Search Results (5,151)

We present an advanced statistical framework for estimating the relative intensity of astrophysical event distributions (e.g., Gamma-Ray Bursts, GRBs) on the sky tofacilitate population studies and large-scale structure analysis. In contrast to the traditional approach based on the ratio of Kernel Density Estimation (KDE), which is characterized by numerical instability and bandwidth sensitivity, this work applies a logistic regression embedded in a Bayesian framework to directly model selection effects. It reformulates the problem as a logistic regression task within a Generalized Additive Model (GAM) framework, utilizing isotropic Splines on the Sphere (SOS) to map the conditional probability of redshift measurement. The model complexity and smoothness are objectively optimized using Restricted Maximum Likelihood (REML) and the Akaike Information Criterion (AIC), ensuring a data-driven bias-variance trade-off. We benchmark this approach against an Adaptive Kernel Density Estimator (AKDE) using von Mises–Fisher kernels and Abramson’s square root law. The comparative analysis reveals strong statistical evidence in favor of this Preconditioned (Precon) Estimator, yielding a log-likelihood improvement of $\Delta \mathcal{L}\approx 74.3$ (Bayes factor $>{10}^{30}$) over the adaptive method. We show that this Precon Estimator acts as a spectral bandwidth extender, effectively decoupling the wideband exposure map from the narrowband selection efficiency. This provides a tool for cosmologists to recover high-frequency structural features—such as the sharp cutoffs—that are mathematically irresolvable by direct density estimators due to the bandwidth limitation inherent in sparse samples. The methodology ensures that reconstructions of the cosmic web are stable against Poisson noise and consistent with observational constraints. Full article

Deep reinforcement learning (DRL) with self-play has emerged as a promising paradigm for solving combinatorial optimization (CO) problems. The recently proposed Gumbel AlphaZero Plan-to-Play (GAZ PTP) framework adopts a competitive training setup between a learning agent and an opponent to tackle classical CO tasks such as the Traveling Salesman Problem (TSP). However, in complex and multi-constrained environments like the Electric Vehicle Routing Problem (EVRP), standard self-play often suffers from opponent mismatch: when the opponent is either too weak or too strong, the resulting learning signal becomes ineffective. To address this challenge, we introduce Two-Stage Self-Play GAZ PTP (TSS GAZ PTP), a novel DRL method designed to maintain adaptive and effective learning pressure throughout the training process. In the first stage, the learning agent, guided by Gumbel Monte Carlo Tree Search (MCTS), competes against a greedy opponent that follows the best historical policy. As training progresses, the framework transitions to a second stage in which both agents employ Gumbel MCTS, thereby establishing a dynamically balanced competitive environment that encourages continuous strategy refinement. The primary objective of this work is to develop a robust self-play mechanism capable of handling the high-dimensional constraints inherent in real-world routing problems. We first validate our approach on the TSP, a benchmark used in the original GAZ PTP study, and then extend it to the multi-constrained EVRP, which incorporates practical limitations including battery capacity, time windows, vehicle load limits, and charging infrastructure availability. The experimental results show that TSS GAZ PTP consistently outperforms existing DRL methods, with particularly notable improvements on large-scale instances. Full article

Deep reinforcement learning (DRL)-based algorithms for solving the Traveling Salesman Problem (TSP) have demonstrated competitive potential compared to traditional heuristic algorithms on small-scale TSP instances. However, as the problem size increases, the NP-hard nature of the TSP leads to exponential growth in the combinatorial search space, state–action space explosion, and sharply increased sample complexity, which together cause significant performance degradation for most existing DRL-based models when directly applied to large-scale instances. This research proposes a two-stage reinforcement learning framework, termed GCRL-TSP (Graph Convolutional Reinforcement Learning for the TSP), which consists of a heatmap generation stage based on a graph convolutional neural network, and a heatmap-assisted Proximal Policy Optimization (PPO) training stage, where the generated heatmaps are used as auxiliary guidance for policy optimization. First, we design a divide-and-conquer heatmap generation strategy: a graph convolutional network infers m-node sub-heatmaps, which are then merged into a global edge-probability heatmap. Second, we integrate the heatmap into PPO by augmenting the state representation and restricting the action space toward high-probability edges, improving training efficiency. On standard instances with 200/500/1000 nodes, GCRL-TSP achieves a Gap% of 4.81/4.36/13.20 (relative to Concorde) with runtimes of 36 s/1.12 min/4.65 min. Experimental results show that GCRL-TSP achieves more than twice the solving speed compared to other TSP solving algorithms, while obtaining solution quality comparable to other algorithms on TSPs ranging from 200 to 1000 nodes. Full article

The three-dimensional borehole-to-surface transient electromagnetic (BSTEM) method plays a critical role in resolving subsurface conductivity structures under complex geological conditions. However, its application is often constrained by the high computational costs associated with large-scale simulations and fine temporal resolution. In this study, a time-parallel forward modeling strategy is employed by integrating the finite volume method (FVM) with the Multigrid Reduction-in-Time (MGRIT) algorithm. Maxwell’s equations are discretized in space using unstructured octree meshes, while the MGRIT algorithm enables parallelism along the time axis through coarse–fine temporal grid hierarchy and multilevel iterative correction. Numerical experiments on synthetic and field-scale models demonstrate that the MGRIT-based solver significantly reduces computational time compared to conventional direct solvers, particularly when a large number of processors are utilized. In a field-scale hematite mine model, the MGRIT-based solver reduces the total runtime by more than 40% while maintaining numerical accuracy. The method exhibits parallel scalability and is especially advantageous in problems involving a large number of time channels, where simultaneous time-step updates offer substantial performance gains. These results confirm the effectiveness and robustness of the proposed approach for large-scale 3D TEM simulations under complex conditions and provide a practical foundation for future applications in high-resolution electromagnetic modeling and imaging. Full article

Global optimization represents a fundamental challenge in computer science and engineering, as it aims to identify high-quality solutions to problems spanning from moderate to extremely high dimensionality. The Differential Evolution (DE) algorithm is a population-based algorithm like Genetic Algorithms (GAs) and uses similar operators such as crossover, mutation and selection. The proposed method introduces a set of methodological enhancements designed to increase both the robustness and the computational efficiency of the classical DE framework. Specifically, an adaptive termination criterion is incorporated, enabling early stopping based on statistical measures of convergence and population stagnation. Furthermore, a population sampling strategy based on k-means clustering is employed to enhance exploration and improve the redistribution of individuals in high-dimensional search spaces. This mechanism enables structured population renewal and effectively mitigates premature convergence. The enhanced algorithm was evaluated on standard large-scale numerical optimization benchmarks and compared with established global optimization methods. The experimental results indicate substantial improvements in convergence speed, scalability and solution stability. Full article

Aiming at the challenges of complex spatial-temporal correlation and strong nonlinearity in the power prediction of large-scale wind farm clusters, this study proposes a short-term wind power prediction method that combines a dynamic graph structure and a Kolmogorov–Arnold Network (KAN) enhanced neural network. Firstly, a spectral embedding fuzzy C-means (FCM) cluster partition method combining geographic location and numerical weather prediction (NWP) is proposed to solve the problem of insufficient spatio-temporal representation ability of traditional methods. Secondly, a dynamic directed graph construction mechanism based on a stacked wind direction matrix and wind speed mutual information is designed to describe the directional correlation between stations with the evolution of meteorological conditions. Finally, a prediction model of dynamic graph convolution and Transformer based on KAN enhancement (DGTK-Net) is constructed to improve the fitting ability of complex nonlinear relationships. Based on the cluster data of 31 wind farms in Gansu Province of China and the cluster data of 70 wind farms in Inner Mongolia, a case study is carried out. The results show that the proposed model is significantly better than the comparison methods in terms of key evaluation indicators, and the root mean square error is reduced by about 1.16% on average. This method provides a prediction tool that can adapt to time and space changes for engineering practice, which is helpful to improve the wind power consumption capacity and operation economy of the power grid. Full article

Data fluency—the ability and confidence to actively make sense of and use data—is increasingly recognized as essential for students’ civic participation and scientific literacy, yet questions remain about implementing data-rich instruction effectively. This exploratory mixed-methods study examined current practices and professional learning needs through surveys with 155 secondary Earth science educators across the United States and focus groups with 21 participants. Educators reported comprehensive engagement with data practices (91% using 5+ practice categories) but showed critical gaps: only 39% used pre-existing datasets despite their importance for investigating large-scale phenomena, 45% employed dynamic visualization tools that could democratize data exploration, and 18% did not foster dispositions for student data agency. Teachers recognized diverse student assets for data work, including community-based knowledge and problem-solving approaches, with 42% seeking support for community-connected pedagogy. Barriers included accessing relevant datasets (53%), time constraints (42%), and integrating data into lessons (47%)—challenges that reflect systemic rather than individual limitations. These findings reveal that while educators serving diverse communities envision data science as an opportunity to value different strengths and ways of knowing, realizing this transformative potential requires systematic support including accessible tools, relevant datasets, and professional learning that bridges recognition of student assets with classroom implementation. Full article

Switching to electric vehicles (EVs) could help reduce air pollution in cities. This is especially important for cities in Russia that have grown quickly because of industry, like those in Siberia, where environmental problems are particularly acute. However, several factors continue to hinder the rapid expansion of EVs on the market, such as an additional strain on the energy infrastructure, which threatens to cause power outages. This study proposes a model for estimating the electricity consumption by EVs in the largest Russian cities, taking into account the technical characteristics of the EV fleet and climatic conditions. The calculations indicate that if 15% of the current car fleet are replaced by EVs, electricity consumption in the 16 largest cities in Russia would increase by 2.2 TWh per year in total. The estimated additional demand in particular cities varies between 33 mln and 769 mln kWh per year, depending on the number of vehicles and the local climate. Furthermore, we conducted an intra-day simulation of electricity consumption from EVs in a conditional Russian city with a population of over one million people. Three scenarios for the power grid load have been developed: (A) the maximum scenario, in which all EVs have a battery level of 0%; (B) the medium scenario, where EVs’ state of charge is distributed between 0% and 100%, and (C) the minimum scenario, involving charging scheduling that allows only EVs with a battery level of 20% or less to charge. The findings show that replacing just 15% of the car fleet with electric vehicles will trigger an increase in current daily household urban consumption of 28.4% in scenario (C), 75.6% in scenario (B) and 141.8% in scenario (A). Consequently, even in Russia’s largest cities, the further proliferation of EVs requires large-scale investments in power infrastructure. An additional 1 mln kWh used by EVs per day may require $160.7 mln investments in energy facilities and urban distribution networks. These findings highlight the necessity of a more thorough cost–benefit analysis of widespread electric vehicle adoption in densely populated urban areas. Full article

The objective of this research was to optimize a technological package for the biosorption of heavy metals in water, using agricultural waste activated with lemon juice, as a sustainable development alternative. Heavy metals such as lead, cadmium, copper, and chromium were characterized in two stages (field and laboratory conditions) using the American Public Health Association (APHA) method, and morphological characterization was performed using electron scanning techniques. Cocoa pod husk (CPH) and banana stem (BS) waste was collected with the informed consent of the native communities to obtain charcoal activated with lemon juice (LJ). In addition, a portable filter was designed that could be adapted to the native communities. The efficiency and validation of the filter were also calculated in the field. Statistical analysis was performed using Student’s t-test and Pearson’s correlation. The results show a significant reduction in lead from 0.209 mg/L to 0.02 mg/L. With regard to morphological characterization, more compact structures were observed after activation with BS, favoring the absorption of heavy metals. The correlations were positive for copper and lead (1.000), evidently due to the alteration of anthropic factors. The efficiency of the cocoa filter reached 87.48% and that of the banana stem reached 88.77%. For the cadmium, copper, and chromium parameters, the values obtained were within the maximum permissible limit (LMP). The validation of the filters showed that 80% of the population agrees with using the filters and hopes for their large-scale implementation. These findings represent a new alternative for native communities and a solution to the problem of heavy metals in drinking water. Full article

Aiming at issues such as reactive power distribution fluctuations and insufficient local support caused by large-scale integration of renewable energy in new power systems, as well as the poor adaptability of traditional methods and bottlenecks of deep reinforcement learning in complex power grids, a two-stage intelligent optimization method for grid reactive power based on dynamic voltage partitioning is proposed. Firstly, a comprehensive indicator system covering modularity, regulation capability, and membership degree is constructed. Adaptive MOPSO is employed to optimize K-means clustering centers, achieving dynamic grid partitioning and decoupling large-scale optimization problems. Secondly, a Markov Decision Process model is established for each partition, incorporating a penalty mechanism for safety constraint violations into the reward function. The DDPG algorithm is improved through multi-experience pool probabilistic replay and sampling mechanisms to enhance agent training. Finally, an optimal reactive power regulation scheme is obtained through two-stage collaborative optimization. Simulation case studies demonstrate that this method effectively reduces solution complexity, accelerates convergence, accurately addresses reactive power dynamic distribution and local support deficiencies, and ensures voltage security and optimal grid losses. Full article

The N Oilfield is the first offshore extra-heavy oilfield developed using thermal recovery methods, adopting cyclic steam stimulation (CSS) and commissioned in 2022. The development of offshore heavy oil reservoirs is confronted with numerous technical and operational challenges. Key constraints include limited platform space, stringent economic thresholds for single-well production, and elevated operational risks, collectively contributing to significant uncertainties in project viability. For effective exploitation of the target oilfield, a comprehensive strategy was proposed, which consisted of effective artificial lifting, steam channeling and high water cut treatment. First, to achieve efficient artificial lifting of the extra-heavy oil, an integrated injection–production lifting technology using jet pump was designed and implemented. In addition, during the first steam injection cycle, challenges such as inter-well steam channeling, high water cut, and an excessive water recovery ratio were encountered. Subsequent analysis indicated that low-quality reservoir intervals were the dominant sources of unwanted water production and preferential steam channeling pathways. To address these problems, a suite of efficiency-enhancing technologies was established, including regional steam injection for channeling suppression, classification-based water shutoff and control, and production regime optimization. Given the significant variations in geological conditions and production dynamics among different types of high-water-cut wells, a single plugging agent system proved inadequate for their diverse requirements. Therefore, customized water control countermeasures were formulated for specific well types, and a suite of plugging agent systems with tailored properties was subsequently developed, including high-temperature-resistant N₂ foam, high-temperature-degradable gel, and high-strength ultra-fine cement systems. To date, regional steam injection has been implemented in 10 well groups, water control measures have been applied to 12 wells, and production regimes optimization has been implemented in 5 wells. Up to the current production round, no steam channeling has been observed in the well groups after thermal treatment. Compared with the pre-measurement stage, the average water cut per well decreased by 10%. During the three-year production cycle, the average daily oil production per well increased by 10%, the cumulative oil increment of the oilfield reached 15,000 tons, and the total crude oil production exceeded 800,000 tons. This study provides practical technical insights for the large-scale and efficient development of extra-heavy oil reservoirs in the Bohai Oilfield and offers a valuable reference for similar reservoirs worldwide. Full article

The temperate Himalayan states of Jammu and Kashmir, Himachal Pradesh, Uttarakhand, Ladakh, Sikkim, and Arunachal Pradesh in India face unique agro-ecological challenges across agriculture and allied sectors, including pest and disease pressures, inefficient resource use, post-harvest losses, and fragmented supply chains. This review systematically examines 21 critical problem areas, with three key challenges identified per sector across agriculture, agricultural engineering, fisheries, forestry, horticulture, sericulture, and animal husbandry. Artificial Intelligence (AI) and Machine Learning (ML) interventions, including computer vision, predictive modeling, Internet of Things (IoT)-based monitoring, robotics, and blockchain-enabled traceability, are evaluated for their regional applicability, pilot-level outcomes, and operational limitations under temperate Himalayan conditions. The analysis highlights that AI-enabled solutions demonstrate strong potential for early pest and disease detection, improved resource-use efficiency, ecosystem monitoring, and market integration. However, large-scale adoption remains constrained by limited digital infrastructure, data scarcity, high capital costs, low digital literacy, and fragmented institutional frameworks. The novelty of this review lies in its cross-sectoral synthesis of AI/ML applications tailored to the Himalayan context, combined with a sector-wise revenue-loss assessment to quantify economic impacts and guide prioritization. Based on the identified gaps, the review proposes feasible, context-aware strategies, including lightweight edge-AI models, localized data platforms, capacity-building initiatives, and policy-aligned implementation pathways. Collectively, these recommendations aim to enhance sustainability, resilience, and livelihood security across agriculture and allied sectors in the temperate Himalayan region. Full article

The particle exhaust system plays a pivotal role in fusion reactors and is essential for ensuring both the feasibility and sustained operation of the fusion reaction. For the successful development of such a system, density control is of great importance and some key design parameters include the neutral gas pressure and the resulting particle fluxes. This study presents a simplified conductance-based model for estimating neutral gas pressure distributions in the particle exhaust system of fusion reactors, focusing specifically on the sub-divertor region. In the proposed model, the pumping region is represented as an interconnected set of reservoirs and channels. Mass conservation and conductance relations, appropriate for all flow regimes, are applied. The model was benchmarked against complex 3D DIVGAS simulations across representative operating scenarios of the Wendelstein 7-X (W7-X) stellarator. Despite geometric simplifications, the model is capable of predicting pressure values at several key locations inside the particle exhaust area of W7-X, as well as various types of particle fluxes. The developed model is computationally efficient for large-scale parametric studies, exhibiting an average deviation of approximately 20%, which indicates reasonable predictive accuracy considering the model simplifications and the flow problem complexity. Its application may assist early-stage engineering design, pumping performance improvement, and operational planning for W7-X and other future fusion reactors. Full article

Defect categorization is essential to product quality assurance during the production of magnetic random access memory (MRAM). Nevertheless, traditional defect detection techniques continue to face difficulties in large-scale deployments, such as a lack of labeled examples with complicated defect shapes, which results in inadequate identification accuracy. In order to overcome these problems, we create the MARMset dataset, which consists of 39,822 photos and covers 14 common defect types for MRAM defect detection and classification. Furthermore, we present a baseline framework (GAGBnet) for MRAM defect classification, including a global attention module (GAM) and an attention-guided block (AGB). Firstly, the GAM is introduced to enhance the model’s feature extraction capability. Secondly, inspired by the feature enhancement strategy, the AGB is designed to incorporate an attention-guided mechanism during feature fusion to remove redundant information and focus on critical features. Finally, the experimental results show that the average accuracy rate of this method on the MARMset reaches 92.90%. In addition, we test on the NEU-CLS dataset to evaluate cross-dataset generalization, achieving an average accuracy of 98.60%. Full article

Electric vehicles (EVs), electric buses (EBs), and battery energy storage system (BESS), as both controllable power sources and load, play a great role in providing flexibility for the power grid, especially with the increased renewable energy penetration. However, there is still a lack of studies on EVs’ pricing strategy as well as the EBs-charging piles matching problem. To address these issues, a multi-objective optimal operation model is presented to achieve the lowest load fluctuation level, minimum electricity cost, and maximum discharging benefit. An improved load boundary prediction method (ICDLBPM) and a novel pricing strategy are proposed. In addition, reduction in the number of EBs charging piles would not only impact normal operation of EBs, but also even lead to load flexibility decline. Thus a handling method of the EBs-charging piles matching problem is presented. Several case studies were conducted on a regional distribution network comprising 100 EVs, 30 EBs, and 20 BESS units. The developed model and methodology demonstrate superior performance, improving load smoothness by 45.78% and reducing electricity costs by 19.73%. Furthermore, its effectiveness is also validated in a large-scale system, where it achieves additional reductions of 39.31% in load fluctuation and 62.45% in total electricity cost. Full article