Temporal heterogeneous network representation learning is a pivotal approach for encapsulating the diversity of nodes and edges along with their temporal evolution into concise, low‐dimensional node representations. This technique has demonstrated remarkable efficacy in various network analysis and inference tasks. However, existing approaches study network evolution mainly by analyzing snapshots of temporal networks, while neglecting the intrinsic formation mechanisms of temporal heterogeneous networks. Few dynamic models delve into the intrinsic factors propelling network evolution. To fill this research gap, we introduce a novel learning framework for temporal heterogeneous network representation learning with dynamic influence modeling, denoted as THNRD. THNRD pioneers the application of the Hawkes process to temporal heterogeneous networks, utilizing the linking process of dynamic events to emulate the network’s formation mechanism, capturing the intrinsic dynamic progression of temporal heterogeneous networks. Subsequently, THNRD introduces a multilayer spatiotemporal aggregation model under a unified spatiotemporal framework, which is designed to harmoniously integrate the semantic and dynamic attributes of the networks. We also take node influence into consideration to further describe the temporal emergent phenomena. We verify the effectiveness of our proposed method via extensive experimental evaluations on real‐world datasets. The results consistently demonstrate that THNRD outperforms current state‐of‐the‐art methods.

The efficiency of large industrial sectors plays a critical role in promoting high‐quality economic growth and enhancing total factor productivity. Data envelopment analysis (DEA) is widely used for such evaluations due to its flexibility and nonparametric structure; however, its core mechanism, which allows each decision‐making unit (DMU) to select optimal weights, often yields extreme and unreasonable weighting schemes that distort efficiency scores and weaken discriminatory power. This study addresses this foundational challenge through three key contributions. First, it introduces the weight extremity function (WEF), a mathematical construct that quantifies the degree of weight concentration within any given weighting scheme. Second, it develops a novel DEA model that incorporates WEF‐based constraints, effectively preventing DMUs from adopting unreasonable weight distributions while preserving the method’s inherent flexibility. The proposed framework transforms the resulting nonlinear programming problem into an equivalent linear formulation, ensuring computational tractability. Third, to eliminate subjective parameter selection, the model employs a volume‐under‐the‐surface calculation method to derive efficiency scores, relying solely on objective statistical properties of the data. An empirical study of China’s large‐scale industrial sectors across 31 provinces (2019–2022) demonstrated the enhanced discrimination capability and evaluation consistency of the proposed approach. The findings reveal significant geographical disparities in industrial efficiency across China, with the proposed model providing more nuanced and robust rankings than existing alternatives.

Blockchain technology has revolutionized numerous industries by providing decentralized, transparent, and immutable ledgers. However, its adoption is hindered by persistent security challenges, including arbitrage attacks, liquidity exploits, and noncompliance with antimoney laundering (AML) regulations. This paper proposes an enhanced framework to address these issues, combining dynamic pricing mechanisms, AI‐based anomaly detection, and regulatory compliance checks within a multilayered architecture. The framework is composed of five interconnected layers: the input layer for data collection and validation, the data warehouse layer for structured data classification, the processing layer for anomaly detection and pricing adjustments, and the decision layer for transaction validation, execution, and reporting. The integration of these layers ensures robust security and compliance mechanisms, reducing system vulnerabilities while optimizing efficiency. To validate the proposed framework, we conducted simulations using real‐world blockchain scenarios, including decentralized finance (DeFi) platforms and cryptocurrency exchanges. Results demonstrate significant reductions in arbitrage opportunities and liquidity risks, with improved accuracy in anomaly detection and compliance adherence. For instance, the dynamic pricing mechanism mitigated 87% of arbitrage attack attempts, while the AI‐based anomaly detection achieved an 89% accuracy rate in identifying high‐risk transactions. This study provides actionable insights and a scalable solution for enhancing blockchain security and trust. Future work will focus on integrating cross‐chain interoperability, real‐time threat intelligence, and privacy‐preserving techniques to further expand the framework’s applicability. By addressing critical vulnerabilities, this research contributes to the development of secure, transparent, and compliant blockchain ecosystems, paving the way for wider adoption across industries. Unlike previous blockchain security models, our framework introduces a real‐time, AI‐enhanced risk assessment mechanism that dynamically updates transaction risk scores, mitigating financial threats in decentralized environments. This holistic approach provides a scalable, explainable, and adaptive security system that not only protects decentralized financial infrastructures but also aligns with emerging regulatory requirements, ensuring long‐term applicability.

Depression is a serious mental disorder, and timely detection and treatment are crucial. Electroencephalography (EEG), as a noninvasive tool directly reflecting brain activity, is suitable for objective depression detection. However, due to difficulty in acquiring depression EEG data, public datasets suffer from limited sample sizes and insufficient exploitation of features, limiting current approaches in training sufficiency and generalization. To this end, this paper proposes an EEG collaborative diffusion framework (EEGCo‐Diff) to address small‐sample learning and sufficient feature utilization. Specifically, this study constructs a multicondition guided diffusion model conditioned on Riemannian manifold features and ground‐truth labels to generate high‐quality EEG samples while maintaining geometric consistency of the original distribution, effectively alleviating sample scarcity and distribution shift. Second, this study proposes a CNN interaction transformer network (CITNet) that fuses multilayer convolution and a transformer to model local details and global temporal dependencies and uses an oscillation‐aware module (OAM) to highlight key channel features. EEGCo‐Diff couples geometry‐prior‐driven data generation with structured temporal modeling, significantly improving discriminative power and generalization in small‐sample, heterogeneous settings. On two public EEG depression datasets, our method achieves accuracies of 89.44% and 95.03%, outperforming the strongest baselines by 8.34% and 1.03%, respectively, and establishing state‐of‐the‐art performance.

Accurate wind‐power forecasting is essential for the successful integration of renewable energy into smart city grids. Traditional long short‐term memory (LSTM) networks face significant challenges in parameter optimization, leading to suboptimal forecasting performance. This paper introduces a novel Bernstein dynamic horned lizard optimization algorithm (BDHLOA) to optimize LSTM parameters for wind power prediction in smart cities. BDHLOA incorporates three key enhancements: a Bernstein‐assisted oppositional‐multiple learning (BOML) strategy that improves exploration–exploitation balance, Bernstein‐based adaptive differential (BAD) strategy for better solution refinement, and a dynamic drift search (DDS) mechanism that prevents premature convergence. The proposed BDHLOA‐LSTM framework is evaluated for one‐step‐ahead (10‐min horizon) wind‐power forecasting using four real‐world datasets from La Haute Borne wind turbines in France, under a rolling‐origin expanding‐window cross‐validation protocol that strictly prevents data leakage. Results demonstrate exceptional performance across all four stations, with mean R 2 = 0.9695, RMSE = 0.0011, and MAE = 0.0007. BDHLOA‐LSTM reduces MAE by 94% compared to persistence forecasting and 91% compared to autoregressive AR(24) models. Against the best competing optimizer (PSO‐LSTM), BDHLOA‐LSTM achieves 42% lower MAE on average across sites. Performance is assessed using R 2 , RMSE, MAE, symmetric mean absolute percentage error (sMAPE), mean absolute scaled error (MASE), and prediction interval coverage probability (PICP) to capture both accuracy and calibrated uncertainty. Furthermore, the proposed model is interpreted using SHapley Additive exPlanations (SHAP) technique. SHAP analysis confirms that BDHLOA‐LSTM learns physically meaningful relationships dominated by wind speed statistics and direction, rather than exploiting spurious correlations. The superior accuracy and stability of BDHLOA‐LSTM make it highly suitable for real‐time grid management and sustainable energy planning in smart cities.

Quantitative evaluation of artificial precipitation enhancement effects remains a critical challenge in meteorological science. Traditional methods rely on idealized assumptions and control area selection, with limited applicability under complex terrain conditions. To address this problem, this paper proposes physics‐informed simple video prediction (SimVP) (PiSim), a hybrid physics‐informed disentangled framework designed to disentangle precipitation evolution. By employing the advection–diffusion equation (ADE) as an inductive bias within the latent space, we transform spatial comparative evaluation into a precise spatiotemporal sequence prediction task. The method adopts a dual‐branch architecture: The physics branch explicitly models deterministic macroscopic motion (advection and diffusion), whereas the data‐driven branch learns complex nonlinear residuals, effectively compensating for microphysical processes and local variations. Experimental results on the Hubei Province Swan radar dataset demonstrate that PiSim achieves a 5.5% improvement in MSE compared to the SimVP baseline, with pronounced advantages in heavy precipitation forecasting. Evaluation of 10 typical artificial precipitation enhancement operations shows hourly net rainfall increments ranging from 0.16 to 4.60 mm, which are highly consistent with historical records, validating the method’s effectiveness.

Parkinson’s disease (PD) is one of the fastest‐growing neurodegenerative disorders, where timely diagnosis is essential for optimizing treatment. In this study, we created a radiomics–MDS‐UPDRS, a robust dataset by integrating DaTscan SPECT radiomics data with the clinical characteristics of MDS‐UPDRS collected from Parkinson’s progression markers initiative (PPMI) to monitor dopamine depletion in the striatum (caudate and putamen) and allow classification and progression analysis of PD. To construct the dataset, the striatum was segmented using a modified K‐means clustering algorithm, extracting 25 radiomics features combined with 59 clinical features. In addition, linear discriminant analysis was used to select 22 significant characteristics, and a four‐way feature selection method was used to identify 30 significant clinical features, resulting in a refined set of 52. Classification with machine learning models improved performance after LDA, achieving over 91% accuracy. We evaluated feature behavior across six PD severity stages and four clinical visits for progression analysis. The clinical features of MDS‐UPDRS were more sensitive to changes in the severity of the initial PD. At the same time, the integrated dataset, radiomics–MDS‐UPDRS, provided more balanced insights, showing a progression of 33.30%–83.30% and 36.36%–45.50% from the first visit to the fourth visit among the clinical and radiomics features and a progression of 73.33%–96.67% and 13.64%–54.55% between the minimal vs mild and minimal vs very severe stage. Our analysis also revealed practical links between progression features and real‐life scenarios, which highlights the practical value of our study for clinical decision‐making.

The rapid advancement of deepfake technology poses significant challenges to athletic celebrities, where altered or falsified media can impact athletes’ reputations, fan engagement, and the integrity of match broadcasting. This paper proposes a novel framework for deepfake image recognition for athletic celebrities using locality sensitive hashing (LSH). LSH, an efficient technique for high‐dimensional nearest neighbor searches, is employed to detect and differentiate deepfake images from authentic media. By extracting high‐dimensional features from images and videos using convolutional neural networks (CNNs), LSH is applied to hash similar content into clusters for quick and accurate deepfake detection. The proposed method is tested on real‐world dataset, showing promising results in terms of accuracy and computational efficiency. This research highlights the importance of integrating advanced hashing techniques like LSH in safeguarding the authenticity of digital content and provides insights into future directions for deepfake detection mechanisms.

This study addresses the pressing need for improved lung cancer diagnosis and treatment by leveraging computational methods and omics data analysis. Lung cancer remains a leading cause of cancer‐related deaths globally, highlighting the urgency for more effective diagnostic and therapeutic approaches. Current diagnostic methods, such as imaging and biopsies, suffer from limitations in sensitivity, specificity, and accessibility, often due to factors such as poor data quality, small sample sizes, and variability in data sources. These limitations highlight the necessity for the development of advanced noninvasive techniques. Computational methods utilizing omics data have shown promise in overcoming these challenges by comprehensively understanding the molecular pathways involved in lung cancer. We propose a novel approach that utilizes RNA‐Seq data and employs LASSO regression with attention mechanisms to identify lung cancer biomarkers. Our results demonstrate the effectiveness of this approach in identifying potential biomarkers for lung cancer, including well‐known genes such as TP53, EGFR, KRAS, ALK, and PIK3CA, validating the model’s ability to uncover key genes associated with lung cancer development and progression. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses revealed significant associations of the identified genes with critical biological processes and pathways, including protein synthesis, folding, cell adhesion, gene regulation, and immune responses. The PPI network analysis, constructed using the STRING database and Cytoscape application, highlighted a highly interconnected interaction landscape, with central hub genes playing pivotal roles in lung cancer progression. RPSA emerged as a crucial hub gene, consistently identified across different centrality measures. This study sheds light on the potential of computational methods and omics data analysis in improving lung cancer diagnosis and treatment, offering new insights for future research directions and personalized medicine strategies.