Search Results (2,487)

Remote sensing research increasingly relies on heterogeneous satellite, UAV, hyperspectral, multispectral, SAR, and environmental monitoring data to support land, urban, hydrological, and environmental applications. However, these data are often affected by sensor differences, spatial and temporal heterogeneity, missing observations, irregular sampling, noise, and non-stationary environmental processes. This systematic review was conducted within the context of the Romanian Hub for Artificial Intelligence (HRIA) project, which supports the development of strategic artificial intelligence technologies. The review synthesizes current research on adaptive neural networks for remote sensing and Earth observation, with particular attention to Liquid Neural Networks and related continuous-time neural models. A systematic search was conducted across IEEE Xplore, Scopus, Web of Science, ScienceDirect, SpringerLink, Wiley Online Library, Google Scholar, and reference lists. After duplicate removal, screening, and full-text assessment, 61 studies published between 2018 and 2026 were included in the qualitative synthesis. The findings show that adaptive neural networks have gained increasing attention after 2022 and are mainly applied to image-centered remote sensing tasks, including classification, mapping, object detection, segmentation, enhancement, and change detection. Most studies adapt established deep learning architectures through multi-scale processing, adaptive feature fusion, attention mechanisms, graph relationships, or task-specific refinement. Continuous-time models are used less frequently but are relevant for irregular observations and dynamic environmental processes. Liquid Neural Networks remain emerging, and current evidence suggests only preliminary, task-specific relevance for irregular, noisy, multimodal, and dynamic remote sensing applications. Full article

Computing capacity has become a metered factor of production for digitally intensive enterprises, yet its consumption exhibits strong temporal heterogeneity—tidal intraday cycles, weekly contrasts, seasonal surges, and project-driven regime shifts—so that individually provisioned capacity is structurally underutilized. This paper proposes a park-level computing power sharing center (CPSC) as an institutional mechanism that converts the temporal complementarity of co-located enterprises into measurable cost savings. We develop a general mode-design framework that separates CPU core-hours from GPU card-hours, characterizes demand via deterministic tides and stochastic modulations, and derives optimal pooled capacity commitments through a newsvendor-type quantile condition. A parametric calibration protocol maps observable temporal features—peak-to-trough ratios, inter-tenant phase spreads, and residual volatility—into closed-form diversity-factor expressions with Monte Carlo confidence intervals. The procurement model covers a multi-option contract menu (on-demand, one–three-year reserved instances, savings plans, and spot), region-specific pricing, hardware class tariffs, and ancillary costs, including network egress, migration, and data sovereignty compliance; benefits are measured relative to each tenant’s individually optimal reserved portfolio, not naive retail procurement. A mechanism design analysis incorporating Shapley value allocation, Bayesian incentive compatibility, and penalty structures ensures individual rationality and robustness to misreporting and strategic load shifting. We further develop an energy model—with utilization-dependent power draw, facility PUE, embodied carbon, and marginal grid emission factors—showing that financial savings translate into genuine emission reductions only when pooling enables physical capacity retirement rather than mere billing reallocation. The framework is applied to twenty representative Chinese parks spanning seven functional categories; all park-level data are reconstructed from public sources using the calibration methodology, and the reported figures are model-derived projections, not empirical measurements. The model yields procurement saving estimates of 4.6–20.2% relative to individually optimal reserved-procurement portfolios, with high-diversity parks at the upper end. Sensitivity analyses across regional tariffs, hardware mixes, and cross-country utilization benchmarks (Uptime Institute, US DOE, EU Commission) confirm robustness and delineate boundary conditions. This paper concludes with a data provenance taxonomy and a phased implementation roadmap. Full article

Current electrical systems are undergoing a profound transformation due to the massive integration of renewable energy sources and power electronics, exceeding the capabilities of traditional simulation tools. Digital Twins (DTs) have emerged as a strategic solution by enabling a bidirectional and real-time connection between physical assets and their virtual replicas. This paper presents a conceptual design proposal for a DT laboratory for electrical networks, developed from the analysis of twelve representative case studies of internationally recognized DT laboratories. The analyzed laboratories were evaluated using a technological integration scale to identify common technological trends and experimental capabilities. The results indicate that most of the selected laboratories operate at Hardware-in-the-Loop (HIL) and Power Hardware-in-the-Loop (PHIL) maturity levels, with a predominant focus on smart grids and distribution systems. Based on these findings, a conceptual laboratory architecture organized into four functional stages is proposed as a scalable cyber–physical environment for technology validation and advanced training in the electrical sector. Full article

The transition toward Open Radio Access Network (O-RAN) architecture has enabled unprecedented intelligence and flexibility in 5G and 6G network slicing. However, a fundamental challenge remains in managing the tension between radio unit energy efficiency and the strict Service Level Agreement (SLA) requirements of Ultra-Reliable Low-Latency Communication (URLLC) slices, particularly under highly dynamic traffic conditions. Existing O-RAN approaches suffer from a timescale conflict where Non-Real-Time (Non-RT) policy planners optimize for long-term energy but fail to react to rapid traffic surges, while Near-Real-Time (Near-RT) controllers prioritize reliability at the cost of significant energy over-provisioning. To address this, we propose H-RLS, a hierarchical multi-timescale framework that decouples control into a Non-RT Proximal Policy Optimization (PPO) agent for strategic, energy-aware policy planning and a Near-RT Recursive Least Squares (RLS)-assisted xApp. By predicting millisecond-level delay risks, the xApp acts as a mathematically constrained safety net, applying bounded tactical adjustments when critical SLA violations are detected. Extensive evaluations across dynamic traffic transitions demonstrate that H-RLS maintains zero SLA violations. By actively preventing resource over-provisioning, the framework achieves the lowest composite Energy-SLA cost across all tested regimes, significantly minimizing dynamic power consumption while preserving Enhanced Mobile Broadband (eMBB) service integrity. Full article

Addressing the dual threats of malicious jamming and time-varying fading faced by wireless communication links in complex dynamic electromagnetic adversarial environments, existing intelligent anti-jamming methods predominantly focus on single-dimensional resource optimization under quasi-static channels. This focus neglects the nonlinear superposition effects of multi-path deep fading and dynamic strong jamming in the time-frequency domain, making it challenging for systems to balance transmission reliability and system energy efficiency in physical environments where fading and suppression coexist. To address this issue, this study proposes a joint intelligent anti-jamming method for channel switching and transmit power control based on a Deep Q-Network (DQN). Initially, a composite communication environment model incorporating Markov time-varying fading and jamming is constructed. Subsequently, the joint resource scheduling problem is formulated as a Markov Decision Process. The environment state space is reconstructed by integrating continuous channel state estimation and jamming observation features, accompanied by the design of a highly aggregated two-dimensional discrete action space for both channel and power. Finally, a composite reward function evaluating both communication success rates and power consumption costs is proposed to guide the agent in multi-dimensional resource joint optimization. Simulation results demonstrate that the proposed algorithm effectively extracts implicit features under the composite state of fading and jamming. When encountering extreme deep fading or full-band blocking, the agent strategically triggers a silent mechanism to avoid exorbitant invalid energy consumption penalties, while precisely matching interference-free channels with the minimum effective transmit power during favorable communication windows. Simulation results show that compared with traditional xx algorithms, the proposed method significantly improves the dynamic successful transmission rate and system energy efficiency in complex, highly dynamic scenarios, achieving an effective optimization of anti-jamming reliability and low power overhead. Full article

Online social networks increasingly serve as platforms for collective action, yet identifying minimal winning coalitions remains challenging due to heterogeneous user features, noisy behavioral traces, and the strategic ambiguity of swing members. Traditional neighborhood rough set models effectively handle numerical data but typically weight either attributes or objects in isolation, neglecting the joint influence of feature importance and user reliability. Moreover, boundary-region users, often critical for tipping coalition outcomes, are frequently discarded or misclassified by hard-thresholding mechanisms. To bridge these gaps, we propose a Dual-Weighted Neighborhood Rough Set framework, called DWNRS, that combines attribute-weighted neighborhood construction with object-reliability-weighted rough-membership estimation. In this formulation, attribute weights determine the geometry of distance-based neighborhoods, while object weights determine how reliably neighboring users contribute to membership estimation and coalition strength. We formalize dual-weighted rough membership, define lower, boundary, outside, and candidate approximation regions, and introduce a dependency-guided reduction algorithm that extracts coalitions that are winning and inclusion-wise minimal under the induced DWNRS strength function, whenever a winning coalition exists within the allowed candidate pool. Theoretically, we prove that DWNRS generalizes classical neighborhood rough sets and Pawlak rough sets, preserves the monotonicity required by simple games, and provides precise conditions under which boundary recruitment and inclusion-wise minimality hold. We further establish a boundary-change accounting identity that characterizes when and how the boundary region contracts, showing that contraction is a data-dependent empirical effect rather than a universal guarantee. Empirically, we validate the framework on a controlled synthetic benchmark and a semi-real US Congress Twitter interaction network under a fair common-strength evaluation protocol. Against the original non-hybrid baselines, DWNRS is the only method that consistently returns coalitions that are both winning and inclusion-wise minimal. A new GWNRS-style hybrid experiment shows that object-weighted geometric denoising can also produce winning and inclusion-wise minimal coalitions; on the synthetic benchmark, the hybrid obtains a slightly higher mean F1 than DWNRS under the fixed-quota protocol, while on the Congress benchmark both methods bypass boundary recruitment because the core alone is sufficient. These results clarify DWNRS’s distinct role without claiming universal classification superiority over the hybrid: DWNRS preserves a regulated boundary region and the strategic option value of swing-user recruitment in regimes where the core alone may be insufficient. Among the original non-hybrid baselines, DWNRS leads all classification metrics on the Congress topology and produces coalitions 23–35% smaller than the original winning baselines. Full article

Artificial intelligence (AI) is changing the way we study transport systems, yet its contribution to sustainable transportation planning remains uneven. While AI-based methods have shown strong performance in operational tasks such as traffic prediction and signal control, their integration into strategic planning is still fragmented. This paper provides a narrative review of AI applications in transportation planning and, more specifically, on-demand modeling, arguably the most challenging side of transport planning, and will focus on three arbitrary field macro-aggregations: machine learning (ML), artificial neural networks (ANN), and neurosymbolic AI (NeSy). The findings show that AI methods can improve predictive performance and capture complex nonlinear mobility patterns; however, predictive accuracy alone is insufficient for planning practice. Strategic planning requires models that are interpretable, transparent, and able to accommodate expert-defined constraints. The European Union AI Act further reinforces this requirement by classifying AI systems used in critical infrastructure, including road-traffic management, as high risk; this creates a regulatory need that many current AI applications do not yet satisfy. The paper argues that neurosymbolic AI architectures offer a promising research direction by combining neural learning from heterogeneous mobility data with symbolic representations of behavioral rules, network constraints, as well as accessibility, equity, and environmental objectives. Full article

The top management team (TMT) plays a key role in shaping corporate strategy. However, how and when TMT faultlines influence corporate digital transformation remains unclear. Drawing on the categorization–elaboration model, this study examines the nonlinear relationship between TMT faultlines and corporate digital transformation and the moderating role of board governance. Using panel data from Chinese A-share-listed manufacturing firms, the results suggest an inverted U-shaped relationship between TMT faultlines and corporate digital transformation. Board monitoring and board networking are associated with a steeper inverted U-shaped curve, whereas board advising is associated with a flatter curve. Heterogeneity analysis shows that the inverted U-shaped relationship is mainly observed in non-state-owned enterprises, younger firms, and firms with high ownership concentration. These findings extend research on faultlines, strategic leadership, and corporate governance and offer practical implications for configuring TMTs and designing board governance mechanisms to support corporate digital transformation. Full article

As professionals increasingly encounter artificial intelligence (AI) in their workplaces, questions around adoption barriers, ethical concerns, and job security have grown in prominence. This paper reports an exploratory cross-sectional descriptive survey of self-reported perceptions among a convenience sample of 324 UK-based industry professionals. The study does not identify determinants, predictors, or causes of AI adoption. The survey examined eight binary items covering daily personal AI use, perceived strategic importance, cost as a barrier, ethical concern, perceived income change, subjective job-displacement anxiety, perceived work-performance change, and preference for conversational AI tools over traditional search engines. All eight items were completed by all 324 eligible respondents. Using Wald 95% confidence intervals, 47.2% (95% CI 41.8–52.7) reported using AI in their daily jobs, while 66.4% (95% CI 61.2–71.5) perceived AI as important for remaining competitive. Cost was identified as a barrier by 76.5% (95% CI 71.9–81.2), and 65.7% (95% CI 60.6–70.9) reported concern about ethical implications. These two figures are separate aggregate proportions and are not treated as an individual-level behavioural gap. Only 37.0% (95% CI 31.8–42.3) reported an income increase associated with AI at work, whereas 71.9% (95% CI 67.0–76.8) reported improved work performance. These two items were separate self-report questions; no respondent-level association was tested, and neither item measured objective productivity or pay. Job-automation concern was reported by 48.5% (95% CI 43.0–53.9). The same share as the performance item, 71.9% (95% CI 67.0–76.8) preferred ChatGPT-like tools over traditional search engines. The sample was recruited through professional networks, email, social media, and organisational mailing lists and is likely to over-represent professionals already interested in digital tools. Findings are therefore presented as descriptive perceptions from this respondent pool and are not generalised to UK professionals as a whole. Full article

This study develops a modular framework for the diagnostic application of microfoundational theory to examine how individual-level actions aggregate into macro-level technological capabilities and operational performance in innovation intermediaries within emerging economies. Grounded in Coleman’s bathtub model and the Viable System Model, we dissect three interdependent modules to diagnose systemic issues within institutional voids. Methodologically, we conduct a qualitative critical case study of a centenary Brazilian Research and Technology Organization, utilizing semi-structured interviews across three distinct organizational levels—senior executives, unit heads, and research leads—triangulated with eleven institutional management documents (2019–2024) through categorical content analysis. Theoretically, this research aims to establish a conceptual taxonomy that synthesizes Coleman’s micro–macro link, potentially serving as an analytical baseline for future cross-context evaluations of innovation intermediaries while exploring how meso–micro actions co-evolve with ecosystem-level environments. Practically, this study seeks to offer actionable governance levers for managers and policymakers, illustrating how the strategic deployment of dedicated internationalization units to buffer external complexity and the targeted cultivation of soft competencies in senior research leads may help overcome collaborative network isolation within late-development settings. Full article

Green taxation has evolved from a fiscal instrument for correcting environmental externalities into a strategic policy mechanism for promoting sustainable development and supporting the transition toward low-carbon economies. As research on this topic has expanded, the literature has become increasingly fragmented, characterized by diverse research streams, limited interdisciplinary integration, and an incomplete understanding of its intellectual and conceptual development. This study provides a comprehensive bibliometric analysis of green taxation research to examine its scientific evolution, map its knowledge structure, and identify emerging research frontiers and knowledge gaps. Conceptually, the literature on green taxation extends beyond conventional Pigouvian foundations to encompass ecological, institutional, and broader heterodox perspectives. The analysis is based on 2952 publications indexed in the Scopus database between 1990 and 2025. Biblioshiny (Bibliometrix in R) and VOSviewer were employed to examine publication trends, co-citation networks, keyword co-occurrence, and international scientific collaboration. The results reveal sustained growth in scientific production, reaching its highest level in 2024, and a heterogeneous research landscape structured around major themes, including the double dividend, innovation for sustainable development, environmental policy related to pollution and welfare, circular economy and sustainability transitions, as well as green investment linked to technological change. Scientific output remains highly concentrated in a limited number of countries, with China emerging as the leading contributor, while international collaboration remains comparatively limited. The analysis also highlights significant geographical disparities, particularly the underrepresentation of Africa and the MENA region, and reveals that the field remains only partially integrated despite its rapid expansion. These findings provide an integrated understanding of the evolution of green taxation research and identify key priorities for future empirical and comparative studies to support more effective and inclusive environmental fiscal policies. Full article

Hydrogen (H₂) is widely regarded as a key energy vector for the transition to a low-carbon energy economy, yet its production, storage, and distribution remain associated with significant environmental burdens. This review analyses the entire low-emission H₂ value chain from an eco-design perspective, synthesising recent life cycle assessment (LCA) literature on water-splitting technologies (electrolysis, thermochemical cycles, and photocatalysis), biomass-based thermochemical and biological routes, and the emerging exploitation of natural (geological) hydrogen. Environmental performance is benchmarked against conventional steam methane reforming and coal gasification across multiple indicators, including global warming potential (GWP), terrestrial acidification, water scarcity, mineral resource scarcity, and human toxicity. The results demonstrate that no single pathway is universally sustainable. Electrolysis remains strongly dependent on the electricity grid mix, while biomass gasification coupled with carbon capture and storage (CCS) achieves deep carbon mitigation but exacerbates acidification concerns. Furthermore, photocatalysis and biological routes show theoretically low carbon footprints but remain severely constrained by low technology readiness levels (TRLs). Downstream, physical compression showed lower life cycle burdens than chemical carriers, and pipeline networks outperformed other transportation modes, in the studies reviewed; both findings are conditional on the distance, scale, pressure and utilisation assumptions adopted. Finally, the integrated economic overview reveals that environmental hotspots correlate directly to financial penalties, ultimately dictating the levelised cost of hydrogen (LCOH). This review consolidates critical technological gaps, highlights the necessity for harmonised LCA boundaries, multi-indicator reporting, and primary industrial data, and proposes strategic research directions to enable a truly sustainable H₂ economy where eco-design choices are matched to localised energy, water, and material realities. Full article

Family firms are distinctive organisational forms in which ownership, governance, leadership, and strategic decision-making are closely intertwined. Although prior research has examined leadership, succession, governance, and strategy in family firms, the literature remains fragmented regarding how leadership styles are associated with both strategic behaviour and strategic choices. This study addresses this gap by conducting a Systematic Literature Network Analysis of 59 studies on leadership and strategy in family firms. Combining systematic review procedures with bibliographic network analysis, this paper maps the intellectual and thematic structure of the field and synthesises the evidence around five research questions. The findings show that the literature is theoretically rich but not yet fully consolidated, with socioemotional wealth, upper echelon theory, agency theory, stewardship theory, and the resource-based view serving as dominant lenses. Leadership appears to be connected with strategy mainly through affective, political, and cognitive mechanisms, while the procedural core of strategic decision-making, including participation, dissent, consensus formation, and formal planning, remains underdeveloped. This review also shows that leadership is more frequently studied in relation to identity-relevant strategies, such as sustainability, innovation, succession, digital transformation, and professionalisation, while scale-and-scope strategies remain less examined. This study contributes by proposing an integrative framework of five leadership-to-outcome pathways and by outlining a future research agenda for leadership, entrepreneurship, and strategic renewal in family firms. Full article

Urban cycling is increasingly recognised as a key component of sustainable urban mobility, yet many medium-sized cities lack integrated analytical tools to support evidence-based cycling infrastructure planning. This study proposes a GIS-based decision-support framework integrating a relative cycling impedance (Bike Cost) model, GIS-based network analysis, Origin–Destination (OD) Cost Matrix analysis, frequency-based corridor identification, and sensitivity analysis to evaluate cycling accessibility and prioritise cycling investments. The framework was applied to the central district of Burdur, Türkiye, using neighbourhood centres as origins and primary schools, middle schools, high schools, the university, parks, and tourism destinations as six destination categories. The results reveal that Burdur’s compact urban structure provides relatively high cycling accessibility within the urban core. In contrast, peripheral neighbourhoods experience lower accessibility because of fragmented network connectivity and higher cycling impedance. Spatial comparison with the existing cycling infrastructure revealed that the identified high-priority corridors do not overlap with the current cycling network, highlighting a clear mismatch between existing infrastructure and the corridors of greatest strategic importance. Sensitivity analysis confirmed that the priority corridors remained highly stable under alternative Bike Cost weighting scenarios, demonstrating the robustness of the proposed framework. These findings indicate that improving network continuity and connectivity is as important as expanding cycling infrastructure. The proposed framework provides a transferable and reproducible methodology for supporting evidence-based cycling infrastructure planning and sustainable urban mobility in medium-sized cities. Full article

Amid the global transition toward carbon neutrality, digital technologies such as blockchain and federated learning offer a viable pathway to alleviating green financing constraints for small- and medium-sized enterprises (SMEs) and advancing supply chain decarbonization. Against this backdrop, this paper proposes a digital intelligence-driven financing model for green supply chains. A dynamic game model is developed to capture strategic interactions between financial institutions and SMEs in financing mode selection and credit decisions, with an evolutionary game-theoretic approach within a two-layered complex network subsequently employed to examine how key factors shape evolutionary outcomes. The results reveal that the federated learning and blockchain-enabled green supply chain financing model reshapes traditional services via digital credit construction, markedly improving lending willingness, lowering default probabilities, and deterring greenwashing. Additionally, technology usage costs, federated training incentives, and data breach risks are identified as critical determinants of bilateral financing mode choices. Full article