Search Results (2,367)

Enhancing system strength to ensure voltage security has become a critical challenge for power systems with high penetration of renewable energy (RE). As China accelerates its clean-energy transition, the conventional grid dominated by synchronous generators is evolving into a dual-high system characterized by both high shares of wind–solar generation and extensive power-electronic interfaces. This shift fundamentally alters the mechanisms of voltage support, rendering traditional short circuit ratio (SCR) index inadequate for describing grid strength. To address this gap, this study proposes a multi-renewable-station short circuit ratio (MRSCR) index that quantitatively evaluates the voltage support strength of RE-dominated systems, and further analyzes the mechanism by which multiple agents on the generation and grid sides affect MRSCR, enhancing the generality and applicability of the proposed index. The MRSCR is further formulated as a voltage security constraint and integrated into an energy storage planning model considering multi-agent collaborative optimization. The proposed model jointly optimizes the siting and capacity configuration of grid-forming energy storage under voltage security constraints. Case studies on the IEEE 14-bus system and a real provincial grid show that incorporating the MRSCR indicator effectively enhances the system’s voltage support performance and operational resilience, achieving these improvements with only a 5.45% increase in daily operating cost compared with baseline planning results. The framework provides a practical offline tool for energy storage planning, enabling both enhanced renewable integration and improved voltage security. Full article

Cellular receptor recognition exerts fundamental roles during coronavirus infection. Clarifying the regulatory mechanism of virus receptor helps to better understand viral infection, transmission and pathogenesis; predict potential host and how viral escape from immune system; prevent coronavirus infection or develop treatment therapy. Herein, we summarize current understanding of host receptor recognition mechanisms in the different genera of coronavirus family. And we also review diverse methodologies of identification and clarification of virus receptors. The integration of structural biology, multi-omics, computational predictions, synthetic biology and artificially engineered viral receptors, provide a powerful framework for elucidating coronavirus‒receptor interactions. This also supports the development of broad-spectrum antiviral agents, smart biosensors, and intervention strategies against emerging coronaviruses. Full article

In this article, the fixed-time, quasi-consensus control problem for heterogeneous multi-agent systems (HMASs) under denial-of-service (DoS) attacks is investigated. Unlike most previous studies in this area, which focus on periodic (or single-type) DoS attacks with static event-triggered control, this paper ensures that HMASs achieve fixed-time quasi-consensus under aperiodic hybrid DoS attacks via dynamic event-triggered control. According to whether DoS attacks are known, two control protocols based on dynamic event-triggered conditions are given, which both ensure that HMASs achieve output quasi-consensus within a fixed time and exhibit less conservative triggering conditions than static event-triggered protocols. Moreover, the proof that the given dynamic event-triggered conditions can avoid Zeno-behavior is provided. Lastly, simulation examples are presented to support the obtained points. Full article

Jailbreak attacks (JAs) represent a sophisticated subclass of adversarial threats wherein malicious actors craft strategically engineered prompts that subvert the intended operational boundaries of large language models (LLMs). These attacks exploit latent vulnerabilities in generative AI architectures, allowing adversaries to circumvent established safety protocols and illicitly induce the model to output prohibited, unethical, or harmful content. The emergence of such exploits underscores critical gaps in the security and controllability of modern AI systems, raising profound concerns about their societal impact and deployment in sensitive environments. In response, this study introduces an innovative defense framework that synergistically integrates language model perplexity analysis with a Multi-Agent System (MAS)-oriented detection architecture. This hybrid design aims to fortify the resilience of LLMs by proactively identifying and neutralizing jailbreak attempts, thereby ensuring the protection of user privacy and ethical integrity. The experimental setup adopts a query-driven adversarial probing strategy, in which jailbreak prompts are dynamically generated and injected into the open-source LLaMA-2 model to systematically explore potential vulnerabilities. To ensure rigorous validation, the proposed framework will be evaluated using a custom jailbreak detection benchmark encompassing metrics such as Attack Success Rate (ASR), Defense Success Rate (DSR), Defense Pass Rate (DPR), False Positive Rate, Benign Pass Rate (BPR), and End-to-End Latency. Through iterative experimentation and continuous refinement, this work endeavors to advance the defensive capabilities of LLM-based systems, enabling more trustworthy, secure, and ethically aligned deployment of generative AI in real-world environments. Full article

In the context of dust pollution contributing more than 30% to PM_2.5 during urbanization, this study optimally designed a multi-component coupled dust suppressant based on the coupling mechanism of chemical dust suppressants, oriented towards environmental friendliness. The concentration range of the core component was determined through single-factor experiments: surfactant sodium dodecylbenzene sulfonate (SDBS) 0.5–1.0% (minimum surface tension 27.8 mN/m), coagulant sodium polyacrylate 0.1–0.2% (viscosity ≥ 42 mPa·s), and water-retaining agent triethanolamine 0.1–1.0% (3 h water retention > 90%). The L9 (3⁴) orthogonal test was used to optimize the formulation with water retention rate, crust hardness, and wind erosion rate as indicators, combined with range and variance analysis (α = 0.05). The results showed that sodium polyacrylate concentration had an extremely significant effect on water retention (contribution rate 98.6%), and an increase in its concentration significantly enhanced shell hardness (up to 51HA) and reduced wind erosion rate (down to 0.05%). The optimal ratio was 0.2% sodium polyacrylate, 1.0% sodium dodecylbenzene sulfonate, and 2.5% triethanolamine. At this time, the 24 h water retention rate reached 35.14%, and the wind erosion resistance was 16 times higher than that of the control group. The system builds a three-dimensional cross-linked structure through a hydrogen bond network to synergistically achieve enhanced dust wetting, particle coalescence, and long-lasting consolidation, providing theoretical support and practical solutions for green dust suppression technology. Full article

Cancer reversion therapy represents a paradigm shift in oncology, focusing on reprogramming malignant cells to a non-malignant state rather than destroying them. This narrative review synthesizes current evidence, emerging technologies, and future directions in this promising field. Cancer reversion is founded on key biological observations: somatic cell reprogramming, spontaneous cancer regression, and microenvironmental influences on malignant behavior. Current approaches include epigenetic reprogramming using HDAC inhibitors and DNA methyltransferase inhibitors; microenvironmental modulation through extracellular matrix manipulation and vascular normalization; differentiation therapy exemplified by all-trans retinoic acid in acute promyelocytic leukemia; and targeting oncogene addiction as demonstrated in BCR-ABL-driven leukemias. Emerging technologies accelerating progress include single-cell analyses that reveal cancer heterogeneity and cellular state transitions; CRISPR-based approaches enabling precise genetic and epigenetic manipulation; patient-derived organoids that model tumor complexity; and artificial intelligence applications that identify novel reversion-inducing agents. Critical evaluation reveals that many reported “reversion” phenomena represent stimulus-dependent plasticity or transient growth arrest rather than stable phenotypic normalization. True cancer reversion requires durable, heritable phenotypic changes that persist after treatment withdrawal, with evidence of epigenetic consolidation and functional restoration. Despite promising advances, significant challenges remain: cancer cell plasticity facilitating therapeutic escape, difficulties in establishing stable reversion states, delivery challenges for solid tumors, and the need for combination approaches to address tumor heterogeneity. Future directions include integrated multi-omics analyses to comprehensively map cellular state transitions, studies of natural regression phenomena to identify reversion mechanisms, advanced nanodelivery systems for targeted therapy, and synthetic biology approaches creating intelligent therapeutic systems. By redirecting rather than destroying cancer cells, reversion therapy offers the potential for reduced toxicity and resistance, potentially transforming cancer from a deadly disease to a manageable condition. Full article

The proliferation of Unmanned Aerial Vehicles (UAVs) enables the large-scale collection of ecological data, yet translating this dynamic sensor data into engaging, personalized public experiences remains a significant challenge. Existing solutions fall short: static exhibitions lack adaptability, while general-purpose LLM agents struggle with real-time responsiveness and reliability. To address this, we introduce CurationAgent, a novel intelligent agent built upon the State-Gated Agent Architecture (SGAA). Its core innovation is an advanced hybrid curation pipeline that synergizes Retrieval-Augmented Generation (RAG) for broad semantic recall with an Intent-Driven Curation (IDC) Funnel for precise intent formalization and narrative synthesis. This hybrid model robustly translates user intent into a curated, multi-modal narrative. We validate this framework in a proof-of-concept virtual exhibition of the Lalu Wetland’s biodiversity. Our comprehensive evaluation demonstrates that CurationAgent is significantly more responsive (1512 ms vs. 4301 ms), reliable (95% vs. 57% task success), and precise (85.5% vs. 52.7% query precision) than standard agent architectures. Furthermore, a user study with 27 participants confirmed our system leads to measurably higher user engagement. This work contributes a robust and responsive agent architecture that validates a new paradigm for interactive systems, shifting from passive information retrieval to active, partnered experience curation. Full article

The accelerating integration of Artificial Intelligence (AI) in Industrial Automation has established Reinforcement Learning (RL) as a transformative paradigm for adaptive control, intelligent optimization, and autonomous decision-making in smart factories. Despite the growing literature, existing reviews often emphasize algorithmic performance or domain-specific applications, neglecting broader links between methodological evolution, technological maturity, and industrial readiness. To address this gap, this study presents a bibliometric review mapping the development of RL and Deep Reinforcement Learning (DRL) research in Industrial Automation and robotics. Following the PRISMA 2020 protocol to guide the data collection procedures and inclusion criteria, 672 peer-reviewed journal articles published between 2017 and 2026 were retrieved from Scopus, ensuring high-quality, interdisciplinary coverage. Quantitative bibliometric analyses were conducted in R using Bibliometrix and Biblioshiny, including co-authorship, co-citation, keyword co-occurrence, and thematic network analyses, to reveal collaboration patterns, influential works, and emerging research trends. Results indicate that 42% of studies employed DRL, 27% focused on Multi-Agent RL (MARL), and 31% relied on classical RL, with applications concentrated in robotic control (33%), process optimization (28%), and predictive maintenance (19%). However, only 22% of the studies reported real-world or pilot implementations, highlighting persistent challenges in scalability, safety validation, interpretability, and deployment readiness. By integrating a review with bibliometric mapping, this study provides a comprehensive taxonomy and a strategic roadmap linking theoretical RL research with practical industrial applications. This roadmap is structured across four critical dimensions: (1) Algorithmic Development (e.g., safe, explainable, and data-efficient RL), (2) Integration Technologies (e.g., digital twins and IoT), (3) Validation Maturity (from simulation to real-world pilots), and (4) Human-Centricity (addressing trust, collaboration, and workforce transition). These insights can guide researchers, engineers, and policymakers in developing scalable, safe, and human-centric RL solutions, prioritizing research directions, and informing the implementation of Industry 5.0–aligned intelligent automation systems emphasizing transparency, sustainability, and operational resilience. Full article

We use a novel type of epistemic logic, employing comparative knowledge assertions, to analyze the relative epistemic powers of individuals or groups of agents. Such comparative assertions can express that a group has the potential to (collectively) know everything that another group can know. Moreover, we look at comparisons involving various types of knowledge (fully introspective, positively introspective, etc.), satisfying the corresponding modal-epistemic conditions (e.g., $S5$, $S4$, $KT$). For each epistemic attitude, we are particularly interested in what agents or groups can know about their own epistemic position relative to that of others. Full article

Precision agriculture increasingly recognizes the transformative potential of unmanned aerial systems (UASs) for crop monitoring and field assessment, yet research consistently highlights significant usability barriers as the main constraints to widespread adoption. Complex mission planning processes, including detailed flight plan creation and way point management, pose substantial technical challenges that mainly affect non-expert operators. Farmers and their teams generally prefer user-friendly, straightforward tools, as evidenced by the rapid adoption of GPS guidance systems, which underscores the need for simpler mission planning in UAS operations. To enhance accessibility and safety in UAS control, especially for non-expert operators in agriculture and related fields, we propose a Secure UAS Control Framework (SAUCF): a comprehensive system for natural-language-driven UAS mission management with integrated dual-factor biometric authentication. The framework converts spoken user instructions into executable flight plans by leveraging a language-model-powered mission planner that interprets transcribed voice commands and generates context-aware operational directives, including takeoff, location monitoring, return-to-home, and landing operations. Mission orchestration is performed through a large language model (LLM) agent, coupled with a human-in-the-loop supervision mechanism that enables operators to review, adjust, or confirm mission plans before deployment. Additionally, SAUCF offers a manual override feature, allowing users to assume direct control or interrupt missions at any stage, ensuring safety and adaptability in dynamic environments. Proof-of-concept demonstrations on a UAS plat-form with on-board computing validated reliable speech-to-text transcription, biometric verification via voice matching and face authentication, and effective Sim2Real transfer of natural-language-driven mission plans from simulation environments to physical UAS operations. Initial evaluations showed that SAUCF reduced mission planning time, minimized command errors, and simplified complex multi-objective workflows compared to traditional waypoint-based tools, though comprehensive field validation remains necessary to confirm these preliminary findings. The integration of natural-language-based interaction, real-time identity verification, human-in-the-loop LLM orchestration, and manual override capabilities allows SAUCF to significantly lower the technical barrier to UAS operation while ensuring mission security, operational reliability, and operator agency in real-world conditions. These findings lay the groundwork for systematic field trials and suggest that prioritizing ease of operation in mission planning can drive broader deployment of UAS technologies. Full article

The shift toward remote and automated admission processes in higher education introduces new challenges, including evaluator subjectivity and risks of applicant fraud. The FAIR-VID project addresses these issues by developing an artificial intelligence system that integrates multimodal data fusion with semi-supervised deep learning to assess applicant video interviews, submitted documents, and form data. This paper presents the project’s data preprocessing pipeline, designed to fuse heterogeneous modalities and to support seamless interaction between AI agents and human decision-makers throughout the admission workflow. The proposed process is intentionally general, making it applicable not only to international university admissions but also to broader human resource management and hiring contexts. Emphasis is placed on the need for robust and transparent AI adoption in admission and recruitment, supported by open-source modules and models at every stage of interaction between applicants and institutions. As a proof of concept, we provide open-source solutions for the analysis of video interviews, images, and documents enriched with semantic descriptions generated by large multimodal and complementary AI models. The paper details the multi-phase implementation of this pipeline to create structured, semantically rich datasets suitable for training advanced deep learning systems for comprehensive applicant assessment and fraud detection. Full article

The escalating global demand for energy-efficient and sustainable built environments has catalyzed the advancement of Building Energy Management Systems (BEMSs), particularly through their integration with cutting-edge technologies. This review presents a comprehensive and critical synthesis of the convergence between BEMSs and enabling tools such as the Internet of Things (IoT), wireless sensor networks (WSNs), and artificial intelligence (AI)-based decision-making architectures. Drawing upon 89 peer-reviewed publications spanning from 2019 to 2025, the study systematically categorizes recent developments in HVAC optimization, occupancy-driven lighting control, predictive maintenance, and fault detection systems. It further investigates the role of communication protocols (e.g., ZigBee, LoRaWAN), machine learning-based energy forecasting, and multi-agent control mechanisms within residential, commercial, and institutional building contexts. Findings across multiple case studies indicate that hybrid AI–IoT systems have achieved energy efficiency improvements ranging from 20% to 40%, depending on building typology and control granularity. Nevertheless, the widespread adoption of such intelligent BEMSs is hindered by critical challenges, including data security vulnerabilities, lack of standardized interoperability frameworks, and the complexity of integrating heterogeneous legacy infrastructure. Additionally, there remain pronounced gaps in the literature related to real-time adaptive control strategies, trust-aware federated learning, and seamless interoperability with smart grid platforms. By offering a rigorous and forward-looking review of current technologies and implementation barriers, this paper aims to serve as a strategic roadmap for researchers, system designers, and policymakers seeking to deploy the next generation of intelligent, sustainable, and scalable building energy management solutions. Full article

Background: Rheumatoid arthritis (RA) is a systemic chronic inflammatory autoimmune disease causing progressive joint destruction, resulting in significant morbidity and increased mortality. Despite advances in treatment, current pharmacological options, including NSAIDs, DMARDs, and biological agents, have limitations in tissue repair and can lead to severe side effects. Objectives: This study aims to explore drug repurposing as a viable approach to identify novel therapeutic agents for RA by utilizing existing FDA-approved drugs. Methods: We applied an integrated computational strategy that uniquely combines network pharmacology with molecular docking and dynamics simulations. The process began with the construction of a protein–protein interaction (PPI) network from 2723 RA-associated genes, which identified five central targets: TNF-α, IL-6, IL-1β, STAT3, and AKT1. We then built protein–drug interaction (PDI) networks by screening 2637 FDA-approved drugs against these targets. Critically, the top candidates from this network analysis were not just docked but were further validated using 100 ns molecular dynamics simulations to thoroughly evaluate binding affinity, complex stability, and interaction dynamics. Results: This multi-tiered computational workflow identified Rifampicin, Telmisartan, Danazol, and Pimozide as the most promising repurposing candidates. They demonstrated strong binding affinities and, importantly, formed stable complexes with TNF-α, IL-6, IL-1β, and STAT3, respectively, in dynamic simulations. The key innovation of this study is this sequential funnel approach, which integrates large-scale network data with atomic-level simulation to prioritize high-confidence drug candidates for RA. Conclusions: In conclusion, this study highlights the potential of repurposing FDA-approved drugs to target key proteins involved in RA, offering a cost-effective and time-efficient strategy to discover new therapies. Full article

To address challenges in agent path planning within complex environments—particularly slow convergence speed, high path redundancy, and insufficient smoothness—this paper proposes KDB-RRT*, a novel algorithm built upon RRT.* This method integrates a bidirectional search strategy with a three-layer optimization framework: ① accelerated node retrieval via KD-tree indexing to reduce computational complexity; ② enhanced exploration efficiency through goal-biased dynamic circle sampling and a bidirectional gravitational field guidance model, coupled with adaptive step size adjustment using a Sigmoid function for directional expansion and obstacle avoidance; and ③ trajectory optimization employing DP algorithm pruning and cubic B-spline smoothing to generate curvature-continuous paths. Additionally, a multi-level collision detection framework integrating Separating Axis Theorem (SAT) pre-judgment, R-tree spatial indexing, and active obstacle avoidance strategies is incorporated, ensuring robust collision resistance. Extensive experiments in complex environments (Z-shaped map, loop-shaped map, and multi-obstacle settings) demonstrate KDB-RRT’s superiority over state-of-the-art methods (Optimized RRT*, RRT*-Connect, and Informed-RRT*), reducing average planning time by up to 97.9%, shortening path length by 5.5–21.4%, and decreasing inflection points by 40–90.5%. Finally, the feasibility of the algorithm’s practical application was further verified based on the ROS platform. The research results provide a new method for efficient path planning of intelligent agents in unstructured environments, and its three-layer optimization framework has important reference value for mobile robot navigation systems. Full article

This study investigates mean-square quasi-consensus for a class of linear discrete-time multi-agent systems with external disturbances, where both the system model and network uncertainties are considered. By introducing adjustable parameters, a more generalized modeling of the internal system uncertainties is achieved, and the network uncertainties among agents are described by Bernoulli variables. This study employs a method combining the parametric algebraic Riccati equation (PARE) and linear matrix inequalities, and a novel auxiliary lemma is developed based on the properties of the PARE. The results demonstrate that, under the designed control protocol, by satisfying the conditions related to the expectations of random uncertainties and network uncertainties, the multi-agent system can achieve mean-square quasi-consensus. Finally, numerical simulation examples are conducted to demonstrate the effectiveness of the results obtained in this study, and the fluctuation in the error trajectory curve is smaller than some existing results. Full article