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The increasing complexity and scale of cyber threats have pushed Cyber Threat Intelligence (CTI) beyond the capabilities of traditional rule-based systems. This article explores how Artificial Intelligence (AI), particularly Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP), and graph-based analytics, is reshaping the CTI landscape. By automating threat data processing, enhancing attribution, and enabling predictive capabilities, AI is transforming CTI into a proactive and scalable discipline. By analysing CTI architectures, real-world use cases, platform comparisons, and current limitations, this study highlights the emerging opportunities and challenges at the intersection of cybersecurity and AI. This analysis concludes that the future of CTI lies in hybrid systems that seamlessly combine human expertise with intelligent automation. Full article

Non-uniform rational B-spline (NURBS) surfaces are effective for accurately modeling curved geometries, and research in this area has recently increased. In this study, point cloud data obtained from two challenging test environments (a convex wooden object and the widely used Stanford Bunny dataset from the literature) were used to predict the u and v parameter values corresponding to positions in the knot vectors, to determine the knot points of NURBS surfaces. The u and v parameters were predicted with accuracies of 92.60% and 93.20% for the wooden object, and 85.50% and 84.40% for the Stanford Bunny. The models’ decision-making processes were analyzed using explainable artificial intelligence (XAI) methods, including SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME). Predicted knot points were compared with the calculated knot points, which are considered as actual, yielding root mean square errors (RMSE) of 0.09 mm for the wooden object and 0.02 mm for the Stanford Bunny. This study fills a gap in the literature by predicting knot points and providing XAI-based analyses, demonstrating that the approach effectively preserves the characteristic features of NURBS surfaces across different geometries. Full article

Acknowledging recent breakthroughs in the context of deep bio-inspired neural networks, several architectural deep network options have been deployed to create intelligent systems. The foundations of convolutional neural networks are influenced by hierarchical processing in the visual cortex. The graph neural networks mimic the communication of biological neurons. Considering these two computation methods, a novel deep ensemble network is used to propose a bio-inspired deep graph network for creating an intelligent supply chain model. An automated smart supply chain helps to create a more agile, resilient and sustainable system. Improving the sustainability of the network plays a key role in the efficiency of the supply chain’s performance. The proposed bio-inspired Chebyshev ensemble graph network (Ch-EGN) is hybrid learning for creating an intelligent supply chain. The functionality of the proposed deep network is assessed on two different databases including SupplyGraph and DataCo for risk administration, enhancing supply chain sustainability, identifying hidden risks and increasing the supply chain’s transparency. An average accuracy of 98.95% is obtained using the proposed network for automatic delivery status prediction. The performance metrics regarding multi-class categorization scenarios of the intelligent supply chain confirm the efficiency of the proposed bio-inspired approach for sustainability and risk management. Full article

Wildfires are environmental hazards with severe ecological, social, and economic impacts. Wildfires devastate ecosystems, communities, and economies worldwide, with rising frequency and intensity driven by climate change, human activity, and environmental shifts. Analyzing wildfire insights such as detection, predictive patterns, and risk assessment enables proactive response and long-term prevention. However, most of the existing approaches have been focused on isolated processing of data, making it challenging to orchestrate cross-modal reasoning and transparency. This study proposed a novel orchestrator-based multi-agent system (MAS), with the aim of transforming multimodal environmental data into actionable intelligence for decision making. We designed a framework to utilize Large Multimodal Models (LMMs) augmented by structured prompt engineering and specialized Retrieval-Augmented Generation (RAG) pipelines to enable transparent and context-aware reasoning, providing a cutting-edge Visual Question Answering (VQA) system. It ingests diverse inputs like satellite imagery, sensor readings, weather data, and ground footage and then answers user queries. Validated by several public datasets, the system achieved a precision of 0.797 and an F1-score of 0.736. Thus, powered by Agentic AI, the proposed, human-centric solution for wildfire management, empowers firefighters, governments, and researchers to mitigate threats effectively. Full article

Generative Artificial Intelligence (Gen AI) offers significant potential to support requirements engineering (RE) education; however, its integration poses challenges regarding accuracy and student engagement. While Gen AI cannot independently specify requirements without hallucinating or overstepping scope, it can serve as a powerful partner in a hybrid intelligence workflow. In this paper, we address the challenge of translating high-level motivational models into detailed user stories, a process that is traditionally labour-intensive for novices. We introduce a structured, human-in-the-loop workflow that uses Gen AI to refine and polish user stories while strictly preserving student agency. By grounding the output from Gen AI in a validated motivational model, the workflow minimises the risk of metacognitive offloading, requiring students to actively critique and validate the initially generated requirements. Our analysis of instructional artefacts demonstrates that Gen AI helps in three ways: suggesting structural improvements, offering alternative professional phrasing, and enhancing readability. However, we also identify risks of intent drift and scope expansion, reinforcing the need for rigorous human oversight. The findings advocate for a pedagogical approach where the Gen AI system acts as a reflective assistant rather than an autonomous generator. Full article

The paper introduces a novel intelligent modeling system of a railway cargo delivery which combines queuing theory and station-level technological activities to model the manner in which re-handling and waiting processes produce delivery delays. The proposed model is in contrast to the available literature, which focuses more on routing or time management; it clearly connects processing of the stations, queue behavior, and reliability of the delivery in one decision system. When applied to a real-life railway route, the optimization of technological sequences is demonstrated to decrease delivery time and congestion rates significantly, as well as decrease the possibility of punishment in case of late deliveries. These findings show that the study is original in terms of the presentation of a data-driven and operationally based approach on the enhancement of railway freight performance. This study introduces a shipment-type-specific intelligent delivery model that integrates queuing theory with real station technological processes. Unlike existing approaches focused mainly on routing or average travel time, the proposed framework explicitly accounts for wagon processing sequences, re-handling operations, and delay-risk assessment. Validation on the Khamza–Bukhara corridor demonstrates a reduction in intermediate re-handlings from four to two and total delivery time from 68 h to 54 h, confirming the operational and economic effectiveness of the model. Full article

Polymathy relates to the exceptional learning abilities, in which individuals cultivate and coordinate Breadth, Depth, and integrative capability across multiple domains. It builds on mechanisms typically associated with intelligence, including abstraction, problem solving, and the transfer and integration of information. Because polymathic disposition has partial biological underpinnings, it may intersect with other biologically based individual differences, such as temperament. Biographical accounts also indicate that many polymaths did not achieve exceptional school grades, raising questions about whether the multiplicity of interests in polymaths is associated with distractibility and impulsivity, or whether there is a deeper institutional mismatch between polymaths and educational systems. Our study examined these issues using estimated high school grades across three subject areas, documented university grades, a neurochemistry-validated temperament assessment (Structure of Temperament Questionnaire; STQ-77), the Trait Polymathy Scale (TPS), the Barratt Impulsivity Scales (BIS-11), and information about aptitudes and interests from 296 participants (M/F = 152/144). Contrary to speculation that polymathy reflects distractibility, the TPS correlated negatively with the BIS-11 Lack of Attention scale and positively with the STQ-77 scales of Intellectual Endurance and Probabilistic Processing, confirming high sustained attention in polymaths. TPSs also had selective negative correlations with the STQ-77 Neuroticism scale and positive correlations with the STQ-77 Plasticity, Social Endurance, Sensation Seeking, dispositional Satisfaction scales, as well as several specific and general aptitudes and interests. These findings refine the dispositional profile linked to polymathy, highlighting the differential nature of the three components of polymathy. Full article

Accurate segmentation of pellet microstructure images is crucial for evaluating their metallurgical performance and optimizing production processes. To address the challenges posed by complex structures, blurred boundaries, and fine-grained textures of hematite and magnetite in pellet micrographs, this study proposes a hybrid intelligently optimized VGG16-U-Net semantic segmentation model. The model incorporates an improved SPC-SA channel self-attention mechanism in the encoder to enhance deep feature representation, while a simplified SAN and SAW module is integrated into the decoder to strengthen its response to key mineral regions. Additionally, a hybrid loss strategy is employed with KL regularization for training optimization. Experimental results show that the model achieves an mIoU of 85.58%, an mPA of 91.54%, and an overall accuracy of 93.58%. Compared with the baseline models, the proposed method achieves improved performance to some extent. Full article

This paper addresses the cooperative jamming task allocation problem for multiple jammers against multiple communication targets in dynamic electronic warfare environments. Traditional algorithms struggle with adaptability and slow decision-making. To overcome these limitations, we propose a deep reinforcement learning (DRL) method enhanced by an improved Vogel’s approximation method (VAM) pre-training strategy, where VAM incorporates situational matrices for initial allocation. The proposed approach aims to maximize the total jamming situational value by intelligently assigning optimal target combinations to each multi-beam jammer. Specifically, the model evaluates the situational value of each target by integrating factors including the distance, target firepower, and threat levels, while adhering to system constraints of both jamming and target platforms. To meet the real-time decision-making requirements in dynamic adversarial environments, we integrate VAM with the proximal policy optimization (PPO) algorithm, leveraging human knowledge to accelerate the training process of DRL. Simulation results demonstrate that the proposed algorithm improves both the training efficiency and decision-making timeliness of the jamming allocation model, achieving cumulative reward increases of 38.45% and 13.86% over the respective baselines, while ensuring target coverage and effectively avoiding redundant or excessive jamming. Full article

Triboelectric nanogenerators (TENGs) have gradually been applied in various practical scenarios, mainly focusing on core areas such as wearable motion monitoring devices, medical security systems, and natural resource exploration technology. However, they have the problem of low output energy and have not yet formed effective integration with mature commercially available products, which has hindered the industrialization process. This situation still significantly limits its global promotion and application. In this study, TENG was used as the sensing module for intelligent automotive airbags. We tested the voltage and current output characteristics of the system under different impact forces and frequency conditions. During the testing process, the electrical energy generated under different operating conditions is transmitted to the control system via Metal-Oxide-Semiconductor Field-Effect Transistor (MOSFET) circuits. The system will quickly determine whether to trigger the airbag deployment based on the received electrical signals, and activate the ignition device when necessary to achieve rapid inflation and deployment of the airbag. Compared with traditional triggering mechanisms, the airbag system based on this designed sensor has higher sensitivity and reliability. The sensor can stably capture collision signals, and experiments have shown that as the collision speed increases, the slope of its open-circuit voltage gradually approaches infinity. Applying TENG to automotive airbags not only effectively improves the triggering efficiency and accuracy of airbags, but also provides more reliable safety protection for drivers and passengers. Finite element simulation of the automotive airbag was conducted to provide specific data support for evaluating its safety performance. With the continuous advancement of TENG technology and further expansion of its application scenarios, we believe that such innovative safety technologies will play a more critical role in the future automotive industry. Full article

Microfluidic chip technologies, also known as lab-on-a-chip systems, have profoundly transformed laboratory medicine by enabling the miniaturization, automation, and rapid processing of complex diagnostic assays using minimal sample volumes. Recent advances in chip design, fabrication methods—including 3D printing, modular and flexible substrates—and biosensor integration have significantly enhanced the performance, sensitivity, and clinical applicability of these devices. Integration of advanced biosensors allows for real-time detection of circulating tumor cells, nucleic acids, and exosomes, supporting innovative applications in cancer diagnostics, infectious disease detection, point-of-care testing (POCT), personalized medicine, and therapeutic monitoring. Notably, the convergence of microfluidics with artificial intelligence (AI) and machine learning has amplified device automation, reliability, and analytical power, resulting in “smart” diagnostic platforms capable of self-optimization, automated analysis, and clinical decision support. Emerging applications in fields such as neuroscience diagnostics and microbiome profiling further highlight the broad potential of microfluidic technology. Here, we present findings from a comprehensive review of recent innovations in microfluidic chip design and fabrication, advances in biosensor and AI integration, and their clinical applications in laboratory medicine. We also discuss current challenges in manufacturing, clinical validation, and system integration, as well as future directions for translating next-generation microfluidic technologies into routine clinical and public health practice. Full article

Due to people’s increasing access to computers, IT security has become extremely important in today’s society. This increase in connectivity has also led cybercriminals to take advantage of the anonymity and privacy offered by the Internet to carry out illegal activities. One of the most innovative solutions for protecting systems and networks is the use of artificial intelligence. However, these same technologies can become attractive targets for attackers seeking to compromise an organisation’s security. This paper analyses attacks targeting machine learning algorithms used in the classification of messaging application traffic, using Generative Adversarial Networks. Three algorithms were specifically evaluated and the results obtained were compared. The analyses show that all algorithms have a certain degree of vulnerability to malicious manipulation, highlighting the need to strengthen their defence mechanisms. Full article

Advancements in energy storage systems (ESS) are important to attaining a sustainable and resilient energy future. Despite significant advancements in battery technologies, including lithium-ion, sodium-ion, and redox flow batteries, numerous problems remain. These include low energy density, thermal instability, resource scarcity, high lifecycle costs, and ineffective recycling methods. Furthermore, the complexity of connecting battery systems to the grid while maintaining operational safety creates further impediments to implementation. Recent advancements, such as hybrid energy storage systems (HESS), better battery chemistries, and intelligent modeling tools based on MATLAB/Simulink R2025b, have shown promise in terms of performance, cost reduction, and more effective energy management. However, the scalability, recyclability, and real-world applicability of these systems require further exploration. The goal here is to provide a comprehensive overview of current and emerging battery technologies, focusing on technical performance, environmental sustainability, lifecycle cost modeling, and grid compatibility. This comprises a techno-economic study that employs process-based cost modeling (PBCM) and leveled cost of storage (LCOS), a thorough examination of green battery chemistries, and system-level modeling of battery and hybrid configurations. The study seeks to provide academics and stakeholders with a comprehensive framework that considers both the innovations and limitations of current ESS technologies in the context of global decarbonization targets. Full article

To address the issues of a low single-seed qualification index and a high missed-seeding index in the process of leafy vegetable plug seedling sowing, this study proposes a lightweight seeding performance detection model named VS-YOLO based on YOLO11n. The model is then deployed on the edge device, the NVIDIA Jetson Xavier NX. A concise and intuitive graphical user interface (GUI) was developed and an automated detection system for vegetable seeding performance was constructed. Based on the empty cells identified by the system, a real-time data transmission mechanism between the Jetson device and a PLC-based control unit is established, enabling the intelligent reseeding device to perform precise reseeding at the designated cell location, achieving row-wise and cell-specific intelligent planting. VS-YOLO incorporates several innovative improvements, including the introduction of a Context Anchor Attention (CAA) module to form the C2PSA_CAA module, the adoption of the Wise Intersection over Union version 3 (WIoU v3) loss function, and the addition of an extra-small object detection head. These enhancements significantly improve the classification and recognition capability for small-sized vegetable seeds while notably reducing the number of model parameters. Experimental results show that VS-YOLO achieves a mAP@0.5 of 96.5% and an F1 Score of 93.45% in detecting the seeding performance of three types of vegetable seeds, outperforming YOLO11n’s 91.5% and 85.19% by 5.0% and 8.26%. The parameter count of VS-YOLO is only 1.61 M, which is 37.6% lower than YOLO11n’s 2.58 M, making it lightweight. Operating at a productivity rate of 120 trays per hour, the system achieved an accuracy of 99.03%, 89.83%, and 92.26% for single-seed prediction, multiple-seeding prediction, and missed-seeding prediction. The single-seed qualification index and missed-seeding index were 93.43% and 4.68%. After reseeding, these indices improved to 97.61% and 0.32%, representing an increase of 4.18% in the single-seed qualification index and a decrease of 4.36% in the missed-seeding index. The significant enhancement offers new ideas and technical approaches for the advancement of seeding performance detection and reseeding systems for vegetable plug seedling production. Full article

Background: The rapid diffusion of large language models (LLMs) such as Claude, ChatGPT, Gemini, LLaMA, and Mistral is reshaping logistics and supply chain management by embedding generative intelligence into planning, coordination, and governance processes. While prior studies emphasize algorithmic capability, far less is known about how differences in diffusion pathways shape productivity outcomes, managerial cognition, and institutional control. Methods: This study develops and applies an integrative analytical framework—the AI Diffusion Triad—comprising Productivity, Perspective, and Power. Using comparative qualitative analysis of five leading LLM ecosystems, the study examines how technical architecture, access models, and governance structures influence adoption patterns and operational integration in logistics contexts. Results: The analysis shows that diffusion outcomes depend not only on model performance but on socio-technical alignment between AI systems, human workflows, and institutional governance. Proprietary platforms accelerate productivity through centralized integration but create dependency risks, whereas open-weight ecosystems support localized innovation and broader participation. Differences in interpretability and access significantly shape managerial trust, learning, and decision autonomy across supply chain tiers. Conclusions: Sustainable and inclusive AI adoption in logistics requires balancing operational efficiency with interpretability and equitable governance. The study offers design and policy principles for aligning technological deployment with workforce adaptation and ecosystem resilience and proposes a research agenda focused on diffusion governance rather than algorithmic advancement alone. Full article