Search Results (6,571)

Critical metals such as gallium and germanium are strategic mineral resources widely used in advanced technology, including semiconductors and solar cells. These metals are recovered as by-products from the processing of Zn–Pb and bauxite ores. In China, the Sichuan–Yunnan–Guizhou (SYG) region is abundant in Zn–Pb and bauxite ore deposits, such as the Huize Zn–Pb–Ge deposit and the Wuchuan–Zheng’an–Daozhen (WZD) area Al–Ga deposit. Although previous studies have proposed models to explain the enrichment mechanisms of critical metals in this area, the metal sources of these deposits remain controversial. In this study, samples were collected from the Paleoproterozoic Kunyang Group to the Permian Emeishan basalts, and the metal sources of these deposits were traced by comparing the Pb isotopic ratios and Zn/Cd ratios of potential source rocks and deposits. The findings indicate: (1) The Pb isotopic compositions of most samples are relatively homogeneous, but certain differences exist among strata from different geological periods. (2) The metal sources of the Yunnan and Guizhou bauxite may both have been controlled by the underlying carbonate rocks, but the specific source horizons differ significantly between the two regions. (3) Based on the Pb isotopic compositions of regional strata and Zn–Pb deposits, it appears that the regional basement and sedimentary cover likely contributed significantly to the ore-forming metals, whereas the Emeishan basalts may have played a relatively minor role. However, due to the complex lithology and substantial thickness of the basement and cover strata in the SYG region, there may be issues of sampling inadequacy. Nonetheless, this study provides important foundational data and insights for tracing the metal sources of deposits in this region using Pb isotopes and Zn/Cd ratios. Full article

This paper investigates the optimal harvesting of a nonlinear, size-structured population governed by a first-order transport equation with nonlocal environmental crowding feedback and exogenous inflow. First, we establish finite-horizon well-posedness for the controlled state system in an $L^1$ framework, proving the existence, uniqueness, positivity, and continuous dependence of weak solutions. Second, we show that the infinite-dimensional stationary problem reduces exactly to a scalar nonlinear closure equation, yielding existence and conditional uniqueness results for stationary states. Within this equilibrium framework, we distinguish the persistence of the forced system from intrinsic demographic self-replacement and introduce size-continuous per-recruit and spawning-potential diagnostics. Finally, we formulate a partial differential equation (PDE)-constrained optimal harvesting problem. Under a compactness assumption on the control-to-state map, we establish the existence of optimal controls. We then formally derive a Pontryagin-type first-order optimality system for the harvesting problem. The variation of the nonlocal environmental feedback produces a coupled integral source term in the adjoint equation. The associated pointwise maximization condition yields a bang–bang harvesting structure, while a monotone size-threshold policy is shown to require an additional single-crossing assumption on the switching function. These hypotheses are illustrated using a fisheries model with density-dependent von Bertalanffy growth. Full article

Ground deformation monitoring is critical for safety and environmental management in modern mining. Active mining sites are highly exposed to terrain instabilities and subsidence, risking infrastructure integrity, disrupting operations, and posing hazards to communities. In this context, Differential Synthetic Aperture Radar Interferometry (DInSAR) techniques provide an effective and non-invasive tool capable of detecting millimetric surface displacements. This study implements the Small Baseline Subset (SBAS) technique through an open-source workflow based on the Python package hyp3_sbas, enabling semi-automated and reproducible interferometric processing by combining HyP3 with MintPy. The workflow is applied to the Björkdal gold mine (Sweden), a pilot site of the Horizon Europe XTRACT project focused on enhancing resilience in critical raw material supply chains. Integrating Sentinel-1 viewing geometries resolves the true vertical deformation field, yielding an overall mean velocity of −3.99 mm/year across the mining complex, with significant displacement rates concentrated below the 25th percentile (Q1) at −11.07 mm/year. Sector-specific analysis reveals localised subsidence accelerating over underground footprints and tailings storage facilities (mean velocities of −6.56 and −3.98 mm/year; Q1 thresholds near −13.00 mm/year), contrasting with the geomechanical stability observed at the open-pit area (mean: −0.45 mm/year). The proposed open-source framework shows strong potential for operational satellite-based monitoring, supporting predictive maintenance and early-warning strategies for risk management in mining environments while simplifying and standardising the interferometric processing workflow. Full article

Under the trend of intelligent transportation and connected vehicles, real-time control plays a vital role in improving bus system efficiency. Existing bus control strategies typically treat buses as homogeneous points and achieve system equilibrium by maintaining consistent headways. However, this simplification overlooks differences in dynamic responses and the evolution of powertrain lifespan arising from vehicle heterogeneity. It converts the sparse constraint problem, which is intended to ensure timely arrival, into a hard constraint on the vehicle trajectory over the entire time horizon, thereby excessively restricting individual optimal evolutionary paths and causing the optimization process to become trapped in a local optimum. To this end, this paper proposes SMATD3, a multi-agent cooperative control algorithm that accounts for vehicle heterogeneity. By adopting a centralized training and decentralized execution paradigm and avoiding the specification of a fixed inter-vehicle spacing target, the algorithm enables each vehicle to adaptively adjust its speed control strategy according to its own dynamic characteristics, thereby achieving the coordinated optimization of system equilibrium and individual objectives. The simulation results indicate that the proposed method can effectively suppress bus tailgating and achieve the coordinated multi-objective optimization of operational stability, passenger travel efficiency, energy consumption, and battery health. From a sustainability perspective, improved headway regularity and service reliability can enhance public transit attractiveness and support mode shift, while smoother energy use and reduced battery degradation lower lifecycle impacts. Full article

With the global energy transition, Integrated Energy Systems (IESs) improve efficiency by coordinating multiple energy sources, including electricity, cooling, and heating. Accurate load forecasting is essential for reliable energy system operation. However, multi-energy loads show complex coupling, non-stationarity, and long-term dependencies. These characteristics pose significant challenges to forecasting tasks. Existing methods have improved short-term forecasting accuracy, but still struggle to jointly capture long-term trends and local fluctuations. To address these issues, this paper proposes FTimeDD, a time–frequency collaborative model for multi-energy load forecasting in IESs. It adopts a dual-path decoupling architecture. The time-domain path separates trend and fluctuation components, while the frequency-domain path extracts dominant periodic features. The two paths are then fused to predict electricity, cooling, and heating loads. Experiments on the ASU Integrated Energy System dataset show that FTimeDD performs well across different forecasting horizons. Compared with the strongest baseline for each metric and horizon, FTimeDD reduces MAE, RMSE, and MAPE by 3.85%, 2.48%, and 1.91% on average, respectively. The method improves forecasting accuracy under the adopted experimental setting while maintaining a compact model scale and low computational cost. Full article

Ultra-short-term electricity load forecasting is crucial for real-time power system operation, but its accuracy is limited by the nonstationary and multiscale characteristics of load data. To address this issue, this study proposes a multi-scale decomposition–clustering and heterogeneous gated fusion framework. The original load sequence is decomposed by ICEEMDAN and then grouped into high-, mid-, and low-frequency components using K-means clustering. MS-gTCN is used to capture high-frequency fluctuations, adaptive DLinear is used to model low-frequency trends, and a gated fusion mechanism is designed for mid-frequency components. A lightweight error correction network is further introduced to reduce residual prediction errors. Experiments on two real-world datasets show that the proposed method achieves the best performance across 1-, 4-, 8-, and 12-step horizons. For the 12-step task, it reduces MAE by 29.3% on Dataset A and 26.2% on Dataset B compared with the second-best baselines. Compared with ICEEMDAN-LSTM on Dataset A, it reduces MAE by 17.7% and improves R² from 0.9127 to 0.9418. Ablation, sensitivity, significance, and complexity analyses further verify the effectiveness, robustness, and real-time feasibility of the proposed framework. Full article

This paper presents a power-based dynamic programming (DP) method for day-ahead battery scheduling in a grid-connected photovoltaic (PV) microgrid under time-of-use (TOU) tariffs. The proposed formulation optimizes battery power directly, rather than SOC setpoints, so the dispatch is easier to apply in practical inverter control and remains computationally tractable over a 48 h horizon. The model includes battery degradation through a linear wear-cost term based on a 200 USD/kWh replacement cost, while also enforcing SOC and charging/discharging power limits. The case study uses a 250 kWh battery and evaluates two power limits, 0.1C and 0.2C, together with two degradation cases, 200 and 400 USD/kWh. The simulation considers two different operating days to test the controller under unequal renewable and demand conditions. Day 1 has stronger PV generation and lower load demand, whereas Day 2 has lower PV output and higher demand. Under the baseline 0.1C limit, DP reduces the net operating cost to 97.47 USD, compared with 122.95 USD for the TOU-aware rule-based benchmark. When the power limit increases to 0.2C, the net operating cost falls further to 78.35 USD because export revenue rises substantially. When the battery replacement cost doubles from 200 USD/kWh to 400 USD/kWh, the optimizer reduces cycling and the net operating cost increases to 129.21 USD. Overall, the results show that power-based DP provides a practical and transparent framework for balancing tariff arbitrage and battery preservation in grid-connected microgrids. Full article

This paper addresses high electricity costs in long-distance crude oil transmission systems due to limited renewable energy integration. A mixed-integer linear programming (MILP) model is formulated to maximize renewable energy use and minimize purchased electricity costs, considering pump station and pipeline constraints. To solve this problem, a hybrid artificial bee colony algorithm with variable neighborhood search (HABC-VNS) is proposed, incorporating memory guidance, discrete uniform crossover, and three neighborhood structures. The algorithm is compared with standard ABC, binary PSO, and GA. In two experimental setups (four pumps/eight stations, 24 h horizon), HABC-VNS achieves average total costs of 33,000 CNY and 43,000 CNY, respectively, compared to 61,000–368,000 CNY for the other methods. Average green power integration rates reach 61.6% and 59.6%, outperforming all baselines. The proposed approach provides effective scheduling under strong operational constraints. Full article

To address the resource-constrained project scheduling problem (RCPSP), the pulse discrete time (PDT) model is commonly used and then solved by integer programming optimization. This method can obtain the exact optimal solution, but it encounters difficulties with low computational efficiency when the RCPSP has a large size and high complexity. This study hypothesizes that a reasonably reduced scheduling horizon, derived from a feasible heuristic solution, can preserve the optimal solution while significantly reducing the computational burden of the PDT formulation. To test this hypothesis, the research analyzes the scheduling horizon of RCPSP and strictly proves that a reasonably reduced scheduling horizon keeps the same optimal solution. Based on this proof, the serial schedule generation scheme algorithm is adopted to obtain the reduced scheduling horizon, and then an improved PDT model is proposed accordingly. The key novelty lies in: (1) the rigorous mathematical proof of optimality preservation under horizon reduction, and (2) the systematic integration of SSGS-based horizon reduction with explicit elimination of redundant pulse variables. Numerical experiments demonstrate that, for RCPSP instances with 90 and 120 activities, compared with the original PDT model, the improved PDT model contains significantly fewer decision variables and constraints, reducing the number of decision variables to 6.6–9.8% and the number of constraints to 24.7–26.5% of those in the original PDT model, and thus achieves significant improvement in computational efficiency while obtaining the exact optimal solution. Full article

Climate change has become a major macroeconomic challenge with profound implications for the real economy. This study examines the influence of perceived climate-related risks, proxied by news-based indices capturing media attention to global warming, natural disasters, US climate policy, and international climate summits, on US business activity across short- and long-term horizons. The methodological framework first employs principal component analysis to condense multiple explanatory variables into a single composite factor. A Fourier autoregressive distributed lag model is then adopted to estimate the effects of these forward-looking informational proxies over time. The results reveal marked heterogeneity across perceived climate-related risks and temporal horizons. Global warming news intensity constitutes a persistent impediment, exerting stronger and more durable effects on business activity. Natural disaster media coverage generates sharp short-term deterioration, although its influence fades over longer horizons. News-based transition-risk proxies exhibit a mixed pattern. US climate policy media coverage consistently dampens business conditions, whereas international climate summit coverage plays a comparatively modest role. Our findings underscore that a one-size-fits-all strategy is ineffective. Climate risk management should differentiate between persistent and transitory forces, recognizing that perceived risks may operate through expectations, uncertainty, and sentiment rather than realized damages or enacted policies alone. Full article

Truck-to-excavator assignment is a time-critical control problem in open-pit earthmoving systems (mines, quarries, and large cut-and-fill construction sites) where stochastic travel and service times, changing queues, and equipment outages continually alter the best dispatch decision. A deep reinforcement learning (DRL) dispatch policy is developed and trained using a discrete-event simulation (DES) digital twin of the Sungun copper mine haulage system. The dispatch task is formulated as a Markov decision process using state features that represent fleet locations, excavator and dump queues, and short-term congestion conditions. The resulting deep artificial neural network (DANN) policy is tuned via systematic hyperparameter optimisation and evaluated against a priority-based rule-of-thumb dispatch baseline under long-horizon operating tracks. Results show that the final trained policy improves the average production rate per truck cycle by approximately 17% while reducing avoidable waiting and maintaining stable performance over extended operation, with inference fast enough for real-time dispatch use. Model fidelity is supported by close agreement between simulated and observed daily completed-cycle counts. Robustness is assessed through controlled truck load-capacity perturbations, and scalability is examined through fleet-size sensitivity, which reveals diminishing returns as additional trucks are added under a fixed excavation–haulage configuration. Practical deployment considerations and implications for construction earthmoving logistics are discussed. Full article

Diamondback moth (DBM, Plutella xylostella) outbreaks are shaped by delayed meteorological conditions, yet most forecasting models compress weather into a few monthly summaries and provide limited ecological interpretation. We propose MeteoSCOPE, an ontology-aware sparse Perceiver framework for interpretable, multi-horizon retrospective forecasting of DBM abundance from historical pest records and rich meteorological descriptors. Each feature-lag value is encoded as a token carrying feature identity, ecological group, descriptor type, lag position, and seasonal information; in the rich setting, 138 descriptors across 12 months yield 1656 tokens per sample. Sparse cross-attention compresses these tokens into a compact latent representation, while horizon-specific queries produce one- to four-month-ahead forecasts. Attention tensors and a common-plus-residual branch are aggregated into feature-, group-, descriptor-, lag-, horizon-, and residual-level explanations. Using DBM records from Huiyang and Shantou, Guangdong, MeteoSCOPE achieved the strongest overall retrospective performance, with robust gains at Shantou and metric-dependent gains at Huiyang. The explanations identified pest history as the leading attended group at both sites and surfaced site-specific secondary attributions for soil moisture, weather state, wind, soil temperature, and humidity, treated as model evidence rather than causal ecological effects and corroborated by independent occlusion and KernelSHAP analyses. Strict zero-shot cross-site transfer degrades substantially, so prospective field validation and broader multi-site testing remain required before operational deployment. MeteoSCOPE thus provides a transferable methodological framework (not a deployable forecaster) for interpretable analysis of high-dimensional agricultural time series. Full article

Adaptive reuse of post-industrial heritage is often studied through technical performance, formal intervention strategies, or decision-support models. While these approaches clarify important aspects of reuse, they give limited attention to how projects evolve through the combined effects of architectural decisions, governance arrangements, financing mechanisms, policy instruments, social programs, and inherited fabric. This paper examines adaptive reuse as a time-structured project trajectory. It applies a hybrid methodology combining within-case reconstruction and comparative cross-case analysis to seven European projects in Brussels, Essen, Rotterdam, San Sebastián, Florence, Vienna, and Barcelona. The cases are analyzed across six dimensions: Asset & Context, Governance & Finance, Circularity, Social & Cultural, Policy & Design, and Outcomes & Transfer. The comparison shows that adaptive capacity depends on the alignment of governance, project time, and intervention strategy. Governance determines who can revise decisions and under what conditions; adaptation time is produced through funding horizons, approval procedures, institutional continuity, and civic or public stewardship; and strategies of retention, replacement, reversible insertion, and incremental occupation distribute future risk differently across project phases. From this synthesis, the paper extracts ten conditional lessons that frame adaptive reuse as configuration knowledge: transferable insights whose relevance depends on the interaction among governance capacity, temporal sequencing, inherited fabric, financing, policy support, and social objectives. The paper argues that knowledge transfer in AR should be understood as disciplined translation across comparable constraints, not as the replication of models, rankings, or best-practice templates. Full article

Strong differential privacy often collapses utility in transformer-based time-series forecasting because noise is injected directly into high-dimensional gradients (e.g., DP-SGD), severely corrupting the optimization process. We introduce Low-Dimensional Feature-Path Privacy for Transformers (LDPT), which enforces privacy by routing calibrated perturbations through a low-dimensional feature bottleneck ($D=16$) that is independent of the model parameter count. LDPT implements noise via classically simulated quantum channels (Lindblad/depolarizing dynamics) and finite-shot POVM measurements, providing an auditable mapping from privacy budget $\epsilon$ to perturbation magnitude while keeping the transformer gradients clean. Across the ETT datasets and multiple prediction horizons, LDPT substantially preserves forecasting utility under its native local $\epsilon$-QDP guarantee. At a nominal per-pass $\epsilon =0.1$, LDPT limits MSE degradation to under 6%. In contrast, DP-SGD with global $(\epsilon ,\delta )$-DP applied to the identical transformer architecture suffers over 100% MSE degradation. Because these methods operate under different privacy definitions (local $\epsilon$-QDP vs. global $(\epsilon ,\delta )$-DP), this comparison illustrates the impact of noise placement rather than equivalent privacy protection. To isolate the effect of the calibration mechanism, we further evaluate a classical Gaussian mechanism on the same feature-path bottleneck, which requires orders-of-magnitude larger noise and severely degrades utility. Membership inference attacks confirm that LDPT does not amplify membership leakage beyond the non-private baseline. These results demonstrate that decoupling privacy noise from optimization through low-dimensional feature-path placement and tight channel-based calibration is critical for practical privacy-preserving transformer forecasting. Full article

Predictive energy management strategies (PEMSs) have attracted increasing attention in hybrid electric vehicles (HEVs) for improving fuel economy and powertrain efficiency using anticipated driving information. For PEMS, data-driven velocity prediction is widely used to capture complex driving patterns from historical trajectories and future traffic priors, but often lacks kinematic awareness, leading to physical causality violations and long-horizon state drift. To address these issues, this paper proposes a physics-informed PEMS, where a Physics-Informed Spatio-Temporal Network (PI-STN) provides control-oriented velocity information for an MPC-based energy management controller. Specifically, to address pseudo-motion in velocity prediction under standstill conditions, a global zero-speed gating mechanism is introduced; to suppress acceleration/deceleration trends that violate vehicle kinematic causality, a causal penalty is designed; and to mitigate temporal phase misalignment between data-driven predictions and physical motion priors, a Differentiable Kalman Filter (DKF) is incorporated. At each receding horizon step, the PI-STN-predicted velocity sequence is converted into future power demand through longitudinal vehicle dynamics and used by MPC for engine–battery power allocation under SOC and engine transient constraints. Under the same tested conditions, the proposed strategy reduces engine power fluctuation by 15.1% compared with BiLSTM-Transformer, and achieves an equivalent fuel consumption of 323.74 g, outperforming Transformer-KF by 3.12%. Full article