Search Results (232)

We study positive–negative guarded systems of language equations over a fixed finite alphabet. The ambient space is the complete ultrametric space of all formal languages equipped with a length-based distance, where two languages are close whenever they agree on all words up to a sufficiently large length. The systems considered here contain both positive recursive dependencies and negative dependencies expressed through language complements. To handle this mixed structure, we introduce a suitable product order on pairs of languages and prove that the associated system operator has the weak monotone property. We show that the complement is an isometry for the length-based ultrametric and establish a signed wrapping estimate for guarded positive and negative language terms. These estimates lead to an ordered contraction principle for comparable pairs. As a consequence, the canonical lower and upper Picard iterations converge to the same limit, which is the unique fixed pair of the system. We also derive an explicit convergence rate and a finite-depth certification result: after a prescribed number of iterations, the approximants agree with the fixed-point semantics on all words below a given length. Additional symmetry assumptions are shown to force the unique fixed pair to be diagonal, reducing the system to a single language equation. Finally, we discuss an application to trace-based policies for tool-using AI agents. In this interpretation, finite executions of an agent are represented as words over an alphabet of observable tool-events, and the two components of the fixed point provide a stable semantics for policy-defined admissible and risky trace classes. The resulting framework gives a mathematically certified method for finite-depth analysis of recursive trace-based policies based on ultrametric fixed-point techniques. Full article

Rock glaciers are important ice-debris landforms in high-mountain permafrost environments, but the development, knowledge base, and emerging directions of this research field remain insufficiently synthesized. This study retrieved English-language article and article/data paper records from the Science Citation Index Expanded database of the Web of Science Core Collection using the query TS = (“rock glacier*” OR “rock glacier*”). After document-type filtering and manual screening, 1125 valid records published between 1910 and 2025 were analyzed. Descriptive bibliometrics were used to characterize scientific production and collaboration patterns, Reference Publication Year Spectroscopy (RPYS) was used to identify historically influential publication years and foundational references, and keyword co-occurrence networks, thematic mapping, and thematic evolution analysis were used to trace associations among research topics. A Logistic life-cycle model was used only as a diagnostic tool for the current publication stage, not as a deterministic forecast. The results indicate that global rock glacier research remains in an active growth stage, although model-derived saturation values should be interpreted cautiously because bibliometric trajectories are affected by database coverage, indexing practices, research funding, technological change, and policy demand. RPYS shows that the knowledge base evolved from geomorphological description, classification, and genetic debate toward permafrost creep, internal structure, thermo-mechanical response, and hydrological significance. Keyword and thematic analyses show increasing attention to climate change, mountain permafrost, InSAR, ground-penetrating radar, hydrological processes, and multi-source monitoring. Because the dataset is restricted to English-language SCI-Expanded records, the results should be interpreted as a map of indexed international literature rather than a complete inventory of all rock glacier knowledge. Full article

In multi-hazard environments, women’s social reproductive labor often constitutes a foundation of community survival, yet remains undertheorized in disaster scholarship. This study contributes to an active scholarly conversation by examining Daya Annisa, a women-led grassroots organization in Bantul Regency, Yogyakarta, Indonesia, a region under continuous geological stress from the Sunda Megathrust, the Opak Fault, and Mount Merapi. Drawing on in-depth interviews and focus group discussions analyzed through Social Reproduction Theory (SRT), with a Strengths, Weaknesses, Opportunities, and Threats (SWOT) framework reinterpreted as an analytical lens on the structural conditions of reproductive labor, the analysis traces four interlinked practices: preparedness embedded in arisan and pengajian gatherings, community-based vulnerability mapping, trust-based crisis response, and informal post-disaster livelihoods. The paper argues that resilience in such settings is best understood not as a passive capacity to absorb shocks, but as the active, gendered, and largely uncompensated labor through which communities are materially sustained when formal systems are stretched. Three policy shifts follow: long-term flexible funding calibrated to continuous reproductive preparedness; institutional integration of community-generated vulnerability data with appropriate privacy and inclusion safeguards; and inclusion of grassroots women’s organizations as autonomous decision-making actors in disaster governance. Full article

Energy-harvesting (EH) AIoT systems enable long-term autonomous operation but suffer from time-varying energy availability, which makes stable learning difficult. In such environments, federated learning (FL) is prone to energy depletion (blackout), while continual learning is required to handle evolving data distributions, leading to a trade-off between energy stability and catastrophic forgetting. In this paper, we propose a stability-controlled continual federated learning framework that jointly regulates local training intensity and rehearsal usage based on the residual energy state. The proposed method is derived from a Lyapunov drift-plus-penalty formulation and implemented as a lightweight mode-based control policy. Simulation results using real solar energy traces show that the proposed method significantly reduces blackout while improving accuracy and mitigating forgetting compared to existing approaches. These results demonstrate the effectiveness of energy-aware joint control for stable continual federated learning in EH-AIoT systems. Full article

The construction sector must simultaneously meet rising global demand and cut embodied carbon deeply enough to satisfy European Green Deal and Bioeconomy Strategy targets—two pressures that conventional petrochemical-derived materials are poorly placed to resolve. Bio-based alternatives offer a credible path: they sequester carbon, carry lower embodied emissions, improve indoor air quality, and fit naturally within circular economy models. Yet they remain marginal in specification practice. This paper reviews the evidence on bio-based construction materials and maps the barriers that keep them there. The analysis organises these barriers into four levels—structural, economic, technical, and enabling—and traces the conditional relationships between them, with direct consequences for how policy interventions should be sequenced. The strategic case for this transition extends beyond environmental policy: the 2026 Strait of Hormuz disruption is used here as a scenario to show how dependent European construction is on fossil-derived material inputs, and how exposed that dependence leaves the sector to geopolitical supply shocks. The principal obstacles to adoption prove to be institutional and economic rather than technical—regulatory fragmentation, absent harmonised standards, fragile supply chains, and market structures that systematically undervalue bio-based solutions. The paper concludes that meaningful scaling requires coordinated action across governance, market design, and industrial policy, and that material and performance advances alone will not deliver it. Full article

The integration of artificial intelligence (AI) in education has made significant advancements in personalized learning and adaptive instruction. However, current systems remain limited by three critical gaps: (a) fragmented architectures that decouple technical performance from ethical governance, (b) the treatment of fairness and accountability as external constraints rather than embedded design principles, and (c) reliance on single-modality data that inadequately represents complex learning environments. These restrictions hinder scalability and limit the capacity of AI systems to deliver equitable, transparent, and context-aware educational experiences. This study aims to address these challenges by designing and validating an ethics-aware, multi-agent conceptual framework for adaptive education in which personalization and responsible AI are co-developed as integrated system properties. The proposed architecture uses five coordinated agents: perception, pedagogy, assessment, feedback, and ethics monitoring. These five agents share one knowledge layer containing learner profiles, domain models, competency structures, interaction histories, and machine-readable policy rules. A four-stage feedback loop comprises: (a) outcome aggregation, (b) system evaluation and validation, (c) teacher review and intervention, and (d) agent update and policy refinement. It enables real-time adaptation, teacher oversight, and iterative system improvement. Adopting a design science research (DSR) methodology and mixed-methods evaluation across functional, pedagogical, ethical, and system-level dimensions, the proposed framework is expected to demonstrate improved learner modeling accuracy, enhanced knowledge tracing, and more robust multimodal engagement analysis compared to centralized and single-modality approaches. Based on design science evaluation against established benchmarks and component-level validation in a simulated learning management system (LMS), this theoretical framework is projected to improve learner modeling accuracy, enhance knowledge tracing, and enable more robust multimodal engagement analysis compared with centralized and single-modality approaches. These projections constitute theoretically derived hypothesis and remain subject to empirical validation in live deployment studies. This study’s theoretical contribution lies in demonstrating that ethics-by-design and adaptive personalization are architecturally compatible and mutually reinforcing design principles. Full article

This study provides a comprehensive overview of emerging contaminants in water resources. It includes a global perspective with specific insights from Portugal. Following PRISMA 2020 guidelines, peer-reviewed studies published between 2020 and 2025 were critically assessed to identify patterns of contamination, monitoring gaps and technological readiness levels. Results indicate frequently detected emerging contaminants including pesticides, antibiotics and antidepressants in surface water, groundwater and wastewater systems. Advanced analytical methods, particularly liquid chromatography coupled with high-resolution mass spectrometry, stands out as the main detection technique, allowing the identification of trace levels of contaminants. These techniques also support the identification of pollution patterns associated with agriculture, urban and industrial effluents. However, significant asymmetries persist between international and Portuguese research. Particularly evident in systematic monitoring networks and integrated risk assessment approaches. Conventional water/wastewater treatment plants show limited removal efficiency, while advanced oxidation processes, adsorption technologies and microalgae-based systems demonstrate promising but variable performance depending on scale and operational maturity. The findings highlight gaps between scientific advances and regulatory implementation, emphasizing the need for strengthened monitoring frameworks and technology scale-up strategies. They also call for improved integration between science, governance, and sustainability policies to ensure resilient water resource management in line with the Sustainable Development Goals. Full article

Maritime emergency response requires broadband and reliable communications in sea areas where shore coverage is limited or emergency connectivity is temporarily unavailable, making rapid on-demand aerial networking essential. Unmanned aerial vehicles (UAVs) acting as aerial base stations can be rapidly deployed to provide on-demand coverage; however, ship mobility, heterogeneous emergency priorities, and UAV endurance limitations make the joint optimization of user association and multi-UAV deployment a challenging mixed-integer, long-horizon decision problem. This paper considers a multi-UAV maritime emergency communication system where ships are categorized into multiple priority classes and served links must satisfy a minimum signal-to-noise ratio (SNR) constraint. We formulate a long-term system-utility maximization problem that jointly determines (i) per-slot association between UAVs and ships under capacity, priority, and SNR constraints, and (ii) dynamic UAV deployment under mobility, geofencing, and battery constraints. To obtain tractable and high-quality solutions, we decompose the problem into two coupled subproblems. For user association, we propose a Priority-Aware Branch-and-Cut (PA-BAC) algorithm that integrates linear programming relaxation, cutting-plane tightening, and priority-guided branching, with a priority-greedy feasible initialization to accelerate incumbent improvement. For dynamic deployment, we develop an Enhanced Multi-Agent Proximal Policy Optimization (E-MAPPO) method featuring a global value network, entropy regularization, and sequential actor updates to enhance learning stability and exploration. Importantly, the PA-BAC association is embedded into the learning loop to provide reliable, constraint-satisfying per-slot rewards and reduce the burden of end-to-end learning over hybrid-action spaces. Simulation results demonstrate that PA-BAC consistently improves normalized priority-weighted throughput over heuristic association baselines. Moreover, by mathematically enforcing priority and QoS feasibility at every slot and delegating only continuous mobility to MARL, the integrated E-MAPPO-PA-BAC framework achieves higher long-term system utility, improved energy efficiency, and strong robustness across varying ship densities—properties that are vital for time-sensitive maritime emergency communications. Additional runtime, sensitivity, and AIS-driven trace evaluations further verify the computational practicality of PA-BAC and the applicability of the proposed framework under realistic ship mobility patterns. Full article

The National Register of Historic Places (NR) is a structured artifact of meaning-making that encodes disciplinary values linking architectural and cultural significance to wealth and stylistic distinction. In doing so, it systematically underrepresents vernacular, working-class, and the built environments of racially and ethnically marginalized communities. This paper uses artificial intelligence (AI) to examine how that meaning is constructed. We analyze the preservation record across three scales: a national dataset of 100,117 NR listings (1966–2025), a state-level profile of Illinois’s 1997 NR listings, and a close analysis of Lake Forest, Illinois, a community whose exceptional concentration of NR-listed estate architecture makes it an ideal site for examining how preservation significance has been defined and what it excludes. Two parallel AI methods are applied to eighteen Lake Forest nomination documents and their associated photographs. Natural Language Processing (NLP) analyzes nomination text to trace how preservation professionals connect buildings to cultural value; blind AI image analysis examines the same properties to assess how a model trained on cultural imagery constructs visual meaning independently. NLP analysis reveals a corpus dominated by architectural description, with social history, landscape, and labor systematically underrepresented. The visual analysis confirms and amplifies the nomination record’s class-based assumptions while reproducing the same omissions regarding labor, diversity, and community context. These findings inform debates about AI’s potential to audit existing listings and support nominations for underrepresented property types, while showing that without deliberate corrective design and policy reform, such tools are as likely to replicate the preservation system’s inequities as to repair them. Full article

International migration research is entering a phase in which digitalization reshapes how migration processes are measured, modeled, and governed. At the same time, recent scholarship emphasizes the need to further develop migration theory so that it reflects contemporary migration dynamics and evolving data environments. This article proposes a global framework for “digital migration systems” that integrates classic migration theories with digital-demography infrastructures and digital trace data. The framework conceptualizes migration as a multi-scalar system in which origin and destination contexts, policy regimes, and network dynamics interact with measurement technologies and data architectures. Building on digital-era demographic scholarship, the article outlines how traditional population sources such as censuses and household surveys can be combined with administrative records and digital trace data while maintaining attention to representativeness, coverage, and bias. The article then presents a modeling pathway connecting spatial interaction models and Bayesian approaches to common migration data constraints. Finally, it develops policy applications illustrating how a digital migration systems perspective can support scenario-based policy evaluation, rapid shock assessment, and local capacity planning. The article contributes a conceptual bridge integrating migration theory, digital measurement infrastructures, and policy analysis. It also clarifies scope conditions for applying the framework across diverse national contexts. Full article

Encrypted storage systems increasingly rely on governance mechanisms such as delegation, revocation, key updates, and policy evolution. While existing approaches provide strong guarantees for access enforcement, integrity, and transparency, they do not address a fundamental question: under which conditions can an observed sequence of governance events be accepted as a semantically valid evolution of authorization state? This work introduces a formal semantic framework for governance validity based on observable evidence. Governance is modeled as an admissibility-constrained state transition system in which events are accepted only if they satisfy explicit authorization, reference, temporal, revocation, and evidence conditions. The framework defines valid governance histories as sequences of admissible events; characterizes the conditions for deterministic state reconstruction; and establishes invariants capturing correctness properties such as revocation soundness, policy-constrained evolution, evidence completeness, non-equivocation, and temporal coherence. It also defines event-specific evidence obligations that support independent verification. The proposed approach is architecture-independent and does not prescribe specific enforcement or logging mechanisms, focusing instead on the semantic conditions required for accepting governance histories as valid from observable evidence. In addition, the framework can be instantiated as an independent verification layer that operates over observable governance traces without requiring access to internal system states. Full article

Autonomous odor source localization remains a challenging problem for aerial robots due to turbulent airflow, sparse and delayed sensory signals, and strict payload and computation constraints. While prior unmanned aerial vehicle (UAV)-based olfaction systems have demonstrated gas distribution mapping or reactive plume tracing, they rely on predefined coverage patterns, external infrastructure, or extensive sensing and coordination. In this work, we present a complete, open-source UAV system for online odor source localization using a minimal sensor suite. The system integrates custom olfaction hardware, onboard sensing, and a learning-based navigation policy that we train in simulation and deploy on a real quadrotor. Through our minimal framework, the UAV is able to navigate directly toward an odor source without constructing an explicit gas distribution map or relying on external positioning systems. We incorporate vision as an optional complementary modality to accelerate navigation under certain conditions. We validate the proposed system through real-world flight experiments in a large indoor environment using an ethanol source, demonstrating consistent source-finding behavior under realistic airflow conditions. The primary contribution of this work is a reproducible system and methodological framework for UAV-based olfactory navigation and source finding under minimal sensing assumptions. We elaborate on our hardware design and open-source our UAV firmware, simulation code, olfaction–vision dataset, and circuit board to the community. Full article

Software-Defined Networking (SDN) enables centralised and programmable traffic control, but adaptive optimization in operational networks remains challenging when safe online exploration is limited and only historical controller traces are available. This study proposes a log-driven Proximal Policy Optimisation (PPO) framework for adaptive SDN traffic control that learns directly from recorded state–action–reward transitions. The method uses a replay-based pseudo-environment constructed from controller logs. It combines clipped PPO updates with action-consistency regularisation and running state normalisation to improve stability under logged-data constraints. The empirical evaluation shows that the learned model reconstructs the dominant response pattern observed in the traces, preserves a positive relationship between the principal control-related predictor and the response, and reveals a non-uniform interaction structure across telemetry features. The framework also differentiates systematically across operating conditions and experimental groups, with category means ranging from 0.78 to 1.24 and group medians ranging from $-0.12$ to 1.12, while receiver operating characteristic analysis yields an area under the curve of 0.714. The practical network evaluation further shows that the PPO-controlled setting improves overall throughput, packet loss, jitter, and flow-completion success relative to the baseline controller. These results indicate that log-driven, stability-constrained PPO can provide a stable and informative basis for adaptive SDN traffic control when policy learning must rely on historical controller data rather than unrestricted live-network experimentation. Full article

Artificial intelligence development offers new solutions for enhancing corporate carbon performance and is crucial for promoting sustainable business practices. This study investigates the dynamic impact of artificial intelligence (AI) policy on corporate carbon performance using time series panel data of Chinese A-share listed companies from 2010 to 2024. Leveraging the staggered establishment of the National New Generation Artificial Intelligence Innovation Development Pilot Zones as a quasi-natural experiment, we develop a multi-period difference-in-differences framework with time-varying treatment. Our time series-based identification strategy addresses serial correlation and time-varying confounding factors through robust clustering and event study specifications. The findings reveal that AI policy significantly improves corporate carbon performance, a conclusion that remains robust after rigorous endogeneity tests, placebo checks, and counterfactual analyses. Using dynamic panel models, this study traces the temporal evolution of policy effects and demonstrates that AI exerts indirect effects through three time-lagged pathways: micro-level technological diffusion, future industry development, and the progressive accumulation of digital infrastructure and computing resources. Heterogeneity analysis reveals differentiated impacts across micro- and macro-levels, providing granular insights for forecasting heterogeneous treatment effects. By integrating panel time series econometrics with causal inference, this study contributes to the literature on corporate carbon performance while expanding analytical frameworks for understanding AI’s enabling effects. The findings offer policy insights and empirical benchmarks for forecasting green transition trajectories, with direct implications for green finance and sustainable economic development. Full article

Enterprise spatial data supply chains (SDSCs) increasingly support high-stakes decision-making; yet, the provenance in operational geospatial systems is often fragmented across metadata records, workflow logs, and application-specific formats. This limits traceability, reproducibility, auditability, and fitness-for-purpose assessment, particularly when organisations need to explain how spatial products were created, with which parameters, spatial references, and dependencies. This study proposes the Enterprise Spatial Data Provenance Knowledge Infrastructure (ESDPKI), a standards-aligned framework that treats provenance as enterprise knowledge infrastructure rather than passive metadata. Using a design science research approach, the study synthesised the literature-derived requirements, standards-based interoperability constraints, and representative spatial data supply chain workflows to develop four artefacts: a six-layer reference architecture, the GeoPROV minimal semantic profile, a validation-gated ingestion and governance mechanism, and a reproducible evaluation blueprint with service-level objectives. Together, these artefacts support provenance capture, semantic normalisation, validation, queryable lineage, catalogue linkage, and policy-aware disclosure across enterprise environments. The resulting design makes geospatial operations, parameters, geometry, and coordinate reference system context machine-actionable, enabling lineage tracing, impact analysis, discovery-time fitness-for-purpose assessment, and stronger governance at scale. ESDPKI therefore provides a coherent architectural pathway for operationalising trustworthy, explainable, and scalable spatial provenance in enterprise settings. Full article