Search Results (17,655)

Carbon storage of terrestrial ecosystems is highly susceptible to land use/cover change (LUCC). In order to optimize land use patterns and advance the dual carbon goals (carbon peaking and carbon neutrality), it is imperative to clarify the role of LUCC in controlling regional terrestrial carbon storage. This study utilized a land use dataset spanning from 1990 to 2020 and incorporated 12 pivotal driving factors. Based on these data and factors, this study constructs four distinct future development scenarios: natural development scenario (ND), cropland protection scenario (CP), ecological protection scenario (EP), and urban development scenario (UD). By integrating the Integrated Valuation of Ecosystem Services and Trade-offs model (InVEST) with the Patch-Generating Land Use Simulation model (PLUS), this study simulated the dynamic changes in land use types and the spatiotemporal evolution of carbon storage in the Qinba Mountains (QBMs). The results revealed that between 1990 and 2020, built-up area and water area experienced substantial expansion with growth rates of 67.89% and 20.39%, respectively. In addition, cropland decreased by 3.09% and grassland decreased by 2.49%. Notably, cropland exhibited the most pronounced conversion intensity among all land use types during this period. Correspondingly, the total terrestrial carbon storage in the study area declined slightly from 7471.08 × 10⁶ t in 1990 to 7437.25 × 10⁶ t in 2020. Forestland dominated the regional carbon pool, accounting for an average of 47.67% of the total carbon storage over the three decades. Further analysis identified natural factors as the primary drivers of LUCC and associated carbon storage changes, with DEM exerting the greatest influence, followed by mean annual temperature and mean annual precipitation. Projection analyses for 2030 reveal divergent carbon storage outcomes across different land use scenarios relative to the 2020 baseline. Under the natural development (ND) and urban development (UD) scenarios, total carbon stocks are projected to decline by 37.63 × 10⁶ t and 19.99 × 10⁶ t, respectively. Conversely, implementation of conservation-oriented strategies yields substantial increases, with the cropland protection (CP) and ecological protection (EP) scenarios enhancing carbon storage by 16.87 × 10⁶ t and 13.07 × 10⁶ t, respectively. These findings underscore the critical role of protection-focused land use policies in strengthening ecosystem carbon sequestration capacity. The study provides a scientific foundation for formulating targeted forestry management policies and enhancing the terrestrial ecosystems’ capacity to act as carbon sinks in mountainous areas. Full article

The global shift toward renewable energy systems raises major challenges related to the variability of solar and wind power and their poor alignment with electricity demand. This paper addresses energy adequacy, defined as the ability of an energy system to reliably meet demand by balancing generation, storage, transmission, and reserves for unforeseen events. Within this framework, energy storage systems are identified as strategic components, requiring a diversified and multi-scale set of solutions-from territorial to building scale-to respond to infrastructural constraints and user behaviour. The study adopts a multi-scalar and interdisciplinary methodology combining deductive and inductive approaches. The deductive analysis examines global, European, and Italian electricity systems, highlighting issues such as overcapacity and grid instability caused by the uncoordinated development of renewable generation and network infrastructures. The inductive approach focuses on existing storage technologies, with particular attention to two types of thermal energy storage selected for their simplicity, scalability, and replicability. Hydropower reservoirs are also considered due to their multifunctional role in energy balancing. Two case studies developed by the research group—a public building energy retrofit in Milan and a modular off-grid housing prototype—demonstrate how integrated storage solutions can enhance system flexibility. The results emphasize the necessity of a systemic design approach that combines storage technologies, adaptable energy use, and active user participation to ensure energy adequacy in scenarios with high renewable penetration. Full article

With the rapid evolution of a new power system characterized by a high proportion of renewable energy, system operations have become increasingly random, variable, and uncertain. The system model exhibits features such as high dimensionality, multiple time scales, stochastic behavior, and nonlinearity. This paper proposes a large-scale computational power system model architecture based on cloud-edge-terminal collaboration. By defining functional roles within the cloud-edge-terminal structure and implementing a global model coordination mechanism, the approach enables an organic integration of global awareness, local adaptation, dynamic training, and online optimization for power system problem models. At the cloud level, various object models and the power grid topology are constructed. The edge generates typical problem models for the power system, while the terminal devices produce lightweight models adapted to local grids. This architecture supports collaborative modeling for key business scenarios such as power flow analysis, stability assessment, and reactive power optimization. The study focuses on the training methods of distilled parameters within the terminal models to enhance their adaptability for real-world deployment in power systems. Simulation results demonstrate that the cloud-edge-terminal model offers excellent scalability, adaptability, and real-time performance for computations in new power systems, effectively supporting localized, intelligent operations and decision-making within the system. Full article

Paraburkholderia fungorum (P. fungorum) is an environmental bacterium with biotechnological applications, yet clinical isolations raise concerns about opportunistic infection risk. Genetically related pathogens exhibit substantial antibiotic resistance, motivating the investigation of alternative control strategies. This paper investigates P. fungorum photoinactivation across ultraviolet (222 nm, 254 nm, 313 nm, and 365 nm) and visible (400 nm and 464 nm) wavelengths including ROS (reactive oxygen species) quantification via DCFH-DA fluorescence assay. A two-way ANOVA analysis demonstrated that the wavelength is the dominant determinant of photoinactivation efficacy (F = 100.4, p < 0.001) with ROS generation as a more powerful predictor of inactivation than fluence dose alone (F = 60.6, p < 0.001) at 365 nm, 400 nm, and 464 nm. Ultraviolet irradiation at 254 nm achieved the highest efficiency (5.4 log reduction at 24 mJ/cm²), while 365 nm irradiation demonstrated a high efficacy of 5.2 log reduction at 122 J/cm² with extraordinary ROS production (12,642-fold fluorescence increase). Conversely, inactivation efficiency declined at 400 nm (4.8 log reduction at 378 J/cm² with 122-fold ROS increase) and 464 nm (3.4 log reduction at 3017 J/cm² with lesser ROS detection at 27-fold increase). Wavelength-dependent ROS production correlated directly with bacterial inactivation efficacy, explaining the approximately 500-fold ROS differential between 365 nm and 464 nm. The demonstrated photosensitivity of P. fungorum across multiple wavelengths, with the statistical validation of wavelength-dependent mechanisms, provides a foundation for developing practical, mechanism-based phototherapy protocols tailored to specific clinical and environmental decontamination scenarios. Full article

This study proposes an explainable machine learning framework for estimating the total project cost (TPC) of AI training-data construction, where cost information is difficult to structure due to heterogeneous workflows and quality requirements. Using 386 public AI training-data projects conducted between 2020 and 2022, we derive 24 numerical predictors from standardized final reports and construct three input tracks: a baseline feature set, a principal component analysis (PCA)-enhanced set, and a factor analysis (FA)–enhanced set capturing latent cost structures. Four regression models (Ridge, Random Forest, XGBoost, and LightGBM) are evaluated using nested cross-validation. XGBoost achieves the best overall performance across all three tracks (Baseline, PCA-enhanced, and FA-enhanced). Among them, PCA-enhanced XGBoost attains the highest predictive accuracy (R² = 0.868; RMSE = 1084.9; MAE = 746.9; MAPE = 0.358; pooled out-of-fold), while Baseline XGBoost yields the lowest MAE (731.4; R² = 0.863). To support transparent decision-making, Shapley Additive exPlanations (SHAP)-based attribution and scenario-based sensitivity analyses are conducted. Results show that project scale and process-level unit costs are dominant cost-drivers, while cloud usage, expert participation, and de-identification requirements exhibit secondary effects. The proposed framework provides an interpretable, data-driven approach to cost information management and decision support for data-intensive AI projects. Full article

The large-scale integration of renewable energy sources and power-electronic-interfaced devices has significantly weakened transient support capability and disturbance tolerance, posing new challenges to the secure and stable operation of modern power systems. Conventional stability analysis methods suffer from high computational burden, long execution time, and limited adaptability to diverse operating scenarios. The rapid development of artificial intelligence (AI) provides effective technical support for fast and accurate assessment of power-system security and stability. This paper presents a comprehensive review of AI-based methods and the interpretability for transient stability assessment (TSA) in modern power systems. First, an intelligent TSA framework is introduced, consisting of three key stages: sample construction and enhancement, intelligent algorithms and learning mechanisms, and model training and interpretability. Subsequently, existing methods for data augmentation, intelligent algorithms, learning mechanisms, and interpretability analysis are systematically reviewed, and the corresponding application scene, technical superiority and limitations are discussed. Finally, from a knowledge–data fusion perspective, four representative integration paradigms combining mechanism-based models and data-driven approaches are summarized, and the application prospects in power-system stability analysis are discussed. Full article

Mitigating heat stress in high-density historical districts remains a critical challenge in urban renewal due to complex morphological heterogeneity. Existing research often relies on isolated intervention measures, lacking systematic, multi-strategy assessments driven by high-precision spatial data. This study addresses this gap by establishing a quantitative framework that couples thermal infrared remote sensing with Computational Fluid Dynamics (CFD) to optimize microclimate responses in Beijing’s Liulichang Historic District. Remote sensing data were utilized to retrieve high-resolution Land Surface Temperature (LST), providing accurate thermal boundary conditions for micro-scale wind-thermal simulations. A baseline scenario (S0) and seven renewal strategies (S1–S7)—integrating varying configurations of greenery, water bodies, and permeable pavements—were evaluated using pedestrian-level comfort indices. Results reveal that single-factor interventions yield marginal improvements or thermodynamic trade-offs; specifically, adding greenery (S1) in narrow street canyons increased aerodynamic roughness, thereby obstructing ventilation and inducing localized warming. Conversely, composite strategies significantly enhanced microclimatic quality. The “greenery-water-permeable pavement” strategy (S4) achieved optimal synergistic effects, characterized by substantial cooling and spatial homogenization. Regression analysis identified water bodies as the dominant cooling driver, where a 10% increase in water coverage resulted in a temperature reduction of approximately 5.17 °C. Conversely, greenery alone showed no statistically significant cooling contribution (p > 0.05) without the synergistic presence of water or pavement modifications. This research suggests that urban renewal in high-temperature zones (>36 °C) should prioritize composite cooling networks. Furthermore, vegetation layouts near wind corridors must be precisely regulated to prevent ventilation degradation. These findings provide a scientific basis for the climate-adaptive sustainable regeneration of culturally significant, high-density urban blocks. Full article

Generative AI enables rapid visualization of sustainable urban design scenarios, yet the question of whether these outputs encode sustainability as operable spatial logic, rather than merely depicting it as a visual impression, remains underexplored. This study proposes a two-level assessment framework that scores the same sustainability dimensions at both the visual-representation level and the spatial-logic level, treating the systematic decoupling between the two as a form of visual greenwashing: system-induced representational distortion rather than deliberate misrepresentation. Using AI-workflow reports from two site-based urban design studios (47 students, 12 teams, 36 coded scenes), the framework integrates rubric-based scoring with qualitative process tracing of breakdown–repair logs. Results show that image-level scores consistently outperform logic-level scores across all five dimensions, with the gap most severe in mobility hierarchy and walkability and smallest in green/blue infrastructure. Case analysis reveals that breakdowns arise from failures in program encoding, urban-scale coherence, functional-boundary demarcation, and relational-condition matching, and that students deploy multi-stage repair pipelines, including prompt restructuring, tool switching, reference injection, and external-source compositing, to re-inject collapsed spatial logic. These findings reframe AI-assisted urban design as repair-centered workmanship rather than automated production. The study proposes three guardrails to prevent visual sustainability from substituting for spatial-logic sustainability: image–logic paired submission, design audit trail formalization, and gap-based red-flag review. Full article

Green ammonia is increasingly recognised as a sustainability enabler for decarbonising fertiliser production, energy storage, and maritime transport, but offshore wind-to-ammonia pathways remain subject to significant economic and operational uncertainty. This study evaluated the techno-economic and sustainability performance of integrating power-to-ammonia (PtA) with an operating offshore wind farm in Denmark under three supply-chain scenarios (SCs): SC1, a fully offshore PtA with vessel-based ammonia transport; SC2, a fully offshore PtA with pipeline export; and SC3, a hybrid offshore–onshore configuration. An hourly dispatch framework allocated wind electricity between grid export and ammonia production by comparing incremental operating margins, while accounting for minimum-load, ramping, storage, and logistics constraints. Hourly wind generation and DK1 electricity-price data for 2020–2025 are used to construct a deterministic base case and a 30-year block-bootstrap Monte Carlo analysis. Sensitivity analysis is performed by varying electrolyser rated power over 10–200 MW and ammonia selling price over 1400–3200 €/tNH3, with additional breakeven-price estimation and flexibility cases based on reduced minimum-load requirements and faster ramping. A screening-level climate indicator was additionally reported by estimating potential CO₂ emissions avoided if delivered green ammonia displaces conventional natural-gas-based ammonia. Results indicated that SC3 is the most favourable configuration under the adopted assumptions, while overall project viability remained highly sensitive to PtA sizing, ammonia market value, operational flexibility, and the assumed infrastructure cost structure. Full article

Gas-insulated switchgear (GIS) is a critical component of modern power systems. During operation, internal defects increase the probability of partial discharge and flashover within the insulation system, thereby constituting a major cause of equipment failure. Considering the diversity of existing GIS insulation condition monitoring methods, it is of great significance to systematically review and evaluate current monitoring technologies. This paper summarizes the detection principles and recent advances in electrical, acoustic, optical, modal analysis, and gas component analysis techniques. Through a comparative analysis of the advantages, limitations, and application scenarios of different methods, in conjunction with failure cases induced by typical GIS insulation defects, the primary bottlenecks faced by various condition monitoring technologies are discussed. Furthermore, future research directions for GIS insulation condition detection are outlined. This study provides a reference for the development of GIS insulation monitoring technologies and the formulation of efficient operation and maintenance strategies. Full article

Renewable Energy Communities (RECs) represent a key instrument for enabling decentralized energy systems and enhancing local renewable energy utilization. Preliminary assessment of REC performance relies on simulation tools that differ in computational complexity, assumptions, and input data. Despite the growing literature, a systematic comparison of tools applied to identical community configurations is still missing. This study provides a systematic cross-comparison of four tools representing different modelling paradigms: a VBA-based prefeasibility model (MERCm), a MATLAB-based detailed framework (UNIPGm), a national open-access simulator (RECON), and a commercial platform (COMMm). The tools were applied to six residential configurations in three Italian provinces representing different solar irradiation levels. Scenarios are defined to ensure high energy sharing potential, considering a ratio of shared energy to energy fed into the grid above 60%. Key performance indicators, including physical self-consumption and shared energy, are analyzed. Results show broadly consistent trends across tools, although these findings refer to PV-only, residential RECs and may differ in more complex community configurations, with coefficients of variation below 15% for most relevant indicators, particularly shared energy, while confirming that differences in input data and modelling assumptions can still influence outcomes. These findings support the reliability of simplified simulation tools for preliminary REC feasibility assessments and provide guidance for policymakers and technical operators. Full article

Urban short-haul cold-chain distribution operates under strict service constraints while facing increasing pressure to reduce carbon emissions under the dual-carbon goals. Existing emission-aware routing studies often treat carbon emissions as external constraints or ex post evaluation indicators, limiting their influence on operational decision making. This study addresses this gap by developing a cold-chain distribution network optimization model that integrates internal carbon pricing (ICP), enabling carbon emissions to be internalized as economic costs within routing and scheduling decisions. Using the student milk cold-chain distribution system serving 54 primary and secondary schools in Fuzhou as an empirical case, the model incorporates multiple cost components, including energy consumption, warehouse operation, carbon emissions, and low-load penalties, while embedding operational constraints such as vehicle capacity, delivery time windows, and minimum economic loading requirements. An improved genetic algorithm is applied to solve the model. Scenario analyses are conducted across carbon price variation and demand fluctuation. Results show that when the internal carbon price increases from 97.49 RMB/t to 2000 RMB/t, the total distribution cost rises from 3531.2 RMB to 4082.842 RMB, indicating that carbon costs become an increasingly important factor in operational decision making. The distribution network exhibits a core-route-dominated structure, with key routes remaining stable across carbon price scenarios, suggesting that the influence of ICP is primarily reflected through cost internalization rather than route substitution. Demand analysis further shows that a 10% demand reduction reduces costs through route consolidation, while a 20% reduction weakens load efficiency and reduces vehicle utilization without triggering low-load penalty costs. These findings demonstrate that integrating ICP into routing optimization provides an effective pathway for aligning operational decisions with low-carbon transition objectives in rigid-demand cold-chain distribution systems. Full article

Phosphate mining companies in Morocco pose many environmental and occupational safety risks, especially through the release of airborne particulates, gas pollutants, and heavy metals. While there is increased implementation of monitoring systems within industrial mining contexts, current methodologies are still predominantly founded on rule-based systems or classical statistics that presume linearity in relationships between an arbitrary set of environmental parameters and the likelihood of an incident. Conversely, mining operations are characterized by intricately dynamic nonlinear combinations of numerous environmental and operational variables. As a result, a potential research opportunity exists for the application of sophisticated machine learning techniques that provide the ability to detect various levels of operational risk within phosphate mining scenarios. This study has three objectives. First, to examine the mining environmental and operational data from the phosphate mining sites to determine the mining operational conditions that present the highest risk. Second, to create a machine learning classification model which utilizes a Feedforward Neural Network (FNN) to identify operational states that are prone to incidents based on multivariate sensor data. Third, to assess the validity and reliability of the model using machine learning validity and reliability evaluation techniques along with statistical validation methods. In this study, an artificial intelligence-based approach for AI-based safety monitoring was proposed by using a Feedforward Neural Network (FNN) on a detailed data set of 1536 hourly measurements, directly recorded onsite at OCP plants in Benguerir and Khouribga. Environmental and industrial parameters (dust concentration, gas emissions, temperature, and toxic metal content) were measured using industrial-grade sensors certified for such a type of application. By means of training the proposed FNN model with adaptive gradient descent and dropout regularization with early stopping, a test mean squared error of 0.057 and over 85% accuracy on incident detection were obtained. Gradient tracking and m-adaptive validation proved the stability and convergence of the model. Emissions and dust were identified as the main risk classifiers in a variable importance analysis. The findings demonstrate that the mining sector may move from reactive to proactive safety management and validate the incorporation of AI into a real-time monitoring infrastructure inside the OCP ecosystem. Practical concerns of industrial data gathering, model interpretability, and the moral application of AI in high-risk settings are also addressed by the study. Full article

Time travel in science fiction, a subgenre distinct yet often overlapping with alternative history, often explores historical contingency through counterfactual scenarios to produce alternative histories. Yet some works deliberately negate this potential, presenting time travelers who refrain from altering the past despite possessing the apparent ability to do so. This essay examines this underexplored narrative mode through a comparative analysis of Octavia E. Butler’s Kindred and Miyabe Miyuki’s The Incident at the Gamō Residence. Framing the narrative device as a non-interventionist history, it explores how both novels deploy time travel not to revise history but to confront the ethical, emotional, and cultural implications of engaging with historically traumatic events that remain causally intact. Drawing on science fiction theory and historiographical debates, the essay argues that these texts redirect the function of time travel toward ethical reflection, embodied experience, and the formation of national identity. While Kindred presents history as an ongoing system of racialized violence that resists reconciliation, The Incident at the Gamō Residence frames historical violence through affective memory and postwar nostalgia, facilitating symbolic closure. Together, these novels demonstrate how time travel can serve as a critical apparatus for negotiating national trauma without recourse to historical revision. Full article

This study investigates the core optimization of a Prismatic Solid Molten-Salt Reactor (PSMSR) to meet key objectives of compactness, high power density, and extended operational life. With graphite irradiation resistance being a paramount concern in high-flux environments, the analysis focuses on the influence of core height-to-diameter ratio, active zone size, and reflector thickness on the graphite displacement per atom (DPA) distribution and burnup performance. The results indicate an optimal active core configuration characterized by a 1:1 height-to-diameter ratio, a 175 cm active zone radius, and a 55 cm reflector. Building on these findings, reactivity-control strategies were refined. An evaluation of burnable-poison addition against fuel-loading optimization revealed that the latter, by adjusting the TRISO (TRi-structural ISOtropic) packing factor and control-rod dimensions, can meet the safety shutdown margin requirements and substantially improve the fuel utilization efficiency, ultimately achieving a burnup depth of 50.3 MWd/kgU and a 10-year operation lifetime without refueling at a 500 MWt power level. This research provides an effective technical solution for the modular deployment of solid-state molten-salt reactors in remote areas and in special application scenarios. This research offers a viable technical pathway for implementing solid-fueled molten-salt reactors in remote and specialized scenarios, enabling their modular deployment. Full article