Search Results (221)

As artificial intelligence systems proliferate across critical societal domains, understanding the nature, patterns, and evolution of AI-related harms has become essential for effective governance. Despite growing incident repositories, systematic computational analysis of AI incident discourse remains limited, with prior research constrained by small samples, single-method approaches, and absence of temporal analysis spanning major capability advances. This study addresses these gaps through a comprehensive multi-method text analysis of 3494 AI incident records from the OECD AI Policy Observatory, spanning January 2014 through October 2024. Six complementary analytical approaches were applied: Latent Dirichlet Allocation (LDA) and Non-negative Matrix Factorization (NMF) topic modeling to discover thematic structures; K-Means and BERTopic clustering for pattern identification; VADER sentiment analysis for emotional framing assessment; and LIWC psycholinguistic profiling for cognitive and communicative dimension analysis. Cross-method comparison quantified categorization robustness across all four clustering and topic modeling approaches. Key findings reveal dramatic temporal shifts and systematic risk patterns. Incident reporting increased 4.6-fold following ChatGPT’s (5.2) November 2022 release (from 12.0 to 95.9 monthly incidents), accompanied by vocabulary transformation from embodied AI terminology (facial recognition, autonomous vehicles) toward generative AI discourse (ChatGPT, hallucination, jailbreak). Six robust thematic categories emerged consistently across methods: autonomous vehicles (84–89% cross-method alignment), facial recognition (66–68%), deepfakes, ChatGPT/generative AI, social media platforms, and algorithmic bias. Risk concentration is pronounced: 49.7% of incidents fall within two harm categories (system safety 29.1%, physical harms 20.6%); private sector actors account for 70.3%; and 48% occur in the United States. Sentiment analysis reveals physical safety incidents receive notably negative framing (autonomous vehicles: −0.077; child safety: −0.326), while policy and generative AI coverage trend positive (+0.586 to +0.633). These findings have direct governance implications. The thematic concentration supports sector-specific regulatory frameworks—mandatory audit trails for hiring algorithms, simulation testing for autonomous vehicles, transparency requirements for recommender systems, accuracy standards for facial recognition, and output labeling for generative AI. Cross-method validation demonstrates which incident categories are robust enough for standardized regulatory classification versus those requiring context-dependent treatment. The rapid emergence of generative AI incidents underscores the need for governance mechanisms responsive to capability advances within months rather than years. Full article

Against the backdrop of heightened global economic uncertainty and increasingly frequent risks in agricultural supply chains, enhancing agricultural supply chain resilience has become a critical issue for safeguarding national food security and promoting high-quality agricultural development. As key actors within agricultural supply chains, the impact of financialization—defined as the shift of resources to non-core financial assets—among agricultural listed firms on supply chain resilience warrants systematic examination. Using panel data from 165 Chinese agricultural listed firms (2010–2022), this study empirically investigates the impact of corporate financialization on agricultural supply chain resilience and its underlying mechanisms. An entropy-weighted composite index based on 16 parameters is used to assess agricultural supply chain resilience. It is composed of three dimensions: resistance capability, recovery capacity, and renewal capacity. The results show that: Financialization significantly undermines supply chain resilience, with the most substantial negative effect on recovery capacity, followed by renewal capacity, and the weakest on resistance capacity. Heterogeneity analyses show more pronounced negative effects among non-state-owned enterprises, non-primary sector firms, and capital-intensive enterprises. Financing constraints and capital expenditures partially mediate the negative relationship between financialization and resilience, while profitability persistence exacerbates the crowding-out effect. These findings suggest that policymakers should strike a compromise between reducing excessive financialization and strengthening agricultural supply chains. While prudently guiding agricultural firms’ financial asset allocation, greater emphasis should be placed on developing a diverse and coordinated industrial support system, thereby diverting financial capital away from crowding out core operations and toward effectively serving the real economy, ultimately contributing to national food security and agricultural modernization. Full article

This article addresses the need to rethink models for managing religious diversity in Europe, which, among other causes, has transformed into a multi-religious society, breaking with Christian hegemony as a result of the migration processes of the last century. The author proposes governance as an essential tool for managing religious diversity, understood as a style of government that promotes interaction and cooperation between the State and non-state actors, including religious denominations, in decision-making processes to regulate this phenomenon and enable individuals and the groups they belong to, to exercise their fundamental right to religious freedom. This approach seeks the social inclusion and effective participation of religious minorities to combat their marginalization and radicalization. To this end, we propose moving away from laicism positions that seek to exclude religion from the public sphere or from those that defend the political use of religion as an element of national identity, proposing instead a model of positive secularism like the Spanish one. Analysing the Spanish model, the article argues how the political participation of religious minorities through a model of religious governance in the management of religious diversity is crucial for building inclusive and safe societies where social cohesion and the full observance of religious freedom and other fundamental rights are achieved. Full article

Disinformation refers to false or misleading information created with the deliberate intention to deceive and cause individual or societal harm. It is typically distinguished from misinformation, which involves falsehoods shared without deceptive intent, and from malinformation, which uses accurate information in misleading or harmful ways. Terms often used interchangeably in public debate—such as fake news, propaganda, and conspiracy theories—describe related but distinct phenomena with differing aims and methods. The term derives from the Soviet concept of dezinformatsiya, originally associated with covert influence operations and strategic deception. Over time, however, its meaning has expanded to encompass a wide range of manipulative practices enacted by both state and non-state actors. Disinformation can take textual, visual, and multimodal forms, including fabricated images and AI-generated content such as deepfakes. Motivations vary and may include political influence, economic gain, ideological mobilisation, or efforts to stigmatise specific groups. Although these practices have long historical precedents, digital and platformised communication environments have amplified their scale, speed, and persuasive potential. This entry provides a narrative overview and conceptual synthesis structured around four dimensions: the history of disinformation, the supply and diffusion mechanisms, the psychological, social, and narrative drivers, and the interventions designed to mitigate its impact. Full article

Massive Internet of Things (IoT) deployments face critical spectrum crowding and energy scarcity challenges. Energy harvesting (EH) symbiotic radio (SR), where secondary devices share spectrum and harvest energy from non-orthogonal multiple access (NOMA)-based primary systems, offers a sustainable solution. We consider long-term throughput maximization in an EHSR network with a nonlinear EH model. To solve this non-convex problem, we designed a two-layered optimization algorithm combining convex optimization with a deep reinforcement learning (DRL) framework. The derived optimal power, time allocation factor, and the time-varying environment state are fed into the proposed long short-term memory (LSTM) attention mechanism combined Deep Deterministic Policy Gradient, named the LAMDDPG algorithm to achieve the optimal long-term throughput. Simulation results demonstrate that by equipping the Actor with LSTM to capture temporal state and enhancing the Critic with channel-wise attention mechanism, namely Squeeze-and-Excitation Block, for precise Q-evaluation, the LAMDDPG algorithm achieves a faster convergence rate and optimal long-term throughput compared to the baseline algorithms. Moreover, we find the optimal number of PDs to maintain efficient network performance under NLPM, which is highly significant for guiding practical EHSR applications. Full article

Maximum power point tracking (MPPT) under partial shading is a nonconvex, rapidly varying control problem that challenges multi-agent policies deployed on photovoltaic modules. We present Fuzzy–MAT3D, a fuzzy-augmented multi-agent TD3 (Twin-Delayed Deep Deterministic Policy Gradient) controller trained under centralized training/decentralized execution (CTDE). On the theory side, we prove that differentiable fuzzy partitions of unity endow the actor–critic maps with global Lipschitz regularity, reduce temporal-difference target variance, enlarge the input-to-state stability (ISS) margin, and yield a global $L_{\infty }$$\gamma$-contraction of fixed-policy evaluation (hence, non-expansive with $\kappa =\gamma <1$). We further state a two-time-scale convergence theorem for CTDE-TD3 with fuzzy features; a PL/last-layer-linear corollary implies point convergence and uniqueness of critics. We bound the projected Bellman residual with the correct contraction factor (for $L^{\infty }$ and $L^2\left(\rho \right)$ under measure invariance) and quantified the negative bias induced by $min\{Q_1,Q_2\}$; an N-agent extension is provided. Empirically, a balanced common-random-numbers design across seven scenarios and 20 seeds, analyzed by ANOVA and CRN-paired tests, shows that Fuzzy–MAT3D attains the highest mean MPPT efficiency (92.0% ± 4.0%), outperforming MAT3D and Multi-Agent Deep Deterministic Policy Gradient controller (MADDPG). Overall, fuzzy regularization yields higher efficiency, suppresses steady-state oscillations, and stabilizes learning dynamics, supporting the use of structured, physics-compatible features in multi-agent MPPT controllers. At the level of PV plants, such gains under partial shading translate into higher effective capacity factors and smoother renewable generation without additional hardware. Full article

Reducing stock price crash risk is vital for capital market stability, particularly in emerging economies such as China. This study investigates whether Employee Stock Ownership Plans (ESOPs) can mitigate crash risk by analyzing panel data from A-share listed firms between 2014 and 2022. In contrast to prior research that has largely centered on managers and controlling shareholders, we highlight employees as active participants in corporate governance. Employing firm, year, and industry fixed effects, together with propensity score matching and instrumental variable techniques, we find robust evidence that ESOPs significantly reduce crash risk. Mediation analyses indicate that this effect operates through reduced agency costs both between managers and shareholders and between controlling and minority shareholders, as well as enhanced corporate productivity. Moderation tests further show that ESOPs are most effective when investor attention is high and when exit threats from non-controlling major shareholders are stronger. Heterogeneity analyses reveal that ESOPs exert greater influence in non-state-owned enterprises, in eastern regions, in firms with higher employee participation, and when shares are sourced from the secondary market. By extending the observation window to nearly a decade and deploying multiple robustness checks, this study provides one of the most comprehensive examinations of ESOPs and crash risk to date. It contributes to the literature by reframing employees as central actors in market stability and offers actionable insights for managers, investors, and regulators seeking to enhance corporate governance and reduce systemic risk. Full article

Participatory Guarantee Systems (PGS) have emerged in Latin America as an alternative to conventional market-driven certification, offering a community-based framework to validate sustainable agricultural and social practices. Rooted in collective responsibility and dialogue between producers, consumers, non-governmental organizations (NGOs) and state institutions, PGS aim to empower smallholders by reducing certification costs and strengthening agroecological transitions. This review examines their development across diverse Latin American contexts, highlighting both their innovative potential and the persistent challenges that limit their scalability and formal recognition. A literature-based approach combined with a stakeholder analysis was employed, integrating case studies from Brazil, Peru, Mexico, Bolivia, and other countries. To systematize findings, SWOT (Strengths, Weaknesses, Opportunities, Threats) and TOWS (Threats, Opportunities, Weaknesses, Strengths) frameworks were applied, assessing strengths and weaknesses from the perspective of producers and consumers and formulating strategies to enhance resilience and legitimacy. Results show that PGS foster social capital, technical learning, and access to local markets; however, they are constrained by high time commitments, reliance on voluntary labour, uneven participation, and limited consumer awareness. The analysis indicates that the most promising pathway is a combination of growth strategies, including leveraging short supply chains, community-based fairs, and digital platforms, with recovery strategies centred on consumer education and producer capacity building. More conservative strategies remain crucial in specific contexts: redistributing workloads, introducing compensation for administrative tasks, and strengthening conflict mediation can help preserve system viability when engagement or resources are scarce. Defence strategies, aimed at reinforcing autonomy and reducing dependence on external actors, are better conceived as long-term goals under current conditions. Full article

The purpose of this work is twofold: first, it aims to derive an exact analytical form of the Pareto index based on the already developed model of the economy as a scale-free network comprising a given amount of either wealth or income (total number of links, each link representing a non-zero amount or quantum of income or wealth) distributed among its variable number of actors (nodes), all of whom have equal access to the system), and second, it aims to employ the derived analytical form of the Pareto index to determine the degree to which the observed inequality in wealth and in income as measured by the respective empirical values of the Pareto index is inherent in the economic system rather than the result of externally imposed factors invariably reflecting a lack of equal access. The derived analytical form of the Pareto index for wealth or for income is described by an exponential function whose exponent is the inverse of the average number of wealth or of income per actor (one-half of the average number of links per node) in the economic model. This exponent features prominently in the scale-free model of the economy and has a numerical value of 0.69 when the Pareto index attains a numerical value of 2, which signifies the optimal, albeit still unequal, distribution of wealth or of income in the economy under the condition of equal access. Because of the correspondence of the scale-free model of the economy to a physical system comprising quantum particles such as photons in thermodynamic equilibrium or state of maximum entropy in accordance with the laws of statistical mechanics, the inverse of the exponent is proportional to the temperature of the economic system, and a new parameter introduced to describe in a comprehensible manner the deviation in the economic system from its optimal distribution of wealth or income. A comparison of the empirical wealth and income Pareto indexes based on economic data for the four largest economies in the word, i.e., USA, China, Germany, and Japan, which account for over 50% of the global GDP, versus the corresponding optimal values per the scale-free model of the economy reveals interesting trends that can be explained away by the prevailing degrees of equal access, as manifested by inadequate education, health care, and housing, as well as the existence of rules and institutions favoring certain actors over others, particularly with regard to the accumulation of wealth. It has also been determined that the newly introduced parameters in the scale-free model of the economy of temperature as well as the quanta of wealth and of income should be expressed in power purchase exchange rates for meaningful comparisons among national economies over time. Full article

This study examines how anti-gender actors represent themselves as legitimate participants in debates on equality politics. Drawing on Mouffe’s distinction between agonism and antagonism, we argue that anti-gender actors foster conflict and exclusion through “moral panic” and the “politics of fear” regarding the issues related to equality politics, while at the same time presenting themselves as neutral, rational, and pluralistic. This dual strategy allows them to insert themselves into democratic debate and present themselves as legitimate “adversaries” rather than “enemies” to those who genuinely advocate for equality politics. We contend that such efforts to simulate agonism are particularly evident in Slovenia, where anti-gender organisations operate as covert allies of the Roman Catholic Church. In a context where public trust in the Church is low and the separation of church and state is strongly valued, efforts to re-Catholicise society rely on secularised means. We argue that this renders strategies of simulating agonism and conforming to secular–democratic values especially salient in the Slovenian context. To identify these strategies, we conducted semi-structured interviews with Slovenian anti-gender actors. Our analysis revealed four interrelated tactics: “self-victimisation”, portraying themselves as excluded and marginalised; “call for dialogue,” stressing a purported willingness to engage with opponents; “depoliticisation”, framing their role as neutral and non-ideological; and “claim of public support”, invoking a “silenced majority” allegedly constrained by a prevailing climate of “leftist” fear and censorship. The significance of this study lies in the fact that, despite extensive scholarly work on anti-gender mobilisations, analyses drawing on interviews with anti-gender actors themselves remain rare. Full article

Terror organizations increasingly utilize the media and especially digital platforms to disseminate strategic messages, particularly during conflicts. This study examines how Hamas employed hostage videos and other related publications as a form of strategic communication during the first 20 months of the 2023–2025 war with Israel. Drawing on qualitative content analysis of 166 media outputs published on Hamas’s official Telegram channel, including videos, infographics, and a few text-based posts, the study identifies five distinct genres: proof of life, revealing the hostages’ fate, rage or call for help, messages to hostage families or the Israeli public, and hostage release videos. Each genre reflects a specific communicative strategy, varying in tone, target audience, emotional appeal, and timing. The findings reveal that Hamas’s media operations are characterized by a high degree of intentionality, with different genres employed to advance political objectives, ranging from negotiation pressure and public mobilization to projecting legitimacy and resilience. The study contributes to the growing literature on terrorism and strategic communication, illustrating how non-state actors leverage visual media and emotional narratives to wage parallel battles over image, perception, and legitimacy. Full article

With the rapid advancement of mobile edge computing and Internet of Things (IoT) technologies, device-to-device (D2D) cooperative computing has garnered significant attention due to its low latency and high resource utilization efficiency. However, workflow task scheduling in D2D networks poses considerable challenges, such as severe heterogeneity in device resources and complex inter-task dependencies, which may result in low resource utilization and inefficient scheduling, ultimately breaking the computational symmetry—a balanced state of computational resource allocation among terminal devices and load balance across the network. To address these challenges and restore system-level symmetry, a novel workflow task scheduling method tailored for D2D cooperative environments is proposed. First, a Non-dominated Sorting Genetic Algorithm (NSGA) is employed to optimize the allocation of computational resources across terminal devices, maximizing the overall computing capacity while achieving a symmetrical and balanced resource distribution. A scoring mechanism and a normalization strategy are introduced to accurately assess the compatibility between tasks and processors, thereby enhancing resource utilization during scheduling. Subsequently, task priorities are determined based on the calculation of each task’s Shapley value, ensuring that critical tasks are scheduled preferentially. Finally, a hybrid algorithm integrating Q-learning with Asynchronous Advantage Actor–Critic (A3C) is developed to perform precise and adaptive task scheduling, improving system load balancing and execution efficiency. Extensive simulation results demonstrate that the proposed method outperforms state-of-art methods in both energy consumption and response time, with improvements of 26.34% and 29.98%, respectively, underscoring the robustness and superiority of the proposed method. Full article

Virtual reality (VR) is often regarded as the “ultimate empathy machine” because of its ability to immerse users in alternative perspectives and environments beyond physical reality. In this study, 105 participants (average age 22.43 ± 5.31 years, range 19–45, 75% female) with diverse educational and professional backgrounds experienced three-dimensional 360° VR videos featuring actors expressing different emotions. Despite the availability of established methodologies in both research and clinical domains, there remains a lack of a universally accepted “gold standard” for empathy assessment. The primary objective was to explore the relationship between the empathy levels of the participants and the changes in their physiological responses. Empathy levels were self-reported using questionnaires, while physiological attributes were recorded through various sensors. The main outcomes of the study are machine learning (ML) models capable of predicting state empathy levels and trait empathy scores during VR video exposure. The Random Forest (RF) regressor achieved the best performance for trait empathy prediction, with a mean absolute percentage error (MAPE) of 9.1%, and a standard error of the mean (SEM) of 0.32% across folds. For classifying state empathy, the RF classifier achieved the highest balanced accuracy of 67%, and a standard error of the proportion (SE) of 1.90% across folds. This study contributes to empathy research by introducing an objective and efficient method for predicting empathy levels using physiological signals, demonstrating the potential of ML models to complement self-reports. Moreover, by providing a novel dataset of VR empathy-eliciting videos, the work offers valuable resources for future research and clinical applications. Additionally, predictive models were developed to detect non-empathic arousal (78% balanced accuracy ± 0.63% SE) and to distinguish empathic vs. non-empathic arousal (79% balanced accuracy ± 0.41% SE). Furthermore, statistical tests explored the influence of narrative context, as well as empathy differences toward different genders and emotions. We also make available a set of carefully designed and recorded VR videos specifically created to evoke empathy while minimizing biases and subjective perspectives. Full article

In modern intelligent buildings, the control of Building Energy Systems (BES) faces increasing complexity in balancing energy costs, thermal comfort, and operational flexibility. Traditional centralized or flat deep reinforcement learning (DRL) methods often fail to effectively handle the multi-timescale dynamics, large state–action spaces, and strict constraint satisfaction required for real-world energy systems. To address these challenges, this paper proposes an energy policy-aware layered control architecture that combines Virtual Energy Storage System (VESS) modeling with a novel Dynamic Constraint-Aware Policy Optimization (DCPO) algorithm. The VESS is modeled based on the thermal inertia of building envelope components, quantifying flexibility in terms of virtual power, capacity, and state of charge, thus enabling BES to behave as if it had embedded, non-physical energy storage. Building on this, the BES control problem is structured using a hierarchical Markov Decision Process, in which the upper level handles strategic decisions (e.g., VESS dispatch, HVAC modes), while the lower level manages real-time control (e.g., temperature adjustments, load balancing). The proposed DCPO algorithm extends actor–critic learning by incorporating dynamic policy constraints, entropy regularization, and adaptive clipping to ensure feasible and efficient policy learning under both operational and comfort-related constraints. Simulation experiments demonstrate that the proposed approach outperforms established algorithms like Deep Q-Networks (DQN), Deep Deterministic Policy Gradient (DDPG), and Twin Delayed DDPG (TD3). Specifically, it achieves a 32.6% reduction in operational costs and over a 51% decrease in thermal comfort violations compared to DQN, while ensuring millisecond-level policy generation suitable for real-time BES deployment. Full article

The concept of governance has gained increasing attention across various fields of study. However, its application within the specific context of educational policies, particularly within compulsory public education, remains fragmented and underexplored. To answer the questions “How is governance conceptualized in the context of the compulsory public education system?” and “What contributions to future research emerge from this review?”, 32 peer-reviewed articles published in open-access journals between 2019 and 2023 were extracted from the Web of Science, Scopus, and ERIC databases and selected following PRISMA guidelines. Results from this systematic literature review analysis suggest a sustained yet moderate interest in the field, as evidenced by the reviewed publications, different theoretical and conceptual approaches, and research themes that illustrate different aspects of educational systems. Research gaps include the lack of a consolidated and integrated theoretical–conceptual framework on educational governance; the under-representation of specific actors, contexts, and points of view about how educational policies intentions are interpreted and enacted; insufficient critical analyses of, among others, educational leadership, digital transformation, and non-state actors’ influence in educational governance; and limited discussion of governance’s effects on educational justice, equity and quality. The main limitations relate to geographic, linguistic, and cultural biases of the analyzed studies, the exclusion of non-open-access articles, and the predominance of qualitative methodological approaches, which restrict generalizability. To address these challenges, future research should follow the adoption of interdisciplinary approaches, longitudinal and context-sensitive studies, and the use of mixed methodologies. These findings could contribute to a more informed discussion, avoiding reductionist interpretations and more open and critical perspectives on how educational governance transcends organizational and technical structures by incorporating political, ethical, and contextual dimensions that challenge the quality of educational systems. Full article