Search Results (33)

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

The electric power grid constitutes a foundational pillar of modern critical infrastructure (CI), underpinning societal functionality and global economic stability. Yet, the increasing convergence of Information Technology (IT) and Operational Technology (OT), particularly through the integration of Supervisory Control and Data Acquisition (SCADA) and Industrial Control Systems (ICS), has amplified the sector’s exposure to sophisticated cyber threats. This study conducts a comparative analysis of five major cyber incidents targeting electric power systems: the 2015 and 2016 Ukrainian power grid disruptions, the 2022 Industroyer2 event, the 2010 Stuxnet attack, and the 2012 Shamoon incident. Each case is examined with respect to its objectives, methodologies, operational impacts, and mitigation efforts. Building on these analyses, the research evaluates the extent to which such attacks could have been prevented or mitigated through the systematic adoption of leading cybersecurity maturity frameworks. The NIST Cybersecurity Framework (CSF) 2.0, the ENISA NIS2 Directive Risk Management Measures, the U.S. Department of Energy’s Cybersecurity Capability Maturity Model (C2M2), and the Cybersecurity Risk Foundation (CRF) Maturity Model alongside complementary technical standards such as NIST SP 800-82 and IEC 62443 have been thoroughly examined. The findings suggest that a proactive, layered defense architecture grounded in the principles of these frameworks could have significantly reduced both the likelihood and the operational impact of the reviewed incidents. Moreover, the paper identifies critical gaps in the existing maturity models, particularly in their ability to capture hybrid, cross-domain, and human-centric threat dynamics. The study concludes by proposing directions for evolving from compliance-driven to resilience-oriented cybersecurity ecosystems, offering actionable recommendations for policymakers and power system operators to strengthen the cyber-physical resilience of electric generation and distribution infrastructures worldwide. Full article

Tourism has emerged as a critical economic pillar for many island communities worldwide, transforming their socio-economic structure and land use strategies. However, intensifying typhoons and other extreme climate events pose escalating risks to these communities, demanding adaptive transformations in disaster knowledge systems and risk management strategies. Local disaster knowledge (LDK), as a place-based knowledge system, plays an essential role in shaping adaptive responses and enhancing resilience within these communities. This study investigates the structure and dynamic adaptation paths of local disaster knowledge amid the shift toward tourism-based communities. Using a qualitative approach, this study conducted an in-depth case study on Shengsi Island, China. The findings reveal that LDK exhibits a three-layered structure: deep-intermediate-surface layers. Beliefs constitute the deep core, while social cohesion, risk knowledge and perception form the middle mediating layer. The surface practical layer encompasses early warning systems, anticipatory measures, structural measures, and livelihood adaptation strategies. The interaction among the three layers constitutes the endogenous dynamics driving knowledge adaptation, while macro-level disaster governance and tourism development act as exogenous drivers. Together, these mechanisms facilitate two adaptive pathways: policy-guided structural transformation and tourism-led practical adaptation. This study advances theoretical understanding of LDK by exploring its dynamics in transforming communities, with a framework that can be extrapolated to other disaster risk contexts. It also provides policy-relevant insights for developing disaster resilience and sustainable land use policies in island communities experiencing tourism transformation. Full article

Cybersecurity represents a critical challenge for data-sharing platforms involving multiple stakeholders, particularly within complex and decentralized systems such as livestock supply chain networks. These systems demand novel approaches, robust security protocols, and advanced data management strategies to address key challenges such as data consistency, transparency, ownership, controlled access or exposure, and privacy-preserving analytics for value-added services. In this paper, we introduced the Framework for Livestock Empowerment and Decentralized Secure Data eXchange (FLEX), as a comprehensive solution grounded on five core design principles: (i) enhanced security and privacy, (ii) human-centric approach, (iii) decentralized and trusted infrastructure, (iv) system resilience, and (v) seamless collaboration across the supply chain. FLEX integrates interdisciplinary innovations, leveraging decentralized infrastructure-based protocols to ensure trust, traceability, and integrity. It employs secure data-sharing protocols and cryptographic techniques to enable controlled information exchange with authorized entities. Additionally, the use of data anonymization techniques ensures privacy. FLEX is designed and implemented using a microservices architecture and edge computing to support modularity and scalable deployment. These components collectively serve as a foundational pillar of the development of a digital product passport. The FLEX architecture adopts a layered design and incorporates robust security controls to mitigate threats identified using the STRIDE threat modeling framework. The evaluation results demonstrate the framework’s effectiveness in countering well-known cyberattacks while fulfilling its intended objectives. The performance evaluation of the implementation further validates its feasibility and stability, particularly as the volume of evidence associated with animal identities increases. All the infrastructure components, along with detailed deployment instructions, are publicly available as open-source libraries on GitHub, promoting transparency and community-driven development for wider public benefit. Full article

Nanotopography refers to the intricate surface characteristics of materials at the sub-micron (<1000 nm) and nanometer (<100 nm) scales. These topographical surface features significantly influence the physical, chemical, and biological properties of biomaterials, affecting their interactions with cells and surrounding tissues. The development of nanostructured surfaces of polymeric nanocomposites has garnered increasing attention in the fields of tissue engineering and regenerative medicine due to their ability to modulate cellular responses and enhance tissue regeneration. Various top-down and bottom-up techniques, including nanolithography, etching, deposition, laser ablation, template-assisted synthesis, and nanografting techniques, are employed to create structured surfaces on biomaterials. Additionally, nanotopographies can be fabricated using polymeric nanocomposites, with or without the integration of organic and inorganic nanomaterials, through advanced methods such as using electrospinning, layer-by-layer (LbL) assembly, sol–gel processing, in situ polymerization, 3D printing, template-assisted methods, and spin coating. The surface topography of polymeric nanocomposite scaffolds can be tailored through the incorporation of organic nanomaterials (e.g., chitosan, dextran, alginate, collagen, polydopamine, cellulose, polypyrrole) and inorganic nanomaterials (e.g., silver, gold, titania, silica, zirconia, iron oxide). The choice of fabrication technique depends on the desired surface features, material properties, and specific biomedical applications. Nanotopographical modifications on biomaterials’ surface play a crucial role in regulating cell behavior, including adhesion, proliferation, differentiation, and migration, which are critical for tissue engineering and repair. For effective tissue regeneration, it is imperative that scaffolds closely mimic the native extracellular matrix (ECM), providing a mechanical framework and topographical cues that replicate matrix elasticity and nanoscale surface features. This ECM biomimicry is vital for responding to biochemical signaling cues, orchestrating cellular functions, metabolic processes, and subsequent tissue organization. The integration of nanotopography within scaffold matrices has emerged as a pivotal regulator in the development of next-generation biomaterials designed to regulate cellular responses for enhanced tissue repair and organization. Additionally, these scaffolds with specific surface topographies, such as grooves (linear channels that guide cell alignment), pillars (protrusions), holes/pits/dots (depressions), fibrous structures (mimicking ECM fibers), and tubular arrays (array of tubular structures), are crucial for regulating cell behavior and promoting tissue repair. This review presents recent advances in the fabrication methodologies used to engineer nanotopographical microenvironments in polymeric nanocomposite tissue scaffolds through the incorporation of nanomaterials and biomolecular functionalization. Furthermore, it discusses how these modifications influence cellular interactions and tissue regeneration. Finally, the review highlights the challenges and future perspectives in nanomaterial-mediated fabrication of nanotopographical polymeric scaffolds for tissue engineering and regenerative medicine. Full article

As a key pillar of China’s economy, the manufacturing industry faces sustainable financial risk management challenges as it undergoes digital and green low-carbon transformation. However, existing financial risk prediction models often suffer from limited accuracy, insufficient robustness, and a suboptimal activation function design. In this study, we investigated advanced deep learning architectures to address these limitations, and we introduced a novel composite triple activation function (CTAF) framework to enhance predictive performance and model robustness. We began by evaluating several deep learning models, such as CNNs, BiLSTM, CNN-AM, and BiLSTM-AM, demonstrating that CNN-BiLSTM-AM achieved the highest performance. On the basis of this model structure, we proposed a CTAF, a composite activation mechanism that combines two distinct functions applied to the raw input x, effectively mitigating gradient instability and enhancing nonlinear expressiveness. Through ablation experiments with different composite activation functions, we verified that the CTAF consistently outperformed alternatives. Meanwhile, the mainstream activation functions and CTAF were applied to different layers for comparison, further verifying the CTAF’s advantages in various structures. The optimal configuration was achieved when tanh was used in the CNN and Dense layers and the CTAF (tanh_relu) was applied in a Lambda layer after a BiLSTM layer, resulting in the highest accuracy of 99.5%. Furthermore, paired t-tests and evaluations on cross-industry datasets confirmed the optimal model’s stability and generalizability. Full article

This article discusses an inclusive research program where colleagues and co-researchers (with intellectual disability) guide and inform future research practice to ensure research is targeted to areas of significance and relevance to them. The research program is about sites of former disability institutions. Many people with intellectual disability in Australia were segregated and forced to live in disability institutions until deinstitutionalisation efforts became mainstream in the late 20th Century. We are a team of four people based in New South Wales, Australia. Our team includes disability advocates and researchers who have contributed to a program of research exploring connections between sites of former disability institutions and contemporary disability rights. In this article, we reflect on conversations about our research undertaken so far and where the research goes from here. We explore five pillars of action informing how research relating to disability institutions can progress: 1. Current use: research exploring erasure of experiences of institutionalisation communicated through educational resources and maps about current use of sites of former disability institutions; 2. Reparative planning processes: research developing frameworks for alternative approaches to planning and heritage processes supporting alternative uses of former sites of disability institutions; 3. Official recognition and redress: research exploring perspectives on governments formally recognising and remedying experiences of people with disability who were institutionalised; 4. Community-led repair and remembrance: research identifying practices for both celebrating advocates with disability and reckoning with and repairing familial and social bonds broken through institutionalisation; 5. Community-inclusive practices: research exploring endurance of institutional practices in disability accommodation in community settings. These five pillars are underpinned by three foundational layers: advancing disability human rights; reckoning with intersections between disability institutions and settler colonialism, other dynamics of oppression, and eugenics; and using inclusive practices. Full article