Search Results (274)

Clinical adoption of large language models (LLMs) in public healthcare faces a structural impasse: the capital expenditure of centralised high-performance computing on one side and the privacy risk of routing patient data through third-party cloud interfaces on the other. Italian local health authorities (Aziende Sanitarie Locali, ASL) operate large fleets of clinical workstations that remain idle outside peak administrative hours. We present OmniMed Federated, a browser-native architecture using the WebGPU application programming interface (API) and the WebLLM framework to distribute LLM inference tasks across these existing workstations. The system federates task allocation rather than model training or partitioned inference: each query executes in full on one node, selected under a data residency constraint. A five-tier escalation model, coordinated by a metadata-only PHP back end, ranks tiers by data exposure rather than capability, with commercial cloud fallback disabled by default. In a pilot three-node testbed (50 queries), federated throughput reached 19.5 versus 8.2 tokens/second standalone, peak per-node memory fell 62%, and node discovery took 140 ms; query content remained within the institutional perimeter throughout. These figures establish infrastructural feasibility at pilot scale. Clinical output quality, security hardening, and scalability remain unevaluated. Full article

Big Data Analytics (BDA) has evolved from a predominantly technical batch function into a socio-technical capability integrating cloud-native platforms, stream processing, Lakehouse architecture, machine learning operations (MLOps), visualization, governance, and managerial judgment. This paper proposes an integrated BDA decision-making framework developed through a structured conceptual synthesis of research on data platforms, analytical capabilities, decision processes, organizational readiness, technology adoption, governance, and responsible artificial intelligence. The framework comprises seven interconnected stages: data sources, ingestion and integration, storage and platform, processing, analytics and artificial intelligence, visualization and interpretation, and decision, action, and learning. Governance, human oversight, organizational readiness, task characteristics, and continuous feedback influence all stages. Key implementation requirements include data quality, interoperability, security, privacy, scalability, cost, explainability, bias, skills, and sustainability. The proposed configurable reference architecture links technical integration, task–analytics fit, governance assurance, human judgment, and organizational readiness with decision quality and organizational outcomes. Organizational size and maturity, sectoral risk, decision criticality, technological context, and regulatory environment are defined as boundary conditions for future empirical validation. Full article

Robot Operating System (ROS) applications are growing increasingly complex and distributed, incorporating multi-node architectures, real-time communication, and compute offloading to cloud or edge resources. Despite advances in containerization, large-scale robotics deployments often rely on ad hoc provisioning, updating, and scalability strategies. This paper’s key contribution is a module extension for an existing ROS-focused container framework that leverages Kubernetes’ capabilities—controlled rolling updates, ROS-adapted readiness checks, distributed workload management, and embedded observability—to bridge cloud-native practices with robotics development. The result is a unified tool that streamlines the entire ROS DevOps cycle. Experimental validation demonstrates stable operation with minimal message loss across diverse ROS communication patterns, including tests on an industrial mobile manipulator system. By simplifying orchestration and monitoring, this approach enables roboticists to concentrate on application logic rather than deployment and networking complexities. Full article

The deployment of 5G standalone (SA) networks introduces cloud-native core architectures, service-based interfaces, and programmable radio access networks that substantially expand the mobile attack surface. Existing work has focused mainly on protocol-level vulnerabilities or isolated anomaly detection, with less attention to SOC-level monitoring, correlation, and investigation of 5G-specific threats. This paper presents a SOC-integrated, protocol-aware detection framework for 5G SA environments, combining structured telemetry ingestion, log normalization, decision-based detection logic, and analyst-oriented visualization within an Elastic-Stack-based SOC architecture. The approach is implemented on an experimental 5G SA testbed at Obuda University and evaluated using a dual-source design: controlled testbed scenarios complemented by an observational analysis of telemetry from an independent, large-scale international cyber-defense exercise whose adversarial complexity is difficult to reproduce synthetically. The evaluation exercises attack scenarios including rogue network element registration, authentication abuse, and control- and user-plane manipulation. The results indicate that embedding 5G-aware detection logic into SOC workflows supports situational awareness and structured incident investigation. A single-layer versus cross-layer ablation of the published detection rules quantifies where cross-layer correlation is strictly required for detection versus where it primarily enriches interpretation. This is extended with a limited generic-SIEM-style baseline check and a live, a priori repeated-trial sensitivity check ($N=30$, Wilson 95% CI [88.6%, 100%]). The framework offers a reproducible methodological foundation for operational 5G security monitoring and practical guidance for next-generation mobile network defense. Future work will extend controlled repeated-trial evaluation to the remaining detection rules, broaden the SIEM baseline comparison, and assess generalizability beyond Open5GS. Full article

Telecommunications infrastructures are increasingly cloud-native, multi-vendor, and mission-critical environments, integrating 5G core networks, virtualized network functions, and software-defined infrastructure that expand the operational attack surface and make robust security architecture a core engineering requirement. Zero-Trust Architecture (ZTA) has emerged as the leading technical paradigm for securing these environments through continuous authentication and policy-based access control; however, technical Zero-Trust controls alone do not guarantee successful deployment, and large-scale deployment introduces significant socio-technical and governance challenges that existing engineering-focused frameworks only partially address. This study makes two contributions: it provides longitudinal evidence on the impact of ZTA on organizational trust using an extended Technology Acceptance Model (TAM) that incorporates Perceived Trust, and it proposes a Proactive Trust Management Playbook (PTMP) for telecommunications infrastructure organizations that complements technical Zero-Trust deployments through organizational governance. The study draws on a five-wave repeated cross-sectional longitudinal case study conducted between 2020 and 2023 in a multinational telecommunications infrastructure organization. The five waves span three organizational phases, enabling an assessment of employee perceptions of usefulness, ease of use, and trust before and after ZTA deployment and following a structured governance intervention. The findings reveal a substantial decline in the composite TAM index following ZTA implementation (−24%, Cohen’s d = 1.12), with no meaningful spontaneous recovery over time (d = 0.08). A structured Communication Campaign was associated with a partial but incomplete recovery (d approximately 0.47), indicating that trust erosion under ZTA is measurable and suggesting that trust recovery is shaped more by governance interventions than by technological adaptation alone. The proposed PTMP complements technical Zero-Trust architectures by strengthening organizational trust and governance in telecommunications infrastructure environments. Full article

Open language models are increasingly considered for institutional decision-support tasks in higher education, including policy interpretation, academic advising, administrative summarization, and quality-assurance workflows. However, their reliable deployment requires more than model availability: it depends on cloud-native orchestration, retrieval quality, evidence grounding, refusal behavior, monitoring, and governance controls. Following a design-science research approach, this paper presents an architectural artifact for deploying open language models in higher education decision support. The artifact operationalizes institutional reliability as a multidimensional construct composed of contextual accuracy, answer faithfulness, retrieval quality, refusal adequacy, latency compliance, auditability, and human-review compatibility, and aggregates these into an institutional reliability index. It proposes a reliability-aware retrieval-augmented generation pipeline that integrates governed document ingestion, embedding generation, hybrid retrieval, reranking, evidence-aware generation, confidence-based refusal, human review, audit logging, and post-deployment monitoring. To support reproducibility, the paper compares four deployment configurations and provides an illustrative worked example of the reliability index. The contribution is a conceptual yet technically grounded deployment artifact that connects cloud computing, data science, and higher education governance; the architecture has not yet been empirically validated, and a protocol for future institutional pilots is specified. Full article

Failures in cloud-native systems can disrupt service availability and system reliability, while their early symptoms are often weak and dispersed across metrics, logs, traces, and interdependent services. Existing methods commonly flatten heterogeneous telemetry or model monitoring variables without preserving service identities. We propose LTFP, a lead-time-aware failure-prediction framework whose graph nodes represent services. LTFP uses modality-specific temporal encoders and gated fusion to form service states, as well as an edge-weight-aware Graph Attention Network to propagate these states over a sparse hybrid graph constructed from known dependencies and training-fitted correlations. Joint graph-level and node-level heads predict whether a failure will occur within a configured future window and rank likely responsible services. We evaluate LTFP on seven subsets from three representative cloud-native systems. Comparisons with representative source-code baselines are reported at the pipeline level, with each method retaining its original learning objective and input configuration. At the 600 s prediction-window setting, LTFP obtains a macro-average window-level precision, recall, and F1 of 92.0%, 90.4%, and 90.5%, respectively. Together with the localization and ablation results, these findings support the effectiveness of service-centered multimodal modeling under the evaluated protocol. Full article

The evolution of modern enterprise architecture has been strongly influenced by distributed web services, especially protocol-based standards such as Simple Object Access Protocol (SOAP) and resource-oriented architectural styles such as Representational State Transfer (REST). Cloud-native ecosystems, microservice architectures, and public API management commonly favor the lightweight, JSON-compatible, and horizontally scalable characteristics of RESTful services, whereas legacy configurations and highly regulated environments continue to use SOAP because of its formal contracts and compatibility with WS-* specifications for message-level security, reliable messaging, and transaction coordination. This paper presents a structured literature review that evaluates the architectural trade-offs, performance patterns, security boundaries, reliability considerations, and enterprise use cases of REST and SOAP. The review follows PRISMA-informed reporting practices and software-engineering review guidance, but it is not presented as an exhaustive systematic review because the original search strategy required REST and SOAP terms to appear together. IEEE Xplore, ACM Digital Library, ScienceDirect, and Scopus were searched for studies published between 2021 and 2026, resulting in 32 selected studies. The selected literature contains different evidence roles, including direct REST-SOAP empirical comparisons, REST-only and SOAP-only empirical studies, implementation studies, analytical papers, surveys, reviews, and contextual technical sources. The synthesis therefore separates direct empirical evidence from contextual and secondary evidence. The findings indicate that RESTful APIs generally show lower latency, smaller payloads, simpler parsing, and better horizontal scalability in the reported benchmark and web-facing settings, while SOAP remains relevant where formal service contracts, message-level protection, reliable messaging patterns, and transaction coordination are required. The paper identifies gaps in production-representative stress testing, empirical security comparison, reference-level traceability, and independent validation of hybrid REST-SOAP decision models. The resulting decision framework is presented as a provisional evidence-informed decision aid, not as an empirically validated tool. Full article

Soil water erosion is one of the most serious environmental problems worldwide, with major consequences for agricultural output, food security, and terrestrial ecosystems, particularly in the Mediterranean basin. This study compares modelled soil-loss conditions across Andalusia, Spain (87,268 km²), between the 2018 and 2025 reference years using a cloud-based implementation of the Revised Universal Soil Loss Equation (RUSLE) in Google Earth Engine (GEE). The framework couples daily precipitation data from the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) at its native information scale of approximately 5.5 km, 10 m satellite imagery, and a machine learning-derived, year-specific soil-erodibility update on a common 30 m output grid. The regional mean annual soil loss in 2025 (45.86 t ha⁻¹ yr⁻¹) was 24.98% higher than in 2018 (36.70 t ha⁻¹ yr⁻¹). The 2025 reference year also showed a 12.28% higher R-factor and a 7.83% higher C-factor. The area under Severe erosion (>50 t ha⁻¹ yr⁻¹) increased from 16,330 to 20,636 km² (+26.37%). Exact signed Shapley attribution on the common erodible support assigned +7.43, +3.52, +0.23, and −2.70 t ha⁻¹ yr⁻¹ to R, C, P, and K, respectively. These results describe a marked contrast between two modelled reference years without establishing a continuous trend or causal change, and demonstrate a transparent framework for regional erosion screening to support sustainable land-use planning and soil-conservation strategies. Full article

Fine-scale identification of Spartina alterniflora (S. alterniflora) is essential for coastal wetland conservation. However, in tidal-flat environments, lodged S. alterniflora often occurs together with upright S. alterniflora and native vegetation. The two-dimensional spectral features of S. alterniflora are easily affected by senescence, canopy posture, tidal stage and mixed pixels, leading to unstable classification. The integration of UAV multispectral imagery and LiDAR data can effectively address this problem. The study focused on Shangsha Island within Jiuduansha Wetland in the Yangtze Estuary and constructed multidimensional spectral–structural features by integrating the two data sources. The separability of upright S. alterniflora, lodged S. alterniflora, Phragmites australis (P. australis) and Scirpus mariqueter (S. mariqueter) was characterized using point-cloud elevation distributions, vertical organization and canopy density. The results showed that P. australis had a multilayered point-cloud structure with broad vertical extent, S. mariqueter showed a compact and sparse structure, and S. alterniflora was characterized by a continuous and dense single-layer point-cloud structure. Lodged S. alterniflora further showed a more concentrated, single-layered point-cloud structure and stronger grass-layer continuity. Multisource classification achieved an overall accuracy of 98.15% and a Kappa coefficient of 0.97. In the lodging-area comparison experiment, point-cloud fusion increased overall accuracy from 95.18% to 97.99% and Kappa from 0.89 to 0.95, improving boundary continuity and discrimination stability. Experimental results demonstrate that the fusion of UAV multispectral imagery and LiDAR data can improve the identification of lodged S. alterniflora in complex tidal-flat environments. Accurate identification and spatial delineation of S. alterniflora can help reduce field-survey effort and associated costs while supporting more targeted and efficient removal operations. Full article

Multi-Key Searchable Encryption (MKSE) enables data owners (DOs) to outsource their data to a cloud server (CS) while supporting fine-grained data sharing with other authorized users. Most existing MKSE schemes can protect data users’ (DUs’) search query privacy against collusion attacks between malicious DOs and the CS. However, the CS is not fully trusted and may maliciously return forged or incomplete search results. To address this issue, Verifiable MKSE (VMKSE) is proposed by leveraging Garbled Bloom Filter (GBF), which can support verifiability even when the search results are empty. Unfortunately, due to the massive native storage redundancy of GBF, the storage and computational overhead of verification evidence generated in the sharing phase increases as the number of shared documents grows. Therefore, in this paper, we present a novel VMKSE scheme (VMKSE-BFF) by adopting BFF, which can simultaneously support verifiability of and secure data sharing in a multi-user setting. We provide a comparison with the existing VMKSE schemes. Experimental results on a real-world dataset show a significant performance improvement of VMKSE-BFF. Full article

Aquaculture requires continuous environmental monitoring, yet low-cost IoT sensing in marine conditions remains poorly characterised, and the data it produces is rarely accompanied by mechanisms establishing its provenance. This paper presents an IoT sensor system for marine and coastal aquaculture, comprising solar-powered 4G multiparameter nodes, a cloud-native back-end with a RESTful layer, and integration with a blockchain-based change-detection mechanism supplying a GS1-compliant digital product passport. Four nodes in adjacent cages were deployed at a marine site on the Montenegrin Adriatic for eight weeks, measuring temperature, pH, dissolved oxygen, oxidation–reduction potential and conductivity at five-minute resolution. Lacking reference instrumentation, we use agreement between nodes for validation. Temperature showed the closest cross-node agreement, with nodes agreeing to within 0.28 °C, and resolved a coherent cold, low-salinity intrusion detected simultaneously by all four nodes. The electrochemical and optical channels proved precise but not accurate: they tracked relative change coherently while their absolute values diverged, with oxidation–reduction potential moving from 9 mV of agreement to 71 mV over the following weeks. Cross-node coherence in conductivity and dissolved oxygen degraded progressively over the deployment, with no electrochemical or optical channel remaining coherent beyond roughly six weeks. Such sensors suit anomaly detection without calibration but require periodic recalibration for absolute reporting. Tamper-evident provenance is therefore necessary but not sufficient: sensor-level quality assurance is its missing half. Full article

Marine cold-air outbreaks (MCAOs) enhance air–sea exchange and are accompanied by substantial cloud-population reorganization over the North Atlantic. Previous satellite studies have documented MCAO-related cloud properties and International Satellite Cloud Climatology Project (ISCCP) weather-state distributions, but the continuous redistribution of the complete cloud population and its dependence on sampling scale remain less well quantified. We combine the merged H-series ISCCP (ISCCP-H) weather-state product with the ERA5 reanalysis over the North Atlantic sector of 35–50°N, 55–20°W during January–March and November–December of 2000–2009. Three complementary analytical strategies are used to examine scale-dependent cloud associations: intensity-binned analysis of positive-MCAO native ERA5 grid-point-days, domain-daily regressions, and ISCCP-H cell fixed-effect models. Across 1459 MCAO dates and 256,518 strict-ocean ISCCP-H cell-days, the combined frequency of shallow-cumulus-like and stratocumulus-like low-cloud weather states decreases systematically with MCAO intensity, with a domain-daily slope of −0.0562 K⁻¹. Compensating regional increases occur mainly in the midlatitude-storm and middle-to-high-cloud weather states, while the deep convective and anvil state shows no robust increase. After cell and calendar-month climatological differences are removed, the low-cloud reduction persists, together with positive associations for the midlatitude-storm and optically thick middle-top weather states. Cirrus is positively associated with MCAO intensity at the regional scale but negatively associated within fixed cells, consistent with geographical composition contributing to its regional response. MCAO intensity is therefore more consistently associated with total low-cloud weather-state occupancy than with the internal composition of the low-cloud subset. The closed weather-state framework provides an observational benchmark for satellite and climate model evaluation. Full article

Urban coastal cities face compounded flood hazards driven by sea-level rise, intense precipitation, and dense impervious surfaces. This study develops and demonstrates a cloud-native Python 3.12 GIS framework for flood susceptibility screening and critical facility exposure analysis in Miami, Florida, one of the most flood-exposed coastal cities in the United States. Defined here as a geospatial workflow that retrieves data dynamically from cloud-hosted APIs and executes entirely within a hosted computing environment, the framework integrates three open-source spatial indicators: terrain elevation from the USGS 3D Elevation Programme via py3dep; Euclidean distance to water bodies from OpenStreetMap via OSMnx; and building footprint density as an impervious surface proxy, also from OpenStreetMap. Indicators were standardised and combined using literature-informed MCDA weights (water proximity: 0.40; elevation: 0.35; building density: 0.25) into a continuous flood susceptibility index, classified at the 33rd- and 66th-percentile thresholds. In this proof-of-concept application, high-susceptibility zones cover 48.66 km² (34.0%) of the city, concentrated along coastal waterfronts and inland canal corridors. Overlaying critical facility locations on the classified surface indicates that 9 of 16 hospitals (56.2%), 61 of 244 schools (25.0%), and 5 of 17 fire stations (29.4%) fall within high-susceptibility zones; because this overlay uses centroid-based facility points that have not been cross-checked against official municipal or state facility registries, these counts should be read as indicative rather than definitive. Exact binomial testing shows that the school exposure deficit is statistically significant (p = 0.00), while elevated hospital exposure, although substantively notable, does not reach significance at the current sample size (p = 0.07). The susceptibility surface itself has not been quantitatively validated against external benchmarks such as FEMA flood maps or historical inundation records, the MCDA weights have not been sensitivity-tested, and spatial autocorrelation in the index has not been assessed; concrete protocols for each of these steps are specified as subsequent calibration work rather than as prerequisites for the architecture demonstrated here. The contribution of this paper is the reproducible, cloud-native workflow architecture and its proof-of-concept application, not a validated operational assessment tool; we present it explicitly as a methodological protocol and workflow demonstration, not as an evaluation of flood risk. The framework is fully reproducible, low-cost, and transferable to other US coastal cities. Full article

Seasonal forest phenology is commonly summarized as canopy greenness or phenophase timing, although leaf development also redistributes observed plant material through three-dimensional space. We evaluated whether repeated RGB-colorized handheld simultaneous localization and mapping (SLAM) can provide height-resolved trajectories of seasonal observed occupancy in adjacent Ailanthus altissima-dominated and native-dominated sectors of a young deciduous forest. We acquired 149 scans on 25 dates from March 2025 to March 2026 at six permanent locations. Point clouds were restricted to date-invariant common support, normalized to a March terrain model, and voxelized at 0.20 m. New occupancy was referenced to the union of two strict March leaf-off scans. A weakly supervised foliage likeness proxy combined geometry-first pseudo-labels with relative color, intensity, and local three-dimensional features; its outputs were interpreted as relative scores rather than leaf fraction, LAI, or biomass. The strongest and most persistent invaded positive signal was localized to 2–4 m. Continuous-time models supported the integrated 1–5 m contrast from late April through October, whereas formal support for the 5–12 m crown domain was limited to the late season invaded positive phase; the earlier native positive crown feature remained descriptive. Height-integrated SLAM showed broad seasonal concordance with intercepted PAR (r_rm = 0.881), GCP-linked Sentinel-2 EVI2 (r_rm = 0.670) and the five-date litterfall comparison (r_rm = 0.914). However, correlations of the invaded minus native trajectories were positive but imprecise. The independent observations therefore supported the broad seasonal cycle rather than the detailed sector-specific or height-specific pattern. The workflow provides a conservative means of localizing relative seasonal observed occupancy in three dimensions, but the resulting contrasts remain site-specific and hypothesis-generating. Full article