Search Results (983)

Ascosphaera apis, an obligate lethal fungal pathogen that infects bee larvae, and causes chalkbrood disease, poses a significant threat to the global beekeeping industry. Long non-coding RNAs (lncRNAs) are employed by pathogens to enhance infectivity and evade host immunity. Here, lncRNAs in A. apis spores (AaCK group) and the guts of 4-, 5-, and 6-day-old Apis cerana cerana worker larvae inoculated with A. apis spores (AaT1, AaT2, and AaT3 groups) were identified, characterized, and validated. Additionally, the expression pattern of fungal lncRNAs during infection was analyzed, followed by an investigation of the regulatory manners and roles of differentially expressed lncRNAs (DElncRNAs). A total of 1379 lncRNAs were identified in AaCK, AaT1, AaT2, and AaT3 groups using bioinformatics, involving various types such as sense lncRNAs, antisense lncRNAs, bidirectional lncRNAs, intergenic lncRNAs, and intronic lncRNAs. Additionally, 4, 9, and 75 up-regulated lncRNAs as well as 2, 1, and 15 down-regulated ones were identified in the 4-, 5-, and 6-day-old larval guts following A. apis inoculation. Fifteen DElncRNAs as potential antisense lncRNAs may interact with 15 sense-strand mRNAs in the AaCK vs. AaT3 comparison group. Cis-acting analysis identified 10, 16, and 136 upstream and downstream genes of DElncRNAs in the aforementioned comparison groups, involving a series of GO terms and KEGG pathways like metabolic process and biosynthesis of secondary metabolites. Following the trans-acting investigation, 752, 821, and 1327 co-transcribed genes with DElncRNAs were discovered, spanning an array of functional terms and pathways such as biological processes and glycerophospholipid metabolism. Analysis of a competing endogenous RNA (ceRNA) network indicated that 1 and 5 DElncRNAs in the AaCK vs. AaT1 and AaCK vs. AaT3 comparison groups potentially targeted 1 and 2 miRNAs, further targeting 208 and 286 mRNAs, respectively. Further analysis identified one ceRNA axis relevant to the MAPK signaling pathway and several ceRNA networks associated with the biosynthesis of secondary metabolites. Finally, RT-qPCR results confirmed that the expression trends of six randomly selected DElncRNAs were consistent with those in the transcriptome data. These findings not only offer a foundation for elucidating the mechanisms underlying DElncRNA-mediated A. apis infection but also enrich our understanding of honeybee host–fungal pathogen interactions. Full article

Traditional linear models in urban informatics struggle to capture the complex, non-linear spatial non-stationarity inherent in metropolitan housing markets. To overcome these constraints, this study introduces a data-driven computational framework integrating a Spatio-Temporal Graph Convolutional Network (STGCN) with gradient-based Explainable Artificial Intelligence (XAI) and Geographically Weighted Regression (GWR). This framework is empirically tested using 217,598 apartment transactions in Busan, the Republic of Korea, augmented with high-resolution micro-demographic grids and Digital Elevation Model (DEM) topographical data. Utilizing unsupervised K-Means clustering, the region is spatially stratified into a dense Urban Core and a dispersed Suburban Periphery. The STGCN demonstrates overwhelming predictive superiority ($R^2=0.802$) over the traditional Spatial Error Model ($R^2=0.437$). Crucially, gradient-based XAI and localized GWR coefficients successfully unspool the deep learning “black box,” visualizing hyper-localized economic realities that global linear models obscure. The analysis expose stark regional market segmentation driven by environmental topography, mathematically quantifying non-linear dynamics such as coastal high-floor premiums, severe mountainous altitude penalties, and latent urban reconstruction premiums. Ultimately, this research bridges the gap between predictive computational power and spatial economic interpretability, offering a robust informatics framework for equitable urban planning. Full article

This article discusses the practical implementation of a prototype academic transcript storage system based on blockchain technology and smart contracts. The digital transformation of higher education requires reliable mechanisms for ensuring the integrity and verifiability of academic documents. It presents the design and experimental validation of a blockchain-based system for storing and verifying academic transcripts within the higher education system of the Republic of Kazakhstan. The proposed solution is based on an Ethereum Virtual Machine-compatible smart contract implemented in Solidity and deployed on a test network. The testnet was used as the experimental environment, and transaction monitoring was performed using the BlockScout v11.0.3 explorer. The architecture of the TranscriptStorage smart contract is presented, including a role-based access model, a data indexing mechanism using keccak-256, and storage of transcripts in a mapping structure (bytes32 => Transcript[ ]). The experimental results confirm the successful recording of the Transcript in the distributed ledger, event recording (Logs), and the correctness of the ABI encoding of input parameters (Raw Input), as well as a change in state (State Changes) reflecting the fee payment. The use of events is shown to enable cost-effective third-party data verification without the need to store the entire text in the contract state. The comparative results showed that the proposed system reduced gas consumption by 804.5% compared to Blockcerts, 48.8% compared to ECertChain, 82.5% compared to ShikkhaChain, and 43.5% compared to zkEVM. These improvements were achieved while maintaining high scalability, robust privacy features, and security, making it a practical solution for Kazakhstan’s educational system. Full article

With the widespread adoption of cryptocurrencies, the ability to conduct continuous offline payments has increasingly become a critical technological requirement. In network-constrained scenarios, current dual-offline payment technologies are useful for single transactions. However, their limitations in continuous payment scenarios have become increasingly evident, making them unable to meet real-world application needs. This has prompted the industry to demand more urgent innovations in research on continuous offline payment capabilities. To address these challenges, this paper proposes a continuous dual-offline payment system capable of supporting multiple continuous payments. The system integrates elliptic curve cryptography (ECC) and zero-knowledge proof (ZKP) technology to generate secure asset credentials, ensuring both immutability and privacy credentials throughout the offline payment lifecycle. A dynamic credential decomposition mechanism enables the splitting of input credentials into change credentials and receipt credentials, facilitating uninterrupted dual-offline payments between hardware wallets. Additionally, it incorporates a batch verification scheme based on smart contracts, utilizing zero-balance verification and chained hash tracing to ensure payment uniqueness and prevent double-spending attacks, thereby guaranteeing the verifiability and validity of payment settlements. Experimental evaluations demonstrate that the proposed system reduces gas consumption per payment and improves execution efficiency during batch processing, combining high security with strong performance. This research provides a feasible solution for the application of digital currencies in offline scenarios, carrying significant theoretical value and practical significance for driving technological innovation and application expansion in the cryptocurrency field. In addition to cryptocurrency payments, the proposed system is also applicable to IoT and sensor network environments. Many IoT devices operate in disconnected or network-limited areas and require secure micro-transactions. Our dual-offline payment mechanism supports such scenarios, as the main cryptographic operations are lightweight enough for typical IoT hardware. This further extends the practical value of our system beyond traditional cryptocurrency payments. Full article

Credit card fraud constitutes a core component of the contemporary cybercrime economy, in which dark web carding forums play a pivotal role in coordinating, commoditising, and disseminating illicit activities. While prior research has primarily focused on transaction-level fraud detection, comparatively limited attention has been devoted to the systematic analysis of the social and organisational ecosystems within which these practices are enacted. This study addresses this gap by proposing and validating a domain-specific taxonomy for the automated classification of content in P2P carding forums. To this end, we adopt an iterative, data-driven methodology that integrates large language models (LLMs), lexical co-occurrence analysis, and semantic network analysis. Using a corpus of 3260 posts, we define and operationalise a taxonomy structured around four predicates: activity context, actor role, products and services, and technical tools, supported by a locally deployed LLM (Llama 4 Scout). A human-annotated subset was additionally used to evaluate inter-annotator agreement and standard classification metrics, complementing the coverage-based assessment and enabling comparison against a keyword-based baseline. Evaluation was further strengthened through manual benchmarking, confidence intervals, sensitivity analysis of key pipeline components, and comparison with alternative open-weight models. The results indicate that the proposed taxonomy achieves broad corpus-level representational coverage, with at least one semantic dimension identified in 98.71% of posts. However, coverage is uneven across predicates: activity-context is highly explicit, whereas actor-role and product-service show only moderate coverage and technique-tool remains substantially underrepresented and ambiguous. Overall, the findings show that combining domain-specific taxonomies with LLM-assisted classification and network analysis offers a robust framework for understanding and monitoring carding ecosystems in the dark web. Full article

Modern company activities depend greatly on inventory management, which covers demand forecasting and inventory optimization to guarantee operational effectiveness and customer happiness. This paper presents a new method fusing blockchain technology with cutting-edge deep learning to overcome these restrictions for better inventory management. Initially, the data are preprocessed using Zmin–max normalization (ZMM), and then feature extraction follows. To extract the spatiotemporal features and capture long-term temporal dependencies in demand data, a hybrid deep learning architecture is presented, built on a Deep Convolutional Koopman Network (CKN) integrated with a Coordinate Attention-Based Gated Recurrent Unit (CKN-CGRU).Genetic Secretary Bird Optimization (GSBO) is used to further tune the model automatically. While the CKN captures complex spatial temporal correlations, the GRU effectively models sequential dependencies. Blockchain architecture with smart contracts and improved Proof-of-Stake consensus is integrated to guarantee data integrity and transparency in stock transactions. This makes it possible to securely, automatically, and in a tamper-proof way record inventory projections, orders, and stock updates. The suggested system improves the stakeholder trust in decentralized inventory management by ensuring complete traceability and real-time auditability throughout the process. Experimental outcomes show the efficiency of the proposed model strategy, with an accuracy of 99.94% and precision of 99.93%. Full article

Real-time fraud detection has become a critical component in ensuring the security and stability of the digital financial ecosystem. However, existing methods struggle to adapt to the highly dynamic and adversarial nature of modern financial fraud, where malicious actors constantly evolve their strategies to evade detection. To address the dual challenges of complex topological relationships and severe concept drift, we propose the Augmented-Driven Graph Network with Adaptive Exploration (AGNAE). First, this paper introduces an augmented graph neural network tailored for financial transaction graphs, which dynamically models the heterogeneous interactions between transacting entities to capture complex, hidden fraud rings. Second, rather than relying on static classifiers, we rigorously formulate the real-time detection process as a sequential decision-making problem. This paper introduces a deep reinforcement learning agent equipped with an adaptive exploration mechanism to continuously update detection strategies, striking an optimal balance between exploiting known fraud patterns and exploring emerging mutations. Furthermore, a novel joint loss function is designed to synergize topological representation learning with the agent’s long-term financial reward optimization. Extensive experiments on the real-world for IEEE-CIS and FDAD-20 datasets demonstrate that AGNAE significantly outperforms state-of-the-art baselines. Crucially, despite its sophisticated architecture, AGNAE maintains an inference latency of 1.12 ms per transaction, fully satisfying the stringent computational requirements of real-world financial infrastructures. Full article

Blockchain technology has emerged as a foundational paradigm for building decentralized, transparent, and secure systems, particularly in environments that operate without centralized authority. At the core of these systems are consensus mechanisms that ensure transaction validity and maintain trust among distributed participants. However, the efficiency of a blockchain network is strongly influenced by how verifier (or validator) nodes are selected, particularly in sharded architectures where transaction processing is distributed across multiple shards. A critical challenge in blockchain design is selecting appropriate nodes for transaction verification in a manner that is efficient, fair, and resilient to adversarial behavior, while also minimizing communication overhead. Existing approaches often rely primarily on resource availability or on the ability to create blocks, particularly in sharded blockchain architectures. Building on these ideas, this paper proposes a Resource Weighted–Block Score selection algorithm, which integrates a node’s block score with its computational resource availability to guide verifier node selection. Simulation-based evaluation demonstrates that the proposed approach significantly reduces transaction verification latency and improves overall node utilization, thereby enhancing network performance and scalability in sharded blockchain systems. Full article

Artificial intelligence applications in mental health diagnosis face persistent challenges related to interpretability, trust, and the integrity of results. This study presents a trust-aware explainable deep learning framework that combines systematic benchmarking, SHAP-based interpretability, and permissioned blockchain verification to achieve secure mental health classification. The Depression & Mental Health Classification Dataset was used, which contains 1999 records, 21 features, and 12 classes. Data preprocessing included categorical encoding and Z-score normalization for continuous variables. To ensure robust evaluation, a stratified train–test split was applied, and class imbalance was addressed using the Synthetic Minority Over-sampling Technique (SMOTE). Eight machine learning and deep learning models were assessed under identical preprocessing and validation settings. In addition, two models were proposed: Feature Attention XGBoost (FA-XGBoost) and Feature Attention Feedforward Neural Network (FA-FNN). The FA-FNN model achieved the best performance, attaining an accuracy of 96.00%, precision of 98.31%, recall of 97.31%, and F1-score of 98.04%. To address deep learning’s black-box limitation, SHapley Additive ExPlanations (SHAPs) were used to provide both global feature importance and instance-level explanations, enabling transparent identification of the most influential mental health markers. Beyond interpretability, a permissioned blockchain layer was added to provide tamper-proof logging and traceable verification of AI results. The framework securely stores cryptographic hashes of model versions, prediction results, and generated SHAP artifacts, including visualization images, without exposing sensitive medical data. By integrating explainable decision-making, high-performance classification, and blockchain-based trust enforcement, the proposed framework creates a transparent and secure pipeline suitable for real-world mental healthcare systems. Controlled experiments on a permissioned Ethereum-InterPlanetary File System (IPFS) network demonstrated predictable latency, stable throughput (≈28–30 transactions/s), and lower operational costs, proving the framework’s suitability for enterprise and healthcare deployments. Full article

Freshwater scarcity is emerging as a critical constraint on industrial clusters, production networks, and urban service systems, where water functions simultaneously as an essential production input and a shared regional resource. This study investigates how post-allocation water-quota imbalances in large inter-basin diversion systems can be addressed through adaptive secondary water rights trading. Focusing on China’s South-to-North Water Diversion Project (SNWDP), the research aims to explain under what institutional and efficiency conditions water rights trading can enhance corporate social responsibility, environmental management, and sustainable supply chain resilience. The study’s main innovation lies in the development of a corporate-oriented evolutionary game model that links water governance with corporate production, urban–industrial demand, and responsible supply chain management. Unlike conventional models, it incorporates bounded rationality, heterogeneous water-use efficiency, information asymmetry, transaction costs, primary allocation water pricing, and the risk of unrecovered basic water fees. Using a case inspired by the Zhengzhou–Nanyang transaction along the Middle Route of the SNWDP, the model simulates the strategic interaction between a water-rich node with surplus quota and a water-scarce node facing deficit demand. The findings show that a socially desirable Trade–Trade equilibrium emerges only when efficiency expectations and institutional conditions are favorable. Lower transaction costs and basic water prices, higher sunk-fee risk, and clearer efficiency differentials significantly increase trading willingness. The study demonstrates the practical value of transparent secondary water markets in improving allocative flexibility, reducing governance rigidity, and promoting more responsible and environmentally efficient regional water management. Full article

With the large-scale integration of electric vehicles (EVs) into the power grid, uncoordinated charging behavior has aggravated load fluctuations in the power system. Deep reinforcement learning can optimize EV charging and discharging strategies through dynamic decision-making, thereby alleviating the operational pressure imposed on the grid by load variations. However, under large-scale EV integration scenarios, challenges still remain, including the excessively high dimensionality of the state space and the resulting decline in training efficiency. In addition, the coupling between existing clustering methods and dynamic scheduling mechanisms is still insufficiently tight. To address these issues, this study proposes a cluster-based deep reinforcement learning method for EV charging and discharging scheduling, referred to as CDRL. First, a probabilistic behavioral model is constructed based on EV charging transaction data to characterize the stochasticity of user charging behavior. A Density–Centroid Hybrid Clustering (DCHC) method is then adopted to cluster the charging behavior characteristics of EVs. Subsequently, at the cluster level, a day-ahead base load forecasting model is introduced, and the forecasting results are fed into a mixed-integer linear programming (MILP) model to generate the charging and discharging power allocation tasks for each cluster. At the individual level, the EV charging and discharging process is formulated as a Markov decision process (MDP), and a deep Q-network (DQN) is employed for policy learning, thereby achieving the decomposition of cluster-level tasks into individual scheduling decisions. The simulation results demonstrate that the proposed method can effectively reduce charging costs and smooth system load fluctuations while improving training convergence speed and policy stability. Full article

Natural disasters are devastating occurrences that have a major influence on the well-being of numerous individuals on a global scale. The primary goal of this study is to facilitate the rapid, transparent, and safe delivery of various aid such as food and clothing to people in disaster areas. For this purpose, a system has been established using blockchain technology in cooperation with institutions and humanitarian organizations. This system is designed to be accountable and reliable; it will supervise all processes from the source of aid materials to their distribution while protecting the personal information of disaster victims. The assistance process is improved using Smart Contracts in order to provide fast, effective, and coordinated assistance. Unlike existing humanitarian frameworks that rely on permissionless networks such as Bitcoin or Ethereum, this study proposes Hyperledger Fabric to ensure beneficiary privacy and eliminate per-transaction fees for end-users, thereby offering a more sustainable economic model for high-frequency aid distribution compared to public blockchains. The proposed system (Chainguard) addresses the ’efficiency gap’ in the current literature JSON Web Token (JWT)-based authentication layer. The results showed that Chainguard achieves a stable throughput of ~180 TPS with an end-to-end latency of less than 1.5 s, outperforming traditional heavy-cryptography models in terms of scalability and resource efficiency during real-time disaster response. Full article

Financial fraud represents one of the most critical operational risks faced by financial institutions, resulting in significant financial losses and destabilizing markets. While machine learning models are effective at prediction, their evaluation is often based on statistical performance metrics that do not directly translate into financial impact. This research develops an evaluation framework that integrates the costs of early fraud detection with predictive effectiveness and economic criteria for decision-making. Several supervised learning models (XGBoost, neural networks, Random Forest, decision trees, and logistic regression) were trained and tested on an imbalanced dataset of credit card transactions. To assess the potential benefit of these models for financial institutions, the savings rate and expected loss were employed alongside conventional metrics such as F1 score, AUC-PR, AUC-ROC, recall, and accuracy. The results show that economic outcomes are highly sensitive even among models with similar predictive performance. The ensemble methods, in particular, achieved the optimal balance between fraud detection capabilities and loss reduction, while models optimized solely for accuracy resulted in higher operating costs due to false positives or undetected fraud. The results indicate that the choice of fraud detection models should not be based solely on predictive accuracy, but also on cost asymmetry and risk tolerance. The proposed framework provides practical guidance to financial institutions seeking to align operational risk management and regulatory requirements with machine learning implementation, enabling risk-informed decision-making. Full article

Financial fraud has become a critical challenge for modern financial systems due to the rapid growth of digital transactions, online banking services, and electronic payment platforms. Traditional rule-based fraud detection systems are increasingly inadequate in addressing the evolving and adaptive strategies employed by fraudsters. Consequently, Machine Learning (ML) and Deep Learning (DL) techniques have emerged as powerful tools for detecting fraudulent activities in large-scale financial datasets. This paper presents a comprehensive survey of ML/DL approaches for financial fraud detection. The survey systematically reviews existing research across multiple methodological paradigms, including classical supervised learning, anomaly detection, graph-based methods, deep neural networks, multimodal architectures, and cost-sensitive learning frameworks. Particular emphasis is placed on emerging techniques such as graph neural networks, transformer-based architectures, and federated learning approaches designed to address privacy and scalability challenges. In addition to reviewing model architectures, this work analyzes key challenges inherent to fraud detection systems, including extreme class imbalance, concept drift, adversarial behavior, data privacy constraints, and real-time deployment requirements. Furthermore, the survey examines evaluation methodologies, highlighting the limitations of commonly used metrics and discussing more realistic evaluation strategies that incorporate operational costs and risk management considerations. This paper also provides a structured taxonomy of fraud detection methods, comparative analyses of commonly used datasets, and a synthesis of current research trends. Finally, open challenges and promising research directions are identified, including adaptive learning systems, interpretable Artificial Intelligence models, graph-based behavioral modeling, and privacy-preserving collaborative fraud detection frameworks. Full article

Recent years have witnessed the rapid proliferation of Industry 4.0 technologies in smart grids, leading to a revolution in energy generation and management, which provides improved operational efficiency and intelligent automation for smart grids. Nevertheless, this highly integrated infrastructure, while making energy more secure and reliable, simultaneously creates greater vulnerability to sophisticated cyber threats such as Distributed Denial of Service (DDoS) attacks, data manipulation and unauthorized access. The task of addressing these challenges requires innovative approaches that maintain the resilience as well as security of critical energy infrastructures. A novel Blockchain-Enhanced Cybersecurity Framework (BCF) specific to Industry 4.0-enabled smart grid systems is presented in this paper. The proposed framework integrates advanced security protocols with real-time threat detection capabilities through the decentralized, transparent and tamper-resistant nature of blockchain technology. Authentication, data validation and secure communication are accomplished through smart contracts to automate it, eliminating human intervention and single points of failures. The framework is able to allow for high transaction volumes, typical of modern smart grid networks, whilst maintaining integrity via a hybrid consensus mechanism that ensures scalability. In addition, the framework is further augmented with a Machine Learning-Based Intrusion Detection System (ML-IDS) to detect and mitigate cyber-attacks in real time. The proposed system achieves excellent performance in identifying malicious activities with high accuracy, precision and recall on the UNSW-NB15 dataset. Analysis with traditional methods indicates that the Blockchain Enhanced Cybersecurity Framework significantly lowers false positive rates and increases detection reliability. The framework is justified in terms of its strength to secure the systems in Industry 4.0-enabled smart grids against emerging cyber threats through extensive simulations and case studies. The value of this work is that it shows that blockchain and machine learning can be used to improve cybersecurity in renewable energy systems, and concrete insights and recommendations on implementing secure and cost-effective systems of energy infrastructure are provided. The proposed framework creates an enabling environment on which the creation of resilient and future-ready smart grids to facilitate the global goal of sustainable and secure energy can be developed. Full article