR of 0.961 and a mean latency of 123.6 ms compared with a PDR of 0.946 and a mean latency of 138.7 ms for fixed-channel operation. Manoeuvring gave a substantial 12.2-percentage-point PDR gain under jamming (periodic hopping: 0.780 vs. 0.658) and a 6.7-percentage-point gain under combined interference (0.674 vs. 0.607). These results indicate that manoeuvring is most worthwhile once interference is persistent and channel-focused rather than purely environmental. Full article

Against global dual-carbon targets, urban residential central heating dominates building energy use and carbon emissions. Conventional LSTM forecasting requires manual hyperparameter adjustment and easily falls into local optima; micro-community carbon prediction also lacks accurate energy models and policy-based multi-scenario analysis for targeted low-carbon renovation. This study adopts the 2018–2023 hourly heating data of a community in H Province. It builds a preprocessing workflow with boxplot-Isolation Forest anomaly detection and MissForest filling, then constructs an SSA-LSTM hybrid model optimized by Sparrow Search Algorithm to predict heat and power loads precisely. Combined with carbon accounting and three policy scenarios, it evaluates carbon peak timing and emission reduction potential of heating renovations. Results show that SSA-LSTM attains 2.48% MAPE for heat and 3.20% for power, surpassing LSTM and BP. Only moderate and ideal renovation scenarios realize carbon peaks in the 2023–2024 heating period, with cumulative cuts of 138.19 t and 254.2 t by 2031–2032; household heat meters deliver 28% of total reductions. The framework offers quantitative support for community heating operation, renovation evaluation and carbon quota management. Full article

Online flatness detection of aircraft rudder surfaces serves as a pivotal core procedure for ensuring the manufacturing precision, aerodynamic performance and operational safety of aeronautical components. Traditional plane fitting-based detection approaches are constrained by low detection efficiency, susceptibility to local optimal solutions, weak anti-noise robustness and limited automation capability, which fail to satisfy the micron-level high-precision online detection requirements for curved composite rudder surfaces in batch manufacturing scenarios. To address the aforementioned technical bottlenecks, this study proposes a bidirectionally coupled PSO-SA hybrid optimization algorithm for non-convex minimum zone flatness evaluation of curved rudder surfaces, which overcomes the unidirectional open-loop iteration limitation inherent in conventional serial PSO-SA composite frameworks. Two targeted algorithmic improvements are elaborated in this work: a residual-adaptive nonlinear inertia weight strategy, which dynamically balances global exploration and local exploitation capabilities based on the fluctuation characteristics of free-form surface measurement residuals; and a measurement noise-modified Metropolis acceptance criterion, which substantially enhances the algorithm’s anti-interference performance against on-machine trigger sampling noise. Integrating with the trigger-type on-machine detection hardware of computer numerical control (CNC) machine tools, an integrated online detection system is established to realize the full-process functions of point cloud data acquisition, error compensation, intelligent plane fitting and flatness error evaluation. Meanwhile, the complete technical workflow involving measurement path planning, probe calibration and algorithm iterative solution is systematically illustrated. Comparative simulation experiments implemented on the MATLAB platform demonstrate that the proposed algorithm exhibits superior performance in convergence speed, fitting accuracy and optimization stability over five mainstream algorithms, including standard particle swarm optimization (PSO), standard simulated annealing (SA), comprehensive learning PSO (CLPSO), adaptive cooling SA and conventional serial PSO-SA. On-machine physical measurement experiments are conducted on 24 aircraft rudder workpieces covering aluminum alloy skins and assembled riveted components. After multi-dimensional systematic calibration, the overall detection error of the developed system is controlled within 1 μm. The experimental results indicate that the average flatness error calculated by the proposed bidirectionally coupled PSO-SA algorithm is 29.7 μm, which is 30.1% and 38.5% lower than that of standard PSO and standard SA, respectively, fully complying with the aviation flatness tolerance specification of 0.1–0.3 mm. Moreover, the full detection cycle for a single workpiece is only 2.1 min, achieving a 34.4% reduction in detection time compared with standard PSO and effectively improving the efficiency of online in-process inspection. One-way analysis of variance (ANOVA) combined with Tukey’s posthoc test further verifies that the accuracy superiority of the proposed algorithm is statistically significant. This research provides a targeted theoretical basis and complete engineering implementation scheme for intelligent flatness detection of aerospace curved thin-walled parts, and offers a valuable technical reference for form and position error evaluation of irregular industrial components under noisy measurement conditions. Full article