Search Results (29,811)

In-depth analysis of epidemic models, particularly for cholera, is crucial because they serve as significant tools for disease transmission prediction, evaluation of control strategies, and optimization of healthcare resource management. The stochastic models provide increased realism by incorporating environmental uncertainty such as variability in water quality, disparities in access to sanitation, and population mobility. The present work generalizes a deterministic two-patch cholera model to a stochastic framework. We first prove the existence and uniqueness of global solutions, then establish the extinction condition $R_0^{*}<1$ for disease eradication in the long term. A key contribution lies in proving the existence of a unique ergodic stationary distribution when $R_0^{\left(1\right)}>1$ and $R_0^{\left(2\right)}>1$. Furthermore, we derive the stochastic threshold $R_0=max\{R_0^{\left(1\right)},R_0^{\left(2\right)}\}$, which corresponds to the basic reproduction number ${\mathcal{R}}_0=max\{{\mathcal{R}}_0^{\left(1\right)},{\mathcal{R}}_0^{\left(2\right)}\}$. Lastly, numerical simulations are employed to confirm theoretical results. Full article

Blockchain-assisted off-chain storage for IoT must simultaneously manage low-latency tiered data placement, trusted and dynamic on-chain mapping, migration consistency, and failure recovery—four concerns that existing designs address in isolation. Tiered storage systems optimize placement without modeling the scalable coordination cost of keeping object–location bindings trustworthy, while blockchain-metadata studies assume static storage topologies with no dynamic tier migration. This paper presents a scalable and trusted metadata-coordinated tiered off-chain storage framework, which bridges traditional trust systems (e.g., legacy authentication) with blockchain networks powered by Proof of Capacity (PoC) consensus. In this framework, adaptive heat-driven placement, dynamic on-chain mapping evolution with batched commitment, migration-aware redirect control, and rollback-safe recovery operate as a single coordinated workflow, with the five-stage write–verify–commit–redirect–retire pipeline acting as a lightweight coordination protocol that maintains ordered and atomic state transitions under message loss, out-of-order delivery, and single-node failures. The distinctive contribution lies in the framework’s coupled control: every placement decision propagates through a verifiable metadata path that can be audited and, when necessary, rolled back. Simulation across multiple workload patterns shows that the proposed method reduces average access latency by 28% and raises the hot-tier hit ratio from 0.19 to 0.65 relative to a dynamic baseline without trusted mapping coordination under the simulated registry write cost. To achieve high-throughput mapping operations, batched on-chain commitment cuts metadata transactions by 50× at the cost of a tunable mapping freshness delay. The framework scales from 1 k to 50 k managed objects, effectively managing tens of millions of bytes of data (10+ MB scale) without disproportionate overhead growth; beyond this scale, hot-tier capacity rather than coordination becomes the dominant bottleneck, and smarter predictive placement becomes the natural next lever. All tested fault types achieve 100% rollback success with sub-millisecond local data plane interruption; audit-visible recovery depends on the assumed chain finality delay and, for heavily regulated IoT domains, such as finance and healthcare, should be treated as the operationally binding recovery time objective. These results, together with extended evaluations—including asymmetric write latency stress, coordination ablation, tail latency analysis, and benefit–complexity assessment—provide quantitative evidence that scalable, dynamic mapping coordination can be integrated into tiered off-chain data management at an acceptable and measurable operational cost under the simulated configuration. Full article

Deep high-temperature and high-pressure (HTHP) oil reservoirs have limited experimental MMP data, large differences between reservoir and saturation pressures, low gas–oil ratios, and pressure-sensitive CO₂–oil phase behavior, which make both minimum miscibility pressure (MMP) prediction and miscibility-mechanism identification challenging. To address these gaps, this study determines the MMP of a CO₂–oil system by integrating slim-tube experiments, empirical formula methods, the Multiple Mixed-Cell (MMC) method, the Method of Characteristics (MOC), compositional numerical simulation, and three intelligent algorithm models (GWO-RBF, GWO-LSSVM, and GWO-SVM). The slim-tube MMP of 44.13 MPa at 140 °C is used as the experimental reference for comparing prediction errors, whereas PVTsim and literature data are used for consistency checks and model benchmarking. The results show that when the injected CO₂ mole fraction exceeds 0.88, the formation oil under original reservoir conditions cannot achieve first-contact miscibility with CO₂, and the maximum dissolved CO₂–oil molar ratio is 7.3:1. Supercritical CO₂ forms dual displacement mechanisms, including front-end vaporizing miscible drive and rear-end condensing miscible drive, but the dominant mechanism for this CO₂–oil system is vaporizing miscible drive. During the vaporizing gas drive, the CO₂ + N₂ + C₁ content in the liquid phase increases from less than 60% to nearly 90%, indicating significant CO₂ dissolution into oil and associated density and viscosity reduction; meanwhile, the C₇₊ content in the gas phase increases to nearly 10%, indicating extraction of heavy components. Relative to the slim-tube reference at 140 °C, the deviations of MMC, GWO-SVM, GWO-LSSVM, compositional numerical simulation, GWO-RBF, MOC, and empirical formula methods are 2.97%, 3.08%, 3.40%, 4.24%, 4.26%, 11.62%, and 19.74%, respectively. The MMC method is the most suitable approach for this specific HTHP oil system, while intelligent algorithms should be regarded as supplementary predictors whose reliability depends on training-domain coverage and independent validation. Full article

Accelerated electric vehicle (EV) adoption imposes complex requirements on grid integration and energy dispatch. Current Vehicle-to-Vehicle (V2V) trading research frequently utilizes monolithic forecasting architectures that fail to account for the stochastic nature of mobility data. Furthermore, traditional optimization strategies often prioritize financial yields at the expense of user-centric utilities, hindering global system optimality. To resolve these limitations, this paper proposes a hierarchical optimization framework, designed to reconcile the interests of stakeholders. The approach first employs a hybrid deep learning architecture, integrating long short-term memory (LSTM), gated recurrent unit (GRU), and Transformer architectures, dynamically weight predictions and refine available dwell time estimations. Then, a multi-objective optimization model is formulated to identify Pareto-optimal solutions that balance economic efficiency with user convenience. Finally, a dynamic greedy matching algorithm is introduced to facilitate rapid transaction pairing for large-scale, real-time V2V requests under multiple constraints. Simulation results demonstrate that this hierarchical framework improves trading success rates, optimizes resource distribution, and enhances overall user satisfaction. Full article

A new era in wireless communication has been witnessed by the emergence of fifth generation (5G) technology, characterized by high data rates, ultra-low latency, and massive device connectivity. To address the growing demand for efficient spectrum utilization, Non-Orthogonal Multiple Access (NOMA) has been introduced as a promising multiple access scheme. This study investigates the energy efficiency (EE) and spectral efficiency (SE) performance of NOMA in comparison with Orthogonal Multiple Access (OMA) under varying bandwidth conditions. In addition to conventional analytical and simulation-based evaluations, artificial intelligence (AI) techniques, including Deep Learning (DL), Decision Tree (DT), K-Nearest Neighbours (KNN), and Logistic Regression (LR), are employed to model and predict system performance. The AI models are trained using simulation-generated datasets to capture complex relationships between bandwidth, transmit power, and user distribution. Simulation results demonstrate improvement in SE and EE of NOMA across different bandwidth scenarios. Furthermore, DL and DT models achieve higher prediction accuracy. The consistency between AI predictions and simulation outcomes confirms the robustness of the proposed framework. These findings highlight the superiority of NOMA over OMA and demonstrate the effectiveness of integrating AI techniques for performance optimization in 5G and beyond wireless networks. Full article

This study presents a framework for developing, emulating, and validating offshore wind turbine models when proprietary blade designs are unavailable. The methodology addresses a critical industry challenge by demonstrating that aero-servo-hydro-elastic models reproducing the measured operational behavior can be constructed using only publicly available reference designs and operational measurements. An inverse design approach based on differential evolution optimization reconstructs blade aerodynamic characteristics from field data, enabling the creation of models that replicate operational behavior without requiring access to proprietary geometries. The framework incorporates statistical error characterization through machine learning techniques to predict simulation errors based on environmental and operational conditions. Validation against extensive field measurements from an operational offshore wind turbine demonstrates the effectiveness of the methodology. The machine-learning models predict the simulation-error distributions (bias and variability). The prediction fidelity is highest for the fore–aft response, which is thrust driven, and lower for the side–side response, for which several influencing factors remain unmodeled. This approach offers a practical pathway for model calibration and error prediction for offshore wind turbines, particularly when complete design documentation is unavailable. Full article

Predicting the wind field around military vehicles during extended missions is crucial to avoid detectability by infrared (IR) devices. This is a challenging task because of the geometric complexity of the vehicles and the unpredictable nature of wind direction, which can shift abruptly and have a significant impact on the flow field and heat transfer. Computational fluid dynamics (CFD) is routinely used to calculate flow fields around ground vehicles. However, this requires extensive computational time and memory, making it unsuitable for real-time analysis. To address these challenges, this paper focuses on machine learning (ML) techniques for accurate wind field prediction in real time for unseen wind directions within the sampled range. Reduced order modeling (ROM) is used for dimensionality reduction of flow field data derived from high-fidelity CFD simulations. ML models are trained using low-dimensional data from the ROM, and the predicted low-dimensional data for unseen wind directions by the trained ML model is used to reconstruct the flow field. ROM, in conjunction with ML techniques, offers a substantial reduction in analysis time while maintaining the ability to predict the flow field accurately. In this study, a neural network architecture with three output formulations trained using ROM data was used for the predictions, and the accuracy of the formulations was evaluated by comparing them with the CFD results. An optimal ML model is identified by varying the number of hidden layers and neurons within those layers. The developed ROM- and ML-based approach was able to predict the unseen flow field in less than a second, while a single CFD simulation required approximately 2.6 h per wind direction. Full article

Gas–solid bubbling fluidized beds involving Geldart Group B particles are fundamental to numerous industrial thermochemical processes, where bubble dynamics dictate the efficiency of heat and mass transfer. However, accurately predicting these complex hydrodynamic behaviors remains a challenge due to the non-linear coupling of phase interactions. This study presents a comprehensive validation of 2D and 3D Eulerian–Eulerian Two-Fluid Models (TFM) against an extensive experimental dataset. A ‘core-flow’ consistency principle is adopted, demonstrating that the 3D cylindrical simulation provides a physically equivalent representation of the central bubbling dynamics in the rectangular experimental bed. A key innovation of this work is a novel post-processing framework that bridges raw CFD datasets and quantitative bubbling metrics. Unlike traditional threshold-based segmentation or localized probe measurements, which are often limited by spatial resolution and noise sensitivity, the integrated use of Autodesk 3DS Max for volumetric reconstruction and customized MATLAB (R2024a) algorithms allows for the seamless processing of heterogeneous 2D and 3D data. This methodology significantly enhances the capability to track complex bubble coalescence and breakup events while improving batch-processing efficiency, providing a high-fidelity alternative for analyzing gas–-solid flow patterns in complex geometries. The results show that both experimental data and 2D simulations align with Werther’s correlation, yielding Mean Relative Errors (MRE) of 8.2% and 10.5%, respectively. In contrast, the 3D simulation tracks Darton’s prediction closely with a lower MRE of 7.4%, demonstrating superior concordance in volumetric bubble growth. The core innovation lies in the definition of a clear dimensional choice framework: 2D simulations are computationally sufficient and accurate for predicting macro-scale bubble heights and frequencies under pseudo-2D or narrow-bed constraints. However, 3D simulations are strictly necessary when evaluating unconstrained radial expansion, core-flow dynamics, and precise volumetric bubble diameters (d_v) where full multi-directional degrees of freedom dictate hydrodynamics. Full article

Flywheel energy storage systems (FESSs) offer millisecond-level response speed, making them highly suitable for providing system inertia/frequency support in emergency grid scenarios. However, the FESSs often have limited energy capacity due to their high capacity cost, which necessitates a comprehensive consideration between remaining stored energy and sustained support capability. Thus, this paper proposes a virtual synchronous generator (VSG) control strategy based on a multi-time-step model predictive control (MPC) that considering flywheel’s state of charge (SOC), which provides both emergency frequency support and autonomous flywheel energy recovery within a single integrated framework. First, a multi-time-step MPC with the objective function aiming for both fast frequency response and smooth power output is introduced to compensate the reference power generated by the VSG strategy. Second, an SOC-adaptive frequency weight function is designed and incorporated into the objective function to balance the frequency deviation and the inertia/frequency support duration. Furthermore, an SOC self-recovery strategy is developed, allowing the flywheel to autonomously adjust its SOC to the desired range when the FESS is not participating in frequency regulation. Finally, the proposed strategy is verified through comprehensive simulations on various scenarios, demonstrating that it can efficiently and rapidly meet the frequency regulation demands when the SOC is sufficient, as well as achieve the balances between the frequency regulation performance and the support continuity when the SOC is insufficient. Full article

Accurate multistep-ahead prediction of natural gas pipeline-network states is essential for intelligent dispatching, yet such networks contain physically heterogeneous components (gas sources, pipelines, compressors, valves), and historical states and future dispatching commands are decoupled in both temporal scale and physical semantics. This causes conventional data-driven models to suffer from semantic entanglement and cumulative error during long-horizon forecasting. This study proposes a deep spatiotemporal surrogate model with three coordinated designs: (i) type-specific feature encoding combined with global latent-graph mapping and a shared graph convolutional network (GCN) to disentangle heterogeneous-equipment attributes and represent network-wide topological coupling; (ii) a residual-gated temporal coupling mechanism that adaptively fuses historical operating inertia with future external disturbances; and (iii) a temporal-gradient multi-objective loss with a 12-step autoregressive rolling strategy over a 6 h horizon to suppress cumulative divergence. On 85,248 samples built from field monitoring data and commercial mechanistic simulations, the model attains median relative errors of 1.15% for nodal pressure and 2.10% for pipeline flow, capturing macroscopic pressure decay and high-frequency transient flow induced by valve and compressor switching without noticeable delay, providing an efficient tool for online simulation, real-time warning, and decision support in complex natural gas pipeline networks. Full article

Accurate characterization of wind-speed distributions is essential for preliminary wind-resource assessment, vertical wind-profile evaluation, and energy-yield estimation. This study presents a CFD-assisted reconstruction and validation framework that integrates two-parameter Weibull statistics with class-conditioned OpenFOAM v13 simulations to reconstruct wind-speed distributions at different measurement heights. Hourly wind-speed records measured at 10 m and 30 m at the Sakarya–Esentepe station during the period of 2009–2010 were used. The 2009 dataset was employed to estimate the Weibull shape and scale parameters by maximum likelihood estimation, while the 2010 dataset was reserved for independent validation. To ensure methodological consistency between statistical wind characterization and steady CFD modeling, the fitted Weibull distribution was discretized into representative wind-speed classes. For each class, a steady Reynolds-averaged Navier–Stokes simulation was performed in OpenFOAM under neutral atmospheric boundary-layer assumptions using the standard k–ε turbulence model, a logarithmic inlet velocity profile, and rough-wall boundary treatment. The class-wise CFD velocity responses extracted at 10 m and 30 m were then weighted by the corresponding Weibull class probabilities to reconstruct height-specific wind-speed probability distributions. The reconstructed distributions showed good agreement with the measured and fitted Weibull references. The RMSE values obtained by CFD for measurements at heights of 10 m and 30 m on the measurement mast were 0.45 m s⁻¹ and 0.52 m s⁻¹, respectively, and the Pearson correlation coefficients were 0.97 and 0.96, respectively; these values indicate that the CFD analyses are reliable. For the Lilliefors-adjusted Kolmogorov–Smirnov statistics, there is no value higher than 0.06. The differences between the reference and CFD-reconstructed AEP estimates were +0.40% at 10 m and −1.97% at 30 m. These findings indicate that the proposed Weibull–OpenFOAM framework provides a reproducible engineering approach for CFD-assisted wind-speed distribution reconstruction and height-specific consistency assessment. However, the method should be interpreted as a class-conditioned reconstruction framework rather than a stand-alone transient atmospheric wind prediction model. Full article

Cost overrun remains a pervasive issue in building construction projects, yet most predictive studies operate within a single data domain, ignoring the systemic interactions across project, schedule, resource, quality, and safety dimensions. This study quantifies the incremental predictive value of integrating these five construction data domains and identifies the cross-domain interaction patterns that explain prediction accuracy. As a simulation-based methodological study, an integrated dataset of 100,000 records was synthesised with theory-grounded causal structures derived from the construction management literature; no real project data were used. Gradient Boosting (GB), Random Forest (RF), and Linear Regression were evaluated on an 80/20 hold-out test split, with robustness verified through alternative domain orderings and hyperparameter sensitivity. SHAP analysis, including exact interaction values, was used to interpret feature importance and cross-domain synergies. The full five-domain GB model achieved R² ≈ 0.97 and MAPE ≈ 6%, a 220% relative R² improvement over the Project-domain baseline (R² rising from 0.305 to 0.975), robust across three ordering schemes. Schedule and Quality contributed the largest marginal gains (ΔR² = +0.312 and +0.255), whereas Resource integration yielded approximately one-thirty-first of Schedule’s return. Because the dataset is synthetic, the results are interpreted as a methodological demonstration rather than empirical evidence from real projects; they provide a reusable framework for prioritising data-integration investment and show that, within the simulated causal structure, cross-domain interactions—particularly Schedule × Risk and Project Type × Change Cost—carry predictive information that single-domain analyses cannot recover. Validation on real, partially integrated datasets is identified as essential future work. Full article

The tree-contact single-phase-to-ground fault (TSF) in 10 kV distribution networks has high transition resistance, weak fault currents, and nonlinear steady-state waveforms. As existing high-impedance fault models cannot accurately describe its complete physical evolution, this paper proposes a novel modeling and parameter identification algorithm for TSF. First, based on recorded data from full-scale experiments, the initiation and development processes of TSF are studied, revealing the main factors affecting fault electrical characteristics—such as moisture evaporation, pyrolysis carbonization, air gap breakdown, and tree body current dissipation. Then, a dynamic resistance series model for TSF is constructed, with parameters identified and calibrated using experimental data, objective functions, and physical constraints. Finally, a 10 kV TSF simulation model is built and verified. Furthermore, a cross-condition predictive validation is performed using different voltage and geometric boundaries. Results demonstrate that the proposed physics-constrained model can effectively reproduce the RMS fault current envelope with asymmetric moisture evaporation characteristics. It also accurately predicts steady-state nonlinear waveform features without parameter re-tuning, providing more physically consistent data support for future TSF identification studies. Full article

Bisphenol A (BPA) is a common industrial chemical primarily used in the manufacture of plastics, and it has been found in more than 90% of people worldwide. As an endocrine disruptor, BPA can impair reproduction, development, immunity, metabolism, and cognition; it also disturbs immune balance and thus fosters chronic inflammation. A number of population-based studies have indicated a link between environmental BPA exposure and atopic dermatitis (AD). Nevertheless, the detailed molecular pathways connecting BPA to AD remain poorly understood. AD is the leading chronic recurrent inflammatory skin disorder, characterized by severe itching and repeated eczema-like lesions. Its prevalence is roughly 13% among children and 5% among adults, and its global incidence continues to rise, imposing heavy health and economic burdens on societies. To clarify whether and how BPA may promote or worsen AD, we carried out a comprehensive computational study that integrated network toxicology, transcriptomic data, machine learning, molecular docking, and molecular dynamics simulations. From the CTD, ChEMBL, and SwissTargetPrediction databases, we collected 5701 potential BPA targets; from GeneCards and OMIM, we obtained 3270 genes linked to AD. The overlap between these two gene sets gave a group of common candidate genes. Enrichment analyses using GO and KEGG showed that these common genes were significantly overrepresented in the PI3K-Akt signaling pathway, Th17 cell differentiation, and the JAK-STAT signaling pathway—all central to immune and inflammatory regulation. We then built a protein–protein interaction (PPI) network by submitting the common genes to the STRING database and employed Cytoscape to extract hub genes from that network. By integrating human AD transcriptomic profiles with the hub genes and applying two machine learning techniques (LASSO and SVM), we identified six core toxic targets of BPA in AD: TIGIT, JAK3, IL22, S100A8, CCL2, and FCER1G. These six targets fall into two main functional categories: immune dysregulation and inflammatory cell infiltration. Subsequent molecular docking and molecular dynamics simulation experiments confirmed that BPA binds well to all six targets and can form stable complexes with them. Collectively, our findings offer a preliminary experimental foundation for future investigations into the pathogenesis of BPA-induced AD and provide important molecular evidence for understanding how environment–gene interactions contribute to complex inflammatory skin diseases such as AD. Full article

As a next-generation automotive display technology, Augmented Reality Head-Up Display (AR-HUD) has demonstrated immense potential in reshaping driving safety and enhancing the human–computer interaction experience. To address the challenges of barrel distortion and perspective distortion inherent in HUD systems, we propose a joint-learning-based dual-path pre-correction method. This approach employs a shared encoder to extract image features, which are then decoupled into two parallel branches: a classification branch and a distortion flow prediction branch. Building upon this architecture, a model-fitting method is introduced to estimate the distortion model parameters in the parameter space using the predicted distortion types and flows, thereby reconstructing a refined distortion flow. Finally, image rectification is achieved through a resampling method. On the ARHDD dataset, the proposed method achieves a PSNR of 24.617 dB (barrel) and 25.062 dB (perspective), an SSIM of 0.845 and 0.873, and an NRMSE of 0.163 and 0.157, respectively. On the Places 365 dataset, it achieves a PSNR of 23.914 dB (barrel) and 21.870 dB (perspective), an SSIM of 0.812 and 0.748, and an NRMSE of 0.174 and 0.211, respectively. Both quantitative and qualitative comparative experiments against other state-of-the-art methods demonstrate that the proposed approach achieves superior correction performance for both types of distortion. Finally, the simulation verification of the HUD system proved that this correction method demonstrated excellent potential, but further verification is still needed in a real or semi-real environment. Full article