Search Results (10,161)

The escalating frequency of extreme hydrological events under environmental uncertainty poses a severe socio-economic threat to floodplains such as the Brazilian Pantanal, the world’s largest tropical wetland. Mitigating dynamic flooding and drying cycles is highly challenging due to a critical scarcity of in situ monitoring, leaving flood risks poorly understood. To address these data gaps, this study presents an advanced deep learning forecasting framework that integrates multisource environmental data, fusing satellite-derived precipitation (CHIRPS) and global land data assimilation evapotranspiration (GLDAS) data with historical river gauge telemetry. Multi-layered neural network architectures were optimized and combined with progressive moving average filters (10− and 15−day windows) to capture the complex hydrometeorological patterns of the data-scarce Miranda River Watershed. The optimal deep learning configuration, utilizing a robust two-hidden-layer topology (15 and 60 neurons), consistently outperformed standard baselines. Although purely exogenous data blocks successfully minimized satellite noise and captured seasonal trends (NSE ≥ 0.92), structural underestimation of peak flows was observed. When incorporating the previous day’s streamflow (lag t−1) as a physical anchor, this limitation was noticeably alleviated, increasing both the Nash–Sutcliffe Efficiency (NSE) and Coefficient of Determination (R²) values above 0.99. While this performance surge is driven by the strong temporal persistence inherent to the autoregressive lag, it introduces an operational trade-off by restricting the forecast to a reactive 24 h window. In this regard, an evaluation of the operational forecast horizons revealed that the exogenous deep learning blocks maximize warning lead times, providing a vital tool for proactive civil defense and disaster risk reduction. Ultimately, this multisource framework establishes a methodological foundation for automated decision support systems, providing the high-accuracy streamflow forecasting capability required to support future flood mitigation frameworks. Full article

Industrial Internet of Things (IIoT) production environments rely on automated communication between control systems and embedded devices while operating under strict availability constraints that limit the deployment of conventional IT security controls. Despite extensive research on intrusion detection systems, empirical studies based on long-term observations of real industrial networks remain scarce. This paper presents a longitudinal 92-day passive monitoring study of a production-line IIoT network comprising 22 monitored devices. A containerised instance of Zeek was deployed in promiscuous mode to collect flow-level and application-layer telemetry without interfering with operations. The resulting dataset contains more than 41.5 million network flows and 520.5 million packets, represented by 48.48 GB of structured Zeek logs. The results reveal highly deterministic communication patterns dominated by periodic HTTP polling between a central server and distributed devices. In particular, the hourly mean HTTP response size remained highly stable at 132.76 bytes, with a standard deviation of 1.37 bytes and a coefficient of variation of 1.0%. Although no confirmed malicious activity was observed, transient deviations were identified and attributed to planned production stoppages restart periods, which caused temporary traffic reductions and short-lived packet bursts. These findings demonstrate that production-line IIoT networks can exhibit predictable behaviour regimes suitable for statistical anomaly detection. The study contributes a longitudinal empirical characterisation of a real operational IIoT network, a reproducible methodology for behavioural baseline extraction using passive telemetry, and practical insights for safe monitoring deployment. Full article

An increasing number of special Internet of Things (IoT) applications are being deployed within federated and zero-trust (ZT) environments. These ad-hoc networks consist of heterogeneous, resource-constrained devices from various administrative domains, all of which are susceptible to compromise. The dynamic nature of these environments necessitates near-real-time Situational Awareness (SA), where processed data varies with its sensitivity and reliability, without dependence on a central authority. Examples include NATO and non-NATO coalitions engaged in hybrid military operations or humanitarian aid scenarios. To address the challenges of security, reliability, and context-aware data dissemination, we propose $FedM$, a multi-level formal model designed for context-aware and policy-driven data dissemination in federated IoT environments. This model is built upon various access control models and Denning’s research on information flow control (IFC), prioritizing the protection and reliability of data flows. A crucial element of this model is the distributed ledger, which facilitates the dynamic modification of label expressiveness, enhances resilience against disruption attacks, and separates policy logic from application functionality to mitigate risks associated with the benevolent developer. Additionally, we delineate a deterministic and history- and precedence-aware policy enforcement procedure to resolve conflicting actions and introduce processing primitives for the ongoing Data Quality Assessment (DQA) process. Our model also aligns with the concepts of Ubiquitous and Continuum Computing. Furthermore, in our paper we illustrate a policy-based dissemination pipeline, incorporating a bounded trustworthiness dimension. Additionally, we present a refined multi-layered framework that proposes the deployment of Information Flow Control (IFC) components, such as the Open Policy Agent decision engine, to facilitate policy-driven contextual data dissemination. We provide preliminary benchmarks for resource-constrained platforms, along with a formal threat model that addresses implicit flows, the benevolent developer problem, and the behavior of a distributed ledger under degraded network conditions. Finally, we conduct a formal verification of our model using the P framework. Full article

Cluster structures are widespread in complex networks, yet conventional network-level measures do not distinguish the accessibility provided inside a cluster from that provided through its external links. This study asks how these two topological contributions can be measured consistently and how rapidly they deteriorate under different node-removal mechanisms. We define internal and external cluster efficiency by combining edge volume, relative cluster size, and a harmonic shortest-path factor based on the mean reciprocal distance rather than the reciprocal of an arithmetic mean distance. The measures are evaluated together with a Cluster Robustness Index (CRI) and Structural Resilience Entropy (SRE) on Barabási–Albert and Lancichinetti–Fortunato–Radicchi networks and five empirical network topologies under six attack strategies. The results show that smaller clusters are generally more vulnerable to attacks on central nodes, whereas larger and less centralized clusters retain more topological efficiency. A source-level audit also shows that replacing the reciprocal of the arithmetic mean distance by the arithmetic mean of reciprocal distances changes most normalized trends only slightly; the clearest quantitative changes occur in the weighted-average efficiency panels. Intra-cluster edge addition increases internal efficiency by 1–5% and CRI-based robustness by up to 11.9%, while cross-cluster edge rewiring improves external efficiency by up to 2.45% and CRI-based robustness by up to 2.33% under the tested targeted attacks. The reported quantities are structural proxies derived from unweighted network topology; they do not represent observed flow, transmission speed, recovery dynamics, or domain-specific functionality. The framework therefore supports cluster-level vulnerability diagnosis and topology-oriented reinforcement without making claims beyond the available network data. Full article

Background/Objectives: Distinguishing melanoma from benign melanocytic nevi remains a central diagnostic challenge, and vascular features may provide additional information beyond surface morphology. Super-resolution ultrasound using the erythrocytes (SURE) is a contrast-free imaging technique that uses endogenous erythrocyte scattering signals to reconstruct microvascular architecture beyond the conventional diffraction limit. This study evaluated the feasibility of SURE for in vivo visualization and quantitative assessment of dermal microvasculature in clinically benign nevi. Methods: Eleven participants with 35 clinically benign dermal nevi were included. All lesions underwent clinical and dermoscopic assessment, conventional B-mode ultrasound, color and power Doppler imaging, and SURE imaging. Eight larger nevi were imaged in two imaging planes, resulting in 43 SURE acquisitions. SURE reconstructions were assessed qualitatively for microvascular morphology and quantitatively for vessel diameter and erythrocyte flow velocity in proximal, intermediate, and distal visible intralesional vessel segments. Results: Dermoscopic and conventional ultrasound images were acquired for all lesions. SURE reconstructions of sufficient quality for quantitative analysis were obtained for all included acquisitions, yielding 129 vessel diameter measurements and 129 corresponding velocity measurements. Conventional Doppler demonstrated absent or minimal detectable vascular signal in the majority of lesions, whereas SURE visualized branching structures consistent with dermal microvascular networks in all the lesions. The mean vessel diameter was 85.0 ± 20.2 µm, with measured diameters ranging from 48.1 to 173.0 µm. The median flow velocity was 1.80 [1.30–2.50] mm/s. No significant differences in vessel diameter or velocity were observed between proximal, intermediate, and distal intralesional segments. Conclusions: SURE enabled contrast-free in vivo visualization and quantitative assessment of low-velocity dermal microvasculature in clinically benign nevi. These findings support the feasibility of SURE for microvascular mapping of melanocytic lesions and provide a basis for future studies including malignant lesions, volumetric imaging, and histopathological validation. Full article

This study investigates emergency scheduling optimization for natural gas networks under unexpected abnormal conditions, such as unplanned valve closures or equipment failures, which may trigger pressure alarms. Unlike normal planned operations, emergency scheduling requires rapid response and accounts for the dynamic lag of gas flow. A mixed-integer programming model is formulated to ensure system safety, satisfy pressure constraints, and minimize compressor energy consumption. To solve the high-dimensional nonlinear problem, a variable neighborhood search algorithm is proposed, which constructs initial feasible solutions via a greedy proximity-based approach and iteratively improves them using four “delete-compensate” neighborhood operators with the Metropolis acceptance criterion. Validation on a real natural gas network containing 645 stations, 69 pipelines, and 26 compressor stations, together with repeated computational experiments under two representative abnormal scenarios, demonstrates the effectiveness and applicability of the proposed method. Full article

Natural fractures strongly influence fluid flow, hydraulic-fracturing performance, and production heterogeneity in tight oil reservoirs. Their identification from conventional logs remains challenging because image-log and core coverage is limited, fracture-related logging responses are non-unique, and discrete fracture interpretations are difficult to align with regularly sampled logging sequences. This study used conventional logging data and electrical image-log interpretations from 17 wells in the Xifeng Oilfield, Ordos Basin, together with core observations from selected intervals, to develop a fracture response mapping and triangular topology-optimized bidirectional long short-term memory model (FRM-BiLSTM-TTAO). After sliding-window construction and density-based undersampling, 1713 samples were retained and partitioned at the well level into 14 training wells and three independent test wells, yielding an approximate training-to-test sample ratio of 75:25. FRM extracts lithologic-background, local-abrupt-change, multiscale-fluctuation, and integrated fracture response features; BiLSTM captures bidirectional depth dependencies; and TTAO selects fracture response features and optimizes the network architecture and training parameters. On the test set, the model achieved a ROC-AUC of 0.9079, a recall of 0.8671, and an F1-score of 0.8464, outperforming CNN, MLP, ResNet1D, XGBoost, and the corresponding ablation models. The predicted high-probability intervals were generally consistent with image-log interpretations and core observations, indicating the feasibility of the proposed method for identifying fracture-prone intervals within the study area. Full article

Reservoir stimulation is essential in enhanced geothermal systems and unconventional reservoirs where low permeability, inadequate fracture connectivity, or near-wellbore damage restricts commercial injection or production. This review evaluates hydraulic fracturing, thermal stimulation, plasma-pulse stimulation, and selected dynamic stimulation technologies. It compares their physical mechanisms, fracture-network development, reservoir applications, permeability enhancement, operational maturity, deployment challenges, and future perspectives. Hydraulic fracturing remains the most mature method for reservoir-scale fracture creation, fracture conductivity, and reservoir connectivity. In enhanced geothermal systems, however, performance depends on the heat-exchange area, distributed flow, thermal sweep, long-term energy recovery, and induced-seismicity control rather than permeability enhancement alone. Thermal stimulation is integral to geothermal reservoir development. Cold-fluid injection generates thermoelastic stress redistribution, enlarges the fracture aperture, activates natural fractures, promotes thermally assisted fracture propagation, and influences thermal breakthrough. Plasma-pulse stimulation, also termed pulsed-power plasma, electrohydraulic, or shock-wave stimulation, provides a low-water method for near-wellbore permeability enhancement, damage bypass, fracture reactivation, and restimulation. Its broader deployment remains constrained by the limited treatment radius, scale-up uncertainty, energy-transfer efficiency, tool durability, completion integrity, and insufficient field validation. Liquid CO₂ phase-transition, propellant, and explosive stimulation provide additional dynamic-loading options with distinct fracture responses, controllability, safety, and technology readiness. Stimulation technologies should therefore be selected according to the dominant reservoir limitation and evaluated using sustained injectivity or productivity, effective reservoir contact, distributed flow, delayed thermal breakthrough, treatment durability, wellbore integrity, and a controlled geomechanical response. Future progress requires hybrid stimulation, coupled thermal–hydraulic–mechanical–chemical (THMC) modeling, integrated monitoring, adaptive control, physics-informed artificial intelligence, digital twins, standardized field validation, and techno-economic and life-cycle assessments. Full article

This study assesses the potential of biomethane integration into a gas distribution net-work to improve supply reliability under constrained operating conditions. A steady-state, isothermal network model based on graph theory and a SIMPLE-type iterative algorithm was developed to simulate pressure distribution and mass flow in a real urban gas network in Margilan, Uzbekistan. The results show that under winter peak-demand conditions, only 30–35% of consumers satisfy the minimum pressure requirements, while under reduced summer demand, supply coverage remains limited to about 46%, indicating significant hydraulic constraints. Increasing the regulator pressures could theoretically improve supply coverage, but this is impractical due to operational limitations at the transmission level. As an alternative, biomethane injection into the medium-pressure network was investigated. A single-point injection strategy increases supply coverage to 94%, while a multi-point configuration achieves full network saturation within allowable pressure limits and with lower total injection. The novelty of this work lies in the quantitative evaluation of biomethane injection strategies in a real gas network and in the application of a SIMPLE-type iterative algorithm to a compressible gas flow simulation in complex distribution systems. Full article

Hydrogen blending into natural gas grids can support early renewable hydrogen deployment, but admissible injection depends on local gas flow, blending limits, and upstream hydrogen concentrations. This paper analyses these effects for a regional high-pressure gas-grid section in Styria, Austria, with two hydrogen injection points. A transient gas-network model derives time- and location-dependent injection limits, which are integrated into an electrolyser dispatch optimisation with fixed trailer demand and annual gas-grid injection demand. Three cases are compared: unrestricted injection, a “CH₄-based” limit without upstream hydrogen, and an “H₂-aware” case representing potential upstream hydrogen injection. For the analysed configuration, blending constraints shift operation away from favourable electricity-price periods, particularly when low prices coincide with reduced gas demand. In the 2025 reference case, the “H₂-aware” constraint increases the electricity-cost contribution from 4.22 to 5.45 EUR/kg_H2. A robustness analysis using electricity-price series for 2020, 2022, and 2025 shows that the “H₂-aware” constraint increases the electricity-cost contribution by 15.8–29.1% relative to unrestricted injection. The results demonstrate that dynamic gas-grid constraints should be considered when assessing blending-based electrolyser projects, while the quantitative findings remain specific to the analysed network and assumptions. Full article

Severe fracturing-fluid retention and low post-fracturing flowback efficiency are common in the Lianggaoshan shale reservoirs of the Sichuan Basin. Liquid loading may also occur during late production. To address these problems, this study investigates a synergistic flowback technology that combines natural fracturing-fluid flowback with CO₂ gas lift. First, based on the complex fracture network characteristics of the Lianggaoshan shale reservoir, the interaction mechanisms between hydraulic fractures and natural fractures were investigated. An energy model for fracturing-fluid flowback under natural flowback conditions was established, revealing that reservoir gas expansion energy, hydromechanical energy, and rock elastic energy are the primary driving forces for fracturing-fluid flowback. Furthermore, considering fracture closure behavior, fluid leakoff, and wellbore flow dynamics, a calculation model for the natural flowback of fracturing fluid was developed, and a staged pressure-controlled flowback strategy was proposed. Subsequently, to address the decline in liquid unloading capacity caused by formation-energy depletion during the late stage of natural flowback, a gas-lift-assisted flowback multiphase flow model for the wellbore was established. The effects of the gas injection pressure, gas injection rate, and wellhead pressure on liquid unloading efficiency were systematically investigated. The results indicate that the liquid unloading rate increases with an increasing gas injection pressure and gas injection rate; however, a pronounced diminishing marginal effect is observed. For the Well H1 reference case, the central recommended gas injection pressure was 12 MPa, the gas injection rate was 8 × 10⁴–10 × 10⁴ m³/d, and the wellhead backpressure was maintained below 0.5 MPa. Furthermore, the CO₂-assisted flowback mechanisms were evaluated by distinguishing between the effects explicitly represented in the model and the potential reservoir-scale physicochemical effects. The reduction in wellbore mixture density and bottomhole flowing pressure was simulated directly, whereas CO₂–oil mass transfer, viscosity reduction, mineral dissolution, and changes in water-blocking behavior were interpreted with reference to published experimental studies. Based on these mechanisms, a three-stage synergistic optimized flowback scheme, consisting of “CO₂ soaking–natural flowback–CO₂ gas lift,” was established. A sequence of stagewise quasi-steady PIPESIM calculations was subsequently performed over the 30-day operating schedule. Under the adopted simulation conditions, the recommended soaking period is 5–7 days. The operation should be switched to gas lift when the wellhead pressure falls below 1.5 MPa or when daily liquid production declines continuously by more than 20%. Under the synergistic scheme, the 30-day cumulative flowback volume was predicted to reach 3492 m³. This value was substantially higher than those obtained by conventional natural flowback and standalone gas-lift processes. Moreover, the flowback curve exhibits a distinct “secondary surge” characteristic. These findings provide a theoretical basis and technical support for efficient fracturing-fluid flowback and stable long-term production in the Lianggaoshan Formation. They may also be applicable to other shale oil reservoirs with low porosity and ultra-low permeability. Full article

Climate change is increasing compound and cascading risks across interdependent transport, water, energy, drainage, communication, and emergency-service systems. Digital-twin research commonly addresses asset monitoring, urban visualization, hazard modeling, and infrastructure interdependency separately. This study develops a review-informed conceptual architecture for climate-adaptation planning. An audit undertaken for this revision identified 22 DOI-verified journal articles published between 2001 and 2022 as the traceable evidence base. Because the original database exports, search histories, and screening records were unavailable, this paper does not claim a reproducible systematic search or retain the earlier numerical PRISMA flow. The evidence map indicates limited integration across multi-hazard climate scenarios, cross-sector dependencies, dynamic vulnerability assessment, and adaptation prioritization. The proposed workflow links GIS, BIM/asset, IoT, network, climate, and socioeconomic information to ordered hazard, interdependency, risk, and prioritization modules, with a conceptual feedback loop for iterative updating. Intended outputs include risk maps, cascading-failure pathways, adaptation rankings, dashboards, and policy decision support. The architecture was not implemented or empirically validated. Full article

To address the collaborative optimization problem caused by the strong coupling between 2D sheet metal nesting and flexible shop floor scheduling in the sheet metal manufacturing process for color-separation ovens of satellite-type flexographic printing presses, this paper proposes a collaborative optimization method for nesting and scheduling based on a dual-flow graph attention network and proximal policy optimization. This problem involves inherent structural and resource symmetry in manufacturing operations and is further complicated by process precedence constraints, multi-resource competition, human–machine collaboration, assembly dependencies, and the dynamic coupling between nesting decisions and downstream production takt. Based on the composition of oven components and actual production methods, a collaborative optimization model integrating nesting and scheduling was constructed; a discrete-event simulation-driven joint scheduling environment was established to uniformly model the nesting, cutting, flexible machining, and assembly processes. On this basis, the collaborative nesting–scheduling process was formalized as a Markov decision process, clearly defining the state space, action space, reward function, and state transition mechanism. To enhance the state representation capabilities of the reinforcement learning agent in a high-dimensional discrete action space and under strongly constrained dynamic scheduling scenarios, this paper embeds a dual-stream graph attention network into the PPO framework to develop the DS-GAT-PPO algorithm. This algorithm simultaneously captures spatial nesting relationships among parts as well as temporal dynamic features such as equipment load, worker fatigue, and production takt time, thereby generating feasible and efficient joint scheduling plans. Experimental results show that the proposed method can reduce completion time by approximately 10.9–33.8% while maintaining a high sheet utilization rate; in a comparison of reinforcement learning algorithms, the proposed method achieved better completion times in most test cases and reduced the number of convergence steps by approximately 6.67–66.67%, validating its effectiveness in improving solution quality, convergence efficiency, and scheduling stability. These findings provide a foundation for further research and practical applications of collaborative nesting–scheduling optimization in dynamic manufacturing environments. Full article

Future sixth-generation (6G)-oriented networks require programmable control that can adapt routing to latency and congestion without unsafe online exploration. This study evaluates offline multi-agent deep deterministic policy gradient (MADDPG) with behavior-adjusted training rewards for latency-aware path control in software-defined networking (SDN). Each traffic pair is modeled as an agent selecting one of three retained candidate paths, while centralized critics learn coordinated decisions from topology-specific Ryu–Mininet transition datasets. Nine policies are compared using ten paired seeds on fat-tree, mesh-grid, and WAN-corridors topologies under a deployed utilization–latency weighting of $0.60/0.40$, together with flow-completion, latency, congestion, architectural-comparison, sensitivity, robustness, statistical, and controller-overhead analyses. The utilization-aware path heuristic achieves the strongest overall reward ranking. MADDPG is the strongest learned policy on fat-tree, is not significantly outperformed by any evaluated policy on mesh-grid, and remains statistically tied with completion-matched policies on WAN-corridors. Behavior adjustment is topology-dependent rather than uniformly beneficial. The exported policy requires approximately $52\mathsf{\mu }\mathrm{s}$ per joint decision, whereas complete control-loop timing is dominated by network-statistics polling. These results support offline multi-agent SDN control as a competitive, low-overhead option when interpreted jointly with topology structure, flow completion, and strong heuristic baselines. Full article

This article reviews the reactive magnetron sputtering of piezoelectric Aluminium Nitride (AlN) thin films, with a focus on process parameter optimisation and system-level deposition challenges. AlN is a leading material for MEMS and RF applications owing to its c-axis (002) orientation, high acoustic velocity, wide bandgap (∼6.2 eV), and CMOS compatibility. We review the influence of sputtering power, nitrogen flow ratio, substrate temperature, and target-to-substrate distance on crystallographic quality and document practical hardware challenges, including vacuum leakage, grounding faults, target erosion, and mass flow controller drift, that critically affect reproducibility but are systematically underreported in the literature. A perspective is provided on emerging application domains where optimised AlN films address current performance gaps, including next-generation RF/telecom systems towards 6G and Future Networks, harsh environment sensing and actuation, biomedical ultrasound, and IoT energy harvesting. The complementarity between AlN and Silicon Carbide (SiC) is discussed for high-temperature, high-power, and radiation-hard MEMS, where AlN/SiC heterostructures combine the piezoelectric activity of AlN with the mechanical and chemical robustness of SiC. It also incorporates a discussion of dopant- and heteroepitaxy-based AlN engineering, AlN deposition on a wider range of substrates, the role of seed and electrode underlayers, and pulsed-DC sputtering as a third power supply mode alongside RF and conventional DC. Full article