Search Results (39)

Urban mobility is undergoing a rapid transition driven by digitalization, electrification, and automation. However, current research remains largely fragmented across specific technological domains, obscuring the interactions required for city-scale deployment. To address this gap, we conducted a literature review (2018–2026) adhering to the PRISMA 2020 guidelines. Using Google Scholar as an aggregate search engine, we screened and synthesized 162 peer-reviewed studies across four foundational pillars: intelligent transportation systems, resilient infrastructure, electric mobility, and autonomous/connected vehicles. The methodological evaluation of the literature reveals a prevalent overreliance on simulation models compared to large-scale field trials. Through a narrative synthesis of the selected studies, we derive a comprehensive five-layer conceptual framework that integrates the infrastructure, mobility, energy, digital, and governance layers. The findings indicate that scaling smart mobility is frequently constrained by institutional fragmentation and infrastructure rigidity, which often act as bottlenecks equal to or greater than technological capability. The review concludes by outlining targeted research priorities to guide the integration of sustainable urban mobility. Full article

Three-dimensional geological structural modelling provides the geometric framework for sub-surface exploration and development. However, conventional workflows, driven primarily by seismic interpretation, often lack explicit constraints from expert knowledge and are difficult to update when interpretations evolve. In particular, the conventional surface-based workflow follows a sequential pipeline—from seismic interpretation through manual intersection editing to surface generation and pillar gridding—in which geological knowledge is embedded only implicitly through operator-dependent parameter tuning, making knowledge transfer and model reproducibility difficult. This study proposes an intelligent modelling methodology guided by a geological structure knowledge graph. The method includes: (i) a three-tier knowledge architecture (TKA) that formalises domain knowledge in entity, relationship and inference layers using RDF/OWL; (ii) a knowledge-driven intersection line generation algorithm (KILGA) coupled with a hierarchical adaptive mesh refinement scheme based on a posteriori error estimation (HAMR-APEE) to integrate geological constraints and mitigate boundary aliasing; and (iii) a bidirectional linkage mechanism between the knowledge graph and 3D models to support incremental updates following knowledge revision. The approach is validated in three petroliferous basins in China (Ordos, Qaidam and Sichuan), representing micro-amplitude, thrust-nappe and deep complex structural styles. Compared with a conventional surface-based workflow, the proposed method reduces modelling RMSE from 15–20 m to 5–8 m, improves geological reasonableness from ~85% to >95%, and shortens modelling cycles from months to weeks. These results demonstrate that explicit integration of formalised geological knowledge into the modelling pipeline can substantially enhance both accuracy and efficiency across a range of structural settings. Full article

Extracellular vesicles (EVs) hold significant potential in cardiovascular diagnosis and treatment. However, their clinical applications are limited by challenges such as isolation efficiency, subpopulation heterogeneity, analytical standardization, and manufacturing scalability. Artificial intelligence (AI) and machine learning (ML) offer a computational framework to address these constraints through data-driven platform engineering. This review examines AI-assisted strategies in three interconnected EV platform pillars in cardiovascular medicine. These include: (i) isolation and processing platforms where ML algorithms optimize microfluidic separation and improve signal accuracy; (ii) analytical and diagnostic platforms where deep learning supports single vesicle phenotyping, multi-omics biomarker engineering, and biosensor interpretation; and (iii) therapeutic and manufacturing platforms where AI guides cargo loading, biodistribution estimation, and process control. We also assess key translational challenges, including MISEV2023 compliance, dataset bias, reproducibility, and regulatory alignment. This review positions artificial intelligence as the fundamental layer of the EV bioengineering process, providing a structured framework for advancing EV-based cardiovascular platforms from laboratory research to clinical application. Full article

The burden of disability is rising rapidly in Africa, where a severe shortage of rehabilitation professionals and limited infrastructure create a major treatment gap. Immersive virtual reality and serious games have shown promise for upper limb rehabilitation, but current extended reality (XR) solutions lack personalization, cultural adaptability, real-time feedback, and scalability. This perspective paper proposes a conceptual AI-enhanced XR framework tailored to African low- and middle-income countries. We identify how generative AI, large language models, multiagent systems, and explainable AI can address specific rehabilitation barriers. The framework integrates these four pillars into a three-layer architecture covering content creation, interaction, and decision support. We analyze implementation considerations specific to African contexts—infrastructure, capacity building, cultural adaptation, ethics, and financing—and outline a detailed research agenda with near, medium, and longer term priorities. Realizing this vision requires co-design with African communities, investment in local capacity, adaptation to infrastructure constraints, and development of ethical frameworks. AI-enhanced XR has the potential to democratize access to quality rehabilitation across Africa, but this potential must be validated through rigorous, context-sensitive research. Full article

Historic urban landscapes (HULs) represent complex environments where contemporary daily life interacts with living heritage. This study focuses on “third places,” informal social spaces outside home and work, as critical infrastructure for urban resilience. Addressing the lack of multidimensional assessments in Southeast Asian heritage contexts, this study develops and applies a context-sensitive assessment framework that integrates the HUL approach with four sustainability pillars: physical, environmental, socio-cultural, and governance–economic. Nakhon Si Thammarat was selected as a representative case study of a multicultural living heritage town where Buddhist, Muslim, and Chinese cultural layers uniquely converge within its urban fabric. Through field surveys and spatial mapping, 17 sites were empirically identified based on Oldenburg’s characteristics and evaluated via a structured rubric. Findings reveal a significant systemic imbalance: while the socio-cultural dimension is highly sustainable (M = 2.44), driven by robust cultural diversity, the environmental (M = 1.03) and governance–economic (M = 1.38) dimensions are considerably weaker. Key deficiencies include poor low-carbon accessibility and limited community participation. Notably, religious courtyards emerged as effective “living heritage” prototypes (M = 2.04), bridging sacred and secular functions. The study suggests that historic urban management should prioritize micro-scale environmental retrofitting and co-management models, leveraging existing social capital rather than wholesale urban restructuring. This flexible framework is transferable to other multicultural historic towns in the region with comparable contextual conditions. Full article

The rapid evolution of Internet-enabled smart systems has accelerated the adoption of the Internet of Things (IoT), Cyber–Physical Systems (CPS), Big Data, Artificial Intelligence (AI), and Human–Computer Interaction (HCI/AR–VR) across distributed digital ecosystems. Despite these advances, the architectural integration of sensing, information processing, and system-level reasoning remains fragmented, limiting system coherence and accountability. This study derives an architectural foundation through a systematic synthesis of smart system architectures. An activity-mediated socio-technical perspective is employed to analyze diverse paradigms—including IoT-centric frameworks, AI-driven infrastructures, digital twins, Big Data pipelines, and cyber–physical systems—as well as reference architectures such as RAMI 4.0, IIRA, and other representative smart system architectures. Here, activity-mediated denotes an architectural mediation mechanism that coordinates sensing, data-driven reasoning, and human–AI interaction. The synthesis reveals a lack of explicit mechanisms for vertical integration and alignment between bottom-up data flows and top-down goal propagation. In response, this study derives three architectural pillars that integrate interaction, governance, and smart technologies. Their operationalization reveals a structured transformation process in which activity-derived signals are translated into actionable intelligence and adaptive interventions, enabling feedback-driven behavior and cross-layer traceability. Full article

The integration of blockchain technology with the Internet of Things (IoT) presents a paradigm shift in securing decentralized networks, yet it introduces critical trade-offs among security, privacy, and scalability. This systematic analytical review examines the inherent tensions within blockchain-enabled IoT systems, focusing on how consensus mechanisms, cryptographic primitives, and architectural choices affect these three pillars. Through a comprehensive analysis of the contemporary literature, we identify that no single blockchain configuration simultaneously optimizes security, privacy, and scalability. Instead, these properties exist in a triadic relationship where enhancing one dimension typically compromises at least one other. Our review categorizes existing solutions based on their approach to balancing these trade-offs, including sharding, layer-2 protocols, zero-knowledge proofs, and hybrid architectures. We further analyze the applicability of these solutions across different IoT domains, identifying context-specific optimal configurations. The findings reveal that while significant progress has been made in addressing individual challenges, integrated frameworks that holistically consider all three dimensions remain underdeveloped. This review contributes a novel analytical framework for evaluating blockchain–IoT systems and identifies critical research directions, including adaptive consensus mechanisms, privacy-preserving scalability solutions, and domain-specific architectural patterns. Unlike prior studies that primarily focus on conceptual discussions of blockchain–IoT integration, this work synthesizes insights from systematically reviewed literature to propose a conceptual lightweight blockchain framework tailored for resource-constrained IoT environments. This study combines a SLR with a conceptual and experimentally evaluated framework, where the review findings and the proposed solution are presented as distinct but complementary contributions. Full article

Vehicle–infrastructure cooperative perception (VICP) extends the sensing capability of single-vehicle systems by integrating multi-source information from onboard and roadside sensors, thereby alleviating limitations in sensing range and field-of-view coverage. However, in complex urban environments, the robustness of such systems—particularly in terms of blind-spot coverage and feature representation—is severely affected by both static and dynamic occlusions, as well as distance-induced sparsity in point cloud data. To address these challenges, a 3D object detection framework incorporating point cloud feature enhancement and spatially adaptive fusion is proposed. First, to mitigate feature degradation under sparse and occluded conditions, a Redefined Squeeze-and-Excitation Network (R-SENet) attention module is integrated into the feature encoding stage. This module employs a dual-dimensional squeeze-and-excitation mechanism operating across pillars and intra-pillar points, enabling adaptive recalibration of critical geometric features. In addition, a Feature Pyramid Backbone Network (FPB-Net) is designed to improve target representation across varying distances through multi-scale feature extraction and cross-layer aggregation. Second, to address feature heterogeneity and spatial misalignment between heterogeneous sensing agents, a Spatial Adaptive Feature Fusion (SAFF) module is introduced. By explicitly encoding the origin of features and leveraging spatial attention mechanisms, the SAFF module enables dynamic weighting and complementary fusion between fine-grained vehicle-side features and globally informative roadside semantics. Extensive experiments conducted on the DAIR-V2X benchmark and a custom dataset demonstrate that the proposed approach outperforms several state-of-the-art methods. Specifically, Average Precision (AP) scores of 0.762 and 0.694 are achieved at an IoU threshold of 0.5, while AP scores of 0.617 and 0.563 are obtained at an IoU threshold of 0.7 on the two datasets, respectively. Furthermore, the proposed framework maintains real-time inference performance, highlighting its effectiveness and practical potential for real-world deployment. Full article

This study maps European countries’ resilience to cognitive warfare by developing a cross-national composite measure. The framework integrates three pillars: information ecology, institutional-digital capacity, and socioeconomic context—drawing on a systemic perspective linking social structures to societal functions. Publicly available secondary indicators are compiled from online sources for EU (European Union) and EEA (European Economics Area) states. The dataset is examined through descriptive analysis, association testing, multivariate modelling, dimensionality reduction to derive a composite resilience score, and unsupervised clustering to produce a country typology. Indicators capture governance effectiveness, e-government maturity, public-sector AI (Artificial Intelligence) readiness, digital connectivity and infrastructure, media freedom and broader media-ecosystem quality, academic freedom, and socioeconomic vulnerabilities such as youth labour market exclusion. Results show that resilience aligns most strongly with institutional capacity and governance performance; a healthy ecology acts as a reinforcing layer. Digital infrastructure appears necessary but insufficient without capable, credible institutions and coherent public policy. Socioeconomic vulnerabilities tend to erode resilience and heighten susceptibility to hostile cognitive influence. The study concludes that policy efforts should prioritise governance integrity and effectiveness, end-to-end digital government, responsible public-sector AI capability, and safeguards for media and academic autonomy, alongside measures that improve youth inclusion. Full article

Background: Lung adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC), the major subtypes of non-small cell lung cancer (NSCLC), exhibit distinct molecular landscapes that demand precision in prognosis and therapy. While deep learning models can achieve high predictive accuracy, their black-box nature limits clinical translation. Methods: We introduce AttentioFuse, an interpretable deep learning framework employing a Reactome-guided mid-fusion strategy for multi-omics integration. AttentioFuse builds on three pillars: (i) dual-phase learning with omics-specific encoders to preserve modality-unique patterns, (ii) hierarchical attention mechanisms (cross-omics, feature-level, and fusion-layer) to quantify layer contributions dynamically, and (iii) integrated explainability combining DeepSHAP and global attention weights for gene-to-pathway interpretation. Two depth variants are instantiated under identical priors: a three-layer configuration (3F) for main discrimination and a five-layer configuration (AttentioFuse-5X) for deeper hierarchical interpretation; the 5X variant is trained end-to-end and yields comparable accuracy while enhancing pathway-level resolution. Results: Evaluated on The Cancer Genome Atlas (TCGA) LUAD/LUSC cohorts, AttentioFuse matches state-of-the-art performance in TNM staging while uncovering actionable biological insights, including pan-NSCLC AKT/mTOR metabolic control, histology-divergent Notch signaling roles, and additional pathways related to developmental reactivation, microbiota-associated metastasis, and extracellular matrix remodeling. Conclusions: By design, AttentioFuse-5X bridges predictive performance with hierarchical, pathway-resolved explanations, advancing oncology by transforming black-box predictions into biologically grounded decision support. Full article

The rapid digitalization of power systems and the growing penetration of variable renewable energy sources have intensified the need for flexible and resilient smart-grid architectures capable of coordinating cross-sector energy flows. This review aims to provide a system-level synthesis of the artificial-intelligence-enabled integration of smart grids and green hydrogen, explicitly addressing coordination across physical infrastructure, digital control layers, market mechanisms, and environmental constraints. Following the PRISMA 2020 framework, 142 high-relevance studies published between 2010 and 2025 were systematically screened and classified into five interdependent thematic pillars: demand-side flexibility, ICT and IoT infrastructures, cybersecurity and resilience, communication and control performance, and AI-based optimization and decision-making. The synthesis reveals three principal findings. First, while core technologies such as photovoltaics, battery storage, and proton exchange membrane electrolyzers exhibit high component-level maturity, system-integration readiness remains limited by interoperability, communication latency, cybersecurity compliance, and market eligibility constraints. Second, electrolyzers can technically provide fast-response and multi-timescale flexibility services, yet their economic viability depends strongly on market product granularity, settlement intervals, and regulatory frameworks. Third, environmental and resource constraints, including water availability and material criticality, are emerging as binding factors that must be embedded directly into planning and optimization models. Overall, the review positions artificial intelligence as a cross-layer coordination mechanism that links operational control, digital observability, market participation, and sustainability boundaries, providing an integrated architecture to guide scalable and resilient smart grid–hydrogen deployment. Full article

We present a Genetic Algorithm (GA)-based inverse design framework for creating a single-layer, fabrication-compatible dielectric nano-patterned surface that enables efficient color routing in both transmissive and reflective optical systems. Unlike traditional multilayer or absorption-based color filters, the proposed structure employs a fabrication-compatible architecture that spatially routes red, green, and blue light into designated output channels, significantly enhancing light utilization and color fidelity. The design process integrates a GA with full-wave finite-difference time-domain (FDTD) simulations to optimize the structural pillar height distribution, using a figure of merit that simultaneously maximizes optical efficiency and minimizes spectral crosstalk. For CMOS image sensor-scale designs, the nano-patterned surface achieved peak optical efficiencies of 76%, 72%, and 78% for blue, green, and red channels, respectively, with an average efficiency of 75.5%. Parametric studies further revealed the dependence of performance on pillar geometry, refractive index, and unit cell scaling, providing practical design insights for scalable fabrication using nanoimprint or grayscale lithography. Extending the approach to reflective displays, we demonstrate tunable-mirror-based architectures that emulate electrophoretic microcapsules, achieving efficient color reflection and an expanded color gamut beyond the sRGB standard. This single-layer, inverse-designed nano-patterned surface offers a high-performance and fabrication-ready solution for compact, energy-efficient imaging and display technologies. Full article

Amid China’s pursuit of the “dual carbon” goals, the development and large-scale integration of renewable energy have become a core pillar of the power system transition. However, the intermittency and uncontrollability of wind and photovoltaic (PV) power have intensified peak-regulation conflicts after large-scale grid integration. Traditional coal-fired units lack sufficient flexibility to accommodate renewable energy fluctuations, while their willingness to participate in deep peak shaving remains low due to high associated costs. Addressing these challenges requires both enhanced system-level peak-regulation flexibility and effective market incentives for thermal units. Motivated by the limitations of existing studies that often consider individual flexibility resources or deterministic market mechanisms in isolation, this study investigates a coordinated multi-resource peak-regulation framework combined with an optimized market-clearing mechanism for deep peak-shaving ancillary services. First, flexibility resources are classified, and the peak-regulation mechanisms of source–load–storage coordination and auxiliary service markets are analyzed. Second, a wind–PV–thermal–storage operation cost model is established, followed by a two-layer peak-regulation market-clearing model that explicitly accounts for wind–PV uncertainty. The upper-level model minimizes total system operating costs through the coordinated dispatch of demand response and energy storage, while the lower-level model minimizes power purchase costs under a unified marginal clearing price. In addition, an uncertainty modeling framework based on Information Gap Decision Theory (IGDT) is introduced to manage renewable generation uncertainty and support decision-making under different risk preferences. Case studies are conducted to verify the effectiveness of the proposed framework. The results show that: (1) synergistic peak shaving through energy storage and demand response reduces the system peak–valley difference from 460 MW to 387.87 MW and decreases wind–PV curtailment costs from 355,000 yuan to 15,700 yuan, thereby alleviating thermal unit pressure and improving renewable energy accommodation; (2) the unified marginal clearing price mechanism reduces total system operating costs by 41.07% and significantly lowers the frequency of deep peak shaving for thermal units, enhancing their participation willingness; and (3) the IGDT-based model effectively addresses wind–PV uncertainty by providing optimistic and pessimistic scheduling strategies under different deviation coefficients. These results confirm that the proposed framework offers an effective and flexible solution for coordinated peak shaving in power systems with high renewable energy penetration. Full article

This paper develops a quantitative framework for climate–financial risk measurement that combines a spatially explicit jump–diffusion asset–loss model with prudentially aligned risk metrics. The approach connects regional physical hazards and transition variables derived from climate-consistent pathways to asset returns and credit parameters through the use of climate-adjusted volatilities and jump intensities. Fat tails and geographic heterogeneity are captured by it, which conventional diffusion-based or purely narrative stress tests fail to reflect. The framework delivers portfolio-level Spatial Climate Value-at-Risk (SCVaR) and Expected Shortfall (ES) across scenario–horizon matrices and incorporates an explicit robustness layer (block bootstrap confidence intervals, unconditional/conditional coverage backtests, and structural-stability tests). All ES measures are understood as Conditional Expected Shortfall (CES), i.e., tail expectations evaluated conditional on climate stress scenarios. Applications to bank loan books, pension portfolios, and sovereign exposures show how climate shocks reprice assets, alter default and recovery dynamics, and amplify tail losses in a region- and sector-dependent manner. The resulting, statistically validated outputs are designed to be decision-useful for Internal Capital Adequacy Assessment Process (ICAAP) and Pillar 2: climate-adjusted capital buffers, scenario-based stress calibration, and disclosure bridges that complement alignment metrics such as the Green Asset Ratio (GAR). Overall, the framework operationalises a move from exposure tallies to forward-looking, risk-sensitive, and auditable measures suitable for supervisory dialogue and internal risk appetite. Full article

This study specifies and validates a three-layer Structural Equation Model (SEM) that accounts for how tourists’ evaluations of destination attributes translate into loyalty; the model is based on UN Tourism’s sustainability pillars. Guided by service-science and Customer Experience (CX) logics, and adopting a Tourist Experience (TX) framework that treats Tourist Experience as a domain-specific case of CX, we define five first-order antecedents—Emotions (EMS), Local Culture (CTL), Authenticity (AUT), Entertainment (ENT), and Servicescape (SVS)—that load onto a higher-order appraisal, Global Perception (GEN), which in turn drives Destination Loyalty (LOY). Using ordinal indicators and a robust diagonally weighted least squares estimator (WLSMV), the model exhibits a good global fit (CFI/TLI = 0.970/0.968; SRMR = 0.049; RMSEA = 0.073 [90% CI = 0.070–0.076]). Standardized effects indicate that GEN is primarily explained by Emotions (β = 0.445, p < 0.001), Authenticity (β = 0.271, p < 0.001), and Servicescape (β = 0.241, p < 0.001), whereas CTL and ENT are not significant when competing with these other predictors. GEN strongly predicts LOY (β = 0.967, p < 0.001), mediating sizable indirect effects from EMS, AUT, and SVS to LOY. The findings corroborate a parsimonious mediational chain in which affective, meaning-related, and infrastructural inputs cohere into a single global appraisal that is proximal to loyalty. Our study provides a decision-focused blueprint for designing emotion-rich, authenticity-protecting, and well-orchestrated servicescapes to enhance GEN and, consequently, LOY; it adheres to established SEM reporting standards and articulates a holistic transactional conceptualization grounded in recent tourism literature. Improvements in GEN reflect not only better experiences but also designs consistent with long-run destination sustainability. Full article