This study presents detailed modelling and comparative analysis of pouch lithium-ion cells (PLCs) configured in combined single and dual battery packs for electric vehicles (EVs), appraising their performance with and without active cooling systems. Pouch cells were selected due to their high volumetric energy density (VED), lightweight design, and suitability for high-power EV applications. Using SolidWorks for computer aided design (CAD) modelling, Analysis System (ANSYS) Fluent software with the multi-scale multi-domain (MSMD) approach, and equivalent circuit model (ECM), simulation based thermal and electrochemical behaviour during 1-hour charging process at specified currents was achieved. Four configurations were examined: single-pack and dual-pack, each with and without liquid active cooling. Main parameters included state of charge (SoC), temperature distribution, and thermal gradients. Results confirmed that dual-pack configuration significantly outperformed single-pack, achieving over 69.3% higher SoC (55.07% against 32.53%) under the same conditions. Without active cooling, maximum temperatures reached 541.11 K (single) and 462.77 K (dual), indicating notable hotspots in the single pack. Active cooling dramatically reduced temperatures to 295 K across both setups, with the dual pack displaying superior uniformity and a 0.45% lower average temperature, adequately averting thermal gradients and enhancing safety. Mathematical validation of SoC dynamics confirmed the dual configuration’s theoretical advantage in charging efficiency, tempered by heat losses. Findings reveal that dual battery packs integrated with active cooling render optimal equilibrium of faster charging, improved thermal management, and extended battery life, addressing limitations associated with conventional single-pack designs.

Solid-particle erosion at pipeline elbows threatens the integrity of oil-and-gas transport systems. This numerical study used the Euler–Lagrange discrete phase model and the Finnie erosion model in ANSYS Fluent to compare a single 90° elbow, two 45° elbows, and three 30° elbows for water–sand flow at inlet velocities of 10–40 m/s and particle diameters of 0.0002–0.0005 m. Two output measures are reported: contour plots show the local cellwise maximum wall erosion rate, whereas line graphs show the area-weighted mean wall erosion rate. At the reference condition of 40 m/s and a particle diameter of 0.0005 m, the area-weighted mean rates were 5.18 $\times$ 10$^{-5}$, 2.89 $\times$ 10$^{-5}$, and 3.59 $\times$ 10$^{-5}$ kg m$^{-2}$ s$^{-1}$ for the single 90° elbow, two 45° elbows, and three 30° elbows, respectively. Relative to the single elbow under the same simulation conditions, the mean erosion rate decreased by 44.2% with two 45° elbows and by 30.7% with three 30° elbows. The corresponding local contour maxima were 3.17 $\times$ 10$^{-3}$, 2.82 $\times$ 10$^{-3}$, and 2.59 $\times$ 10$^{-3}$ kg m$^{-2}$ s$^{-1}$. These results show that distributing the change in flow direction across multiple elbows reduces severe particle–wall impacts, with two 45° elbows providing the lowest area-weighted mean erosion rate.

Transit-oriented development (TOD) offers a planning basis for compact, mixed-use, and vibrant station areas. However, in many rapidly developing Chinese cities, metro-network expansion has not been matched by coordinated station-area commercial renewal, resulting in spatially uneven street-level activity despite improved transit accessibility. Taking the North Ring Road Station area in Changchun, China, as a case, this study examines the associations between TOD-related spatial conditions and observed human activity intensity as a proxy for commercial spatial vitality, and how these spatial associations can inform targeted regeneration strategies. Within an 800 m TOD analytical boundary, commercial point of interest (POI) data, Baidu Heatmap-derived human activity data, building footprints, road-network data, land-use information, and field observations were integrated. Average nearest-neighbor (ANN) analysis, kernel density estimation (KDE), standard deviational ellipse (SDE) analysis, ordinary least squares (OLS) regression, and geographically weighted regression (GWR) were applied within a 5D TOD framework. The results reveal a dual-core, corridor-oriented, and spatially uneven commercial structure. Commercial facility agglomeration and functional diversity are positively associated with observed activity intensity, whereas transit distance, road-network configuration, building morphology, and proximity to commercial anchors exhibit spatially heterogeneous associations. These results suggest that transit proximity and commercial concentration alone do not necessarily correspond to spatially continuous activity when pedestrian permeability, interface openness, and functional mixing are limited. The study translates these associations into evidence-informed transport-development responses that coordinate metro-entrance access, pedestrian transfer and first/last-mile connections, commercial functions, and surrounding land uses, thereby providing a micro-scale diagnostic approach to station–street–commercial integration.

The rapid expansion of dockless shared bicycle systems creates persistent challenges for parking management and the efficient use of urban public space, particularly around transport hubs and major commuting corridors. Conventional hotspot-identification methods mainly indicate where parking demand is concentrated but provide limited information on the spatial form and potential public-space impact of bicycle accumulation. This study develops a morphology-aware framework using shared bicycle order and trajectory data from Guangzhou. After removing operational repositioning events, dense parking cores were extracted through grid-based connected components. Principal component analysis and robust spread measures were then used to quantify cluster orientation, length–width ratio, and bandwidth. Road proximity and orientation consistency were incorporated to distinguish roadside-band (RB), intersection-corner (IC), designated-parking-area-like (DPA), and scattered points (SP) morphologies. Short-duration demand surges during morning and evening peaks were identified separately and combined with high demand and band-shaped morphology to screen priority governance locations. Analysis of 6.902 million orders identified 27,687 dense parking cores, with the top 1% accounting for 43.7% of within-cluster orders. Band-shaped clusters represented 13.4% of all cores but accounted for 32.1% of orders. Among 26,142 strictly classified cores, RB clusters accounted for 3.23%, and only 21.8% were aligned within 30° of adjacent roads, indicating that oblique and multi-row occupation was more common than simple linear roadside parking. Among 1,842 high-demand cores, 320 showed morning-peak surges and 80 evening-peak surges. High-demand locations within 200 m of metro entrances recorded 1.7 times the mean order volume of those outside the metro buffer. The combined surge–demand–morphology criterion identified 240 priority governance points, 64.6% near arterial or sub-arterial roads and 50.0% near designated parking facilities. The findings show that parking pressure is shaped by both demand intensity and accumulation morphology, providing a practical basis for differentiated parking management, pre-peak dispatch, and improved use of urban public space.

Selecting an appropriate antihypertensive drug class for older patients with multimorbidity requires multiple clinical considerations to be evaluated simultaneously, including comorbidity-specific suitability, treatment-related risks, therapeutic priorities and professional judgement. A transparent decision-support framework is therefore needed to structure these heterogeneous considerations without implying that a mathematical ranking constitutes a clinical recommendation. An entropy-weighted group decision-support framework was developed and evaluated using a hypothetical 72-year-old patient with multiple comorbidities. Seven antihypertensive drug classes—diuretics, beta-blockers, Angiotensin-Converting Enzyme (ACE) inhibitors, Angiotensin II Receptor Blockers (ARBs), calcium-channel blockers (CCBs), alpha-1 blockers and central alpha-2 agonists—were assessed against eight criteria: physician experience, suitability for older patients, suitability for patients with diabetes, suitability for patients with kidney disease, suitability for patients with congestive heart failure, suitability for patients with a history of myocardial infarction, medication-related complication risk and rapidity of therapeutic effect. Assessments were provided independently by an internist, a cardiologist and a urologist. Criterion weights were derived using the entropy method from transformed rank-score distributions, while rank-frequency linear assignment and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) were used to aggregate expert assessments and obtain alternative rankings. ARBs were ranked first by TOPSIS and third in both optimal linear-assignment solutions. Two equally optimal linear-assignment solutions were obtained, with beta-blockers and alpha-1 blockers exchanging the first and sixth positions. Central alpha-2 agonists were ranked last by both approaches. Importantly, the complete and tie-free rankings provided by every expert resulted mathematically in identical entropy weights of 0.125 for all eight criteria, indicating that criterion differentiation was not achieved under the adopted elicitation format. The resulting rankings therefore represent methodological outputs rather than evidence of clinical superiority among antihypertensive drug classes. The framework provides a transparent means of structuring multi-criteria and multi-expert assessments in complex clinical scenarios, while its preliminary nature, limited expert panel and absence of patient-level validation preclude direct clinical application. Validation using larger and more diverse expert panels, clinically validated criteria and patient-level outcomes is warranted before the framework can be considered for clinical decision-support applications.