丰田

Designing cost-effective, highly competent, and resilient transition metal-based electrodes has gained significant research interest towards energy storage and conversion. Herein, we present a rational design of a ZnO/CoNi2S4 heterojunction nanoarray electrode by incorporating ZnO nanoflakes with transition metal sulfides to construct a multifunctional hybrid array. Extensive investigations along with theoretical analysis revealed the nano- engineered interfaces promote interfacial charge redistribution, thereby accelerating rapid charge-transfer kinetics. Furthermore, the hetero-hybrid architecture affords ample of electroactive sites and strengthens the synergy created due to interfacial electronic interaction. The redistribution of interfacial electrons between ZnO and CoNi2S4 promotes robust synergistic effects from multimetallic centers, optimizing the overall electronic structure for superior catalytic activity. As a result, the ZnO/CoNi2S4 electrocatalyst delivered small overpotentials of 102.2 mV for HER and 198.7 mV for OER at 10 mA cm- 2, along with excellent long-term stability. The assembled hybrid electrolyzer requires only 1.56 V to achieve 10 mA cm- 2. In addition, this electrode also exhibits an ultrahigh specific capacity of 1319.0C g- 1 at 1 A g-1 and retained over 94% of its value after 15,000 cycles at 10 A g-1. This hybrid supercapattery combination (ZnO/CoNi2S4//activated carbon) achieved a high energy density of 62.7 W h kg-1 at 2100 W kg-1, preserving 84.8% specific capacity retention after prolonged cycling, highlighting its great potential for multifunctional energy storage applications. This study constructs an effective pathway for fabricating progressive electrodes with superior performance.

This study examined whether individuals' willingness to engage in new leisure activities is systematically related, across activities, to activity-specific mean daily decision-making states. Decision-making was conceptualized along two dimensions: orientation (reflecting a reliance on System-1 versus System-2 processing) and intensity (reflecting the degree of cognitive engagement in judgment and choice). Because new leisure activities provide limited evaluative anchors, we tested whether their perceived cognitive characteristics show complementary correspondence with activity-specific mean decision-making states among participants highly willing to engage in them despite limited experience. In an online survey of 600 adults, participants reported their daily orientation and intensity and evaluated leisure activities in terms of their own willingness to engage in them, their experience, and activities' perceived activity-level orientation and intensity. Treating leisure activities as the unit of analysis, we found systematic complementary patterns. Activities perceived as more System-1-oriented were associated with more System-2-oriented activity-specific mean daily states among high-willingness inexperienced participants, whereas activities perceived as more System-2-oriented were associated with more System-1-oriented activity-specific mean daily states. Activities perceived as low-intensity were associated with higher activity-specific mean daily intensity among high-willingness inexperienced participants. Our findings suggest structured activity-level correspondences between perceived activity characteristics and activity-specific mean daily decision-making states.

This study presents a sampling-based method to guarantee robust stability of general control systems with uncertainty. The method allows the system dynamics and controllers to be represented by various data-driven models, such as Gaussian processes and deep neural networks. For nonlinear systems, stability conditions involve inequalities over an infinite number of states in a state space. Sampling-based approaches can simplify these hard conditions into inequalities discretized over a finite number of states. However, this simplification requires margins to compensate for discretization residuals. Large margins degrade the accuracy of stability evaluation, and obtaining appropriate margins for various systems is challenging. This study addresses this challenge by deriving second-order margins for various nonlinear systems containing data-driven models. Because the size of the derived margins decrease quadratically as the discretization interval decreases, the stability evaluation is more accurate than with first-order margins. Furthermore, this study designs feedback controllers by integrating the sampling-based approach with an optimization problem. As a result, the controllers can guarantee stability while simultaneously considering control performance.

We provide a sober look at the application of Multimodal Large Language Models (MLLMs) in autonomous driving, challenging common assumptions about their ability to interpret dynamic driving scenarios. Despite advances in models like GPT-4o, their performance in complex driving environments remains largely unexplored. Our experimental study assesses various MLLMs as world models using in-car camera perspectives and reveals that while these models excel at interpreting individual images, they struggle to synthesize coherent narratives across frames, leading to considerable inaccuracies in understanding (i) ego vehicle dynamics, (ii) interactions with other road actors, (iii) trajectory planning, and (iv) open-set scene reasoning. We introduce the Eval-LLM-Drive dataset and DriveSim simulator to enhance our evaluation, highlighting gaps in current MLLM capabilities and the need for improved models in dynamic real-world environments.

On-demand services are gradually being introduced worldwide. Each city usually provides both fixed-route and on-demand mobility services, improving mobility convenience in city areas. However, the benefits to users cannot easily be estimated. Fixed-route services facilitate understanding of users’ convenience by measuring access times to stations using conventional methods. However, on-demand services, which are usually not mass-transit services, change service quality spatiotemporally. Moreover, mixing the two types makes understanding more challenging. In this study, we propose a multi-mobility service availability rate (MA) that enables us to easily assess the convenience of mobility services, including on-demand services, across subareas. As case studies, we conducted simulations using MA in two areas with different multiple-service situations. In the first case, we evaluated the convenience of multiple mobility services. We selected the best service plan among three candidates based on the equity of MA in each subarea. In the second case, we searched for improvement measures and decided on the improvement direction in the context of an additional on-demand bus service. These findings underscore the value of MA as a practical tool for transportation planners and operators seeking to enhance multi-mobility service environments.