Search Results (950)

Blockchain is a fast-changing field that is highly useful in such areas as finance, supply chain management, voting systems, and healthcare. As a consequence, software developers are increasingly creating Blockchain-Based Applications (BBAs) and Smart Contracts (SCs). However, the development of BBAs has been associated with various problems, especially in the process of updating and debugging such systems with a high degree of reliability. This is due to the immutability of deployed SCs. In this paper, we conduct an in-depth analysis of 61 published BBA articles between 2017 and 2025 to identify some causes of these challenges. Our results indicate that there is inadequate adaptation of the Software Development Life Cycle (SDLC) for BBAs. In particular, few BBA projects—only 32% of the reviewed projects—address the analysis phase, and only 29% deal with the design phase, frequently ignoring formal modeling methods. Based on these observations, we propose a new, context-adaptive methodology that facilitates BBA developers passing through the requirements, analysis, design, and implementation processes. Formal modeling techniques—such as Use Case Maps (UCMs), Finite State Machines (FSMs), and extended Unified Modeling Language (UML) class and sequence diagrams—are used within the methodology to document BBA structural and behavioral features and maintain complete traceability between requirements and implementation. In order to overcome the blockchain-specific drawbacks of traditional UML, we present formal stereotype extensions of UML class diagrams, where a four-compartment structure is introduced to differentiate state variables, functions, events, and access modifiers on SCs. We also provide analogous extensions to UML sequence diagrams using differentiated arrow notations to distinguish between function calls and event emissions to support accurate modeling of decentralized transaction flows. These extensions are described with a rationale and are formally defined and justified by mapping rules. Our methodology is justified by two case studies that prove its applicability in different fields of blockchain. The initial case study thus designs and executes a system of a halal chicken meat supply chain on Ethereum, showing the complete traceability of requirements that are based on UCM-based requirements and FSM-generated algorithms to implement SCs. The second case study applies the methodology to a decentralized Electronic Health Record (EHR) management system, and it shows coverage and completeness modeling. The methodology was evaluated through two case studies using a structured questionnaire and quantitative metrics, including traceability accuracy, reduction-in-error indicators, SC defect and gas-analysis results, modeling overhead measurements, and static security analysis with Slither. It is also evaluated based on a group of seven literature-based qualitative evaluation criteria that include workflow expressiveness, reusability, technical concept coverage, intelligibility, completeness, tool support, and blockchain limitation modeling. Full article

Urban plant communities are widely regarded as important nature-based solutions for climate mitigation, yet their actual carbon benefits remain uncertain: vegetation growth is accompanied by carbon emissions from construction and long-term maintenance, and existing assessments rarely integrate community succession, interspecific competition, and maintenance-related emissions within a consistent life-cycle framework. To address these limitations, this study developed a dynamic succession-based life-cycle simulation model to project the 50-year carbon source–sink transitions of 150 typical urban plant communities in Tianjin, China. The model updates plant structural attributes—diameter at breast height, crown width, and tree height—iteratively by linking individual plant growth to environmental suitability and neighborhood competition through a Plant Health Index. Simulated structural trajectories were coupled with biomass equations and carbon content coefficients to estimate aboveground carbon sequestration, while construction and maintenance emissions were quantified using life cycle assessment, enabling evaluation of modeled net carbon balance rather than gross carbon sequestration alone. Under the modeled 50-year scenario, most communities were projected to act as carbon sources during the early stage but gradually shifted toward carbon sinks as biomass accumulated; 86.1% of the communities were projected to become net carbon sinks after 50 years (a scenario-based projection under specified growth, maintenance, and emission assumptions). The highest modeled net carbon balance reached 3186.08 kg C ha⁻¹, whereas the weakest community remained a slight carbon source at −81.21 kg C ha⁻¹. Vertical structural complexity and species richness were the strongest positive predictors of modeled net carbon balance, followed by three-dimensional green quantity and canopy closure. Among maintenance processes, fertilization was the dominant emission source, followed by pesticide application and irrigation; comparative scenario analysis showed that resource-saving maintenance consistently improved projected net carbon balance relative to high-maintenance management. These results suggest that low-carbon planting design should prioritize locally adapted species, multi-layered vertical structures, and adaptive maintenance over simply maximizing planting density or minimizing inputs. The results represent scenario-based projections of aboveground vegetation carbon balance; belowground biomass, soil carbon, litter carbon, dead organic matter, and parameter uncertainty were not fully incorporated, and future studies should address these limitations to improve the robustness and transferability of the proposed framework. Full article

To achieve non-contact and continuous monitoring of body length in Micropterus salmoides and overcome the stress damage and subjective error associated with traditional manual measurement, this paper proposes an improved YOLOv8-based multi-target tracking framework for intensive recirculating aquaculture systems. The system employs a geometric measurement framework based on monocular vision that achieves conversion from pixel coordinates to actual body length through camera calibration, water-surface refraction correction, and pose projection correction. Under a collaborative optimization framework integrating detection and tracking, the model incorporates multi-scale feature enhancement, lightweight re-identification (ReID), and a robust data association mechanism, which improves system stability under conditions of high fish density, variable illumination, and turbid water. A shallow feature fusion path is introduced to enhance small-target perception, and a MobileNetV3_ReID model is adopted to extract highly discriminative appearance features, which improves identity consistency while maintaining model compactness. In the data association stage, a hybrid cost matrix integrating IoU, cosine similarity, and motion consistency is constructed, and optimal matching is realized through the Hungarian algorithm. Dynamic threshold adjustment and an exponential moving-average feature-update strategy are introduced to effectively suppress identity switching. Experiments were conducted on an overhead video dataset of Micropterus salmoides collected at a recirculating aquaculture system facility. The results show that the proposed method achieves 82.7% mAP50 while maintaining a real-time throughput of 88 FPS, with MOTA reaching 76.9% and IDF1 reaching 81.5%—the latter representing an improvement of 3.2 percentage points over BoT-SORT and 5.3 percentage points over the YOLOv8 baseline tracker. The number of identity switches (IDSW) decreased from 89 in the baseline configuration to 39, a reduction of 56.2%. Crucially, these component-level improvements translate into a body length error (BLE) of 5.2 ± 1.8% (MAE = 1.35 cm, Pearson r = 0.972), representing a 38.8% improvement over the baseline BLE of 8.5% and satisfying the 5–10% tolerance required for aquaculture growth monitoring. Ablation analysis confirms that both detection enhancements (contributing −1.3% BLE) and tracking optimizations (contributing −2.0% BLE) are necessary to achieve this application-level accuracy. Full article

We propose a family of spectral hypergraph algorithms for early detection of connectivity collapse in pharmaceutical supply chain networks. The Fiedler eigenvalue ${\lambda }_2$ of the normalised hypergraph Laplacian serves as the order parameter. Five geometry-aware early warning indicators (TSI, HSST, HOMFA, HOTV, ORC) monitor network topology rather than scalar residuals, with provable detection guarantees under geometric ergodicity. A Greedy Dejamming algorithm restores connectivity via rank-2 Laplacian updates, achieving a $(1-1/e)$-approximation within a procurement budget constraint. Monte Carlo validation on a calibrated pharmaceutical distribution hypergraph demonstrates substantially higher detection sensitivity and shorter lead times than classical statistical process control. Hyperedge representation yields detection gains exceeding $90\%$ for simultaneous multi-party failures that pairwise graph projections miss entirely. A COVID-19 lockdown episode provides a held-out directional consistency check. Full article

Point cloud denoising is a fundamental preprocessing task in 3D vision and geometry processing, where the main challenge is to suppress corruption while preserving sharp features, thin structures, and local surface fidelity. Classical geometric filters are computationally efficient and interpretable, but they commonly rely on fixed local supports or globally selected parameters, which limits their effectiveness under spatially heterogeneous corruption. More adaptive non-local and learning-based approaches can improve robustness, yet they often introduce higher computational complexity, stronger modeling assumptions, or substantial training-data dependency. In this work, we propose Noise-Adaptive Bilateral Normal Projection (NABNP), a learning-free point cloud denoising framework that introduces explicit patch-wise adaptation to local corruption conditions. NABNP estimates a robust dimensionless local noise level from point-to-plane residuals and uses this quantity to adapt the angular bandwidth of bilateral normal refinement, the balance between weighted local averaging and normal projection, and the magnitude of the positional update. This design enables conservative smoothing in locally reliable neighborhoods while applying stronger geometric correction in more severely corrupted regions. We evaluate NABNP on standard benchmark models under 13 stress scenarios covering additive Gaussian noise, outlier contamination, sparsity, and rotation perturbation, resulting in 650 trials per method. The experimental results show that NABNP provides strong aggregate behavior among the evaluated learning-free baselines, particularly under low-to-medium corruption, sparsity, and rotation perturbations, while its advantage becomes less pronounced under the most severe noise and outlier settings. The method remains training-free and interpretable, with a moderate computational cost associated with repeated neighborhood analysis and covariance-based local updates. Full article

The increasing penetration of distributed renewable energy sources and electric vehicles (EVs) introduces significant operational challenges for residential energy management systems (HEMS), including stochastic renewable generation, uncertain load demand, device coupling, and physical safety constraints. This paper proposes a digital-twin-aided safe multi-agent reinforcement learning framework for coordinated energy management in renewable-integrated residential systems. The proposed approach models the battery energy storage system and the EV as independent agents and employs a multi-agent soft actor–critic (MASAC) algorithm with a centralised critic to capture the interactions among distributed energy resources. To improve decision quality under uncertainty, a digital twin module is developed to maintain a virtual representation of the residential energy system, synchronise operational states, update degradation-sensitive parameters, and generate short-term predictive information on photovoltaic (PV) generation and household load. The updated digital twin states and forecasts are incorporated into the observations of the reinforcement learning agents. In addition, a safety projection layer is incorporated to improve operational feasibility during both training and deployment. The environment considers realistic residential characteristics, including time-of-use electricity prices, battery degradation, EV mobility patterns, and grid energy trading. Simulation results show that the proposed framework reduces daily energy costs compared with rule-based baselines while maintaining EV charging reliability and operational feasibility. These results highlight the potential of combining predictive information, safety-constrained action execution, and multi-agent reinforcement learning for intelligent residential energy management. Full article

Intelligent cost control in engineering, procurement, and construction (EPC) projects depends on the continuous transmission, updating, warning, correction, and reuse of cost data across multiple project stages. To analyse the resilience of this process, this study constructs an EPC project cost-data transmission network using complex network theory and Monte Carlo simulation. Eighteen core nodes and 27 directed weighted edges are identified according to EPC cost-management logic and expert evaluation. Node importance is analysed using weighted degree centrality, betweenness centrality, and PageRank, while network efficiency is used to evaluate cost-data reachability and transmission-path efficiency. Node failure, edge-weight perturbation, random edge failure, random failure and targeted attack, feedback enhancement, critical-node failure–recovery, and robustness checks are then conducted. The results show that Dynamic cost, Cost deviation warning, and Historical cost database are the three most critical nodes. Their failures reduce network efficiency by 44.54%, 37.43%, and 45.27%, respectively. Random edge failure has a stronger effect on network efficiency than edge-weight perturbation; when the edge failure probability increases from 5% to 20%, the average efficiency loss rate rises from 10.54% to 37.30%. Feedback-link enhancement increases network efficiency from 0.1858 to 0.2009 and produces a larger improvement than forward-link enhancement and random seven-edge enhancement. Robustness checks under alternative network assumptions indicate the relative stability of the critical-node identification results within the proposed network structure. The findings provide a scenario-based network perspective for identifying structurally critical nodes, vulnerable transmission links, and feedback-improvement priorities in EPC cost-data transmission. They also offer a methodological basis for future project-level calibration using BIM/5D BIM records, procurement data, cost-management platform logs, and settlement audit data. Full article

Vision Transformer (ViT) has been widely adopted in the computer vision community. However, the standard ViT often contains many parameters, usually performs poorly when trained from scratch on medium-scale datasets, and does not explicitly preserve the local spatial and channel-wise structures within each token. This work proposes a novel model called the Token-Shared Convolutional Projection Vision Transformer (TSCP-ViT). The core idea of TSCP-ViT is to integrate convolutional layers into the multi-head attention mechanism and to apply the same convolutional operation independently to each token, where each token exhibits spatial 2D multi-channel characteristics. In addition, this work introduces a Transformer decoder immediately after each Transformer encoder, enabling the classification tokens to aggregate information from all tokens and be updated using statistical information. Moreover, a trainable Non-Reversing Gate GELU (NRG-GELU) activation is also proposed. Comparative experiments on CIFAR-100, Food-101, and ImageNet100 show that, under comparable parameter counts and without pretraining or knowledge distillation, TSCP-ViT substantially surpasses ViT, outperforms CvT, outperforms ResNet on Food-101, and approaches ResNet on CIFAR-100 and ImageNet100, although with considerably higher FLOPs. Full article

Diadromous fish provide key ecological and socio-economic services in European Atlantic catchments, yet their marine phase remains poorly understood and weakly integrated into management. Involving nine partners from Portugal, Spain, France and Ireland, the DiadSea Interreg Atlantic Area initiative aims to fill these critical knowledge gaps on the marine and estuarine phases and to translate this information into coordinated conservation and fisheries management tools. To do so, the project combines historical and newly collected fishery-dependent and -independent data (landings, by-catch, cooperative surveys with commercial and recreational fishers) with advanced microchemical, genetic and environmental DNA (eDNA) analyses to characterize marine distributions, mixing areas and connectivity for shads, Atlantic salmon, sea trout, European eel, sea lamprey and other diadromous species. It also includes innovative case studies on lamprey tagging and intestinal metabarcoding, coastal habitat suitability mapping for shads using river plumes and environmental variables, and joint otolith microchemistry–genomics approaches to reassess European eel panmixia and maternal origin at the Atlantic scale. Species distribution models under present and future climate scenarios, specifically RCP4.5 and RCP8.5, are used to identify priority marine areas for conservation, zones of high temporal turnover and key interfaces ensuring longitudinal (river–sea) and latitudinal connectivity, which will feed into an updated interactive web Atlas of diadromous species. In parallel, DiadSea establishes a transnational observatory of stakeholders to harmonize legislation, co-develop adaptive fisheries management plans and produce climate-aware policy guidelines, while capacity-building actions include an origin-labeling scheme for sustainably harvested diadromous fish, educational games and a comic book to raise awareness among younger generations and the wider public. Together, these work packages will deliver the first integrated, marine-focused, evidence-based and decision-support framework for diadromous fishes in the North-Eastern Atlantic, strengthening conservation, sustainable fisheries and stakeholder engagement under ongoing climate change. Full article

Post-Quantum Cryptography (PQC) migration to National Institute of Standards and Technology (NIST) Federal Information Processing Standards (FIPS) 203, 204, and 205 under the National Security Agency (NSA) Commercial National Security Algorithm Suite (CNSA) 2.0 is a multi-year, multi-domain transformation across cloud, enterprise, embedded, operational technology (OT), tactical, and national-security systems. Anthropic’s Claude Mythos Preview (April 2026) introduces artificial intelligence (AI)-accelerated cybersecurity capabilities that intersect this migration directly, performing autonomous reasoning against previously unknown vulnerabilities in production software—a qualitative departure from signature-based and static and dynamic application security testing (SAST/DAST) tooling. Drawing on federal guidance from NIST, NSA, the Office of Management and Budget (OMB), and the Cybersecurity and Infrastructure Security Agency (CISA), and on independent analyses from the Centre for Emerging Technology and Security (CETaS) and the UK AI Security Institute, we present a lifecycle and architecture analysis of how Mythos-class models alter PQC migration timelines, risk surfaces, lifecycle dependencies, and architectural constraints. Modeling Mythos as both accelerator and destabilizer, we derive an analytic projection of a compressed two-to-four-year migration window for highest-exposure systems, against traditional baselines of five-to-ten years for small organizations and twelve-to-fifteen-plus years for large enterprises. The compression collapses human-labor bottlenecks in discovery, planning, and code modification, not cryptography itself. We propose a lifecycle-aligned migration model, an updated cost model, and governance requirements for frontier-model access. The binding constraint shifts domain-conditionally: defender capacity at adversary tempo governs software-analytical phases, while non-compressible external cadence governs embedded and regulated domains. Full article

The discovery of the Higgs boson in 2012 at the Large Hadron Collider marked a major milestone in our understanding of electroweak symmetry breaking. Since then, increasingly precise measurements by the ATLAS and CMS Collaborations, based primarily on proton–proton collision data at $\sqrt{s}=13\mathrm{TeV}$ corresponding to about $140{\mathrm{fb}}^{-1}$ per experiment, have confirmed its compatibility with Standard Model predictions within current uncertainties. The Higgs boson mass is now measured with a precision of about 0.08%, while its couplings to fermions and bosons are determined at the 7–20% level. The completion of the LHC programme and the High-Luminosity LHC, will probe Higgs boson couplings at the few-percent level. However, sub-percent precision is required for stringent tests of the Standard Model, as any deviation would signal new physics beyond it. This strongly motivates future collider facilities, designed both as high-precision Higgs factories and, in many cases, as energy-frontier machines. Within the framework of the update of the European Strategy for Particle Physics, we discuss the physics case and main characteristics of the proposed particle collider options, highlighting their complementarity, technological challenges, and expected performance. The 2026 Strategy Update identifies the FCC-ee collider as the preferred next flagship project at CERN. Operating at the Z pole and at centre-of-mass energies between 240 and 365 GeV, it would enable model-independent, per-mille-level precision on Higgs boson couplings, while providing a pathway to a future high-energy hadron collider. The Higgs sector thus constitutes a central laboratory for precision tests of the Standard Model and for exploring the fundamental structure of our universe. Full article

The formation of direction-selective visual circuits is thought to involve the progressive refinement of synaptic connections during development. In biological visual systems, patterned spontaneous activity, such as retinal waves, has been proposed to provide structured spatiotemporal activity that contributes to the refinement of visual pathways before mature sensory experience is fully established. Motivated by this view of activity-dependent circuit organization, this study develops a Self-Organizing Map-Based Artificial Visual System, termed SOM-AVS, to examine how organized connectivity may emerge in a motion direction-detecting circuit. In the proposed model, local motion-detecting units extract elementary direction-related responses from visual input and project them to a global motion direction layer represented by a self-organizing map. Connections are progressively reshaped by winner selection and local cooperative updating, allowing initially unstructured connections to gradually acquire organized direction preference. After repeated exposure to generated retinal-wave-like activity data, the SOM layer develops topographically arranged regions corresponding to distinct motion directions. This organization suggests that direction-related response domains can emerge from activity-dependent self-organization without externally imposed labels. The proposed model should be regarded as a biologically motivated computational abstraction rather than a direct physiological reproduction of retinal-wave-driven circuit development. Within this scope, the model provides a computational framework for examining how retinal-wave-like activity and self-organizing plasticity may contribute to the formation of motion direction-related connectivity, offering a possible developmental interpretation for bio-inspired visual motion processing. Full article

Highway construction projects face interacting risks that affect time, cost, regulatory compliance, and delivery resilience, all of which are closely linked to sustainable infrastructure development. This study develops a hybrid decision-analytical and machine learning framework for sustainability-oriented risk assessment in highway construction under limited-data conditions. The framework combines (i) the Analytic Hierarchy Process (AHP) and tabular Generative Adversarial Networks (GANs) to structure and stress-test expert judgement, and (ii) Probability-Impact (P-I) scoring with a Bayesian Networks (BNs) to model dependencies and derive posterior weights for probability of occurrence, impact on time, and impact on cost across four headline risk factors: weather-related risks, lack of labour, design-related risks, and permitting/regulatory risks. AHP provides transparent and auditable priorities with consistency checks, while GAN-generated synthetic tables support diagnostics for central tendency (P50) and tail behaviour (P90) under data scarcity. The calibrated P-I scores parameterise BN conditional probability tables, enabling the updating of BN scores; and factor-level decomposition of expected contributions. The framework produces model-ready posterior weights that support early planning, contingency allocation, mitigation prioritization, scenario analysis, and subsequent simulation and optimization studies. In sustainability terms, the proposed approach helps project teams improve climate resilience, strengthen regulatory and environmental preparedness, and reduce inefficient use of time, cost, and project resources in data-constrained settings. The results show that permitting/regulatory risks have the highest contribution to probability of occurrence and time impact, while weather-related risks exert the greatest cost impact. The framework therefore offers a practical tool for supporting more resilient, transparent, and sustainable highway project delivery when large historical datasets or questionnaire surveys are unavailable. Full article

Digital transformation projects in the construction sector are usually implemented through staged processes involving changing technical conditions, organizational priorities and expert participation. Existing risk assessment studies are often based on static or single-round models, thus limiting their ability to support structural comparisons when assessments are repeated under changing project conditions. To address this issue, this study proposes an unsupervised learning-enhanced DEMATEL–ISM framework for stage-sensitive risk structure analysis in construction digital transformation. DEMATEL and ISM are used to identify causal roles and hierarchical relationships among risk factors within each assessment round, while K-means clustering and principal component analysis are introduced to extract historical relational patterns and incorporate them into subsequent structural modeling. The framework is applied to a digital transformation project in a large construction enterprise using a two-round expert assessment with partial panel continuity. The results show that the baseline structure is mainly driven by tangible resources and strategic planning, whereas the follow-up structure places greater emphasis on data management and intangible organizational capabilities. Comparative and robustness analyses further indicate that the main structural interpretation is not driven by the enhancement layer, threshold selection, panel reduction or individual expert judgement. This study offers a decision-support approach for updating risk structures across assessment rounds and for adjusting risk governance as construction digital transformation progresses. Full article

We assessed the current and possible future predicted distributions of the Mexican narrow-mouthed toad, Amphibia, Microhylidae Hypopachus variolosus (Cope, 1866) across its range to evaluate vulnerability under global change. (2) Methods: We integrated 481 validated occurrence records across the species’ distribution range, including 120 records from Mexico, with bioclimatic and land-cover predictors to build ensemble ecological niche models. We additionally incorporated human footprint metrics to evaluate anthropogenic pressure and projected future habitat suitability under climate and land-use change scenarios. (3) Results: Models showed high performance (TSS > 0.80; AUC > 0.90), identifying temperature and precipitation extremes as main drivers. Suitable habitats extended across both coasts and revealed novel areas in central Mexico. The most suitable habitat occurred under low human pressure, although localized impacts were detected. Deforestation in the Yucatán Peninsula reduced tree cover despite high climatic suitability. Future projections for 2050 under RCP 8.5 indicated marked reductions in modeled high-suitability areas, particularly in central Mexico. (4) Conclusions: These findings indicate high vulnerability to climate and land-use change and support updating distribution limits, incorporating new regions into conservation planning, and reassessing threat status to promote long-term persistence. Full article