ABSTRACT The convergence of artificial intelligence, digital identity technologies, and biogerontological ambitions has produced a novel set of philosophical problems that have not yet been adequately addressed within mainstream bioethical discourse. Digital life extension—encompassing AI‐generated avatars, neural data preservation, mind uploading, and postmortem digital personhood—raises philosophical questions that require urgent attention. This article argues that the Aretai model of practical wisdom, as systematically articulated by Mario De Caro, Claudia Navarini, and Maria Silvia Vaccarezza, offers useful conceptual resources for navigating these challenges. Drawing on Shannon Vallor's foundational work on technology and the virtues, and situating the argument within a broader body of scholarship that includes recent work on the morality of life extension, the ethics of virtual avatars, and the contested quest for biological immortality, this paper contends that practical wisdom—phronesis—cannot be eliminated from the ethical evaluation of technologies that purport to extend, preserve, or reconstruct human selfhood. The cultivation of virtuous digital selves is not a luxury but an ethical necessity, demanding attention from bioethicists, AI researchers, policymakers, and the public at large.
ABSTRACT The rapid advancement of large language model (LLM) technology is profoundly transforming the practice of social science research. Scholarly discussions on Artificial Intelligence (AI)'s role in social science research can be organised into three levels: AI as a research tool, AI as a methodological infrastructure and AI as a quasi‐cognitive actor. Existing research studies predominantly focus on individual levels, with limited attention to how the tool and infrastructure levels interact and mutually support each other. Drawing on 2 years of practical exploration, this paper proposes a systematic framework spanning both the tool and infrastructure levels and demonstrates the bidirectional interaction mechanism between these levels through the development and operation of over 40 application systems. The framework comprises three tiers: the ontological tier (five core principles), the methodological tier (a technical architecture of ‘one core, three repositories, four domains’) and the practical tier (over 40 application systems). Top–down guidance and bottom–up feedback mechanisms among the three tiers form a continuously evolving closed loop. As the nexus connecting infrastructure and tools, this paper distils POMASA (pattern‐oriented multi‐agent system architecture), a methodological framework containing 20 design patterns that can guide the rapid construction of declarative multi‐agent research systems. The case studies provide detailed accounts of representative applications, including the digital sovereignty index (DSI) assessment system and the report production system (RPS), demonstrating the framework's operation across research tasks of varying scales and domains. This paper's contributions lie in demonstrating the bidirectional interaction mechanism between tools and infrastructure in AI‐assisted social science research, distilling a reusable methodological framework, and validating the framework's feasibility through extensive case studies.
ABSTRACT The detection and classification of diseases have become a field of interest for artificial intelligence in recent years, where the development of methods and models that allow support for specialists in different health fields has allowed early detection of diseases and the provision of timely treatment to patients. This work proposes the classification of diabetic retinopathy: binary and multi‐class. The first classification consists of detecting whether a patient has diabetic retinopathy, whereas the second classification seeks to detect and determine the level of the disease. The classification is performed using convolutional neural networks, which are optimized using a grey wolf optimizer algorithm. The optimizer has the objective of finding the architecture of the convolutional neural network, as well as the number of convolutional layers, filters, hidden layers, and neurons. The combination of this optimization algorithm with fuzzy logic allows dynamic adjustment of parameters based on current information. This work presents a comparison with other optimization methods such as genetic algorithms and particle swarm optimization. Optimization allows the convolutional neural network model to achieve a maximum accuracy of 0.979536176 for the detection study case and 0.980900407 by adding fuzzy logic, while a maximum accuracy of 0.780354679 is obtained for the classification study case and 0.76807642 by adding fuzzy logic. The results obtained show that the application of optimization methods and fuzzy logic allow the convolutional neural networks to have an advantage over other optimized architectures.
ABSTRACT This article focuses on knowledge innovation and its contemporary reshaping, with particular attention to the growing role of AI in this process. This article first examines the general structure and phases of knowledge innovation cycles, aiming to identify AI's role within them. The discussion then focuses on the first phase, innovativeness, as the phase in innovation cycles that may still require human action, even when other phases of knowledge innovation cycles turn fully digital. This article then elaborates on AI's deepening engagement in innovation cycles, notably in the first phase of the innovation generation cycle. Finally, two implications of AI‐based transitions are addressed: The potential damage to human innovation processes within organizations and the need for broader changes in the academic teaching of cross‐disciplinary tools for knowledge management.
ABSTRACT Artificial intelligence (AI) has become a foundational component of contemporary social, economic, and political life. Yet, the ways in which AI reshapes patterns of exclusion beyond questions of access and technical capability remain insufficiently theorized. This article argues that the AI divide is best understood as a multidimensional sociotechnical phenomenon, in which technological inequality is intertwined with psychological, institutional, and geopolitical dynamics that shape both participation and agency. Drawing on digital inequality studies, philosophy of technology, and psychology, and supported by a synthesis of recent empirical findings, the article conceptualizes the AI divide across three interrelated dimensions—access, use, and outcomes—while foregrounding the role of power asymmetries, opaque governance structures, and market concentration in producing unequal participation in AI‐mediated systems. Building on this structural framework, the analysis advances a second contribution by integrating the psychological consequences of AI‐related exclusion: algorithmic anxiety, diminished self‐efficacy, technostress, and collective vulnerability, operating across individual, organizational, and societal levels. This study demonstrates that these psychological effects are not merely secondary consequences but actively reproduce and intensify technological inequality by weakening agency, undermining trust, and constraining sociotechnical imaginaries. These dynamics are better understood as contingent on governance, design, and patterns of use rather than as inevitable outcomes of AI deployment. By linking structural conditions with psychological experience, the study underscores the normative importance of transparency, explicability, and participatory governance as prerequisites for more inclusive and human‐centered AI systems. Finally, the paper outlines concrete and operational policy recommendations, including algorithmic impact assessments and regulatory sandboxes, to mitigate these multidimensional inequalities.
ABSTRACT Open‐source artificial intelligence is widely promoted as a democratising pathway to digital sovereignty for African states, offering access to frontier architectures without prohibitive capital investment. This paper investigates whether open‐source AI represents a credible route to autonomy or generates a new form of structural dependency. Drawing on the National Innovation System (NIS) theory and the political economy of cloud infrastructure, the paper argues that open‐source AI transfers model weights but neglects the structural foundations of capability: compute infrastructure, localised data and indigenous human capital. A structured narrative review demonstrates that Africa's research marginalisation, chronic infrastructure financing deficits and reliance on foreign cloud services collectively undermine the promise of open‐source AI's sovereignty. The analysis establishes that the compute layer, not the code layer, is the primary locus of power: consequently, adoption of foreign infrastructure relocates rather than resolves dependency. Three analytical contributions are advanced: a theoretically grounded critique of the open‐source paradox that integrates NIS and Big AI scholarship; an integrative framework that applies these resources to the African case; and a Digital Bandung collective action framework. This proposal presents the Digital Bandung framework as a heuristic device and an ideal type. By utilising this historical analogy, the paper illustrates a strategic logic for collective action rather than a rigid policy prescription, acknowledging that the original Bandung spirit must be modernised for the digital age. While treating Africa as a focal case, the study acknowledges that this approach risks obscuring significant intra‐continental variation.
ABSTRACT Nonlinear control systems are an integral part of complex engineering systems. The main difference from linear systems is their ability to adapt to changes and unpredictable conditions. These systems exhibit behaviors that cannot be predicted by simple linear equations, making them essential for applications requiring precise control over a range of operating conditions and are widely used in industry, engineering, and technology. In this article, we focus on the water level in a tank. Based on the nonlinear control model of the water tank, a type‐3 fuzzy interval controller was designed with the objective of maintaining the water level in a tank at a desired value. To evaluate the controller's performance, different interval type‐3 fuzzy control systems (IT3‐FLS) were designed by varying the lower scale and lower lag parameters, which represent the uncertainty domain (DOU) of the IT3‐FLS, to achieve the best parameterization for handling the uncertainty of the problem. Once the optimal parameterization of the lower scale and lower lag parameters of the controller was obtained, the interval type‐3 fuzzy harmony search algorithm (IT3‐FHS) optimized the controller antecedent parameters. The results of simulations performed under different conditions are presented: with and without noise applied to the IT3‐FLS controller.
ABSTRACT Aiming at the coupled vibration problem of a multi‐degree‐of‐freedom (MDOF) vibration isolation platform under eccentric excitation, this paper proposes a semi‐active vibration control strategy based on Proximal Policy Optimization (PPO) ‐based reinforcement learning (PPO RL). First, the mechanical model of a 3‐DOF platform supported by four magnetorheological dampers (MRDs) and isolators is established. On this basis, the semi‐active vibration control problem is formulated as a Markov Decision Process (MDP). A 20‐dimensional state space is constructed, integrating the motion of the centroid, local support point motion, vibration excitation, and MRD internal hysteresis. Accordingly, a 4‐dimensional continuous action space corresponding to the input current of each MRD is designed. A multi‐objective reward function is developed, which accounts for vibration suppression, differential vibration penalty and control input efficiency. The actor‐critic dual network of the PPO algorithm is optimized with carefully tailored hyperparameters, and GPU acceleration is employed to enhance training efficiency. Simulation results demonstrate that the proposed strategy significantly suppresses both the vertical translation and the angular vibration of the platform's centroid, while effectively controlling the differential vibration of support points. More importantly, by suppressing the angular vibration of the centroid and ensuring the consistency of support point displacement, the strategy achieves high‐precision horizontal attitude maintenance of the platform under eccentric excitation. The maximum relative deviation of the support point displacement in z ‐direction is reduced from 28.5% to 6.2%. This research verifies the effectiveness and superiority of the PPO algorithm for semi‐active vibration control of MDOF vibration isolation platforms, and provides a new intelligent control method for the attitude and vibration control of isolation platforms under asymmetric excitation.
ABSTRACT Artificial intelligence research has historically relied on games such as chess as benchmarks for progress in strategic reasoning. While chess and Go domains have produced remarkable advances—from Deep Blue to AlphaZero—they share a fundamental property that limits their relevance for many real‐world applications: perfect information. In contrast, most strategic environments in domains such as international relations are characterized by incomplete, uncertain, and asymmetric information. This perspective article makes three contributions. First, it proposes a structured taxonomy of imperfect‐information chess variants—including Kriegspiel and related “invisible board” environments—organized by the type and degree of information asymmetry they impose. Second, it presents an empirically grounded case study of Darkboard, the strongest computer Kriegspiel player to date, whose decade‐long record on the Internet Chess Club and three Computer Olympiad gold medals provides concrete evidence of both the power and the structural limits of AI under adversarial uncertainty. Third, it derives governance implications for the design and evaluation of AI systems operating in adversarial real‐world environments, where an opponent can model and exploit the inference procedure of an AI agent. We examine how the digital transformation of chess has shaped the development of AI methods and highlight the epistemic limitations of perfect‐information benchmarks. We discuss how games with imperfect information introduce challenges central to modern AI systems, including belief‐state reasoning, partial observability, and adversarial uncertainty. Finally, we outline implications for the future of AI innovation, suggesting that imperfect‐information strategic games may serve as valuable interdisciplinary laboratories for exploring the design, governance, and evaluation of intelligent systems operating under uncertainty.
The rapid evolution of the Internet of Things (IoT) has significantly advanced the field of electrocardiogram (ECG) monitoring, enabling real‐time, remote, and patient‐centric cardiac care. This paper presents a comprehensive survey of AI assisted IoT‐based ECG monitoring systems, focusing on the integration of emerging technologies such as wearable sensors, wireless connectivity, cloud and edge computing, and advanced artificial intelligence (AI) frameworks. Special attention is given to the application of Foundational AI Tools, Generative AI ecosystem components (GenAI Stack), and TinyML, which have empowered intelligent data processing and low‐power on‐device inference in resource‐constrained environments. We explore the architecture, enabling components, and communication protocols that underpin IoT‐ECG systems, along with their security and privacy challenges. Moreover, we discuss the role of Blockchain, federated learning, and homomorphic encryption in safeguarding patient data, while also examining novel diagnostic approaches using retinal imaging, facial recognition, speech analysis, and emotional state monitoring. This review identifies existing research gaps and emphasizes the need for interoperability, energy‐efficient design, and personalized analytics in future deployments. The survey also aligns with the objectives of Sustainable Development Goals (SDG 3, 9, and 16), advocating for inclusive, innovative, and secure healthcare solutions.
