Thermal contact resistance (TCR) at the gas diffusion layer–catalyst layer (GDL–CL) interface significantly affects heat transfer, water distribution, and electrochemical performance in proton exchange membrane fuel cells (PEMFCs). However, previous studies have mainly focused on bipolar plate-related interfaces, while the coupled effects of GDL–CL interfacial TCR, electrode structures, and advanced flow-field configurations remain unclear. In this study, a three-dimensional two-phase non-isothermal computational fluid dynamics (CFD) model of a PEMFC with a bamboo-shaped (BS) cathode flow field is developed to investigate the effects of GDL–CL TCR, GDL thickness, and catalyst layer (CL) thickness on cell performance. The results demonstrate that the BS flow field enhances oxygen transport, liquid-water removal, and heat transfer, improving maximum power density by 2.81% under zero-TCR conditions and up to 3.97% under high-TCR conditions compared with the parallel flow field. Furthermore, within the investigated GDL thickness range of 0.1–0.3 mm, the preferred configuration shifts from 0.2 mm under low-TCR conditions to 0.1 mm under high-TCR conditions. Increasing CL thickness from 0.01 mm to 0.03 mm improves cell performance and TCR tolerance within the investigated range. These findings clarify the coupling between interfacial thermal resistance, flow-field structure, and electrode design for improved PEMFC performance.
District heating system (DHS) operation requires allocating heating loads among buildings with heterogeneous thermal responses while maintaining indoor comfort and avoiding excessive heat supply, particularly under volatile weather and capacity constraints. We propose a spatiotemporal forecasting-based multi-objective reinforcement learning (STF-MORL) framework that leverages building-level differences in thermal response to guide adaptive heating load allocation. The framework first uses building-specific autoregressive with exogenous inputs (ARX) models to derive coefficient-based empirical control features and generate look-ahead indoor-temperature trajectories. These features are then embedded in the agent state and reward formulation, while Pareto solution sets generated using the non-dominated sorting genetic algorithm II (NSGA-II), together with adaptive objective weighting, are used to balance fairness, comfort, and efficiency under extreme operating conditions. Using measured data from five residential buildings in Tianjin, China, STF-MORL reduces inter-building temperature dispersion by 11.7–13.8 % relative to proportional allocation across four extreme operating conditions while maintaining 100 % compliance with the 18-22℃ comfort range. It also increases the heating-load saving index by 2.7–6.2 percentage points relative to Proportional Allocation. These results provide a case-study demonstration of how building thermal heterogeneity can be translated into actionable heating-load allocation policies.
Given a planar graph H, let fBP(n,H) denote the maximum number of copies of H in a bipartite planar graph on n vertices. Let Pk denote the path on k vertices. In this paper, we determine the exact value of fBP(n,Pk) and characterize all bipartite planar graphs containing fBP(n,Pk) copies of Pk for each k∈{3,4,5}. In addition, we give a conjecture of asymptotic value of fBP(n,Pk) for all k≥6.
Planned special events (PSEs) bring considerable economic benefits to cities but simultaneously challenge the performance of urban transportation systems. Existing studies focusing on PSEs either rely on survey data with limited population coverage or adopt aggregate analyses that cannot precisely identify PSE-related trips. Utilizing ride-hailing data with detailed textual addresses, this study accurately identifies concert-related trips in Tianjin, China, and investigates the spatiotemporal dynamics and determinants of ride-hailing demand using machine learning models and SHAP-based interpretation. A total of 7,022 ingress trips and 3,381 egress trips were identified. Approximately 70 % of ingress trips occur between one hour before venue opening and one hour before the concert starts, while around 70 % of egress trips are generated within one hour after the concert ends. In terms of travel distance, 86.1 % of ingress trips and 77.5 % of egress trips are concentrated within 12 km of the venue. Spatially, ingress trip origins cluster in the urban core and several prominent urban attractions, whereas egress trip destinations are oriented toward hotels, university districts, and train stations. Machine learning results indicate that concert-related ride-hailing demand is primarily shaped by location-related factors and built-environment characteristics, with clear phase-specific differences. Distance to Tianjin Olympic Center (TJOC) and Hotel Points of Interest (POIs) density consistently emerge as the most influential variables. The distance effect is stronger during ingress and weaker during egress, while Hotel POIs density maintains a positive association, exerting greater influence during egress. Population density exhibits a consistently negative relationship with ride-hailing demand. Parking POIs density, Restaurant POIs density, Bus stops density, and Land use diversity, display stage-dependent effects, jointly reflecting the influence of transport-related infrastructure and built environment on ride-hailing demand during large-scale events. These findings enhance understanding of PSE-related ride-hailing patterns and provide practical insights for proactive traffic management during large-scale events.
The solar-driven photocatalytic reduction of CO₂ represents a vital frontier, offering substantial potential to concurrently alleviate the energy crisis and reduce environmental pollution. In this study, a core-shell BaTiO3/B-In2O3 (BTO/B-In2O3) heterojunction was constructed to combine photothermal effect of black indium oxide (B-In2O3) and polarization of tetragonal BTO. The BTO/B-In2O3 exhibited the excellent photocatalytic efficiency in reduction of CO2 to CO, with the yield of 146.56 and 188 μmol·g−1·h−1 under full-spectrum light with and without cooling water, being 6.07 and 7.79 times of that by BTO (24.14 μmol·g−1·h−1). Spin-polarized DFT is applied to further explore how CO2 reduction proceeds on BTO/B-In2O3, focusing on its electronic structure, electron flow, and underlying reaction mechanism. The heterojunction's photocatalytic CO2 reduction performance is enhanced by modulating its electronic structure and facilitating the reaction process. This study provides a theoretical foundation for the design of efficient ferroelectric-based photocatalysts.
