Search Results (1,021)

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

Unconventional gas-well production is jointly controlled by fracture conductivity, stimulated-region supply, matrix replenishment, boundary propagation, and low-pressure fluid-property changes. In practice, RTA diagnostic curves are often affected by variable operating schedules, pressure-measurement errors, and production disturbances, making flow-stage boundaries difficult to define consistently. To reduce the subjectivity of manual interpretation and to capture stage evolution rather than whole-well classes, an ordered flow-state identification method based on a five-region analytical model and RTA sliding-window features is developed. A fully random, large-sample production-response library is generated with the five-region model. Each well production curve is divided into local time windows, from which dynamic features, including RNP, material-balance time, local slopes, pseudopressure derivatives, and normalized cumulative gas production are extracted. K-means clustering is then used to identify local states, which are reordered by material-balance time to form an ordered S1–S5 sequence. Results from 10,000 synthetic wells yielded 689,394 RTA windows, an inter-cluster separation of 1.8924, a stage-regression rate of 0.0238, and an average of 4.24 states per well. S1–S5 represent early fracture–stimulated-region response, stimulated-region supply development, matrix composite supply transition, enhanced boundary/control-volume effects, and late low-pressure property response, respectively. Application to Well M1 shows that S4 contributes the most gas (37.83%), followed by S5 (23.47%), indicating dominant mid-to-late effective supply and low-pressure long-tail production. The method converts empirical flow-regime division into reproducible and comparable window-state identification results, supporting stage diagnosis and production-strategy adjustment for unconventional gas wells. Full article

In order to improve the energy extraction capability of oscillating hydrofoils, a dual-Fowler-flap structure is adopted as a device to increase lift. According to the motion of the oscillating hydrofoil, the double Fowler flaps retract and swing to increase the chord length and curvature of the entire hydrofoil. This article investigates the geometric parameter characteristics of an oscillating hydrofoil with Fowler flaps. Under the condition of a fixed Reynolds number Re = 2 × 10⁶, the effects of Fowler motion F and slot value S on the overall performance of the hydrofoil are studied. The numerical results show that the Fowler flap structure can increase the camber and chord length of the integral hydrofoil, and the movement of the Fowler flap is combined with the motion of the oscillating hydrofoil to increase the lift coefficient of the hydrofoil, thus increasing the energy collection efficiency of the oscillating hydrofoil—the maximum increase is 50%. By affecting the flow structure and pressure distribution around the trailing edge of the hydrofoil, the Fowler flap helps to generate lift, resulting in a higher power coefficient. U (overlap amount) = 0% is a dividing point, and the S (gap amount) value at this position has the greatest influence on the average power coefficient. The structure of the Fowler flaps maintains the streamlined state of the entire hydrofoil, and when S = 0 and F = 200, the lift and drag fluctuations of the oscillating hydrofoil are minimal. This is very beneficial for the stable operation of the hydrofoil. Full article

Rapid surrogate models are attractive for iterative computational fluid dynamics (CFD) design loops, though defining their operating envelope remains crucial. This study proposes RenaNet, a Reynolds-aware convolutional gated recurrent unit (ConvGRU) surrogate, for predicting two-dimensional laminar and transitional flows past cylinder and square obstacles. Using two initial flow snapshots and a Reynolds-number map, the model predicts spatiotemporal flow states up to 2000 time steps into the future, with Lattice Boltzmann Method (LBM) simulations serving as ground truth. Trained on Reynolds numbers of $1\le \mathrm{Re}\le 500$ (cylinder) and $1\le \mathrm{Re}\le 250$ (square), RenaNet achieves a minimum validation mean squared error (MSE) of $1.47\times {10}^{-5}$. A Reynolds-number ablation shows that removing the conditioning channel increases the validation MSE to $1.17\times {10}^{-3}$, while a ConvLSTM baseline gives $9.94\times {10}^{-4}$ with 24% more parameters. RenaNet also uses a direct long-horizon prediction interface for distant target frames. Auxiliary physics diagnostics confirm that predictions trained via MSE maintain acceptable continuity residuals across fitting, interpolation, and extrapolation cases. The average inference time for a 1000-step prediction horizon is approximately 1.25 s, delivering a 500-fold speedup over the reference LBM solver. Interpolation errors range from ${10}^{-4}$ to ${10}^{-2}$ depending on Reynolds number and geometry, while extrapolation beyond the training regime increases errors to the order of ${10}^{-2}$. These results establish RenaNet as a robust, parameter-efficient surrogate for laminar and transitional flows, with a clearly characterized operational boundary that informs future extensions into turbulent regimes. Full article

Forecasting multi-asset volatility spillovers and turning the forecasts into risk-aware portfolios requires methods that uncover directional information flow between assets, compress the state into a minimal sufficient representation, deliver calibrated uncertainty, and respect explicit tail-risk limits. We propose TDV (Transfer-entropy, Dynamic-graph-attention, Variational-information-bottleneck), an information-theoretic artificial intelligence framework that couples a time-varying transfer entropy network with a graph attention encoder regularised by a variational information bottleneck, and demonstrates the practical value of the calibrated predictive distribution through a downstream entropy-regulated, CVaR-constrained portfolio application. We establish three theoretical results: $L^2$ consistency of the k-nearest-neighbour transfer entropy estimator on $\alpha$-mixing returns with rate $O_P\left(n^{-2/(2+d)}\right)$, a PAC–Bayes generalisation bound of order $O\left(\sqrt{\left(I\right(X;Z)+log(1/\delta \left)\right)/n}\right)$ for the bottleneck-encoded forecaster, and asymptotic CVaR feasibility of the plug-in allocation. In simulations across sparse Granger networks, contagion DCC–GARCH ensembles, and regime-switching factor models, the framework cuts spillover forecasting errors by 24 to 42 percent against LSTM, vanilla GAT, and Transformer baselines, and it recovers 1.6 additional nats of mutual information with the realised connectedness matrix. On a 32-asset global panel covering 2014 to 2025, the model delivers an out-of-sample $R^2$ of 0.331, an annualised Sharpe ratio of 1.46 against 0.83 for an equally weighted benchmark, a maximum drawdown of 7.8 percent, and 95 percent CVaR reductions of 28 to 36 percent across sub-periods relative to a shrinkage minimum-variance baseline. Full article

Graphite foil is widely used as a sealing material in flange joints in the form of gaskets or gasket components. Predicting gasket permeability during stress relaxation remains challenging because both the compression state and the gas pressure affect leakage. No unified semi-empirical model based on the Darcy–Klinkenberg framework with compression pressure as a direct input has been available for use in flange-joint numerical simulations. Graphite foil gaskets with a density of about 1.0 g/cm³ and a thickness of ~1.5 mm were tested under compression pressures from 5 to 100 MPa. Helium leakage was measured at helium pressures from 0.5 to 8 MPa. Leakage and deformation during loading and unloading were recorded using EN 13555-based procedures. The results were analyzed using a Darcy–Klinkenberg formulation and equivalent slit- and capillary-based representations of the leakage channels. The second-order model reproduced the pressure-dependent leakage more accurately than the first-order Darcy approximation (R² ≥ 0.9985 vs. 0.916–0.992), particularly where slip-flow effects were significant. Exponential dependences of the intrinsic permeability and the Klinkenberg coefficient on deformation and power-law relations with compression pressure are proposed to model leakage during unloading. The proposed semi-empirical model allows estimation of graphite-foil permeability under stress relaxation with the use of EN 13555 test procedures and its subsequent implementation in numerical simulations of flange joints. Limits of the model’s applicability, including loading regime, ranges of compression pressure, gas pressure and anisotropic nature of permeability, are discussed. Full article

While vanadium redox flow batteries (VRFBs) are considered a promising technology for grid-scale energy storage, the combined influence of electrical load and electrolyte flow rate on overall system performance is often simplified in existing models. A MATLAB/Simulink-based system-level model of a 1 kW vanadium redox flow battery (VRFB) was developed to investigate the influence of load variation and electrolyte flow rate on battery performance. The model accounts for the flow-dependent behavior of key electrochemical parameters, including open-circuit voltage, internal resistance, and polarization losses. State of charge (SOC) is estimated using the Coulomb counting method, and a lumped first-order thermal model is included to represent stack temperature dynamics. The impact of auxiliary pump power is also considered to provide a realistic assessment of system efficiency. Results show that increasing the electrolyte flow rate from 5 LPM to 30 LPM reduces concentration polarization and improves voltage stability, leading to an increase in stack-level electrical efficiency from approximately 85% to more than 92.5%. However, the improvement in overall system efficiency becomes less pronounced at higher flow rates because of the nonlinear increase in pump power consumption.Thermal analysis indicates that stack temperature rise is mainly influenced by electrical loading, whereas higher electrolyte flow contributes to enhanced heat removal and produces only a slight reduction in overall stack temperature. The study highlights the importance of considering both electrochemical performance and auxiliary energy consumption when evaluating VRFB systems and provides useful insights into the coordinated operation of load and electrolyte flow conditions. Full article

Maintenance management increasingly depends on effective information flow and coordination between internal teams and external service providers. This study investigates the use of Business Process Model and Notation (BPMN) to support the formalization of Computerized Maintenance Management System (CMMS) workflows and improve transparency, decision-making, and interorganizational coordination. A single case study was conducted in the maintenance department of an electricity distribution company characterized by tacit knowledge, informal communication practices, and limited process formalization. Existing corrective maintenance workflows were analyzed and modeled using BPMN to identify inefficiencies, decision points, and opportunities for improvement. The proposed BPMN models were aligned with CMMS operational states associated with anomaly management and work-order execution processes and supported by a procedural manual. Results obtained during a three-month observation period suggest reductions in training time, email communications, and dependence on individual decision-makers, together with increased use of CMMS workflow functionalities and improved process traceability. These findings provide preliminary evidence, derived from operational indicators within a single case study, that BPMN-supported process formalization may contribute to workflow standardization, operational clarity, and knowledge management in maintenance-intensive environments. Given the single-case design and limited observation period, the results should be interpreted as context-specific and not directly generalizable to the broader energy sector. Full article

The constitutive analysis model and hot processing map of the ZM6 alloy across various deformation conditions were investigated during hot compression experiments. True stress-strain curves within 300–450 °C and 0.0001–0.1 s⁻¹ were obtained from compression tests on a Gleeble-1500 platform. The results showed that higher strain rates (e.g., 0.1 s⁻¹) induced pronounced work hardening, whereas high temperatures (300–400 °C) combined with low strain rates (10⁻⁴ s⁻¹) promoted conditions conducive to dynamic recrystallization (DRX), leading to a softening tendency of steady-state flow stress. Additionally, a modified strain-compensated constitutive model was built for flow stress prediction. Material constants were plotted as fifth-order polynomial functions of strain (0.025–0.80) for precise stress predictions. The derived activation energy (Q = 182.38 kJ/mol) falls within the typical range for Mg-RE alloys. Leave-one-temperature-out cross-validation showed average AARE values of 7.2–9.8%, demonstrating the model’s interpolation capability and its sensitivity to extrapolation. Cross-validation within the training dataset showed reasonable consistency between experimental and predicted stresses (R > 0.997, AARE < 4.35%). Using the dynamic materials model, hot processing maps identified safe deformation zones and instability zones of the ZM6 alloy. Flow instability was observed at strain rates >0.01 s⁻¹, particularly at low temperatures (300–350 °C). Optimal processing windows appeared in high-energy dissipation (η > 30%) regions, e.g., 400–450 °C/10⁻⁴–10⁻³ s⁻¹. Optical microscopy confirmed that at high temperatures (≥400 °C) and low strain rates (≤0.001 s⁻¹), a uniform, fine-grained, fully recrystallized structure can be obtained, whereas low temperatures (350 °C) and high strain rates (0.1 s⁻¹) produce coarse elongated grains with limited DRX, consistent with the instability regime predicted by the processing maps. Under intermediate conditions (e.g., 400 °C, 0.01 s⁻¹), a bimodal grain distribution indicates incomplete recrystallization. Although EBSD analysis was not performed in this study, the optical microstructures directly validate the predicted safe and unstable windows. Together, all these findings provide preliminary model-based guidance for optimizing hot working parameters to balance microstructural stability and processing efficiency. Full article

Intrusion detection systems (IDSs) are commonly evaluated through aggregate classification metrics, although operational workflows require detected flows to be interpreted, prioritized, and transformed into actionable evidence. This study proposes a detection-to-triage framework for multiclass intrusion detection using a CSE-CIC-IDS2018-derived experimental subset containing 213,463 records across one benign class and fourteen attack classes. The framework combines supervised multiclass classification, SHAP-style post hoc explanation, class-specific false positive analysis, and a Suspicious Flow Priority Score (SFPS) for analyst-oriented suspicious flow ranking. The practical role of SFPS is to reorder suspicious flows by combining model confidence, explanation strength, predefined attack severity, and validation-based false positive control, thereby producing a transparent triage list rather than a probability-only alert queue. Three detection backbones were evaluated under a shared preprocessing protocol: Random Forest, XGBoost, and a lightweight multilayer perceptron baseline. To assess stability, experiments were repeated across five random seeds. XGBoost achieved the strongest mean performance across most aggregate indicators, with an accuracy of 0.9494 ± 0.0011, a macro F1-score of 0.8366 ± 0.0193, a weighted F1-score of 0.9494 ± 0.0011, and a Matthews Correlation Coefficient of 0.9429 ± 0.0012. Random Forest produced closely comparable results, while the lightweight MLP remained lower on aggregate and macro-level indicators. False positive analysis showed that the alert burden was concentrated in selected classes and differed across models, confirming that aggregate performance alone is insufficient for assessing IDS usefulness. SHAP-style analysis identified stable flow-level contributors to XGBoost discrimination, while SFPS substantially changed the post-detection ordering of suspicious flows compared with probability-only ranking. The study does not claim universal state-of-the-art superiority, causal explanation, or deployment validation; instead, it demonstrates how multiclass IDS outputs can be extended into explainable, false positive-aware, and triage-oriented rankings for analyst review. Full article

Photovoltaic (PV) generation and electric vehicle (EV) charging infrastructure are changing the dynamic behavior of current power systems, especially in terms of voltage stability and LVRT capabilities. In this work, 50% PV penetration on a modified Kundur two-area power system was tested to mitigate transient instability under severe fault circumstances. With PV units running at unity power factors under steady-state conditions, 50% PV penetration was defined relative to the system’s total active load demand. A steady-state power-flow study ensured generation–load balance before MATLAB/Simulink dynamic simulations. Controllable reactive power compensation was used as an EV aggregator on Bus 7. We constructed and evaluated a genetic algorithm (GA)-optimized fractional-order proportional–integral–derivative (FOPID) controller with a traditional PID controller utilizing identical optimization conditions. An inter-area tie-line critical three-phase fault was applied and removed after 100 ms to evaluate system performance. While the GA-PID controller increased transient performance, it did not restore system stability. Instead, the GA-FOPID controller provided superior dynamic support by restoring Bus 7 voltage to 0.9–1.1 pu within 250 ms after fault clearance and maintaining about 95% LVRT compliance. The suggested controller also reduced rotor angle oscillations and enhanced inter-area damping. Fractional-order control increased EV aggregators’ reactive power response during transient shocks. Thus, in renewable-energy-dominated power systems, the GA-FOPID-controlled EV support technique may improve voltage stability and LVRT compliance. Full article

The relationship between water and sediment in the lower reaches of the Yellow River is uncoordinated, leading to frequent floods. In this area, the floodplain is situated below the main channel and embankment foundations, increasing the likelihood of overbank flooding. Ecological protective forests serve as a nature-based mitigation measure by reducing flow velocities along embankments and lowering the risk of structural failure during near-bank flood events. To assess the role of ecological protective forests, laboratory experiments were conducted, and field data informed parameterization and geometry selection. A total of 24 scenarios were designed, combining four forest arrangements (A1, A2, A3, and A4), two submergence degrees (H₀/H = 0.5 and 1.0), and three water and sediment conditions. Results show that sediment deposition increases with vegetation density. Under constant vegetation density and embankment-aligned flow, a larger along-flow to cross-flow spacing ratio promoted deposition upstream, whereas a smaller ratio extended deposition further downstream. Deposition thickness was greater under fully submerged conditions than under semi-submerged conditions. Among the arrangements, sediment deposition effectiveness followed the order A1 > A2 > A4 > A3, with arrangement A1 providing the strongest promotion of deposition. Under varying flow–sediment conditions, the A1 arrangement enhanced sediment deposition by 6.8% to 20.6%. Flow structure was also modified: under semi-submerged conditions, the vertical profile of longitudinal velocity approximated a logarithmic distribution, whereas full submergence produced a different profile due to combined drag from tree trunks and canopy. Vertical sediment concentration profiles were similar under both submerged states, with minimum values near the water surface and maximum concentrations near the bottom. These changes confirm that ecological protective forests contributed to reducing flow velocity and diminishing sediment transport capacity. Full article

This work presents a case study of sensitivity analysis applied to the modeling of ethanol steam reforming (SRE) in a catalytic porous medium, with a focus on hydrogen production. Considering the high variability of parameters reported in the literature, the objective is not to propose a universal model, but rather to assess the impact of uncertainties associated with input parameters on the model outcomes. The model was developed under steady-state conditions, coupling flow in porous media, species transport, and heat transfer, with kinetics described as a function of partial pressures. The sensitivity analysis was conducted through the systematic variation of kinetic and physicochemical parameters within ranges associated with their uncertainties. The results indicate that activation energy is the parameter most sensitive to uncertainty variation, exhibiting the greatest impact on hydrogen production. The thermal properties of the medium, particularly thermal conductivity and solid density, also stand out, highlighting the role of thermo-kinetic coupling. In contrast, parameters such as porosity, water reaction order, and particle diameter exhibited low sensitivity under the analyzed conditions. As a main contribution, this work establishes a sensitivity hierarchy associated with parameter uncertainties and provides guidance for other researchers regarding the prioritization of their determination and calibration in hydrogen production models. Full article

Selecting a compact subgraph of a document graph while maximising a learned coherence function, subject to flow conservation and temporal ordering, is important in storyline detection, event threading, and Narrative Map extraction. Existing Narrative Map methods either recover a single optimal path (a Narrative Trail) or solve a linear program with an output size which grows with graph density (Narrative Maps). We propose a hybrid classical–quantum pipeline that casts the problem as a Quadratic Unconstrained Binary Optimisation (QUBO) problem and solves it both with the Quantum Approximate Optimisation Algorithm (QAOA) and with off-the-shelf classical QUBO solvers (simulated annealing, Tabu search) on the same Hamiltonian; this approach uses a classical mean field active space reduction and Matrix Product State tensor network simulation to scale beyond 16 qubits. We evaluate node- and edge-level qubit encodings under a range of QAOA circuit variants (transverse field and XY mixers; classical warm-start deeper circuits) on a 418-document news corpus across four graph densities and ten endpoint pairs, and audit their reproducibility across optimiser seeds. The QUBO formulations—whether solved by QAOA or by classical QUBO solvers on the same Hamiltonian—produce maps averaging $4.7$–$9.0$ nodes versus $26.6$ for Narrative Maps ($p<{10}^{-7}$) and they are far more focused on their main storyline (main path fraction $0.61$–$0.99$ versus $0.20$). The Hamming-weight-preserving XY mixer goes the furthest: the node-level XY mixer variant produces the most compact ($4.7$ nodes) and most spine-focused ($0.99$ main path fraction) maps of any method tested, and a multi-seed audit identifies it as the most reproducible of the eight QAOA variants we compared. Main path coherence is on par with Narrative Maps’ and $0.031$–$0.072$ below the bottleneck-optimising baselines—Narrative Trails ($0.770$) and Iterative Maximin ($0.758$). These results position QAOA not as a uniformly stronger alternative but as a distinct trade-off region favouring compactness and spine focus over raw bottleneck coherence and corpus topic breadth. Full article

To achieve efficient separation of Sr²⁺ under complex ionic-strength conditions, porous geopolymer particles (PGs) were used as a support to construct a K₂SbPO₆-loaded porous geopolymer composite, denoted as K₂SbPO₆@PGs, via in situ loading of one-dimensional K₂SbPO₆ by a high-temperature solid-state route. Its adsorption performance and mechanism were systematically compared with those of pristine PGs. Structural characterization (SEM/EDS, XRD, FTIR, XPS, and BET) confirmed that the K₂SbPO₆ crystalline phase was uniformly anchored onto the PGs framework while preserving interconnected mesoporous channels. K₂SbPO₆@PGs exhibited excellent Sr²⁺ removal over a wide pH range (3–12), with a removal efficiency of approximately 92% at pH 3, which was significantly higher than that of PGs (approximately 5%). The isotherm data were better fitted by the Sips model (R² = 0.982), and the maximum adsorption capacity reached 189.35 mg·g⁻¹ (theoretical qm = 201.14 mg·g⁻¹). Kinetic fitting showed that PGs followed the pseudo-first-order model, whereas K₂SbPO₆@PGs were better described by the pseudo-second-order model, indicating that chemical adsorption dominated the process through K⁺/Sr²⁺ exchange and surface complexation. Coexisting-ion experiments demonstrated strong resistance to monovalent ions, whereas Ca²⁺ and Mg²⁺ caused more pronounced competitive effects. The results indicate that PGs mainly provide interconnected mass-transfer pathways and granular structural support, whereas K₂SbPO₆ provides selective exchange sites with high affinity for Sr²⁺. The synergy between these two components endows the composite with good pH adaptability and enhanced adsorption performance and suggests its potential for subsequent continuous-flow separation studies. Full article