Search Results (433)

The main goal of this study is to create a computational solver that analyzes the effects of magnetohydrodynamics (MHD) on heat radiation in Cu–water-based Prandtl nanofluid flow using artificial neural networks. Copper nanoparticles are utilized to boost the water-based fluid’s thermal effect. This study primarily focuses on heat transfer over a horizontal sheet, exploring different scenarios by varying key factors such as the magnetic field and thermal radiation properties. The mathematical model is formulated using partial differential equations (PDEs), which are then transformed into a corresponding set of ordinary differential equations (ODEs) through appropriate similarity transformations. The bvp4c solver is then used to simulate the numerical behavior. The effects of relevant parameters on the temperature, velocity, skin friction, and local Nusselt number profiles are examined. It is discovered that the parameters of the Prandtl fluid have a considerable impact. The local skin friction and the local Nusselt number are improved by increasing these parameters. The dataset is split into 70% training, 15% validation, and 15% testing. The ANN model successfully predicts skin friction and Nusselt number profiles, showing good agreement with numerical simulations. This hybrid framework offers a robust predictive approach for heat management systems in industrial applications. This study provides important insights for researchers and engineers aiming to comprehend flow characteristics and their behavior and to develop accurate predictive models. Full article

As traditional connection studies ignore the unbalanced distribution of connection demand and the variability of connection situations, this results in a poor match between passenger demand and connection mode, increasing passenger travel costs. Combining the economic efficiency of metro network operations with the unique accessibility advantages of ride-hailing services, this study clusters origin and destination points based on different travel needs and proposes four transfer strategies for integrating ride-hailing services with urban rail transit. Four nested strategies are developed based on the distance between the trip origin and the subway station’s service range. A reinforcement learning approach is employed to identify the optimal connection strategy by minimizing overall travel cost. The guided reinforcement learning principle is further introduced to accelerate convergence and enhance solution quality. Finally, this study takes the Fengtai area in Beijing as an example and deploys the Guided Q-Learning (GQL) algorithm based on extracting the hotspot passenger flow ODs and constructing the road network model in the area, searching for the optimal connecting modes and the shortest paths and carrying out the simulation validation of different travel modes. The results demonstrate that the GQL algorithm improves search performance by 25% compared to traditional Q-learning, reduces path length by 8%, and reduces minimum travel cost by 11%. Full article

Fiber orientation is an important descriptor of the microstructure for short fiber polymer composite materials where accurate and efficient prediction of the orientation state is crucial when evaluating the bulk thermo-mechanical response of the material. Macroscopic fiber orientation models employ the moment-tensor form in representing the fiber orientation state, and they all require a closure approximation for the higher-order orientation tensors. In addition, various models have more recently been developed to account for rotary diffusion due to fiber-fiber and fiber-matrix interactions which can now more accurately simulate the experimentally observed slow fiber kinematics in polymer composite processing. It is common to use explicit numerical initial value problem-ordinary differential equation (IVP-ODE) solvers such as the 4th- and 5th-order Dormand Prince Runge–Kutta (RK45) method to predict the transient and steady-state fiber orientation response. Here, we propose a computationally efficient method based on the Newton-Raphson (NR) iterative technique for determining steady state orientation tensor values by evaluating exact derivatives of the moment-tensor evolution equation with respect to the independent components of the orientation tensor. We consider various existing macroscopic-fiber orientation models and several closure approximations to ensure the robustness and reliability of the method. The performance and stability of the approach for obtaining physical solutions in various homogeneous flow fields is demonstrated through several examples. Validation of our orientation tensor exact derivatives is performed by benchmarking with results of finite difference techniques. Overall, our results show that the proposed NR method accurately predicts the steady state orientation for all tensor models, closure approximations and flow types considered in this paper and was relatively faster compared to the RK45 method. The NR convergence and stability behavior was seen to be sensitive to the initial orientation tensor guess value, the fiber orientation tensor model type and complexity, the flow type and extension to shear rate ratio. Full article

We consider the flow of gravity currents of Newtonian and power-law non-Newtonian viscous fluids, injected over a horizontal boundary in rectangular and cylindrical (axisymmetric) systems. We focus on some novel box model (BM) predictions. Previously published theoretical studies consider a power-law volume $\mathcal{V}=q{t}^{\alpha }$ (influx rate $\Theta =\alpha q{t}^{\alpha -1}$) where $q>0$ and $\alpha \ge 0$ are constants and t is time. The lubrication simplification equations predict a self-similar flow: the propagation is $K_L{t}^{\beta }$, and the height (thickness) profile is determined by a second-order ODE in the reduced length $\xi \in [0,1]$. The predicted $\beta$ and $K_L$ are in good agreement with laboratory data. Previous studies reported that a basic BM predicts $K_1{t}^{\beta }$ propagation with the same $\beta$ as the lubrication model, but the discrepancy between $K_1$ and $K_L$ is in general not small. This work points out two inconsistencies of the basic BM with the physical system and presents an improved, more consistent, BM prediction, $K_2{t}^{\beta }$. We show that $K_2$ is in general more accurate than $K_1$ (including in comparison with experimental data). Next, we consider a general influx $\Theta \left(t\right)$ (not a power law). We demonstrate that the BM provides a simple and flexible framework of initial-value time-dependent ODEs, though for such systems the lubrication theory lacks analytical reduction and requires numerical solution of a non-linear PDE (in time and length). Full article

Nanofluids are an innovative working medium in solar hot water installations (DHWs), thanks to their increased thermal conductivity and heat transfer coefficient. The aim of this work was to assess the effect of Al₂O₃ nanofluids in a water–ethylene glycol base (40:60%) and with the addition of Tween 80 surfactant (0.2 wt%) on thermal efficiency (ε) and exergy (η_ex) in a plate heat exchanger at DHW flows of 3 and 12 L/min. The numerical NTU–ε model was used with dynamic updating of thermophysical properties of nanofluids and the solution of the ODE system using the ode45 method, and the validation was carried out against the literature data. The results showed that the nanofluids achieved ε ≈ 0.85 (vs. ε ≈ 0.87 for the base fluid) and η_ex ≈ 0.72 (vs. η_ex ≈ 0.74), with higher entropy generation. The addition of Tween 80 reduced the viscosity by about 10–15%, resulting in a slight increase of Re and h-factor; however, the impact on ε and η_ex was marginal. The environmental analysis with an annual demand of Q = 3000 kWh/year and an emission factor of 0.2 kg CO₂/kWh showed that for ε < 0.87 the nanofluids increased the emissions by ≈16 kg CO₂/year, while at ε ≈ 0.92, a reduction of ≈5% was possible. This paper highlights the need to optimize nanofluid viscosity and exchanger geometry to maximize energy and environmental benefits. Nowadays, due to the growing problems of global warming, the analysis of energy efficiency and carbon footprint related to the functioning of a building seems to be crucial. Full article

This review surveys recent advances and emerging prospects in phase-transfer catalysis (PTC) for fuel desulfurization. In response to increasingly stringent environmental regulations, the removal of sulfur from transportation fuels has become imperative for curbing SO_x emissions. Conventional hydrodesulfurization (HDS) operates under severe temperature–pressure conditions and displays limited efficacy toward sterically hindered thiophenic compounds, motivating the exploration of non-hydrogen routes such as oxidative desulfurization (ODS). Within ODS, PTC offers distinctive benefits by shuttling reactants across immiscible phases, thereby enhancing reaction rates and selectivity. In particular, PTC enables efficient migration of organosulfur substrates from the hydrocarbon matrix into an aqueous phase where they are oxidized and subsequently extracted. The review first summarizes the deployment of classic PTC systems—quaternary ammonium salts, crown ethers, and related agents—in ODS operations and then delineates the underlying phase-transfer mechanisms, encompassing reaction-controlled, thermally triggered, photo-responsive, and pH-sensitive cycles. Attention is next directed to a new generation of catalysts, including quaternary-ammonium polyoxometalates, imidazolium-substituted polyoxometalates, and ionic-liquid-based hybrids. Their tailored architectures, catalytic performance, and mechanistic attributes are analyzed comprehensively. By incorporating multifunctional supports or rational structural modifications, these systems deliver superior desulfurization efficiency, product selectivity, and recyclability. Despite such progress, commercial deployment is hindered by the following outstanding issues: long-term catalyst durability, continuous-flow reactor design, and full life-cycle cost optimization. Future research should, therefore, focus on elucidating structure–performance relationships, translating batch protocols into robust continuous processes, and performing rigorous environmental and techno-economic assessments to accelerate the industrial adoption of PTC-enabled desulfurization. Full article

Topical drug administration is a common method of delivering medications to the eye to treat various ocular conditions, including glaucoma, dry eye, and inflammation. Drug efficacy following topical administration, including the drug’s distribution within the eye, absorption and elimination rates, and physiological responses can be predicted using physiologically based pharmacokinetic (PBPK) modeling. High-resolution computational models of the eye are desirable to improve simulations of drug delivery; however, these approaches can have long run times. In this study, a fast-running computational quasi-3D (Q3D) model of the human tear film was developed to account for absorption, blinking, drainage, and evaporation. Visualization of blinking mechanics and flow distributions throughout the tear film were enabled using this Q3D approach. Average drug absorption throughout the tear film subregions was quantified using a high-resolution compartment model based on a system of ordinary differential equations (ODEs). Simulations were validated by comparing them with experimental data from topical administration of 0.1% dexamethasone suspension in the tear film (R² = 0.76, RMSE = 8.7, AARD = 28.8%). Overall, the Q3D tear film model accounts for critical mechanistic factors (e.g., blinking and drainage) not previously included in fast-running models. Further, this work demonstrated methods toward improved computational efficiency, where central processing unit (CPU) time was decreased while maintaining accuracy. Building upon this work, this Q3D approach applied to the tear film will allow for more seamless integration into full-body models, which will be an extremely valuable tool in the development of treatments for ocular conditions. Full article

A model for the flow and thermal explosion of a micropolar gas is investigated, assuming the equation of state for a real gas. This model describes the dynamics of a gas mixture (fuel and oxidant) undergoing a one-step irreversible chemical reaction. The real gas model is particularly suitable in this context because it more accurately reflects reality under extreme conditions, such as high temperatures and high pressures. Micropolarity introduces local rotational dynamic effects of particles dispersed within the gas mixture. In this paper, we first derive the initial-boundary value system of partial differential equations (PDEs) under the assumption of spherical symmetry and homogeneous boundary conditions. We explain the underlying physical relationships and then construct a corresponding approximate system of ordinary differential equations (ODEs) using the Faedo–Galerkin projection. The existence of solutions for the full PDE model is established by analyzing the limit of the solutions of the ODE system using a priori estimates and compactness theory. Additionally, we propose a numerical scheme for the problem based on the same approximate system. Finally, numerical simulations are performed and discussed in both physical and mathematical contexts. Full article

This paper explores the complex dynamics of mixed convective flow in a Casson fluid saturated in a non-Darcy porous medium, focusing on the influence of gyrotactic microorganisms, internal heat generation, and multiple convective mechanisms. Casson fluids, known for their non-Newtonian behavior, are relevant in various industrial and biological contexts where traditional fluid models are insufficient. This study addresses the limitations of the standard Darcy’s law by examining non-Darcy flow, which accounts for nonlinear inertial effects in porous media. The governing equations, derived from conservation laws, are transformed into a system of no linear ordinary differential equations (ODEs) using similarity transformations. These ODEs are solved numerically using a finite differencing method that incorporates central differencing, tridiagonal matrix manipulation, and iterative procedures to ensure accuracy across various convective regimes. The reliability of this method is confirmed through validation with the MATLAB (R2024b) bvp4c scheme. The investigation analyzes the impact of key parameters (such as the Casson fluid parameter, Darcy number, Biot numbers, and heat generation) on velocity, temperature, and microorganism concentration profiles. This study reveals that the Casson fluid parameter significantly improves the velocity, concentration, and motile microorganism profiles while decreasing the temperature profile. Additionally, the Biot number is shown to considerably increase the concentration and dispersion of motile microorganisms, as well as the heat transfer rate. The findings provide valuable insights into non-Newtonian fluid behavior in porous environments, with applications in bioengineering, environmental remediation, and energy systems, such as bioreactor design and geothermal energy extraction. Full article

Chronic kidney disease (CKD) is associated with heart failure and neurological disorders. Therefore, point-of-care (POC) detection of CKD is essential, allowing disease monitoring from home and alleviating healthcare professionals’ workload. Lateral flow immunoassays (LFIAs) facilitate POC testing for a renal function biomarker, serum Cystatin C (CysC). LF devices were fabricated and optimised by varying the diluted sample volume, the nitrocellulose (NC) membrane, bed volume, AuNPs’ OD value and volume, and assay formats of partial or full LF systems. Notably, 310 samples were analysed to satisfy the minimum sample size for statistical calculations. This allowed for a comparison between the LFIAs’ results and the general Roche standard assay results from the Southern Health and Social Care Trust. Bland–Altman plots indicated the LFIAs measured 0.51 mg/L lower than the Roche assays. With the 95% confidence interval, the Roche method might be 0.24 mg/L below the LFIAs’ results or 1.27 mg/L above the LFIAs’ results. In summary, the developed non-fluorescent LFIAs could detect clinical CysC values in agreement with Roche assays. Even though the developed LFIA had an increased bias in low CysC concentration (below 2 mg/L) detection, the developed LFIA can still alert patients at the early stages of renal function impairment. Full article

With the advancement of intelligent transportation systems, vehicle trajectory data have become a key component in areas like traffic flow prediction, route planning, and traffic management. However, high-quality, publicly available trajectory datasets are scarce due to concerns over privacy, copyright, and data collection costs. The lack of data creates challenges for training machine learning models and optimizing algorithms. To address this, we propose a new method for generating synthetic vehicle trajectory data, leveraging traffic flow characteristics and road maps. The approach begins by estimating hourly traffic volumes, then it uses the Poisson distribution modeling to assign departure times to synthetic trajectories. Origin and destination (OD) distributions are determined by analyzing historical data, allowing for the assignment of OD pairs to each synthetic trajectory. Path planning is then applied using a road map to generate a travel route. Finally, trajectory points, including positions and timestamps, are calculated based on road segment lengths and recommended speeds, with noise added to enhance realism. This method offers flexibility to incorporate additional information based on specific application needs, providing valuable opportunities for machine learning in intelligent transportation systems. Full article

As urban traffic systems become increasingly complex, traditional traffic data collection methods based on fixed detectors face challenges such as poor data quality and acquisition difficulties. Traditional methods also lack the ability to capture complete vehicle path information essential for signal optimization. While vehicle trajectory data can provide rich spatiotemporal information, its sampling characteristics present new technical challenges for traffic parameter extraction. This study addresses the key issue of extracting traffic parameters suitable for signal timing optimization from sampled trajectory data by proposing a comprehensive method for traffic parameter extraction and analysis based on vehicle trajectory data. The method comprises five modules: data preprocessing, basic feature processing, exploratory data analysis, key feature extraction, and data visualization. An innovative algorithm is proposed to identify which intersections vehicles pass through, effectively solving the challenge of mapping GPS points to road network nodes. A dual calculation method based on instantaneous speed and time difference is adopted, improving parameter estimation accuracy through multi-source data fusion. A highly automated processing toolchain based on Python and MATLAB is developed. The method advances the state of the art through a novel polygon-based trajectory mapping algorithm and a systematic multi-source parameter extraction framework specifically designed for signal control optimization. Validation using actual trajectory data containing 2.48 million records successfully eliminated 30.80% redundant data and accurately identified complete paths for 7252 vehicles. The extracted multi-dimensional parameters, including link flow, average speed, travel time, and OD matrices, accurately reflect network operational status, identifying congestion hotspots, tidal traffic characteristics, and unstable road segments. The research outcomes provide a feasible technical solution for areas lacking traditional detection equipment. The extracted parameters can directly support signal optimization applications such as traffic signal coordination, timing optimization, and congestion management, providing crucial support for implementing data-driven intelligent traffic control. This research presents a theoretical framework validated with real-world data, providing a foundation for future implementation in operational signal control systems. Full article

The rapid expansion of urban rail networks has resulted in increasingly complex passenger flow patterns, presenting significant challenges for operational management, especially during incidents and emergencies. Disruptions such as power equipment failures, trackside faults, and train malfunctions can severely impact transit efficiency and reliability, leading to congestion and cascading network effects. Existing models for predicting passenger origin–destination (OD) matrices struggle to provide accurate and timely predictions under these disrupted conditions. This study proposes a deep counterfactual inference model that improves both the prediction accuracy and interpretability of OD matrices during incidents. The model uses a dual-channel framework based on multi-task learning, where the factual channel predicts OD matrices under normal conditions and the counterfactual channel estimates OD matrices during incidents, enabling the quantification of the spatiotemporal impacts of disruptions. Our approach which incorporates KL divergence-based propensity matching enhances prediction accuracy by 4.761% to 12.982% compared to baseline models, while also providing interpretable insights into disruption mechanisms. The model reveals that incident types vary in delay magnitude, with power equipment incidents causing the largest delays, and shows that incidents have time-lag effects on OD flows, with immediate impacts on origin stations and progressively delayed effects on destination and neighboring stations. This research offers practical tools for urban rail transit operators to estimate incident-affected passenger volumes and implement more efficient emergency response strategies, advancing emergency response capabilities in smart transit systems. Full article

Urban interstices are transitional spaces that emerge between expanding metropolitan regions. Despite increasing scholarly interest, the empirical analysis of these cities’ spatial development and functional integration remains scarce, particularly within the contexts of state-led urbanization, where administrative boundaries significantly shape development outcomes. This study quantitatively investigates urban interstice dynamics through a detailed analysis of Jurong City, which is located between the cities of Nanjing and Zhenjiang in the Chinese Yangtze River Delta. Utilizing mobile phone signaling data and origin–destination (OD) flow analysis, this research study systematically measures the intensity, directionality, and spatial patterns of human mobility flows between Jurong and its neighboring cities. The findings demonstrate that Jurong has a strong functional connection to Nanjing, with nearly 60% of its outbound mobility directed toward the city, despite being governed by Zhenjiang. This misalignment reveals a structural tension between functional integration and administrative hierarchy, fostering distinct subcenters such as Baohua (residential) and Guozhuang (innovation). Overall, the findings highlight the need to move beyond territorially bounded governance toward functionally coordinated regional strategies. Urban interstices can serve as effective connectors across fragmented systems, supporting more balanced and adaptive metropolitan integration. Leveraging real-time mobility data enables planners to identify spatial–functional linkages that transcend administrative boundaries, informing more responsive governance without requiring formal realignment. Full article

An isocratic reverse-phase high-performance liquid chromatography (RP-HPLC) method, coupled with photodiode array detection (PDA), was developed for the identification and characterization of stress degradation products and an unknown process-related impurity of rivaroxaban in bulk drug form. Rivaroxaban, a selective and direct Factor Xa inhibitor, underwent forced degradation under hydrolytic (acidic, alkaline, and neutral), photolytic, thermal, and oxidative stress conditions, following the ICH’s guidelines. The drug displayed significant susceptibility to acid, base, and oxidative environments leading to the formation of eleven degradation products. All degradation products, along with process impurities and Rivaroxaban, were effectively separated using a (4.6 × 250 mm, 5 µm) C18 Thermo ODS Hypersil column at ambient temperature. The mobile phase composed of acetonitrile and monobasic potassium phosphate (pH 2.9) in a 30:70 (v/v) ratio, with a flow rate of 1.0 mL/min, and detection was carried out at 249 nm. The LC-PDA method was validated in accordance with the ICH’s guidelines and USP38-NF33, demonstrating specificity, linearity, accuracy, precision, and robustness. Recovery studies showed results within the range of 98.6–103.4%, with a % RSD LT 2%. The limits of detection (LOD) and quantitation (LOQ) for rivaroxaban were determined to be 0.30 ppm and 1.0 ppm, respectively. Stress studies confirmed that the degradation products did not interfere with rivaroxaban detection, establishing the method as stability-indicating. Specific impurities were identified, including impurity G at 2.79 min, impurity D at 3.50 min, impurity H at 5.32 min, impurity C at 6.14 min, impurity E at 8.36 min, impurity A at 9.03 min, and impurity F at 9.49 min. Additionally, several unknown impurities were observed at 3.20, 4.00, 4.59, and 4.77 min. Statistical evaluation confirmed the method’s reliability, making it suitable for routine analysis, quality control of raw materials, formulations of varying strengths, dissolution studies, and bioequivalence assessments of rivaroxaban formulations. Full article