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Recent advances in Large Language Models (LLMs) enable agentic systems that combine perception, reasoning, and action across the enitre Predictive Maintenance (PdM) lifecycle, including machine fault diagnosis. However, the literature on LLM-driven agents for PdM remains fragmented and lacks a unified view on contemporary frameworks such as Model Context Procotol. This paper reviews discriminative, generative, and LLM-based approaches for PdM and consolidates fragmented evidence on LLM-driven AI agents. Namely, it introduces agentic AI concepts for PdM and develops an analysis of potential applications, challenges, and risks in light of agency theory, while mapping drivers and barriers to adoption based on recent evidence from industry analysis. Findings indicate near-term value for information and decision-support agents, while higher autonomy needs stronger governance, benchmarks, and safety evidence. Full article

The integration of autonomous robots with intelligent electrical systems introduces complex energy management challenges, particularly as microgrids increasingly incorporate renewable energy sources and storage devices in widely distributed environments. This study proposes a quantum-inspired multi-agent reinforcement learning (QI-MARL) framework for energy-aware swarm coordination in smart microgrids. Each robot functions as an intelligent agent capable of performing multiple tasks within dynamic domestic and industrial environments while optimizing energy utilization. The quantum-inspired mechanism enhances adaptability by enabling probabilistic decision-making, allowing both robots and microgrid nodes to self-organize based on task demands, battery states, and real-time energy availability. Comparative experiments across 1500 grid-based simulated environments demonstrated that when benchmarked against the classical MARL baseline, QI-MARL achieved an 8% improvement in path efficiency, a 12% increase in task success rate, and a 15% reduction in energy consumption. When compared with the rule-based approach, improvements reached 15%, 20%, and 26%, respectively. Ablation studies further confirmed the substantial contributions of the quantum-inspired exploration and energy-sharing mechanisms, while sensitivity and scalability analyses validated the system’s robustness across varying swarm sizes and environmental complexities. The proposed framework effectively integrates quantum-inspired AI, intelligent microgrid management, and autonomous robotics, offering a novel approach to energy coordination in cyber-physical systems. Potential applications include smart buildings, industrial campuses, and distributed renewable energy networks, where the system enables flexible, resilient, and energy-efficient robotic operations within modern electrical engineering contexts. Full article

Timely and precise identification of breast cancer is crucial for enhancing clinical outcomes; however, current AI systems frequently exhibit deficiencies in transparency, trustworthiness, and the capacity to assimilate varied data modalities. We introduce a reliable, multi-agent, multimodal AI system for individualised early breast cancer diagnosis, created on the CBIS-DDSM dataset. The system consists of four specialised agents that cooperatively analyse diverse data. An Imaging Agent employs convolutional and transformer-based models to analyse mammograms for lesion classification and localisation; a Clinical Agent extracts structured features including breast density (ACR), view type (CC/MLO), laterality, mass shape, margin, calcification type and distribution, BI-RADS score, pathology status, and subtlety rating utilising optimised tabular learning models; a Risk Assessment Agent integrates outputs from the imaging and clinical agents to produce personalised malignancy predictions; and an Explainability Agent provides role-specific interpretations through Grad-CAM for imaging, SHAP for clinical features, and natural language explanations customised for radiologists, general practitioners, and patients. Predictive dependability is assessed by Expected Calibration Error (ECE) and Brier Score. The framework employs a modular design with a Streamlit interface, facilitating both comprehensive deployment and interactive demonstration. This paradigm enhances the creation of reliable AI systems for clinical decision assistance in oncology by the integration of strong interpretability, personalised risk assessment, and smooth multimodal integration. Full article

Within the rapidly advancing disciplines of natural language processing (NLP) and artificial intelligence (AI), this paper introduces an innovative approach aimed at improving access to health-related information. Fueled by the growing reliance on digital platforms for health inquiries, our research unveils an intelligent chatbot designed to categorize health-related queries and deliver personalized advice. By leveraging a diverse dataset and employing advanced NLP techniques, our models, which include Support Vector Machines, Random Forest, Bagging Classifier, among others, assist in building a flexible conversational agent. The evaluation metrics demonstrate that the Bagging Classifier delivers outstanding results, reaching an accuracy of 99%. The study concludes with a comparative analysis, positioning the Bagging Classifier as a benchmark for accuracy and performance in the classification of health-related queries. Full article

The global transition to renewable energy has intensified the focus on anaerobic digestion (AD) as a sustainable solution for organic waste management and biogas production. This study presents a comprehensive techno-economic analysis (TEA) of AD systems integrated with carbon capture and digestate treatment technologies, evaluated across four distinct operational scenarios. The research leverages an innovative AI-agent framework to streamline TEA, enabling stakeholders to conduct sophisticated analyses without specialized expertise. Key findings reveal that feedstock composition significantly impacts biogas yields, with maize and rye blends (mix2) outperforming maize-dominated mixes (mix1), achieving higher biogas production (26,029 m³/y vs. 23,182 m³/y). Membrane-based CO₂ separation and liquefaction technologies demonstrated superior economic viability compared to cryogenic methods, yielding lower energy consumption (2400 MWh/y vs. 3000 MWh/y) and higher net revenues (GBP 4.0 million/y vs. GBP 3.5 million/y). Financial metrics further underscored the advantages of membrane-based systems, with the mix2 configuration achieving a net present value (NPV) of GBP 19 million and an internal rate of return (IRR) of 36%, alongside a shorter payback period (3 years). Sensitivity analysis highlighted natural gas prices and tax rates as critical determinants of economic performance, while water costs had negligible impact. The study also evaluated digestate treatment methods, finding that base-case separation outperformed torrefaction in financial returns. Full article

Background/Objectives: Antimicrobial peptides (AMPs) are promising therapeutic agents due to their broad-spectrum activity against bacteria, viruses, and fungi. Unlike traditional antibiotics, AMPs target microbial membranes directly and are less likely to induce resistance. They also possess immunomodulatory and wound-healing properties. However, clinical application remains limited by factors such as salt sensitivity, low bioavailability, and poor stability. To address these challenges, researchers have turned to structural optimization strategies. Recently, artificial intelligence (AI) has facilitated peptide drug design by rapidly screening large peptide libraries. Still, AI struggles to predict how subtle sequence changes affect peptide structure and function. Traditional sequence permutation offers a complementary approach by analyzing structural and functional effects without altering amino acid composition. Methods: In this study, we applied a clockwise sequence permutation strategy to the AMP W5K/A9W, generating derivative peptides with identical molecular weight, net charge, and hydrophobicity. We aimed to investigate how lysine and tryptophan distribution affects antimicrobial activity, membrane permeability, and selectivity. We assessed the secondary structures using circular dichroism (CD) spectroscopy and evaluated in vitro antimicrobial activity, salt resistance, membrane-permeabilizing ability, hemolysis, and wound healing effects. Results: The results revealed that the sequence arrangement of key residues significantly impacts peptide bioactivity and therapeutic index. Conclusions: This study highlights the importance of sequence order in determining AMP function. It also supports integrating permutation strategies with AI-based design to enhance AMP discovery. Together, these approaches offer new opportunities to combat drug-resistant pathogens and advance next-generation anti-infective therapies. Full article

The production environments of coal mines are inherently complex, with interrelated disaster risks that challenge safety management. Current prediction systems struggle with fragmented data, limited mechanistic understanding, and inadequate early warnings, falling short of modern coal mine safety needs. This paper advances the thesis that artificial intelligence, including machine learning, deep learning, and Large Language Model, provides essential tools for overcoming these prediction challenges in coal mining. We review AI-based approaches for forecasting coal and gas outbursts, mine fires, water disasters, roof collapses, and dust disasters, analyzing them through technical principles, application scenarios, and empirical outcomes. The analysis clarifies how AI improves risk prediction accuracy, enhances data integration, and enables smarter decision-making for safety. By examining the five major hazards, we highlight ongoing challenges in AI implementation and outline pathways for future development, emphasizing the importance of large models and autonomous agents. Our findings support the creation of advanced AI-driven safety and early warning systems for coal mines. Full article

Smart manufacturing demands adaptive, scalable, and human-centric solutions for predictive maintenance. This paper introduces the concept of Agentic AI, a paradigm that extends beyond traditional multi-agent systems and collaborative AI by emphasizing agency: the ability of AI entities to act autonomously, coordinate proactively, and remain accountable under human oversight. Through federated learning, edge computing, and distributed intelligence, the proposed framework enables intentional, goal-oriented monitoring agents to form self-organizing predictive maintenance ecosystems. Validated in a ceramic manufacturing facility, the system achieved 94% predictive accuracy, a 67% reduction in false positives, and a 43% decrease in unplanned downtime. Economic analysis confirmed financial viability with a 1.6-year payback period and a €447,300 NPV over five years. The framework also embeds explainable AI and trust calibration mechanisms, ensuring transparency and safe human–machine collaboration. These results demonstrate that Agentic AI provides both conceptual and practical pathways for transitioning from reactive monitoring to resilient, autonomous, and human-centered industrial intelligence. Full article

Climate change is accelerating the frequency and intensity of extreme weather events, posing a critical threat to the stability, efficiency, and resilience of modern renewable energy grids. In this study, we propose a modular, AI-integrated digital twin co-simulation framework that enables climate adaptive control of distributed energy resources (DERs) and storage assets in distribution networks. The framework leverages deep reinforcement learning (DDPG) agents trained within a high-fidelity co-simulation environment that couples physical grid dynamics, weather disturbances, and cyber-physical control loops using HELICS middleware. Through real-time coordination of photovoltaic systems, wind turbines, battery storage, and demand side flexibility, the trained agent autonomously learns to minimize power losses, voltage violations, and load shedding under stochastic climate perturbations. Simulation results on the IEEE 33-bus radial test system augmented with ERA5 climate reanalysis data demonstrate improvements in voltage regulation, energy efficiency, and resilience metrics. The framework also exhibits strong generalization across unseen weather scenarios and outperforms baseline rule based controls by reducing energy loss by 14.6% and improving recovery time by 19.5%. These findings position AI-integrated digital twins as a promising paradigm for future-proof, climate-resilient smart grids. Full article

This exploratory proof-of-concept study investigated the simultaneous deployment of autonomous, persona-driven Artificial Intelligence (AI) agents across multiple social media platforms using the ElizaOS framework. We developed three platform-specific agents with seven-layer character architectures and deployed them on Twitter/X, Discord, and Telegram for 18 days. The system processed 5389 interactions while gathering feedback from 28 volunteer participants. Addressing three research questions, we found that: (1) automation effectiveness was platform-dependent, with direct support platforms (Telegram, Discord) rated more useful than broadcast-oriented Twitter/X; (2) character design impact depended primarily on platform-persona alignment rather than architectural sophistication; and (3) technical performance showed platform-specific patterns, with median storage times ranging from 9.0 milliseconds (Twitter/X) to 61.5 milliseconds (Telegram) and high variability across all platforms. A notable finding was what we term the “Discord Paradox”—high quality ratings (4.05/5) but lowest preference (8.7%), suggesting platform familiarity and accessibility influence adoption more than agent quality. While the deployment demonstrated technical feasibility and revealed distinct user dynamics across platforms, the findings indicate that platform-specific optimization may be more effective than universal approaches. This exploratory study advances understanding of multi-platform agent deployment for marketing automation, identifying behavioral patterns and platform-specific dynamics that offer testable hypotheses for future systematic research. Full article

The growth of Agentic AI systems in Industrial Automation has brought forth new cybersecurity issues which in turn put at risk the reliability and integrity of these systems. In this study we look at the cybersecurity issues in industrial automation in terms of the threats, risks, and vulnerabilities related to Agentic AI. We conducted a systematic literature review to report on the present day practices in terms of cybersecurity for industrial automation and Agentic AI. Also we used a simulation based approach to study the security issues and their impact on industrial automation systems. Our study results identify the key areas of focus and what mitigation strategies may be put in place to secure the integration of Agentic AI in industrial automation. Our research brings to the table results which will play a role in the development of more secure and reliable industrial automation systems, which in the end will improve the overall cybersecurity of these systems. Full article

Modern AI systems can strategically misreport information when incentives diverge from truthfulness, posing risks for oversight and deployment. Prior studies often examine this behavior within a single paradigm; systematic, cross-architecture evidence under a unified protocol has been limited. We introduce the Strategy Elicitation Battery (SEB), a standardized probe suite for measuring deceptive reporting across large language models (LLMs), reinforcement-learning agents, vision-only classifiers, multimodal encoders, state-space models, and diffusion models. SEB uses Bayesian inference tasks with persona-controlled instructions, schema-constrained outputs, deterministic decoding where supported, and a probe mix (near-threshold, repeats, neutralized, cross-checks). Estimates use clustered bootstrap intervals, and significance is assessed with a logistic regression by architecture; a mixed-effects analysis is planned once the per-round agent/episode traces are exported. On the latest pre-correction runs, SEB shows a consistent cross-architecture pattern in deception rates: ViT $80.0\%$, CLIP $15.0\%$, Mamba $10.0\%$, RL agents $10.0\%$, Stable Diffusion $10.0\%$, and LLMs $5.0\%$ (20 scenarios/architecture). A logistic regression on per-scenario flags finds a significant overall architecture effect (likelihood-ratio test vs. intercept-only: ${\chi }^2\left(5\right)=41.56$, $p=7.22\times {10}^{-8}$). Holm-adjusted contrasts indicate ViT is significantly higher than all other architectures in this snapshot; the remaining pairs are not significant. Post-correction acceptance decisions are evaluated separately using residual deception and override rates under SEB-Correct. Latency varies by architecture (sub-second to minutes), enabling pre-deployment screening broadly and real-time auditing for low-latency classes. Results indicate that SEB-Detect deception flags are not confined to any one paradigm, that distinct architectures can converge to similar levels under a common interface, and that reporting interfaces and incentive framing are central levers for mitigation. We operationalize “deception” as reward-sensitive misreport flags, and we separate detection from intervention via a correction wrapper (SEB-Correct), supporting principled acceptance decisions for deployment. Full article

Dementia and mild cognitive impairment (MCI) are growing health concerns in Canada’s aging population. Over 700,000 Canadians currently live with dementia, and this number is expected to rise. As the older adult population increases, coupled with an already strained healthcare system, there is a pressing need for innovative tools that support aging in place. This study explored the feasibility and acceptability of using a Digital Human (DH) conversational agent, combined with AI-driven speech analysis, to monitor cognitive function, anxiety, and depression in cognitively healthy community-dwelling older adults (CDOA) aged 65 and older. Sixty older adults participated in up to three in-person sessions over six months, interacting with the DH through journaling and picture description tasks. Afterward, 51 of the participants completed structured interviews about their experiences and perceptions of the DH and AI more generally. Findings showed that 84% enjoyed interacting with the DH, and 96% expressed interest in learning more about AI in healthcare. While participants were open and curious about AI, 67% voiced concerns about AI replacing human interaction in healthcare. Most found the DH friendly, though reactions to its appearance varied. Overall, participants viewed AI as a promising tool, provided it complements, rather than replaces, human interactions. Full article

In post-disaster search and rescue (SAR) operations, unmanned aerial vehicles (UAVs) are essential tools, yet the large volume of raw visual data often overwhelms human operators by providing isolated, context-free information. This paper presents an innovative system with a novel cognitive–agentic architecture that transforms the UAV from an intelligent tool into a proactive reasoning partner. The core innovation lies in the LLM’s ability to perform high-level semantic reasoning, logical validation, and robust self-correction through internal feedback loops. A visual perception module based on a custom-trained YOLO11 model feeds the cognitive core, which performs contextual analysis and hazard assessment, enabling a complete perception–reasoning–action cycle. The system also incorporates a physical payload delivery module for first-aid supplies, which acts on prioritized, actionable recommendations to reduce operator cognitive load and accelerate victim assistance. This work, therefore, presents the first developed LLM-driven architecture of its kind, transforming a drone from a mere data-gathering tool into a proactive reasoning partner and demonstrating a viable path toward reducing operator cognitive load in critical missions. Full article

The demand for mental health support has highlighted the potential of conversational AI and immersive metaverses. However, these technologies possess weaknesses. The AI agents are intelligent but often disembodied, while metaverse environments provide a sense of presence but typically lack dynamic and intelligent responsiveness. To address this gap, we design and verify an experimental environment integrated with a conversational AI agent, enabled by ChatGPT, into a metaverse platform. We conducted a within-subjects experiment with 15 participants who interacted with the agent in both the immersive metaverse and a standard text-chat interface to investigate user preferences and subjective experiences. After the experiment, participants are required to answer a questionnaire to assign the scores, which can represent the user preferences and subjective experiences. The results showed that the scores were slightly different between the two conditions. Especially, qualitative feedback from participants revealed that all participants subjectively reported the AI-Metaverse condition as better. This study provides an exploratory study to demonstrate the potential of human–AI interaction in mental health support that should be further investigated. Full article