Search Results (29,726)

Escalating climate volatility, particularly the El Niño/Southern Oscillation (ENSO), poses severe operational and financial risks to corporate sustainability in the energy sector. However, quantitative evidence regarding how macro-level climate shocks transmit to micro-level operational performance remains scarce. Integrating dynamic capability and social network theories, this study analyzes a panel of 103 Chinese listed energy firms (2005–2022) using System GMM, mediation, and moderation models. The results indicate that ENSO intensity significantly impairs performance; specifically, a 1 °C rise in sea surface temperature anomalies decreases firms’ return on assets (ROAs) by 0.142%. We identify supply chain resilience as a critical strategic mechanism for climate adaptation, where response capacity acts as the dominant mediating channel, while recovery capacity functions as an independent compensatory mechanism. Conversely, supply network complexity—across horizontal, vertical, and spatial dimensions—amplifies the negative impact of climate disruptions by hindering resource mobility. Heterogeneity analysis reveals that state-owned enterprises exhibit stronger institutional resilience, and firms in southern regions partially offset impacts through hydropower advantages. This study bridges climate science with operations management, offering strategic guidance for managers to configure resilient, sustainable supply chains capable of withstanding environmental turbulence. Full article

Dispersed archaeological landscapes are often rich in heritage value but weakly integrated into regional tourism systems. This creates difficulties in visitor orientation, interpretive continuity, and conservation-sensitive tourism planning. In response to this problem, this study examines the Adana–Kozan–Anavarza axis in southern Türkiye and proposes a spatial corridor framework for organising tourism development within a dispersed archaeological landscape. The research integrates spatial accessibility assessment, service-capacity evaluation, field observation, and sequential route design in order to establish a hierarchical gateway–transition–anchor configuration. Anavarza, one of the largest archaeological complexes of Cilicia, represents a monumental urban heritage site and a biocultural landscape situated within a Mediterranean ecological zone historically associated with Pedanius Dioscorides. Although current visitor volumes remain moderate, official statistics indicate a substantial increase in annual entries between 2022 and 2024, reflecting rising destination visibility. This emerging growth trajectory underscores the need for proactive spatial governance mechanisms prior to the onset of congestion and environmental degradation pressures. The findings suggest that Adana can function as a metropolitan gateway, Kozan as an intermediate staging node, and Anavarza as the archaeological anchor within a realistic multi-day visitor sequence. In this configuration, visitor functions are distributed across multiple nodes, while the ecological and archaeological sensitivity of the anchor landscape is more cautiously managed through spatial sequencing. Rather than proposing a predictive model, the study develops and assesses a context-responsive spatial planning framework grounded in accessibility, infrastructural feasibility, and conservation-sensitive visitor distribution. Beyond the local case, the study offers a transferable hierarchical staging logic for corridor-based heritage planning. Full article

With the increasing demand for earth observation and communication missions, semi-rigid airships have emerged as critical aerial platforms due to their long endurance and high payload capacity. However, high-precision dynamic modeling and robust autonomous flight control remain challenging because of large hull volume and strong aerodynamic nonlinearities. This study proposes an integrated framework combining computational fluid dynamics (CFD) aerodynamic modeling with full-envelope gain scheduling control. First, nonlinear aerodynamic characteristics over wide ranges of angles of attack and sideslip are identified via CFD simulation, and a six-degree-of-freedom (6-DOF) nonlinear dynamic model incorporating added-mass effects is established. Subsequently, a gain scheduling linear quadratic regulator (LQR) controller is then designed using airspeed, climb rate, and yaw rate as scheduling variables, enabling coordinated control allocation between low-speed thrust vectoring and high-speed aerodynamic surfaces. Simulation results demonstrate improved three-dimensional (3D) path following performance and smooth flight mode transitions. The mean absolute errors (MAEs) in altitude, airspeed, and heading are limited to 0.711 m, 0.028 m/s, and 2.377°, respectively. Furthermore, the system’s robustness is validated under composite wind disturbances, confirming effectiveness of the proposed approach across the full flight envelope. Full article

Baihua Reservoir, a typical large waterbody in the karst region of southwestern China and an essential drinking water source, is characterized by a high carbonate buffering capacity that profoundly shapes the structure and function of its phytoplankton community. This study systematically elucidates the multi-dimensional driving mechanisms underlying seasonal phytoplankton community assembly in karst reservoirs by integrating multiple analytical models—including the Neutral Community Model, β-diversity decomposition, co-occurrence network analysis, XGBoost-SHAP machine learning, and Partial Least Squares Path Modeling—based on monthly sampling at five sites from 2020 to 2024. The results revealed that: (1) Stochastic processes dominated community assembly across all four seasons, while deterministic processes played a crucial role in local species turnover. (2) The co-occurrence network structure showed significant seasonal dynamics, with the composition of keystone species adaptively shifting in response to changing environmental conditions. (3) The key environmental factors influencing the phytoplankton community exhibited clear seasonal patterns, primarily pH, NH₃-N, and COD_Mn in spring; water temperature, COD_Mn, and NH₃-N in summer; TN, TP, and pH in autumn; and pH, water temperature, and DO in winter. To support the sustainable management of karst reservoirs, we propose seasonally differentiated strategies derived from our phytoplankton community analysis: target COD_Mn reduction in spring and summer, focus on TN and TP load control in autumn, prioritize water column stability in winter, and maintain hydrological connectivity and pH monitoring year-round. This approach enhances phytoplankton community stability, safeguards drinking water safety, and provides a targeted management model for similar reservoir ecosystems globally. Full article

At the dawn of the 20th century, seminal studies revealed that muscle fibers produce less heat and generate greater force during elongation than during shortening actions, laying the foundation for contemporary research on eccentric exercise. Today, eccentric exercise is widely used by athletes to enhance strength and by older adults to maintain functional capacity, yet it may cause muscle damage, particularly in unaccustomed muscles. Despite more than a century of investigation, the precise mechanisms of eccentric exercise-induced muscle damage remain incompletely resolved. Nevertheless, eccentric exercise serves as a valuable model for studying muscle injury and repair and adaptation. This review organizes current evidence into nine key themes: (1) eccentric exercise-induced muscle damage and flawed biomarkers, (2) satellite cell-mediated and alternative repair pathways, (3) high-force, low-cost contractions and metabolic impact, (4) repeated bout effect and protective adaptations, (5) architectural remodeling of fascicles, sarcomeres and tendon, (6) distinct neural control, proprioception, and cross-education adaptations, (7) mitochondrial, sarcoplasmic reticulum, and cytoskeletal stress remodeling, (8) connective tissue perturbation, remodeling, and joint stability, and (9) targeted, cautious use of antioxidant supplementation. Rather than offering a comprehensive overview, this review highlights pivotal experiments, concepts, and controversies within these themes to guide readers to the most impactful discoveries in eccentric exercise and muscle damage. Full article

This study examines the compositional differentiation of two Cucurbita species, C. maxima Duchesne and C. moschata Duchesne, to identify chemical markers relevant for their nutritional and functional potential. Multivariate statistical analysis, including principal component analysis (PCA), was applied to chromatographic, chemical, and antioxidant descriptors to visualize patterns of variability among samples. Classification artificial neural network (cANN) models were used to explore the potential of machine learning for sample differentiation, using integrated lipidomic, carotenoid, phenolic, and liquid chromatographic datasets, providing a multidimensional biochemical characterization of Cucurbita samples, achieving good classification within the analyzed dataset, reflecting the model’s capacity to describe the available data. The integration of chemometric and ANN approaches provides a framework for the compositional profiling and quality assessment of Cucurbita species, offering insights into their sustainable valorization as sources of bioactive compounds for food and nutraceutical applications while acknowledging the need for further validation on larger datasets. Full article

The rapid expansion of e-commerce and on-demand logistics has intensified the need for cost-effective and reliable urban distribution systems. This paper investigates an energy-aware routing problem for electric vehicle fleets operating from multiple depots under time-varying traffic conditions. We propose a novel multi-depot vehicle routing model that jointly incorporates time-dependent travel speeds, simultaneous pickup and delivery operations, and time window constraints. The model explicitly captures key operational realities, including battery capacity limitations, load- and speed-dependent energy consumption, synchronized pickup-delivery requirements, and soft time windows. The objective is to minimize total operational cost by simultaneously optimizing depot assignments, vehicle routes, and service schedules. Given the NP-hard nature of the problem, we develop a two-stage heuristic solution framework. In the first stage, a spatio-temporal clustering strategy is employed to assign customers to depots efficiently. In the second stage, route construction and improvement are performed using an enhanced Adaptive Large Neighborhood Search (ALNS) algorithm equipped with problem-specific destroy and repair operators. Computational experiments on adapted benchmark instances demonstrate that the proposed approach consistently produces high-quality solutions and exhibits robust convergence behavior. In addition, sensitivity analyses provide managerial insights, revealing an optimal range of vehicle energy capacity and an economically efficient speed band that balances travel time and energy consumption. Full article

Aiming at the problems of difficult hard-constraint enforcement and weak environmental generalization ability in the safe trajectory planning of manipulators in complex environments, a Policy-Guided Model Predictive Path Integral (PG-MPPI) planning framework is proposed. This framework integrates the advantages of reinforcement learning and model predictive control to construct a global prior guidance, local real-time optimization and hard-constraint safety assurance: a Constraint-Discounted Soft Actor–Critic (CD-SAC) offline learning policy is designed, which incorporates the configuration-space distance field as a safety guidance term to realize the learning of obstacle avoidance behavior; the offline policy is used to guide the online sampling and optimization of MPPI, improving sampling efficiency and planning quality; and a Control Barrier Function (CBF) safety filter is introduced to revise control commands in real time, ensuring the strict satisfaction of constraints. Taking the SIASUN T12B manipulator as the research object, simulation comparison experiments are carried out in multi-obstacle scenarios. The results show that the PG-MPPI algorithm outperforms the comparison algorithms in the success rate of collision-free target reaching, ensures the smoothness and feasibility of the trajectory, and has a good adaptive capacity to complex environments with unknown obstacle configurations, thus providing an efficient solution for the autonomous and safe operation of manipulators. Full article

Task scheduling for vehicle–crane coordination is crucial for the operational efficiency of electrified automated container terminals (ACTs). However, under fully shared dispatching, existing studies rarely capture how charging-induced capacity fluctuations disrupt bidirectional service–arrival matching and propagate service-window shifts. To address this gap, this study proposes a comprehensive spatio-temporal optimization approach. Firstly, a bi-objective model is established to minimize service–arrival mismatch and vehicle energy consumption under state-of-charge (SOC) and charger-capacity constraints, explicitly quantifying vehicle–crane alignment at both handling interfaces. Secondly, an enhanced multi-objective algorithm (ST-NSGA-II) is developed, integrating a feasibility-preserving recursive decoding mechanism and a spatio-temporal variable neighborhood search (VNS) procedure. Finally, numerical experiments demonstrate that ST-NSGA-II significantly reduces mismatch and energy consumption compared to standard NSGA-II in large-scale scenarios. It also outperforms MOEA/D in Pareto-set quality, yielding a higher hypervolume (1.301 vs. 0.960) and a lower Spacing value (0.102 vs. 0.185). The results demonstrate that the proposed spatio-temporal optimization approach can effectively reduce handover mismatch compared to conventional scheduling modes, thereby achieving seamless task scheduling for vehicle–crane coordination. Full article

Systemic risk in banking systems arises from losses transmitted through networks of contractual exposures. Yet, most widely used measures rely on market-implied volatility and equity prices rather than structural balance sheet fragilities. This paper develops a flow network framework that models systemic risk as a capacity-constrained loss-diffusion process governed by flow conservation, contractual seniority, and interbank topology. Using regulatory balance sheet data for four major U.S. banks across six quarters of the 2007–2008 financial crisis, we simulate millions of unit-consistent cascade scenarios to characterize the distribution of bank failures and aggregate losses. Despite severe macro-financial stress, the system remains in a subcritical contagion regime, exhibiting frequent single-bank failures, virtually no multi-bank cascades, and quasi-stationary aggregate losses concentrated around USD 420–430B.We extend the model to a stochastic setting in which the initial shock magnitude is randomized while propagation mechanics remain deterministic. The resulting loss distribution remains tightly concentrated and scales approximately linearly with shock size, suggesting that uncertainty in shock realizations does not induce nonlinear cascade amplification. Applying an efficient network benchmark, we estimate that 10–23% of expected systemic loss is attributable to suboptimal network architecture, implying potential gains from structural policy intervention. A comparison with SRISK reveals early divergence and convergence only at peak stress, highlighting the complementary roles of structural and market-based systemic risk measures. Finally, a graph neural network trained on synthetic flow network data fails to reproduce threshold-driven cascade dynamics, underscoring the importance of considering network structures vis-à-vis data-driven approaches. Full article

We study the complex point spectral distribution of the underlying operator of the system consisting of a reliable machine, a storage buffer with infinite capacity and an unreliable machine. This system is described by infinitely many partial differential equations with integral boundary conditions. The known literature proved that all points in a set in the left half of the complex plane are eigenvalues of the underlying operator and indicated that all points outside of the set remain undetermined. In this paper, we study the spectrum outside of the set and, under certain conditions, prove that some points outside the set are eigenvalues of the underlying operator, whereas other points are not. By combining our result with the results in the existing literature, we give a description of the point spectral distribution of the underlying operator on the whole complex plane. Our idea and method are suitable for studying point spectral distribution of the underlying operators of some queueing models described by infinitely many partial differential equations with integral boundary conditions. Full article

Climate change and rapid urbanization are intensifying flood risks in China, particularly in regions with complex terrain and dense populations. Traditional risk assessment methods often lack the flexibility to handle uncertainties in multi-dimensional risk systems. This study proposes a probabilistic flood risk assessment framework integrating Monte Carlo simulation with a composite indicator system from the perspective of disaster system theory. Taking Hunan Province as a case study, we constructed a hierarchical indicator system encompassing environmental susceptibility, hazard intensity, exposure vulnerability, and mitigation capacity. The analytic hierarchy process (AHP) and coefficient of variation (CV) methods were combined for indicator weighting, and Monte Carlo simulation was employed to quantify uncertainties and classify risk levels. Results reveal significant spatial heterogeneity in flood risk across the province, with high-risk areas concentrated in regions exhibiting intense rainfall, dense river networks, and insufficient mitigation infrastructure. The study provides a transferable, data-driven approach for spatially explicit flood risk zoning, offering evidence-based insights for land-use planning, resilient infrastructure development, and sustainable flood governance. This research contributes to the integration of probabilistic modeling into land system science, supporting disaster risk reduction and climate adaptation strategies aligned with SDG 11. This study also provides policy-relevant insights for regional flood governance by supporting risk-informed land-use planning, targeted infrastructure investment, and adaptive flood management strategies, thereby contributing to more resilient and sustainable land system development under increasing climate uncertainty. Full article

Despite pressure to scale green building (GB) adoption in Indonesia, many government building projects underperform against their initial intended design, creating a compliance gap between the design and construction phases and reducing the GB rating and its potential benefits. This study investigated the barriers and drivers affecting the Indonesian government’s GB projects’ compliance using a systems thinking (ST) approach. A causal loop diagram (CLD) was constructed from stakeholder interviews and literature scoping, followed by semi-qualitative analysis, combining systems archetype identification, eigenvector centrality (EC), and influence mapping to propose potential leverage points as a basis for policy analysis of the current regulatory scenario. Key findings show that knowledge development, sustained stakeholder integration, project documentation readiness, and government support reinforce GB compliance, but are undermined by financial constraints. CLD analysis identified that the more sustainable factors, including regulation alignment, capacity building, and enhancing collaboration, should become a focus of interventions in the system, instead of focusing solely on the provision of funding. This study presents a novel exploration of the GB adoption problem in an Indonesian governmental context through a comprehensive and systems approach. Further research might require narrowing the system boundaries, broadening the literature and stakeholder validation, and performing quantitative modelling to test intervention scenarios to support rigorous decision-making processes. Full article

Anti-unmanned aerial vehicle (Anti-UAV) detection is critical for airspace security, yet existing single-modality approaches suffer from severe performance degradation under adverse illumination, thermal crossover, and extreme scale variation. In this paper, we propose CSFADet, a dual-modal detection framework that jointly exploits visible and infrared imagery through four tightly integrated modules. First, a Cross-Spectral Feature Alignment (CSFA) module performs early-stage spectral calibration by computing cross-modal query–value attention maps, generating modality-aware channel descriptors that re-weight and concatenate the two spectral streams. Second, a Dual-path Texture Enhancement Module (DTEM) enriches fine-grained spatial details via cascaded convolutions with residual connections. Third, a Dual-path Cross-Attention Module (DCAM) introduces a feature-shrinking token generation strategy followed by symmetric cross-attention branches with learnable scaling factors, Squeeze-and-Excitation recalibration, and a $1\times 1$ convolution fusion head, enabling deep bidirectional interaction between modalities. Fourth, a Dual-path Information Refinement Module (DIRM) embeds Adaptive Residual Groups (ARGs) that cascade Multi-modal Spatial Attention Blocks (MSABs) with channel and dynamic spatial attention, culminating in a Multi-scale Scale-aware Fusion Refinement (MSFR) unit that employs three parallel multi-head attention branches with a Scale Reasoning Gate and Channel Fusion Layer to produce scale-discriminative enhanced features. Experiments on the public Anti-UAV300 benchmark show that CSFADet achieves 91.4% mAP@0.5 and 58.7% mAP@0.5:0.95, surpassing fifteen representative detectors spanning single-stage, two-stage, YOLO-family, and Transformer-based categories. Ablation studies confirm the complementary contributions of each module, and heatmap visualizations verify the model’s capacity to focus on small, distant UAV targets under challenging conditions. Full article

Natural anthraquinones possess a wide range of biological activities, including antibacterial, antiviral, antitumor, and antioxidant effects. However, studies on their ability to inhibit amyloid protein aggregation remain relatively limited. In this study, we used insulin as a model protein to investigate the anti-amyloidogenic potential of several natural anthraquinones. Specifically, the inhibitory mechanisms of five anthraquinones (emodin, anthraflavin, aloe-emodin, alizarin, and purpurin) on insulin amyloid fibrillation were explored in both dilute and crowded environments (PEG 2000 and PEG 4000). Multidisciplinary analytical results demonstrated that all five anthraquinones could effectively inhibit insulin amyloid fibrillation in both dilute and crowded environments. Simultaneously, crowded agents themselves also exhibited inhibitory effects on insulin amyloid aggregation. However, the inhibitory efficacy of anthraquinones was weaker in crowded environments than in dilute solutions, indicating that although crowded agents themselves suppressed insulin aggregation, they may interfere with the regulatory roles of anthraquinones on insulin aggregation behavior. Interestingly, purpurin showed stronger inhibitory activity in crowded environments compared to dilute solutions. Furthermore, fluorescence spectral analysis suggested that the quenching mechanism of insulin by all these anthraquinones was identified as static quenching mode. Molecular simulation studies revealed that anthraquinones could bind to the aggregation-prone regions of insulin via hydrogen bonding and hydrophobic interactions, thereby inhibiting insulin amyloid aggregation. Notably, the inhibitory capacity of these compounds was correlated with their structural features and the binding affinities to insulin. Collectively, this study explored the anti-amyloid activity of anthraquinones, which held significant research value for the development of potential therapeutic agents for amyloid-associated proteinopathies. Full article