Dynamic multi-objective optimization problems (DMOPs) have attracted significant attention in recent years. However, some existing approaches do not fully exploit the geometric structure of the Pareto optimal front, or rely on relatively coarse distance-based matching, which may lead to less accurate solution alignment and degraded prediction performance under dynamic changes. To address these issues, this paper proposes a Topological Prior and Vector Migration-based Multi-Objective Evolutionary Algorithm (TPV-MOEA) to enhance sub-region partitioning and improve prediction accuracy. Specifically, TPV-MOEA incorporates a two-layer graph-based topological aggregation module to aggregate neighborhood information of shared points, enabling topology-aware ordering along the Pareto front manifold for more reliable sub-region partitioning. Within each sub-region, a vector migration strategy is employed to adaptively transfer global MOEA/D reference vectors into local regions, enabling better alignment with the underlying Pareto front structure and achieving more accurate association between non-dominated solutions and their historical counterparts. New populations are generated through direction-guided alignment and position prediction, thereby enabling effective adaptation to dynamic environmental changes. Comparisons with five state-of-the-art algorithms show that TPV-MOEA achieves superior convergence and diversity in tracking the Pareto front under dynamic environments.
This paper questions the form of power attributed to migrant women entrepreneurs often emanating from their pre-established social positions, which limits their engagements to mere reaction and adaptation to dominant power holders (i.e. specific groups, contexts, structures). Drawing on the life stories of four migrant women entrepreneurs of Turkish origin in Sweden and the Netherlands, we examine how they experience power as an embodied and affective phenomenon through a multiple case study approach. Going beyond the positional power attributed to women, we adopt a critical feminist perspective on power and demonstrate how migrant women entrepreneurs actively exercise different 'modalities of power' (power-over, power-to and power-with) and build 'power agility'. The study contributes to migrant women's entrepreneurship by uncovering their power as an emergent attribute rather than as a fixed parameter and by articulating how this 'agility' is at the core of migrant women entrepreneurs' capacity to generate transformative effects crossing over individual, relational, contextual, and systemic aspects and levels.
Autonomous AI agents powered by large language models (LLMs) with structured function-calling interfaces have greatly expanded capabilities for real-time data retrieval, computation, and multi-step orchestration. However, the rapid growth of plugins, connectors, and inter-agent protocols has outpaced security practices, leading to brittle integrations — plugin APIs and protocol adapters that rely on ad-hoc authentication, inconsistent schemas, and weak validation — making them vulnerable to failures and exploitation. This survey introduces a unified end-to-end threat model for LLM-agent ecosystems, spanning host-to-tool and agent-to-agent communications, and catalogs over thirty attack techniques across Input Manipulation, Model Compromise, System and Privacy Attacks, and Protocol Vulnerabilities. For each category, we provide a formal mathematical formulation of the underlying threat model, defining attacker capabilities, objectives, and affected layers to enable systematic analysis. Representative examples include Prompt-to-SQL (P2SQL) injections and the Toxic Agent Flow exploit in GitHub’s MCP server. For each category, we assess feasibility, review defenses, and outline mitigation strategies such as dynamic trust management, cryptographic provenance tracking, and sandboxed agentic interfaces. The framework was validated through expert review and cross-mapping with real-world incidents and public vulnerability repositories (e.g., CVE, NIST NVD) to ensure practical relevance. Compared to prior surveys, this work provides the first integrated taxonomy bridging input-level exploits and protocol-layer vulnerabilities in LLM-agent ecosystems while introducing formal system definitions for each threat class. Ultimately, it offers actionable insights for securing next-generation AI agents through layered defense and continuous verification. Our work provides a comprehensive reference to guide the design of secure and resilient LLM-agent workflows.
Minimising honey bee colony losses requires healthy colonies. An important contributor to maintaining good colony health and vitality is effective colony management, but individual beekeepers vary greatly in their knowledge and application of optimal management practices. Beekeepers become knowledgeable through the acquisition of reliable information, but whilst there are many available information sources for the beekeepers, these vary greatly in quality. The COLOSS B-RAP (Bridging Research and Practice) group, a Core Project of the COLOSS (prevention of honey bee COlony LOSSes) honey bee research association, studies the means for the effective transfer of the latest beekeeping knowledge from scientists and extension workers to practising beekeepers. A purpose-designed questionnaire was used in an international online survey, translated and published by volunteer national coordinators, to collect data on the information sources preferred and most used by the beekeepers, in order to understand the best means for communication and beekeeping education. The study covered 71 countries and received 11,351 responses, mainly from Europe, Asia, North America and Latin America. It was found that knowledge acquisition differed significantly according to various beekeeper characteristics, with the most influential factors being continent, beekeeper age, beekeeping experience and beekeeping education. The results demonstrate the necessity for researchers and beekeeping advisors to diversify their usage of information channels so that a majority of the beekeeping community can access important new bee research results.
With increasing global urbanization and climate change, urban flooding has become more frequent, making urban flood resilience (UFR) a critical research focus. This study assesses UFR in the Yangtze River Delta (YRD) region by extending the Pressure-State-Response model with a 'Recovery' dimension, integrating Nature, Economy, Society, and Infrastructure systems. Using data from 27 YRD cities (2014-2023), a CRITIC-TOPSIS model evaluates UFR, while Moran's I and LISA analyses reveal spatiotemporal patterns. The XGBoost-SHAP model identifies key influencing factors. Results show: (1) YRD's UFR is medium-to-high, steadily rising, with disparities narrowing and values concentrating at 0.4-0.5 by 2023; (2) A 'high southeast, low northwest' spatial pattern emerges, with high-high clusters (e.g. Jinhua, Shaoxing) and low-low clusters (e.g. Yancheng, Taizhou-JS); (3) slope and population density primarily drive UFR differences, supported by ecological, economic, and social factors. This study informs targeted flood resilience strategies and regional governance in the YRD.
