Search Results (6,807)

The Cascade Reservoir System (CRS) in the upper Yellow River delivers integrated benefits (flood control, water supply, hydro-power generation, and ecological regulation), but it also alters the natural runoff regime and exerts non-negligible impacts on the regional eco-environment. However, the long-term trajectory of reservoir-cascade effects on land use/land cover (LULC) in alpine basins has not yet been systematically quantified. Here, we focused on the Hualong-Xunhua reach and delineated two impact domains—the Reservoir Influence Zone (RIZ, enclosed by the first-order mountain ridge lines closest to the river channel representing direct hydrological impacts) and the Local Microclimate Influence Zone (LMIZ, spanning from the first-order ridges to the outer watershed boundary representing indirect climatic impacts)—to investigate the spatiotemporal dynamics of LULC associated with reservoir development. Results show that, from 1985 to 2023 in the CRS area, cropland and shrub-land decreased by 89.56 km² (−16.09%) and 9.41% (−9.41%), respectively, whereas forest and grassland increased by 79.92 km² (+14.36%) and 7.74% (+7.74%). Within the RIZ, cropland declined by 29.49 km² (−20.14%), while water bodies increased markedly by 32.19 km² (+22%); forest cover also expanded by 9.09 km² (+6.21%). In the LMIZ, forest and grassland exhibited pronounced increases of 70.83 km² (+17.27%) and 39.37 km² (+9.60%), respectively. Correlation analysis indicates that GDP and air temperature are strongly and positively correlated with forest, water bodies, and impervious surfaces (Pearson’s r > 0.9), whereas cropland shows significant negative correlations with GDP, forest, and grassland (Pearson’s r < −0.8). Overall, the distinct spatiotemporal contrasts between the RIZ and LMIZ, coupled with the temporal alignment of cropland-to-forest transitions post-2000, suggest that reservoir-cascade construction and the Grain-for-Green Project are associated with these major LULC transitions, serving as contributing factors, while temperature rise and GDP growth represented the background environmental and socioeconomic context. These findings provide data support and a conceptual basis for long-term monitoring and assessment of eco-environmental responses to reservoir cascade development, and offer scientific evidence particularly relevant to reservoir planning and management in high-altitude cold regions. Full article

Short-term power load forecasting is fundamental to the secure operation and optimal dispatch of modern power systems. This study proposes an Improved Pied Kingfisher Optimization–Temporal Convolutional Network–Bidirectional Gated Recurrent Unit (IPKO-TCN-BiGRU) model to address the challenges of strong non-stationarity, high randomness, and multi-factor coupling in load time series. The model employs a multi-scale TCN for simultaneous extraction of local and global temporal features, a BiGRU enhanced with an Improved Self-Attention (ISA) mechanism for bidirectional dependency modeling, and an Autoregressive (AR) module combined with an election mechanism to jointly capture linear and nonlinear load components. The Improved Pied Kingfisher Optimization (IPKO) algorithm—incorporating SPM chaotic initialization, a planetary optimization strategy, and adaptive t-distribution perturbation—is applied to globally optimize key hyperparameters, demonstrating superior convergence accuracy and global search capability over the original PKO and other benchmark optimizers. To ensure evaluation integrity, dataset splitting precedes all normalization operations, with StandardScaler fitted exclusively on the training set and applied to the test set without leakage. Validation is conducted on two benchmark datasets: a U.S. residential electricity load dataset (hourly, 2012, 13-dimensional features including HVAC and lighting systems) and a China Electrical Engineering Mathematical Modeling Competition dataset (15 min intervals, three years, enriched with five meteorological variables). The U.S. dataset exhibits a clear annual double-peak seasonal pattern, while the Chinese dataset shows strong intraday fluctuations significantly coupled with temperature and humidity, both posing substantial forecasting challenges. On the U.S. dataset, the proposed model achieves MAE = 0.0190 kW, RMSE = 0.0301 kW, MAPE = 1.7673%, and R² = 0.9947; on the China dataset, MAE = 79.8125 MW, RMSE = 109.4154 MW, MAPE = 1.1124%, and R² = 0.9955. The proposed model consistently outperforms six mainstream baseline models—including Transformer, Autoformer, and FEDformer—reducing RMSE by up to 34.4% and 18.9% on the two datasets, respectively, while maintaining a compact architecture of 15.2 MB and 74.6–78.9 MFLOPs. Ablation experiments confirm the significant and synergistic contribution of each module, and the direct comparison between PKO-TCN-BiGRU and IPKO-TCN-BiGRU validates that the algorithmic improvements translate into measurable forecasting gains beyond benchmark function optimization. The proposed model is most suitable for ultra-short-term to short-term single-step-ahead forecasting within a horizon of 15 min to 24 h, with an inference latency of 2.3–2.7 ms per sample, fully meeting the real-time requirements of practical power dispatching systems. Full article

This paper presents the sizing and performance evaluation of a natural draft air-cooled condenser, with a nominal heat rejection rate of 75 MW_th, for implementation at a concentrated solar power plant in the Northern Cape province of South Africa. Initial sizing and optimization of the tower geometry is done with the aid of a one-dimensional thermofluid model at design point conditions. A high-density Latin hypercube sampling-based parametric sweep was conducted that covers the geometric design envelope, which is defined via the tower and heat exchanger heights, and the tower base and outlet diameters. Following this, the performance of the best-performing tower geometry is verified via detailed three-dimensional computational fluid dynamics (CFD), and the geometry adjusted slightly to achieve the desired heat rejection rate. This process includes refinement and validation of the CFD model compared to previous work, with the heat rejection rate matching the previous results within 0.1%, as well as performing grid convergence studies to ensure mesh independence. The refinements include a more direct coupling with the solver continuity equation, improving the accuracy of the heat exchanger integration via porous media, and a decrease in computational overhead to reduce the time required for parametric studies. The best-performing geometry implemented in the CFD model features a tower height of 80 m, base diameter of 58 m, outlet diameter of 40.15 m, heat exchanger height of 11.25 m and heat exchanger width of 3.551 m, with the model predicting a conservative heat rejection rate of 76 MW_th at the design point. Finally, a methodology is presented to evaluate the performance of the system over the full range of ambient conditions encountered during an annual operating cycle. The methodology will be applied in further work to develop a reduced-order surrogate model for application in annual performance studies. Full article

To accurately characterize the warm deformation behavior and workability of the 5A06 aluminum alloy, this study presents an innovative workflow that develops and systematically validates machine learning-assisted Johnson–Cook (ML-JC) frameworks based on artificial neural network (ANN) surrogate models. Two predictive frameworks—the parallel-decoupled PD-ANN-JC and the multi-objective integrated MOI-ANN-JC—were constructed. Quantitatively, both developed ML-JC frameworks achieve significantly higher stress prediction accuracy and superior generalization capability compared with the conventional JC model. Specifically, on the testing set, the MOI-ANN-JC framework yields an average absolute relative error (AARE) of 1.424% and an R² of 0.997, outperforming the PD-ANN-JC framework (AARE of 3.246%, R² of 0.988). On the validation set, the MOI-ANN-JC framework also demonstrates exceptional generalization, with an AARE of 3.302% and an R² of 0.987. Scientifically, the superior performance of the MOI-ANN-JC framework stems from its ANN-mnδ surrogate model, which simultaneously predicts the strain hardening exponent n, thermal softening exponent m, and relative error δ directly from deformation parameters. This mutual coupling establishes an intrinsic correlation between m and n, successfully aligning with the physical reality wherein strain hardening and thermal softening are inherently linked during deformation. Qualitatively and practically, by integrating the MOI-ANN-JC framework into finite element (FE) simulation software, dynamic tracking and visualization of the thermal softening exponent m during warm deformation were achieved. Combined with FE simulations, Vickers hardness testing and EBSD observations, this study successfully establishes a direct qualitative spatial correspondence between low-m regions and macroscopic defects, which was further verified through the warm forging of a thin-walled dual-cavity component. Crucially, this approach for evaluating deformation stability bridges the gap caused by the inapplicability of conventional processing maps within this temperature regime, offering a robust and broadly applicable workflow for complex forming optimization. Full article

Background: This study aimed to comprehensively investigate the complex association between the use of fluoroquinolone (FQ) antibiotics and cancer risk, with a specific focus on patients with interstitial lung disease (ILD)—a unique clinical population characterized by a high inflammatory burden and a high susceptibility to infections. Methods: We conducted a large-scale retrospective cohort study using a high-quality clinical database. A total of 7906 matched patients (3953 pairs) were included after propensity score matching (PSM). Three complementary statistical models were applied: the standard Cox proportional hazards model, the time-dependent Cox regression model, and the Fine–Gray competing-risks model, to provide a multidimensional assessment of cancer risk. Results: A total of 7906 matched patients (3953 pairs) were followed. After strictly defining the index date to eliminate immortal time bias, FQ exposure was associated with an increased risk of all-cause cancer in the standard Cox model (adjusted HR 1.45; 95% CI, 1.20–1.76) and the competing risk model (adjusted SHR 1.28; 95% CI, 1.06–1.55). Site-specific analyses revealed elevated risks for certain malignancies, notably prostate cancer. Importantly, when modeled as a continuous variable, the cumulative dose of fluoroquinolones showed no significant dose–response relationship with overall cancer risk (adjusted HR 0.99; 95% CI, 0.99–1.00). Conclusions: After correcting for immortal time bias, the previously hypothesized protective effect of fluoroquinolones on cancer risk was not observed. The increased risk observed in categorical models, coupled with a lack of a continuous dose–response, strongly suggests that these findings are driven by confounding by indication and reverse causation (i.e., frequent infections masking undiagnosed malignancies or reflecting severe underlying ILD), rather than a direct pharmacological effect. Full article

Spacecraft electrostatic discharge (ESD) can generate transient electric-field disturbances, pulse currents, and electromagnetic coupling that threaten onboard electronics and mission reliability. Localizing discharge sources in confined spacecraft spaces remains difficult because conventional time-difference-of-arrival, radio-frequency, acoustic, and optical methods often require strict synchronization, large arrays, suitable propagation media, line-of-sight conditions, or explicit propagation models. This study develops a spacecraft-oriented, ground-validated electrostatic-induction sensor-array framework for three-dimensional localization of transient discharge events. Different from conventional received-signal-strength-based Apollonius localization, the proposed approach uses relative transient electrostatic-induction response features, including root-mean-square value, envelope energy, and integrated energy, and calibrates their feature ratios into distance-ratio constraints using a Power + offset mapping. These calibrated constraints are interpreted as Apollonius-sphere constraints, and the source coordinate is estimated by nonlinear residual minimization without relying on high-precision arrival-time picking. The method is evaluated on a controlled one-cubic-meter confined-space laboratory platform with known sensor geometry. Ten independent test positions show centimeter-level localization accuracy in the present calibration domain. The envelope-energy representation performs best, with a mean three-dimensional error of 7.01 cm, a median error of 6.94 cm, and a maximum error of 10.05 cm. These results demonstrate the feasibility of the sensing–calibration–inversion chain as a preliminary ground-based proof of concept. The reported accuracy should not be interpreted as direct on-orbit performance because the present experiment does not include vacuum, plasma, thermal gradients, spacecraft materials, metallic enclosures, or electronics integrated for spacecraft onboard operation; further environment-specific calibration and validation are required before practical spacecraft deployment. Full article

Optical emission spectroscopy was used to investigate CN-related emission in atmospheric-pressure microsecond-pulsed spark discharges of pure N₂, pure CO₂, and CO₂/N₂ mixtures. This study examines how gas composition and pulse repetition frequency affect the relative evolution of C-, N-, O-, and CN-containing emitting species in plasma-assisted CO₂/N₂ carbon–nitrogen coupling. Pure N₂ discharge was dominated by N₂ second positive bands and N atomic lines, whereas pure CO₂ discharge mainly showed C- and O-related emissions. In mixed gases, the CN violet band was observed together with C, N, and O emissions, indicating the coexistence of carbon- and nitrogen-containing reactive species. CN band fitting and atomic-line analysis indicated a non-equilibrium plasma state, with T_vib and Trot of approximately 7850 and 2150 K, an excitation temperature of 0.8–1.0 eV, and an electron density on the order of 10¹⁵ cm⁻³. An emission-based CN characterization index was introduced to compare the relative variation in CN-related emission. Changes in gas composition and pulse frequency modified the relative CN-, N-, and O-related emission responses, consistent with competition between C–N coupling and oxygen-related pathways. The index is a semi-quantitative spectral descriptor, not a direct measure of CN concentration or formation efficiency. Full article

Huntington’s disease (HD) has traditionally been conceptualized as a neuron-centric disorder primarily attributed to cell-autonomous toxicity of mutant huntingtin (mHTT) in striatal medium spiny neurons. However, this framework inadequately explains the prolonged presymptomatic phase, selective network vulnerability, early non-motor manifestations, and limited success of neuron-targeted therapeutic interventions. Accumulating evidence from molecular biology, transcriptomics, neuroimaging, and preclinical therapeutics supports a reframing of HD as a disorder of neuroglial systems dysfunction. We synthesize data demonstrating that astrocytes, microglia, and oligodendrocyte lineage cells are not passive bystanders but play direct and interactive roles in HD pathogenesis through defined molecular mechanisms. Expression of mHTT in glial populations impairs synaptic homeostasis, metabolic coupling, immune resolution, and myelin integrity, generating self-amplifying pathological feedback loops that destabilize neural circuits long before overt neuronal death. Critically, we evaluate glial replacement therapy as a potential disease-modifying strategy. Preclinical studies demonstrate that transplantation of healthy human glial progenitor cells substantially ameliorates motor, cognitive, and neuropathological deficits in multiple HD models through oligodendroglial remyelination and lactate-mediated metabolic support, despite persistent neuronal mHTT expression. Effective HD therapy will likely require strategies that jointly target the genetic cause and the dysfunctional neuroglial microenvironment. By integrating systems neuroscience with glial biology and translational strategy, this review defines a neuroglial framework for HD that opens a plausible path toward meaningful disease modification and positions HD as a model disorder for glial-centric interventions in neurodegeneration. Full article

The structural dynamic characteristics of super high-rise buildings are key to understanding how they respond to wind-induced vibration. Currently, one widely adopted method involves using vibration sensors to capture structural vibration responses through on-site measurements, followed by identifying structural dynamic characteristics using output-only methods. However, when measuring and analyzing the dynamic characteristics of super high-rise buildings, the modal directions associated with the vibration modes of the structure are often ignored, which can lead to identification errors. This is particularly true for super high-rise buildings with irregular cross-sections, for which research into the impact of actual structural vibration modes is notably lacking. Therefore, this study uses a normal mode decomposition method to examine the determination of structural vibration mode directions in detail. This method identifies the angular deviation between the viewing and normal coordinates by analysing the spectral energy distribution. It then decomposes the measured signal in the viewing coordinate system based on this deflection angle. This achieves decoupling of modes that are coupled in both directions. Specifically, the study analyses the phenomenon of modal aliasing in structural modal parameters from both time-domain and frequency-domain perspectives based on the measured acceleration response signals of a super high-rise building with a non-circular cross-section during Super Typhoon Saola, and employs the structural modal orthogonal decomposition method to determine the modal directions. The fundamental sway modes of the structure exhibit aliasing between the two adjacent modes at 0.1748 Hz and 0.1825 Hz due to a 46° angle of deviation between the viewing and normal coordinates. Based on the clarification of modal directions, the study further refines the identification of structural modal parameters. Following decoupling, the dispersion of the damping ratio and frequency identification results in the normal coordinate system decreased significantly (concentrating at 1.0–2.0% and 0.175–0.185 Hz, respectively). The damping ratio increased by 1.0% with increasing amplitude, while the frequency decreased by 0.005 Hz with increasing amplitude. The research findings help to improve the accuracy with which the dynamic characteristics of super high-rise buildings can be identified, thereby enabling the structure’s wind-induced response to be assessed more rationally. Full article

Organic solar cells (OSCs) are a potential photovoltaic technology because they can be manufactured in scalable systems, are lightweight, and have mechanical flexibility. Power conversion efficiencies close to 20% have been achieved in recent years due to the quick development of donor–acceptor material systems. Better control over nanoscale shape and the creation of non-fullerene acceptors are major factors driving this advancement. Nevertheless, there are still complicated connections between morphology, interfacial energetics, and molecular structure. It is yet unclear how these elements interact to affect charge creation and transport. In this review, donor–acceptor interactions in organic solar cells are examined from a fundamental chemical and physical perspective. From conventional fullerene derivatives to contemporary non-fullerene acceptors, we first look at the development of acceptor materials. We demonstrate how molecular engineering has enhanced device efficiency, energy level adjustment, and light absorption. We then examine the energetic alignment at donor–acceptor interfaces, paying particular attention to charge-transfer state creation, border orbital offsets, and the factors influencing voltage losses. We also investigate how intermolecular interactions, including hydrogen bonding, π-π stacking, and noncovalent interactions involving heteroatoms, control electrical coupling and nanoscale shape in bulk heterojunction active layers. We also go over device engineering techniques including processor control, interface engineering, and bulk heterojunction architecture optimization. These tactics demonstrate how improved solar performance might result from molecular design. Lastly, we highlight new possibilities for next-generation OSCs, such as scalable production techniques, adaptive molecular design, and morphological stabilization. This work provides a strong framework for comprehending donor–acceptor interactions and for directing the careful design of high-performance organic photovoltaic systems by combining knowledge from molecular chemistry, morphological control, and device engineering. Full article

A central premise of climate adaptation is that better information and smarter decisions can keep escalating hazards within manageable bounds. We test this premise for one of the most information-sensitive decisions in disaster management—ordering a hurricane evacuation—and find that it has limits. Taking Hurricane Irma (2017), the storm behind Florida’s largest evacuation (6.5 million people, 4 million vehicles), as a reference event, we add Coupled Model Intercomparison Project Phase 6 (CMIP6) perturbations to the historical storm and use the Pangu-Weather artificial intelligence (AI) forecasting system to generate 20,000 ensemble members for present-day and future climates (Shared Socioeconomic Pathway (SSP) 2-4.5 and SSP5-8.5; 2050s and 2080s). As the climate warms, storm intensity rises by 15–20% and forecast uncertainty roughly doubles. A reinforcement learning (RL) framework that optimizes evacuation orders under these conditions then exposes a paradox: although RL’s advantage over fixed policies grows from 7% today to 17% under the 2080s SSP5-8.5, absolute evacuation performance still deteriorates by 44% despite optimization. The optimized future climate outcome (objective: 0.239) is in fact worse than that of suboptimal fixed policies today (0.178)—better decisions cannot compensate for a decision environment that has itself degraded. This is direct, scenario-specific evidence that optimization-based adaptation has a ceiling, with consequences for the long-term sustainability of hazard-exposed coastal regions: keeping such communities safe and livable will require coupling evacuation optimization with structural risk reduction, equitable access to decision-support technology, and aggressive greenhouse gas mitigation that holds future risk within adaptable—and therefore sustainable—bounds. The framework supplies quantitative support for sustainable disaster risk reduction and resilient infrastructure planning aligned with global sustainability goals. Full article

With the strategic advancement of energy structure transformation and the implementation of carbon peaking and carbon neutrality goals, the Integrated Energy System (IES) has become a core research direction owing to its superior performance in multi-energy complementation, operational efficiency, and low-carbon emission characteristics. Nevertheless, existing studies reveal that the optimal operation of IES still faces significant challenges, including the high complexity of multi-energy coupling, supply–demand imbalance caused by renewable energy penetration, and insufficient exploitation of demand-side flexibility. As a core measure of demand-side management, demand response (DR) provides an effective approach to motivate users to adjust power load via price incentives or direct load control. DR can effectively smooth load profiles, improve resource utilization, and boost the consumption level of renewable energy. To meet the operational demands of modern IES, this paper establishes a security-constrained economic dispatch model embedded with multi-level demand response mechanisms. The proposed framework is divided into four key modules: First, a price-based demand response strategy is developed to dynamically guide users in regulating multi-energy consumption behaviors. Second, electric vehicles (EVs) are considered flexible demand-side resources with unique response characteristics. An aggregated EV charging–discharging model is established to suppress power fluctuations and support high proportions of renewable energy integration. Third, to precisely calculate the overall operating cost of IES, a combined economic evaluation index integrating time-of-use tariff and Levelized Cost of Electricity is adopted. It maintains a balance between amortized long-term generation investment and short-term operational expenditure, and coordinates the economic benefits and operational reliability of the whole system. Finally, numerical simulations are performed on a coupled test system comprising an IEEE 33-bus distribution network and a 20-node natural gas network. Simulation results verify that the proposed co-optimization model can effectively reduce total system operating costs and greatly improve the local assumption of fluctuating renewable energy. Full article

To tackle the challenges in oriented object detection, such as discontinuous angle regression, low precision in discrete classification, and high complexity in probabilistic modeling, this paper proposes VODet (Vertex Offset-based Detector), a novel oriented object detection method. VODet models the geometric structure of rotated objects through a coupled vertex offset mechanism, inherently avoiding boundary discontinuity. Specifically, it first regresses the horizontal enclosing rectangle of a target from horizontal anchors, then predicts only two offset ratios to reconstruct the four vertices of the rotated bounding box. For near-horizontal critical cases, a lightweight direction classifier adaptively distinguishes horizontal boxes from rotated ones, reducing regression complexity and improving robustness. To further enhance feature representation, we design a hierarchical special attention model (HSAM) based on Swin Transformer blocks, which effectively captures both local details and global contextual dependencies. Extensive experiments on DOTA, HRSC2016, and UCAS-AOD demonstrate that VODet achieves competitive detection accuracy (80.44% mAP on DOTA, 90.42% AP on HRSC2016, and 90.11% mAP on UCAS-AOD), with particular advantages for objects of large aspect ratios and dense arrangements. Moreover, VODet achieves the highest inference speed (26.7 FPS) and the lowest computational complexity (192.3 GFLOPs) among the compared methods, demonstrating a favorable accuracy-efficiency trade-off. These results confirm the effectiveness and practical value of the proposed method. Full article

Gas-insulated switchgears (GISs) and gas-insulated transmission lines (GILs) are essential for large-capacity power transmission in demanding environments, such as high drops, large spans, and heavy pollution. As the core components providing both electrical insulation and mechanical support, ultra-high voltage (UHV) epoxy-based insulators often suffer from high internal residual stress. This issue, compounded by a lack of reliable detection methods, frequently results in equipment being commissioned with hidden defects. To address this, this review first examines the formation mechanisms of curing deformation and residual stress in oversized insulators based on cure kinetics and thermo-chemical coupling models. Subsequently, it provides a comprehensive summary of current residual stress measurement techniques, comparing the applicability and limitations of embedded sensors, direct mechanical measurements, and indirect non-destructive testing (NDT) methods. Finally, by coupling residual stress with filler sedimentation, the stress distribution patterns and mechanical reliability of epoxy-based insulators across different life-cycle stages are analyzed. These insights offer valuable theoretical references for the structural design, process optimization, and performance evaluation of oversized epoxy-based insulators, ultimately contributing to the intrinsic safety of UHV power equipment. Full article

Energy Strategy 2050 promotes photovoltaic (PV) deployment to reduce fossil fuel dependence in Switzerland. However, limited available land constrains conventional solar farms, motivating the deployment of linear PV (LPV) systems along transport corridors such as highways or railways. This paper contributes to a systematic design and modeling methodology for an LPV power plant interconnected through a medium-voltage direct current (MVDC) collection network. The main methodological contribution is the development of a modified iterative modified nodal analysis (MNA) framework tailored to LPV–MVDC systems. In long-distance feeders with high line impedance, nonlinear voltage–current coupling becomes significant. These nonlinearities cannot be accurately captured by conventional MNA assuming fixed current injections. The proposed iterative approach can more accurately capture these effects compared to conventional MNA. A case study of a 5 km railway-based LPV system is investigated to present the design and modeling methodology, including layout design, network modeling, and cable sizing. The LPV power plant reaches a peak power of 3.4 MW and requires 40 DC/DC converter stations rated at 100 kW each. Cable analysis shows that 6 mm² copper conductors satisfy voltage drop limits at a string level, while 95 mm² conductors maintain MVDC voltage variations within 3%. These results highlight technical feasibility of MVDC-based integration for efficient long-distance renewable energy distribution. Full article