Search Results (843)

Traditional electricity market bidding typically focuses on unilateral structures, where independent energy storage units and flexible loads act merely as price takers. This reduces bidding motivation and weakens the balancing capability of regional power systems, thereby limiting the large-scale utilization of renewable energy. To address these challenges and support sustainable power system operation, this paper proposes a double-sided auction market strategy for heterogeneous multi-resource (HMR) participation based on multi-agent reinforcement learning (MARL). The framework explicitly considers the heterogeneous bidding and quantity reporting behaviors of renewable generation, flexible demand, and energy storage. An improved incentive mechanism is introduced to enhance real-time system power balance, thereby enabling higher renewable energy integration and reducing curtailment. To efficiently solve the market-clearing problem, an improved Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (MATD3) algorithm is employed, along with a temporal-difference (TD) error-based prioritized experience replay mechanism to strengthen exploration. Case studies validate the effectiveness of the proposed approach in guiding heterogeneous resources toward cooperative bidding behaviors, improving market efficiency, and reinforcing the sustainable and resilient operation of future power systems. Full article

(1) Background: Geothermal resources are enriched in Jiangsu Province, particularly in its mid-deep geothermal reservoirs. The thermal properties and thermal effects of magmatic rocks, which are largely unknown in Jiangsu Province, are fundamental for analyzing the genetic mechanisms of geothermal resources and evaluating resource potential. (2) Methods: Representative magmatic rock samples from different geological periods and different tectonic settings are collected from the main tectonic units of Jiangsu Province. Key thermophysical parameters such as thermal conductivity, heat production rate, rock density, and porosity are systematically tested. (3) Results: The variation patterns of these thermal property parameters are analyzed, and the sources and spatiotemporal evolution characteristics of radiogenic heat production, and the thermal effects of magmatic rocks, are specifically explored. (4) Conclusions: Magmatic rock lithology from acidic to basic is negatively correlated with thermal conductivity, thermal diffusivity, and radiogenic heat production rate, and positively correlated with volumetric heat capacity. The radiogenic heat production of magmatic rocks is primarily controlled by the contents of U and Th, increasing with the increasing SiO₂ content. The formation of geothermal anomalies in areas with thin or absent sedimentary cover is significantly influenced by the thermal effect of magmatic rocks, especially by the high heat-producing granites. The radioactive thermal contribution of the Taolin and Suzhou plutons was calculated. Full article

Accurate short-term wind power forecasting plays a critical role in maintaining grid stability due to the inherently irregular and fluctuating nature of wind resources. Deep learning models such as LSTM, GRU, and CNN are widely used to learn temporal dynamics; however, their ability to capture or adapt to the underlying symmetries and asymmetries inherent in real-world wind energy data remains insufficiently explored. In this study, we evaluate and compare these models using authentic production and meteorological data from the Tokat Wind Farm in Türkiye. The forecasting scenarios were designed to reflect the temporal structure of the dataset, including seasonal patterns, recurrent behaviors, and the symmetry-breaking effects caused by abrupt changes in wind speed and operational variability. The results demonstrate that the LSTM model most effectively captures the temporal relationships and partial symmetries within the data, yielding the lowest error metrics (RMSE = 0.2355, MAE = 0.1249, MAPE = 25.16%, R² = 0.8199). GRU and CNN offer moderate performance but show reduced sensitivity to asymmetric fluctuations, particularly during periods of high variability. The comparative findings highlight how symmetry-informed model behavior—specifically the ability to learn repeating temporal structures and respond to symmetry-breaking events—can significantly influence forecasting accuracy. This study provides practical insights into the interplay between data symmetries and model performance, supporting the development of more robust deep learning approaches for real-world wind energy forecasting. Full article

Sandstone-hosted uranium deposits represent one of the most critical global uranium resources suitable for in situ recovery, with their formation closely associated with organic matter (OM). We conducted a systematic literature review to synthesize over 100 published studies sourced from authoritative databases (Elsevier, Google Scholar, Web of Science, Scopus, CNKI, etc.). This study systematically summarizes the types and geological characteristics of OM in sandstone reservoirs and thoroughly analyzes the geochemical mechanisms by which OM regulates the transport and precipitation of aqueous uranium. By integrating case studies of representative sandstone uranium deposits globally, three major OM-related metallogenic models are proposed with distinct core characteristics: the humic-dominated model is driven by the complexation and direct reduction of uranium by humic substances/coal-derived OM; the roll-front model relies on reactions between oxidized uranium-bearing fluids and scattered OM, as well as microbially generated sulfides at the migration front; and the seepage-related model is fueled by upward-migrating deep hydrocarbon fluids (petroleum, methane) that act as both uranium carriers and reductants. Furthermore, this review explores the spatial coupling relationships between OM distribution and uranium mineralization in typical geological settings, evaluates the guiding significance of OM for uranium exploration, and outlines key unresolved scientific issues. The findings refine the genetic theoretical framework of sandstone-hosted uranium deposits and provide important technical support and theoretical guidance for future uranium exploration deployment and resource potential evaluation. Full article

The dolomite of the fourth member of Dengying Formation in Gaoshiti-Moxi area of central Sichuan Basin is rich in hydrocarbon resources. It has experienced superimposition-reformation of multistage karstification, and is the key target for studying deep ancient carbonate reservoirs. Exploration and development practices show that there are great differences in the development of karst reservoirs of the fourth member of Dengying Formation between the platform margin and intraplatform in Gaoshiti-Moxi area. However, the differences in the genetic mechanism of karst reservoirs between these two zones are unclear. Therefore, based on an integrated analysis of core, thin section, drilling, logging, and geochemical test data, this study clarifies the differences in karstification between the platform margin and intraplatform and conducts a comparative analysis of the controlling factors for the differences in karst reservoirs. Results show that the fourth member of Dengying Formation experienced superimposition-reformation of four types of paleokarstification, including eogenetic meteoric water karst, supergene karst, coastal mixed water karst, and burial karst. Large-scale dissolved fractures and caves are mainly controlled by meteoric water karstification, primarily developing three types of reservoir space: vug type, fracture-vug type, and cave type. Dolomite and quartz fillings are mainly formed in the medium-deep burial period. Four types of paleokarstification are developed in the platform margin, while the coastal mixed water karst is not developed in the intraplatform. Eogenetic meteoric water karst and supergene karst in the platform margin are stronger than those in the intraplatform, while burial karst shows no notable difference between the two zones. The thickness of soluble rock (mound-shoal complex), karst paleogeomorphology, and different types of paleokarstification are the main controlling factors for the difference in karst reservoirs between the platform margin and the intraplatform. Full article

Traditional geological mapping is often time-consuming, labor-intensive, and restricted by rugged terrain. This study addresses these challenges by proposing a novel methodology for automated lithological identification in the Ququleke area of the eastern Kunlun Mountains, which pioneers the integration of portable UAV oblique photogrammetry with a Coordinate Attention-enhanced DeepLabV3+ (CA-DeepLabV3+) semantic segmentation framework for geological mapping. Using a DJI Mavic 3M quadcopter, high-resolution oblique photogrammetric orthophotos were captured to build a pixel-level lithology dataset containing four classes: sandstone, diorite, marble, and Quaternary sediments. The CA-DeepLabV3+ model, adapted from the DeepLabV3+ encoder–decoder framework, integrates a lightweight MobileNetV2 backbone and a Coordinate Attention mechanism to strengthen spatial position encoding and fine-scale feature extraction, crucial for detailed lithological discrimination. Experimental evaluation demonstrates that the proposed model achieves an overall accuracy of 97.95%, mean accuracy of 97.80%, and mean intersection over union of 95.71%, representing a 5.48% improvement in mean intersection over union (mIoU) over the standard DeepLabV3+. These results indicate that combining UAV oblique photogrammetry with the CA-DeepLabV3+ network enables accurate lithological mapping in complex terrains. The proposed method provides an efficient and scalable solution for geological mapping and mineral resource exploration, highlighting the potential of low-altitude UAV remote sensing for field-based geological investigations. Full article

Background: Snipe eels (family Nemichthyidae) are a group of pelagic fishes with unique specializations; yet, species within this study are not well-studied due to a lack of molecular data. As typical mesopelagic-to-bathypelagic fishes, snipe eels exhibit extreme body elongation, reduced skeletal ossification, and highly specialized beak-like jaws that facilitate survival in deep-sea midwater environments. Methods: The complete mitochondrial genome of the deep-sea eel Nemichthys curvirostris (Anguilliformes: Nemichthyidae) was sequenced and annotated, representing the first mitogenomic resource for this species. The phylogenetic position of N. curvirostris was also explored. Results: The circular genome of N. curvirostris was determined to be 16,911 bp in length and contained 37 genes, including 13 protein-coding genes, 22 tRNAs, 2 rRNAs, and a single control region, with an overall A + T bias of 56.67%. The maximum-likelihood phylogeny inferred from concatenated mitochondrial protein-coding genes recovered a well-supported monophyletic Nemichthys clade, with N. curvirostris positioned as the sister taxon to N. scolopaceus. The genera Avocettina and Labichthys were recovered as sister taxa, and Nemichthys clustered within a broader clade alongside them. The COX1 haplotype phylogeny showed that the two public database sequences (HQ563894.1 and MN123435.1) appeared as long, isolated branches outside the main N. curvirostris lineage, with COX1 genetic distances from typical N. curvirostris haplotypes reaching 12–13%, far exceeding the expected range of intraspecific variation. Conclusions: This mitogenome provides a valuable molecular resource for phylogenetic, evolutionary, and population genetic studies of deep-sea Anguilliformes. Full article

With increasing quality demands in pharmaceutical and cosmetic packaging, this work presents a unified inspection platform for transparent ampoules that synergistically integrates stress measurement, dimensional measurement, and surface defect detection. Key innovations include an integrated system architecture, a shared-resource task scheduling mechanism, and an optimized deployment strategy tailored for production-like conditions. Non-contact residual stress measurement is achieved using the photoelastic method, while telecentric imaging combined with subpixel contour extraction enables accurate dimensional assessment. A YOLOv8-based deep learning model efficiently identifies multiple surface defect types, enhancing detection performance without increasing hardware complexity. Experimental validation under laboratory conditions simulating production lines demonstrates a stress measurement error of ±3 nm, dimensional accuracy of ±0.2 mm, and defect detection mAP@0.5 of 90.3%. The platform meets industrial inspection requirements and shows strong scalability and engineering potential. Future work will focus on real-time operation and exploring stress–defect coupling for intelligent quality prediction. Full article

As a core driver of high-quality and sustainable economic development, the deep integration of the digital economy with foreign trade has emerged as a critical pathway to enhance the resilience of China’s foreign trade while advancing Sustainable Development Goals (SDGs)—particularly those related to decent work and economic growth, industry, innovation and infrastructure, and partnerships. This study employs panel data from 30 Chinese provinces spanning 2012–2021, combined with a two-way fixed effects model, mediating effect model, and threshold panel model, to empirically explore how the digital economy shapes foreign trade resilience and its implications for sustainable development. The findings demonstrate that the digital economy significantly empowers the enhancement of foreign trade resilience, with industrial structure advancement serving as a key mediating channel. This mechanism aligns with sustainable development principles by promoting resource allocation efficiency, reducing environmental footprints through optimized trade processes, and fostering inclusive industrial upgrading. To advance sustainable foreign trade development, policy implications include strengthening digital infrastructure, promoting the integration of digital economy and green industries, and optimizing institutional frameworks for inclusive digital trade. Full article

This review explores the deployment of Artificial Intelligence (AI) technologies to augment key industry processes in the new paradigm of Industry 5.0. Based on a handpicked collection of 35 peer-reviewed articles and leading resources, the study integrates the latest breakthroughs in Machine Learning (ML), Deep Learning (DL), Reinforcement Learning (RL), and Federated Learning (FL) with their applications in predictive maintenance, process planning, real-time monitoring, and operational excellence. The results emphasize AI’s central role in making manufacturing smarter, minimizing system downtime, and facilitating decision-making based on information in various industries like aerospace, energy, and intelligent manufacturing. Yet, the review also highlights significant challenges, ranging from data heterogeneity to model interpretability, security risks, and the ethics of automation. Solutions in the making, including Explainable AI (XAI), privacy-enhancing collaborative models, and enhanced cybersecurity protocols, are postulated to be the key drivers for the development of dependable and resilient industrial AI systems. The study concludes by postulating directions for further research and practice to secure the safe, transparent, and human-centered deployment of AI in industrial settings. Full article

The dynamics of chemical components in sediment porewater are crucial for marine ecological research, resource assessment, and environmental monitoring. A scientific sampling strategy is key to obtaining high-quality porewater. This study aims to explore the effects of circular sampling hole size and layout on sampling effectiveness to optimize the sampling strategy. First, this study analyzed the flow field from time and spatial flow. Then, a simulation model was built using COMSOL Multiphysics 6.2 to simulate changes in the flow field, Darcy velocity, and effective sampling depth under different conditions. The results showed that the sampling holes finished sampling earlier due to being close to the open boundary; small sample hole sizes could suppress this time lag but reduce efficiency, and the effective sampling range increased exponentially with volume. When R = 5 mm, D = 150 mm, and V = 10 mL, interference between adjacent layers was effectively avoided, balancing timeliness and sample representativeness. Laboratory experiments and sea trials validated the effectiveness of the sampling strategy. This study provides theoretical and practical guidance for deep-sea porewater sampling technology, supporting marine scientific research. Full article

The Cimabanshuo deposit, situated in the western Gangdese Belt, is a recently discovered porphyry Cu deposit formed in a post-collisional setting, approximately 10 km from the giant Zhunuo porphyry Cu deposit. Despite its proximity to Zhunuo, Cimabanshuo remains poorly studied, and the current exploration depth of 600 m leaves the potential for deeper resources uncertain. In this study, 840 samples from four drill holes along the NW-SE section (A-A′) were analyzed using portable X-ray fluorescence (pXRF). Based on the geochemical characteristics of primary halos, the deep mineralization potential of Cimabanshuo was evaluated. The results show that Co, Pb, and Ag are near-ore indicator elements; Zn, Cs, Hg, Sb, As, and Ba represent the frontal elements; and Te, Sn, and Bi occur as tail elements. Based on these distributions, a 14-element zoning sequence is defined along the A-A′ profile according to Gregorian’s zoning index, showing Mo-Co-Cu-Pb-Bi-Ag-Sn-Te-Sb-Hg-Cs-Zn-Ba-As from shallow to deep. This sequence shows a distinct reverse zonation pattern, in which tail elements occur in the middle and frontal elements appear at depth, suggesting the existence of a concealed ore body in the lower part of the deposit. Horizontally, the geochemical ratios Ag/Mo and Ag/Cu decrease from northwest to southeast along the profile, implying hydrothermal flow from southeast to northwest. Vertically, the ratios As/Bi, (As × Cs)/(Bi × Te), (As × Ba)/(Bi × Sn), and (As × Ba × Cs)/(Bi × Sn × Te) display a downward-decreasing then upward-increasing trend, further indicating hidden mineralization at depth. This inference is supported by the predominance of propylitic alteration and the deep polarization anomaly revealed by audio-magnetotelluric imaging. pXRF analysis provides a fast, efficient, and environmentally friendly approach, showing strong potential for rapid geochemical evaluation in porphyry Cu exploration. Full article

Geological interfaces are crucial elements governing deposit formation, such as silica–calcium surfaces, intrusive contact interfaces, and unconformities can serve as key symbols for mineral exploration prediction. Geological maps provide relatively detailed representations of primary geological interfaces and their interrelationships. However, in previous mineral resource predictions, the type differences in different geological interfaces were ignored, and the types of different geological interfaces vary greatly, thus affecting the validity of the mineral prediction results. Manual interpretation and analysis of geological interfaces involve substantial workloads and make it difficult to effectively apply the rich geological information depicted on geological maps to mineral exploration prediction processes. Therefore, this study proposes a model for intelligent identification of geological interface types based on deep learning. The model extracts the attribute information, such as the age and lithology of the geological bodies on both sides of the geological boundary arc, based on the digital geological map of the Gouli gold mining area in Dulan County, Qinghai Province, China. The learning dataset comprising 5900 sets of geological interface types was constructed through manual annotation of geological interfaces. The arc segment is taken as the basic element; the model adopts natural language processing technology to conduct word vector embedding processing on the text attribute information of geological bodies on both sides of the geological interface. The processed embedding vectors are fed into the convolutional neural network (CNN) for training to generate the geological interface type recognition model. This method can effectively identify the type of geological interface, and the identification accuracy can reach 96.52%. Through quantitative analysis of the spatial relationship between different types of geological interfaces and ore points, it is known that they have a good correlation in spatial distribution. Experimental results show that the proposed method can effectively improve the accuracy and efficiency of geological interface recognition, and the accuracy of mineral prediction can be improved to some extent by adding geological interface type information in the process of mineral prediction. Full article

The surface–borehole transient electromagnetic method (TEM) employs surface-based transmission and downhole reception to collect electromagnetic data. This configuration offers distinct advantages over traditional TEM approaches by effectively attenuating surface electromagnetic noise and cultural interference, leading to enhanced signal strength and vertical resolution. As a result, it has emerged as a key technique for the exploration of deep mineral resources. Although a relatively comprehensive three-dimensional (3D) theoretical system for surface–borehole TEM has been established, most existing studies remain focused on forward modelling, with inversion interpretation receiving comparatively limited attention. In this study, a one-dimensional (1D) inversion algorithm for surface–borehole TEM data is developed. The approach begins with forward modelling based on numerical simulation, followed by the integration of a prior model to formulate an objective function. Optimization is carried out using the Broyden–Fletcher–Goldfarb–Shanno (quasi-Newton) method. A parameter transformation approach was further applied to convert the constrained inversion into an unconstrained optimization problem. The effectiveness of the proposed algorithm is validated through inversions performed on synthetic data derived from theoretical models. This method offers a reliable interpretation tool for practical surface–borehole TEM applications and provides a theoretical basis for the design and optimization of related instrumentation. Full article

5G technology represents a transformative shift in mobile communications, delivering improved ultra-low latency, data throughput, and the capacity to support huge device connectivity, surpassing the capabilities of LTE systems. As global telecommunication operators shift toward widespread 5G implementation, ensuring optimal network performance and intelligent resource management has become increasingly obvious. To address these challenges, this study explored the role of advanced clustering methods in optimizing cellular networks under heterogeneous and dynamic conditions. A systematic literature review (SLR) was conducted by analyzing 40 peer-reviewed and non-peer-reviewed studies selected from an initial collection of 500 papers retrieved from the Semantic Scholar Open Research Corpus. This review examines a diversity of clustering approaches, including spectral clustering with Bayesian non-parametric models and K-means, density-based clustering such as DBSCAN, and deep representation-based methods like Differential Evolution Memetic Clustering (DEMC) and Domain Adaptive Neighborhood Clustering via Entropy Optimization (DANCE). Key performance outcomes reported across studies include anomaly detection accuracy of up to 98.8%, delivery rate improvements of up to 89.4%, and handover prediction accuracy improvements of approximately 43%, particularly when clustering techniques are combined with machine learning models. In addition to summarizing their effectiveness, this review highlights methodological trends in clustering parameters, mechanisms, experimental setups, and quality metrics. The findings suggest that advanced clustering models play a crucial role in intelligent spectrum sensing, adaptive mobility management, and efficient resource allocation, thereby contributing meaningfully to the development of intelligent 5G/6G mobile network infrastructures. Full article