Search Results (1,137)

The static nature of traditional military symbology, such as MIL-STD-2525D, hinders effective real-time threat detection and response in modern cybersecurity operations. This research introduces the Dynamic Adaptive Symbol System (DASS), a novel framework enhancing cyber situational awareness in military and enterprise environments. The DASS addresses static symbology limitations by employing a modular Python 3.10 architecture that uses machine learning-driven threat detection to dynamically adapt symbol visualization based on threat severity and context. Empirical testing assessed the DASS against a MIL-STD-2525D baseline using active cybersecurity professionals. Results show that the DASS significantly improves threat identification rates by 30% and reduces response times by 25%, while achieving 90% accuracy in symbol interpretation. Although the current implementation focuses on virus-based scenarios, the DASS successfully prioritizes critical threats and reduces operator cognitive load. Full article

Industrial engineering research has been reshaped by the integration of artificial intelligence (AI) within the framework of Industry 4.0, characterized by the interplay between cyber-physical systems (CPS), advanced automation, and the Industrial Internet of Things (IIoT). While this integration opens new opportunities, it also introduces biases that undermine the reliability and robustness of scientific and industrial outcomes. This article presents a systematic literature review (SLR), supported by natural language processing techniques, aimed at identifying and classifying biases in AI-driven research within industrial contexts. Based on this meta-research approach, a taxonomy is proposed that maps biases across the stages of the scientific method as well as the operational layers of intelligent production systems. Statistical analysis confirms that biases are unevenly distributed, with a higher incidence in hypothesis formulation and results dissemination. The study also identifies emergent AI-related biases specific to industrial applications such as predictive maintenance, quality control, and digital twin management. Practical implications include stronger reliability in predictive analytics for manufacturers, improved accuracy in monitoring and rescue operations through transparent AI pipelines, and enhanced reproducibility for researchers across stages. Mitigation strategies are then discussed to safeguard research integrity and support trustworthy, bias-aware decision-making in Industry 4.0. Full article

The exponential growth of scientific literature has made it increasingly difficult for researchers to identify relevant and timely publications within vast academic digital libraries. Although academic search engines, reference management tools, and recommender systems have evolved, many still rely heavily on metadata and lack mechanisms to incorporate full-text content or time-awareness. This review systematically examines the landscape of scholarly publication recommender systems, employing the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology for a comprehensive and transparent selection of relevant studies. We highlight the limitations of current systems and explore the potential of integrating fine-grained citation knowledge—such as citation proximity, context, section, graph, and intention—extracted from full-text documents. These elements have shown promise in enhancing both the contextual relevance and recency of recommendations. Our findings highlight the importance of moving beyond accuracy-focused metrics toward user-centric evaluations that emphasise novelty, diversity, and serendipity. This paper advocates for the development of more holistic and adaptive recommender systems that better align with the evolving needs of researchers. Full article

Contemporary artificial intelligence excels at pattern recognition but lacks genuine understanding, temporal awareness, and ethical reasoning. Critics argue that AI systems manipulate statistical correlations without grasping concepts, time, or moral implications. This article presents Phase 2, a component of the emerging infrastructure called Sophimatics, a computational framework that translates philosophical categories into working algorithms through the integration of complex time. Our approach operationalizes Aristotelian substance theory, Augustinian temporal consciousness, Husserlian intentionality, and Hegelian dialectics within a unified temporal–semantic architecture. The system represents time as both chronological and experiential, allowing navigation between memory and imagination while maintaining conceptual coherence. Validation through a Data Protection Policy use case demonstrates significant improvements: confidence in decisions increased from 6.50 to 9.40 on a decimal scale, temporal awareness from 2.00 to 9.50, and regulatory compliance from 6.00 to 9.00 compared to traditional approaches. The framework successfully links philosophical authenticity with computational practicality, offering greater ethical consistency and contextual adaptability for AI systems that require temporal reasoning and ethical foundations. Full article

This paper presents a novel gateway-centric architecture for context-aware trust evaluation in Internet of Battle Things (IoBT) environments. The system is structured across multiple layers, from embedded sensing devices equipped with internal modules for signal filtering, anomaly detection, and encryption, to high-level data processing in a secure cloud infrastructure. At its core, the gateway evaluates the trustworthiness of sensor nodes by computing reputation scores based on behavioral and contextual metrics. This design offers operational advantages, including reduced latency, autonomous decision-making in the absence of central command, and real-time responses in mission-critical scenarios. Our system integrates supervised learning, specifically Decision Tree Regression (DTR), to estimate reputation scores using features such as transmission success rate, packet loss, latency, battery level, and peer feedback. The results demonstrate that the proposed approach ensures secure, resilient, and scalable trust management in distributed battlefield networks, enabling informed and reliable decision-making under harsh and dynamic conditions. Full article

Smart crop technologies offer promising solutions for enhancing agricultural productivity and sustainability, particularly in the face of global challenges such as resource scarcity and climate variability. However, their deployment in infrastructure-limited regions, especially across Africa, faces persistent barriers, including unreliable power supply, intermittent internet connectivity, and limited access to technical expertise. This study presents a PRISMA-guided systematic review of literature published between 2015 and 2025, sourced from the Scopus database including indexed content from ScienceDirect and IEEE Xplore. It focuses on key technological components including multimodal sensing, data fusion, IoT resource management, edge-cloud integration, and adaptive network design. The analysis of these references reveals a clear trend of increasing research volume and a major shift in focus from foundational unimodal sensing and cloud computing to more complex solutions involving machine learning post-2019. This review identifies critical gaps in existing research, particularly the lack of integrated frameworks for effective multimodal sensing, data fusion, and real-time decision support in low-resource agricultural contexts. To address this, we categorize multimodal sensing approaches and then provide a structured taxonomy of multimodal data fusion approaches for real-time monitoring and decision support. The review also evaluates the role of IoT virtualization as a pathway to scalable, adaptive sensing systems, and analyzes strategies for overcoming infrastructure constraints. This study contributes a comprehensive overview of smart crop technologies suited to infrastructure-limited agricultural contexts and offers strategic recommendations for deploying resilient smart agriculture solutions under connectivity and power constraints. These findings provide actionable insights for researchers, technologists, and policymakers aiming to develop sustainable and context-aware agricultural innovations in underserved regions. Full article

With buildings accounting for 40% of global energy consumption, heating, ventilation, and air conditioning (HVAC) systems represent the single largest opportunity for emissions reduction, consuming up to 60% of commercial building energy while maintaining occupant comfort. This critical balance between energy efficiency and human comfort has traditionally relied on rule-based and model predictive control strategies. Given the multi-objective nature and complexity of modern HVAC systems, these approaches fall short in satisfying both objectives. Recently, reinforcement learning (RL) has emerged as a method capable of learning optimal control policies directly from system interactions without requiring explicit models. However, standard RL approaches frequently violate comfort constraints during exploration, making them unsuitable for real-world deployment where occupant comfort cannot be compromised. This paper addresses two fundamental challenges in HVAC control: the difficulty of constrained optimization in RL and the challenge of defining appropriate comfort constraints across diverse conditions. We adopt a safe RL with a neural barrier certificate framework that (1) transforms the constrained HVAC problem into an unconstrained optimization and (2) constructs these certificates in a data-driven manner using neural networks, adapting to building-specific comfort patterns without manual threshold setting. This approach enables the agent to almost guarantee solutions that improve energy efficiency and ensure defined comfort limits. We validate our approach through seven experiments spanning residential and commercial buildings, from single-zone heat pump control to five-zone variable air volume (VAV) systems. Our safe RL framework achieves energy reduction compared to baseline operation while maintaining higher comfort compliance than unconstrained RL. The data-driven barrier construction discovers building-specific comfort patterns, enabling context-aware optimization impossible with fixed thresholds. While neural approximation prevents absolute safety guarantees, reducing catastrophic safety failures compared to unconstrained RL while maintaining adaptability positions this approach as a developmental bridge between RL theory and real-world building automation, though the considerable gap in both safety and energy performance relative to rule-based control indicates the method requires substantial improvement for practical deployment. Full article

Reliable individual detection under dense and cluttered conditions is a prerequisite for automated monitoring in modern poultry systems. We propose CF-DETR, an end-to-end detector that builds on RT-DETR and is tailored to chicken face detection in production-like environments. CF-DETR advances three technical directions: Dynamic Inception Depthwise Convolution (DIDC) expands directional and multi-scale receptive fields while remaining lightweight, Polar Embedded Multi-Scale Encoder (PEMD) restores global context and fuses multi-scale information to compensate for lost high-frequency details, and a Matchability Aware Loss (MAL) aligns predicted confidence with localization quality to accelerate convergence and improve discrimination. On a comprehensive broiler dataset, CF-DETR achieves a mean average precision at IoU 0.50 of 96.9% and a mean average precision (IoU 0.50–0.95) of 62.8%. Compared to the RT-DETR baseline, CF-DETR reduces trainable parameters by 33.2% and lowers FLOPs by 23.0% while achieving 81.4 frames per second. Ablation studies confirm that each module contributes to performance gains and that the combined design materially enhances robustness to occlusion and background clutter. Owing to its lightweight design, CF-DETR is well-suited for deployment in real-time smart farming monitoring systems. These results indicate that CF-DETR delivers an improved trade-off between detection performance and computational cost for real-time visual monitoring in intensive poultry production. Full article

With the rapid growth of Artificial Intelligence of Things (AIoT), identity management and trusted communication have become critical for system security and reliability. Continuous AI learning and large-scale device connectivity introduce challenges such as permission drift, cross-domain access, and fine-grained API calls. Traditional identity management often fails to balance privacy protection with efficiency, leading to risks of data leakage and misuse. To address these issues, this paper proposes a blockchain-based self-sovereign identity (SSI) management mechanism for AIoT. By integrating SSI with a zero-trust framework, it achieves decentralized identity storage and continuous verification, effectively preventing unauthorized access and misuse of identity data. The mechanism employs selective disclosure (SD) technology, allowing users to submit only necessary attributes, thereby ensuring user control over self-sovereign identity information and guaranteeing the privacy and integrity of undisclosed attributes. This significantly reduces verification overhead. Additionally, this paper designs a context-aware dynamic permission management that generates minimal permission sets in real time based on device requirements and environmental changes. Combined with the zero-trust principles of continuous verification and least privilege, it enhances secure interactions while maintaining flexibility. Performance experiments demonstrate that, compared with conventional approaches, the proposed zero-trust architecture-based SSI management mechanism better mitigates the risk of sensitive attribute leakage, improves identity verification efficiency under SD, and enhances the responsiveness of dynamic permission management, providing robust support for secure and efficient AIoT operations. Full article

Digital Twin (DT) technology is increasingly recognized as a key enabler for optimizing design, operation, and management across the built environment. While several reviews have addressed DTs in either building- or city-scale contexts, a comprehensive integration of these two perspectives remains limited. This paper provides an updated overview of DT developments from Building Digital Twins (BDTs) to Urban Digital Twins (UDTs), aiming to identify convergences, divergences, and future directions. The analysis is conducted through a review of recent literature and selected case studies, considering technical, environmental, economic, and social dimensions. Findings reveal that although BDTs and UDTs share common conceptual and technological foundations, scaling from single assets to complex urban systems introduces new challenges in terms of interoperability, governance, and data management. Furthermore, while environmental and economic benefits are relatively well-documented, social implications, such as citizen engagement, inclusivity, and behavioral modeling, remain underexplored. This review highlights the novelty of adopting a cross-scale perspective, emphasizing the importance of integrating technical and social aspects to fully exploit the potential of DTs for sustainable and resilient transitions. The study concludes by outlining research gaps and recommending strategies for developing more integrated, socially aware DT frameworks in both building and urban contexts. Full article

Knowledge graphs (KGs) represent a promising approach for detecting and correcting errors in automated audio and video transcripts. Yet the lack of accessible tools leaves human reviewers with limited support, as KG construction from media data often depends on advanced programming or natural language processing expertise. We present the Custom LLM Automated Relationship Extractor (CLARE), a system that lowers this barrier by combining context-aware relation extraction with an interface for transcript correction and KG refinement. Users import time-synchronized media, correct transcripts through linked playback, and generate an editable, searchable KG from the revised text. CLARE supports over 150 large language models (LLMs) and embedding models, including local options suitable for privacy-sensitive data. We evaluated CLARE on the Measure of Information in Nodes and Edges (MINE) benchmark, which pairs articles with ground-truth facts. With minimal parameter tuning, CLARE achieved 82.1% mean fact accuracy, exceeding Knowledge Graph Generation (KGGen, 64.8%) and Graph Retrieval-Augmented Generation (GraphRAG, 48.3%). We further assessed interactive refinement by revisiting the twenty-five lowest-scoring graphs for fifteen minutes each and found that the fact accuracy rose by an average of 22.7%. These findings show that CLARE both outperforms prior methods and enables efficient user-driven improvements. By streamlining ingestion, correction, and filtering, CLARE makes KG construction more accessible for researchers working with unstructured data. Full article

Background: Immunisation remains one of the most effective and cost-efficient public health interventions for preventing infectious diseases in children. Despite global progress, Sub-Saharan Africa (SSA) continues to face challenges in achieving equitable immunisation coverage. Gender-related disparities, rooted in sociocultural and structural inequalities, significantly influence the prevalence of zero-dose and under-immunised children in the region. This review critically examines the gender-associated barriers to routine childhood immunisation in SSA to inform more inclusive and equitable health interventions. Methods: A critical literature review was conducted generally following some steps of the PRISMA-P and CRD guidelines. Using the Population–Concept–Context (PCC) framework, studies were selected that examined gender-related barriers to routine immunisation for children under five in Sub-Saharan Africa. Comprehensive searches were performed across PubMed, Google Scholar, and relevant organisational websites, targeting articles published between 2015 and 2025. A total of 3683 articles were retrieved, with 24 studies ultimately meeting the inclusion criteria. Thematic analysis was used to synthesise the findings. Results: Four major themes emerged: (1) women’s empowerment and autonomy, including limited decision-making power, financial control, and the impact of gender-based violence; (2) male involvement and prevailing gender norms, where patriarchal structures and low male engagement negatively influenced vaccine uptake; (3) socioeconomic and structural barriers, such as poverty, geographic inaccessibility, maternal workload, and service availability; and (4) education, awareness, and health system responsiveness. Conclusions: Gender dynamics have a significant impact on childhood immunisation outcomes in Sub-Saharan Africa. Future policies must integrate these insights to improve immunisation equity and reduce preventable child morbidity and mortality across the region. Full article

Corporate greenwashing has emerged as a pervasive and systemic threat to global sustainability efforts, undermining regulatory effectiveness and obstructing progress toward multiple United Nations Sustainable Development Goals. As environmental opportunism increasingly diffuses across interconnected industrial supply networks, it evolves from isolated corporate misconduct into a chain-level governance challenge with significant systemic risks. Traditional governance mechanisms—whether market-based self-regulation or top-down administrative control—have proven insufficient, while the effectiveness of hybrid approaches integrating administrative coordination with market dynamics remains largely unexplored. This study investigates China’s Chain Leader Policy, a novel hybrid governance model that combines formal administrative authority with market coordination mechanisms to systematically address environmental opportunism across industrial supply networks, and its impact on mitigating greenwashing. Employing a multi-period difference-in-differences design on 12,334 firm-year observations of Chinese A-share listed companies from 2011 to 2023, we find that the policy reduces corporate greenwashing by 10.8% through four pathways: stabilizing supply–demand relationships, reducing coordination costs, fostering green collaborative innovation, and enhancing external scrutiny via social networks. Coercive isomorphism strengthens these effects, while mimetic isomorphism weakens them; impacts are more pronounced in state-owned enterprises, firms with stronger green awareness and higher levels of internationalization, and in more concentrated industries. By operationalizing embedded autonomy theory in an environmental governance context, this research extends theoretical understanding of hybrid governance mechanisms, offers robust empirical evidence for designing policies to curb greenwashing, and provides a replicable framework for achieving corporate environmental sustainability worldwide. Full article

The digital transformation of low-voltage distribution networks demands a renewed perspective on both regulatory frameworks and metering technologies. This article explores the intersection between incentive structures and metering technologies, focusing on how smart metering can act as a strategic enabler for flexibility in electricity distribution. Starting with the Spanish regulatory evolution and European benchmarking, the shift from asset-based regulation and how it can be complemented with performance-oriented incentives to support advanced metering functionalities is analyzed. On the technical side, the capabilities of smart meters and the performance of communication protocols (such as PRIME, G3-PLC, and 6LoWPAN) highlighting their suitability for real-time observability and control are examined. The findings identify a way to enhance regulatory frameworks for fully harnessing the operational potential of smart metering systems. This article calls for a hybrid, context-aware approach that integrates regulatory evolution with metering structures innovation to unlock the full value of smart metering in the energy transition. Full article

Smart grid operators face escalating cyber threats and tight resource constraints, demanding the transparent, defensible prioritization of security controls. This paper asks how to select cybersecurity controls for smart grids while retaining picture fuzzy evidence throughout and supporting policy-sensitive “what-if” analyses. We propose a hybrid Picture Fuzzy Stepwise Weight Assessment Ratio Analysis (SWARA) and Combinative Distance-based Assessment (CODAS) framework that carries picture fuzzy evidence end-to-end over a domain-specific cost/benefit criteria system and a relative-assessment matrix, complemented by multi-scenario sensitivity analysis. Applied to ten prominent solutions across twenty-nine sub-criteria in four dimensions, the model highlights Performance as the most influential main criterion; at the sub-criterion level, the decisive factors are updating against new threats, threat-detection capability, and policy-customization flexibility; and Zero Trust Architecture emerges as the best overall alternative, with rankings stable under varied weighting scenarios. A managerial takeaway is that foundation controls (e.g., OT-integrated monitoring and ICS-aware detection) consistently remain near the top, while purely deceptive or access-centric options rank lower in this context. The framework contributes an end-to-end picture fuzzy risk-assessment model for smart grid cybersecurity and suggests future work on larger expert panels, cross-utility datasets, and dynamic, periodically refreshed assessments. Full article