Search Results (298)

With the acceleration of the global energy transition, the photovoltaic industry has become a significant force in the promotion of green development, and photovoltaic raw materials play a crucial role in this process. In this paper, 177 countries during the period of 2001 to 2024 were taken as the research subjects, with a focus on polysilicon and silicon wafers as components of upstream photovoltaic raw materials. Through a combination of the evolutionary analysis of nodes, the overall structure, and the three-dimensional structure with an exponential random graph model, the evolution and dynamic mechanisms of the global photovoltaic raw material trade network are explored. The study reveals the following: (1) The global PV raw material trade volume tended to increase from 2001 to 2024. (2) The global photovoltaic raw material trade network showed a tendency towards the “enhanced dominance of core countries and denser trade connections,” with the trade volume between core countries continuously expanding and the network density, average clustering coefficient, and connection efficiency increasing annually, which is a reflection of the globalization and regional cooperation of the global photovoltaic industry. (3) From the weighted out-degree and in-degree ranking evolution of the global photovoltaic raw materials trade network, it can be seen that China consolidated its core position, while Southeast Asian countries tended to transfer their processing and manufacturing links. The status of the United States and traditional industrial powers gradually declined, which is a reflection of the restructuring of the global industrial chain along with regional geopolitical agglomeration effects. (4) Internal attributes such as the national economic level, population size, and urbanization rate, as well as external network effects such as common language and geographical proximity, significantly influence the formation path of the photovoltaic raw material trade network. Moreover, the network exhibits distinct heterogeneous complementarity mechanisms and path dependence characteristics, with a structural evolution that tends toward stability and cooperative relationships showing significant time inertia. Overall, the global trade volume of photovoltaic raw materials continues to grow, and the core positions of major countries such as China, the United States, and Germany remain prominent but show a transitional trend towards Southeast Asian countries. The strengthening of the level of coordination and cooperation among global photovoltaic raw material producers to ensure supply chain stability, promote resource sharing and technological progress, and achieve the sustainable development of green energy policies is necessary. Full article

The rapid proliferation of mobile devices, particularly smartphones and tablets, has transformed digital entertainment, with mobile gaming emerging as one of the fastest-growing digital segments. Such applications are inherently latency-sensitive and require effective resource management and seamless mobility support. To overcome these issues, this paper suggests a four-layered infrastructure that combines edge, fog, and cloud computing with Software-Defined Networking (SDN) and is assisted by a lightweight proximity-aware heuristic placement strategy and mobility management. The suggested structure follows a microservices contained breakdown of the gaming functionality and uses clustering algorithms to permit coordinated access to resources by edge and fog nodes. A dynamic lightweight proximity-aware virtual machine placement algorithm is presented to deploy application modules nearer to the users depending on the availability and mobility of the resources. The proposed work is simulated using IFogSim2. The proposed model reduces the latency by up to 73 percent and the rate of task completion by 25 percent relative to baseline configurations in the case of dynamic mobility of users. These results indicate that the suggested strategy can be effective in improving the latency-sensitive mobile gaming applications performance in the edge-fog networks. Full article

The emergence of edge computing introduces significant opportunities to improve real-time responsiveness and reduce latency by deploying computational resources closer to end users, at the edge, compared to traditional centralized cloud computing. However, stochastic and fluctuating workloads pose challenges in maintaining Quality of Service, often leading to resource fragmentation, service node saturation, and energy inefficiencies. In addition, imbalances in service node utilization, arising from either under-utilization or over-utilization, degrade the overall system performance and lead to unnecessary operational costs. Furthermore, finding an optimal balance between total latency cost and load balancing in different network topologies remains a significant challenge. In this research, we propose and evaluate a workload-aware orchestration framework that integrates short-term workload forecasting with dynamic resource scaling to efficiently manage edge node infrastructure under dynamic processing demands. The framework employs heuristic schemes that consider both workload distribution and service proximity throughout the edge network to optimize the distribution of edge users’ service requests across service nodes. Simulation results on grid and irregular edge network topologies, utilizing both synthetic and real-world dataset, demonstrate that the proposed framework and the integrated heuristics outperform other benchmark approaches. Specifically, our framework achieves up to 20% lower load imbalance variance, maintains high resource utilization, decreases system reconfigurations and increases service reliability, providing a robust, low-overhead and adaptive solution for dynamic orchestration in edge computing environments and infrastructures. Full article

Background: Follicular lymphoma (FL) and diffuse large B-cell lymphoma (DLBCL) exhibit substantial heterogeneity, reflecting the diversity of the germinal center (GC). Histologic transformation of FL to DLBCL is associated with poor prognosis, yet robust biomarkers predicting transformation remain limited. Methods: We integrated single-cell DNA sequencing, single-cell RNA sequencing, and spatial transcriptomics in diagnostic lymph-node biopsies from non-transformed FL (ntFL), transformed FL (tFL), and DLBCL to characterize clonal states and immune niches in GC lymphomas. T-cell signatures associated with transformation were evaluated in an independently published single-cell FL dataset. Results: Transcriptional profiling revealed similarities between tFL and DLBCL, consistent with a GC-related malignant program. The tFL microenvironment showed enrichment of exhausted CD4⁺ regulatory and CD8⁺ effector T cells, together with CD4⁺ follicular helper T cells (Tfh) displaying an adhesion-related phenotype. Spatial analysis suggested increased proximity of exhausted/immunosuppressive T cells and enhanced Tfh-B-cell interactions in tFL compared with ntFL. These immune signatures were also observed in an external cohort and were associated with early transformation. In addition, clonal hematopoiesis-associated mutations were detected in microenvironmental cells across samples, suggesting a potential contribution to the lymphoma microenvironment. Conclusions: This work demonstrates the feasibility of integrating single-cell and spatial analyses in GC lymphomas and provides a framework for investigating tumor heterogeneity and immune organization. These findings may inform future studies on biomarker development and the rational design of immunotherapies. Full article

Urachal carcinoma is a rare and aggressive malignancy for which standardized management remains limited, particularly in patients with locally advanced and node-positive disease. We report the case of a 34-year-old woman with urachal adenocarcinoma involving the bladder dome and radiographically suspicious pelvic lymph nodes who underwent robot-assisted partial cystectomy with urachal resection and extended bilateral pelvic lymph node dissection. Because there was no clinical, radiologic, or intraoperative evidence of umbilical involvement, the umbilicus was preserved after preoperative counseling and intraoperative confirmation of a negative proximal margin. Final pathology demonstrated a 4.5 cm enteric-type urachal adenocarcinoma, pT3a pN2 (2/17), with lymphovascular invasion, perineural invasion, and negative surgical margins. Immunohistochemistry and DNA- and RNA-based next-generation sequencing showed microsatellite stability, mismatch-repair proficiency, low tumor mutational burden, and no actionable genomic alteration. Given the absence of an established adjuvant standard, the multidisciplinary tumor board selected adjuvant FOLFOX as a non-standard postoperative strategy based on the overall clinicopathologic context. The patient remained continent, experienced no postoperative complications or treatment-limiting toxicity, and showed normalization of carcinoembryonic antigen and carbohydrate antigen 19-9 levels. This case provides a carefully contextualized example of transparent surgical reasoning and restrained multidisciplinary management in a rare malignancy with limited prospective evidence. Full article

Modern microgrids increasingly rely on learning-based energy management systems (EMSs) for real-time decision-making, yet remain vulnerable to cyber–physical disturbances, sensor tampering, and model uncertainty. Existing resilient control and robust reinforcement learning methods provide useful foundations, but rarely address adversarial measurement perturbations that distort belief evolution under partial observability. This gap is critical, as structured perturbations in sensing channels can destabilize learning-based policies and propagate into voltage-regulation violations. This paper proposes an adversarially robust reinforcement learning framework for energy management with voltage regulation under partial observability in microgrids. The EMS decision-making problem is formulated as a partially observable Markov decision process (POMDP) that accounts for adversarial measurement perturbations, belief evolution, and system-level economic and voltage constraints. To avoid excessive conservatism under worst-case uncertainty, an adversary-aware belief construction based on adversarial belief balancing (A3B) is employed to focus on policy-relevant perturbations. Building on this belief representation, an adversarially robust learning framework is developed by incorporating adversarial counterfactual error (ACoE) as a learning regularization mechanism, enabling a balance between nominal operating efficiency and robustness under adversarial measurement distortion. The case study is conducted on a medium-voltage radial distribution feeder (IEEE 123-Node Test Feeder). Case study results demonstrate that the proposed ACoE-regularized policies substantially reduce voltage-deficit events, improve policy stability, and maintain operational constraints under adversarial perturbations, consistently outperforming standard proximal policy optimization (PPO)-based controllers. These results indicate that counterfactual-aware, belief-based learning substantially enhances voltage quality and operational resilience in microgrids with high penetration of distributed energy resources. Full article

The parafoil is highly susceptible to deviations from its reference trajectory under wind disturbances. Given its constrained longitudinal control authority, it has limited capability to correct these deviations and regain the intended glide path. To overcome this limitation, we propose a dynamic planning framework based on a layered homing strategy. The airdrop mission trajectory is initially designed as a traditional multi-segment path. To approximate non-uniform glide characteristics under wind disturbances, this planning problem incorporates a predicted wind model as an external input. Node parameters of the segmented trajectory are then solved using an improved grey wolf optimizer (IGWO). By tracking this reference trajectory, the parafoil is guided into the proximity of the target. To ensure landing precision, the terminal phase is formulated and discretized using an adaptive pseudo-spectral method (APSM). The online planner computes a real-time trajectory to account for actual motion characteristics. This dynamic replanning (DRP) compensates for deviations caused by model mismatches and external disturbances. The proposed homing method is statistically verified via extensive Monte Carlo simulations under different wind conditions. Finally, the airdrop experiment is conducted to validate the DRP method. Full article

This study analyzes 1441 industry–university–research (I-U-R) collaborative invention patents (2004–2023) in China’s new energy vehicle (NEV) industry using social network analysis. We propose the “Proximity–Industry Life Cycle” Fit Theory to systematically investigate the influence mechanisms of industrial proximity, geographical proximity, and technological proximity on the evolution of the industry–university–research collaborative innovation network of the new energy vehicle industry across three industry life cycle stages. Key findings include: (1) the network scale expanded significantly while density declined; (2) State Grid Corporation emerged as the core node after 2010; (3) all three proximity dimensions positively influence network evolution, with varying effects across stages—industrial proximity dominates in the emergent stage, while technological proximity becomes the primary driver in later stages. Policy implications: Governments should formulate stage-differentiated policies—encouraging industrial chain collaboration in early stages while promoting technology alliances in mature stages. Core enterprises should be supported to strengthen I-U-R collaboration, and cross-regional innovation platforms should be established to optimize proximity-driven knowledge transfer. Full article

The spread of Xylella fastidiosa, a xylem-limited bacterial pathogen, has caused widespread mortality among olive trees in Apulian region, Italy in more than a decade, and represents a significant threat to Mediterranean agroecosystems. To encourage evidence-based containment strategies, we developed a stochastic, spatiotemporal simulation model that represents pathogen transmission at the individual-tree level. This work integrates high-resolution georeferenced olive-tree data and implicitly incorporates vector population dynamics through a tree-specific vulnerability index, which considers local host density and landscape connectivity. Vector dispersal is approximated using a radial transmission kernel, which preserves host–vector spatial interactions while avoiding the explicit modeling of insect trajectories. The system’s spatial structure is additionally formulated as a proximity graph, facilitating network-based analysis of spread pathways. A series of Monte Carlo simulation experiments is employed for calibration against the observed epidemic footprint, while validation utilizes independent infection records and global sensitivity analysis of key parameters. The findings indicate that the model effectively replicates realistic propagation patterns, and its calibrated parameters are consistent with out-of-sample data. This makes it an appropriate exploratory tool for scenario testing, assessing the potential impact of intervention strategies, and offering risk-based decision support for handling Xylella fastidiosa outbreaks. Subsequently, graph centrality metrics are used to identify epidemiologically critical trees that function as transmission bridges, thus representing priority targets for surveillance or removal efforts. Thus, multiple tests have been conducted using betweenness and closeness centrality, while comparing both methods leads to effective node-tree removal decisions. Full article

R-loops, three-stranded nucleic acid structures formed by an RNA-DNA hybrid, have emerged as important regulators of transcription and genome stability. Although advances in high-throughput sequencing have revealed widespread R-loop landscapes, platform-specific biases hinder the identification of conserved R-loops in specific cell types. Mouse embryonic stem cells, which are transcriptionally active, provide an ideal system for investigating the potential roles of stable R-loops in RNA biology. Here, we integrated 13 independent R-loop profiling datasets from four experimental platforms to define 27,950 Common R-loop regions in mouse embryonic stem cells and characterized their chromatin environment and associated biological functions. Common R-loop regions were reproducibly detected across methods and were preferentially localized to promoter-proximal and genic regions enriched in CpG islands. Genes associated with Common R-loops were highly and stably expressed, showing strong functional enrichment in RNA metabolic processes such as mRNA processing, RNA splicing, and ribonucleoprotein complex biogenesis. Chromatin state analysis revealed that Common R-loops are enriched in transcriptionally active and regulatory contexts. Sequence feature analysis further identified GC skew as a prominent signature of Common R-loops, particularly within transcribed chromatin states. Transcription factor motif analyses have identified distinct regulatory environments in Common R-loop regions, including pluripotency-associated OCT4-SOX2-TCF-NANOG motifs in enhancers, CTCF motifs in open chromatin, and YY1 motifs in promoters. Together, this study provides the first integrated analysis of conserved R-loop regions in mouse embryonic stem cells, revealing their preferential localization at regulatory loci linked to RNA metabolism and highlighting R-loops as structural and functional nodes in RNA biology. Full article

Vehicle networking is a new paradigm in wireless technology that facilitates communication between vehicles in close proximity and in-vehicle internet access. This technology paves the way for a variety of safety, convenience and entertainment applications, including safety message exchange, real-time traffic information sharing and public internet access. The overall goal of vehicular networks is to create an efficient, safe and convenient environment for vehicles on the road. This paper presents a Sensitive Node Election (SNE) algorithm adapted to routing protocols in certain opportunistic network environments. The algorithm focuses on selecting the best agent for communication using an innovative approach for message forwarding. Quality of Service (QoS) metrics targeted for optimization include network end-to-end throughput and packet delivery, with the aim of improving the overall performance of the network. Our algorithm includes a stochastic rebroadcasting scheme that takes into account parameters, such as vehicle density, distance between vehicles and transmission distance, and adapts to various network conditions. Furthermore, the SNE algorithm uses a metric based on transmission distance and can dynamically adapt to application requirements, such as prioritization. It provides high throughput and minimizes delay. The results demonstrate the effectiveness of this approach in improving QoS in various vehicular ad hoc network (VANET) simulations and influencing the neural network ensemble (NNE Algorithm). Full article

Chronic, dysregulated inflammation contributes to colitis-associated colorectal cancer (CRC), and the cGAS–STING pathway represents a central but therapeutically challenging node because both insufficient and excessive STING activity can be pathogenic. Here, we integrate AlphaFold3 (AF3) receptor modeling, diffusion-based docking, and explicit-solvent molecular dynamics (MD) simulations to characterize how the marine briarane diterpenoid excavatolide B (ExcB) engages the human STING (hSTING) cyclic dinucleotide (CDN)-binding cleft. The structural integrity of the AF3 hSTING model was validated through both intrinsic confidence scores (pLDDT, PAE) and comparative benchmarking against experimental CTD structures (PDB: 4EF5, 6A05). Notably, the local geometries of key pocket-defining residues—including His157, Tyr167, and Thr263—remained consistent with established crystallographic data. Across three independent 100 ns MD replicas, ExcB exhibits a consistent spatial progression from an entrance-proximal pose at the solvent-accessible rim of the cleft (Site-2) to a more embedded, non-canonical corridor on the Cys148-adjacent side (Site-2′). Distance and contact analyses support a predominantly non-covalent within-cleft mechanism and do not indicate a persistent approach to the literature-reported covalent regime near Cys91. Residue-level profiling over the stabilized sampling window defines a reproducible corridor “contact signature” and reveals within-cavity sub-pose diversity rather than a single rigid bound pose. Mechanistically, competitive docking of the native agonist cGAMP to ExcB-conditioned receptor snapshots yields consistently less favorable docking outcomes in ExcB-conditioned conformations than docking to the native/open receptor; retaining ExcB coordinates does not further penalize cGAMP, supporting a receptor-reshaping (conformational conditioning) component rather than persistent static steric clash. Our findings characterize ExcB as a non-covalent modulator targeting a cryptic pocket within the STING CDN-binding cleft, establishing a structural basis for targeted mutagenesis and structure-activity relationship (SAR) studies. Full article

Recent GraphRAG methods based on knowledge graphs (KGs) primarily rely on either under-reasoning or a structural path-level retriever, which prevents them from jointly capturing fine-grained semantic relevance and explicit multi-hop reasoning paths. This separation often results in semantic mismatch—where logical links are missing—or structural over-constraint in reasoning— where rigid dependencies limit flexible reasoning—thereby degrading both answer accuracy and the reliability of evidence in complex KGQA tasks. To address these issues, we propose HybRAG, a hybrid retrieval framework that synergistically integrates a semantic node-level retriever and structural path-level retriever. HybRAG constructs a hybrid subgraph that jointly reflects the semantic proximity of entities and the relational structures encoded in the KG. Furthermore, we incorporate retrieval-augmented fine-tuning, which enables the model to internalize advanced reasoning strategies for interpreting disparate semantic and structural signals, rather than merely memorizing domain facts. Through extensive experiments on the WebQSP and CWQ benchmarks, we demonstrate that HybRAG effectively bridges the gap between LLM-centric semantic approaches and GNN-centric structural approaches, outperforming single-retriever baselines. Our findings, including detailed sensitivity and ablation analyses, provide empirical evidence that the systematic alignment of semantic and structural signals is essential for ensuring the reasoning reliability and scalability of next-generation GraphRAG systems. Full article

Accurate multi-modal traffic demand forecasting is key to optimizing intelligent transportation systems (ITSs). To overcome the shortcomings of existing methods in capturing dynamic high-order correlations between heterogeneous spatial units and decoupling intra- and inter-mode dependencies at multiple time scales, this paper proposes a Dynamic Multi-Relation Learning with Multi-Scale Hypergraph Transformer method (MST-Hype Trans). The model integrates three novel modules. Firstly, the Multi-Scale Temporal Hypergraph Convolutional Network (MSTHCN) achieves collaborative decoupling and captures periodic and cross-modal temporal interactions of transportation demand at multiple granularities, such as time, day, and week, by constructing a multi-scale temporal hypergraph. Secondly, the Dynamic Multi-Relationship Spatial Hypergraph Network (DMRSHN) innovatively integrates geographic proximity, passenger flow similarity, and transportation connectivity to construct structural hyperedges and combines KNN and K-means algorithms to generate dynamic hyperedges, thereby accurately modeling the high-order spatial correlations of dynamic evolution between heterogeneous nodes. Finally, the Conditional Meta Attention Gated Fusion Network (CMAGFN), as a lightweight meta network, introduces a gate control mechanism based on multi-head cross-attention. It can dynamically generate node features based on real-time traffic context and adaptively calibrate the fusion weights of multi-source information, achieving optimal prediction decisions for scene perception. Experiments on three real-world datasets (NYC-Taxi, -Bike, and -Subway) demonstrate that MST-Hyper Trans achieves an average reduction of 7.6% in RMSE and 9.2% in MAE across all modes compared to the strongest baseline, while maintaining interpretability of spatiotemporal interactions. This study not only provides good model interpretability but also offers a reliable solution for multi-modal traffic collaborative management. Full article

This study constructs a global transportation carbon emission spatial correlation network via a modified gravity model and explores its evolutionary characteristics and dynamic mechanisms by integrating three-dimensional evolutionary analysis (node, overall, structural) and temporal exponential random graph model (TERGM). The main findings are as follows: (1) Global transportation carbon emission spatial correlation intensity keeps rising, with improved connectivity and integration, forming three regionally agglomerated correlation poles centered on the United States (America), China (Asia) and major European countries (Europe). (2) Network centrality distributes asymmetrically: Switzerland, Norway and the United States remain core nodes, while China, Japan and other Asian economies with strong direct correlation radiation are not in the core tier. (3) Third, evolutionary dynamics stem from the synergistic interaction of multidimensional attributes. ① Economic level positively drives bidirectional connection emission and attraction; economic scale and openness curb emission but boost attraction, while tertiary industry structure inhibits both. ② Only economic level and government efficiency exert significant positive effects on absdiff, fostering network heterophilic attraction. ③ Spatial and institutional proximity in edgecov effectively facilitate connection formation. ④ Endogenous network variables present a collaborative mechanism of reciprocity and transmission, constrained by network density. ⑤ Temporal effects show early connection structure forms path dependence, resulting in low dynamic variability and overall network stability. Full article