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Reducing traffic energy consumption is crucial for smart cities, and vehicle carbon emissions are a key energy indicator. Traffic signal control (TSC) is a useful method because it can affect the energy consumption of vehicles on the road by controlling the stop-and-go of vehicles at traffic intersections. However, setting traffic signals to reduce energy consumption will affect traffic efficiency and this is not in line with traffic management objectives. Current studies adopt multi-objective optimization methods with high traffic efficiency and low carbon emissions to solve this problem. However, most methods use static weights, which cannot adapt to complex and dynamic traffic states, resulting in non-optimal performance. Current energy indicators for urban transportation often fail to consider passenger fairness. This fairness is significant because the purpose of urban transportation is to serve people's mobility needs not vehicles. Therefore, this paper proposes Multi-objective Adaptive Meta-DQN TSC (MMD-TSC), which introduces a dynamic weight adaptation mechanism to simultaneously optimize traffic efficiency and energy saving, and incorporates the per capita carbon emissions as the energy indicator. Firstly, this paper integrates traffic state data such as vehicle positions, velocities, vehicle types, and the number of passengers and incorporates fairness into the energy indicators, using per capita carbon emissions as the target for reducing energy consumption. Then, it proposes MMD-TSC with dynamic weights between energy consumption and traffic efficiency as reward functions. The MMD-TSC model includes two agents, the TSC agent and the weight agent, which are responsible for traffic signal adjustment and weight calculation, respectively. The weights are calculated by a function of traffic states. Finally, the paper describes the design of the MMD-TSC model learning algorithm and uses a SUMO (Simulation of Urban Mobility) v.1.20.0 for traffic simulation. The results show that in non-highly congested traffic states, the MMD-TSC model has higher traffic efficiency and lower energy consumption compared to static multi-objective TSC models and single-objective TSC models, and can adaptively achieve traffic management objectives. Compared with using vehicle average carbon emissions as the energy consumption indicator, using per capita carbon emissions achieves Pareto improvements in traffic efficiency and energy consumption indicators. The energy utilization efficiency of the MMD-TSC model is improved by 35% compared to the fixed-time TSC. [ABSTRACT FROM AUTHOR]

Mechanical errors seriously affect the transmission performance of mechanical structure, especially for high-precision mechanical structure. With the development of virtual simulation technology, the performance test of mechanical structure in virtual environment is gradually applied. Under the background, this paper takes the movable tooth reducer as the research object and puts forward a virtual performance test method by combing test errors with virtual simulation technology. The tooth profile errors of key components of the reducer are obtained by optical scanning, and the simulation test is carried out. Meanwhile, the accuracy of the simulation test is verified by the implementation of vibration test. The results show that both the simulated vibration acceleration and the experimental test vibration acceleration show a double-peak variation trend, and the frequency difference between the two peaks is less than 5%. Therefore, the simulation test method proposed in this paper can effectively evaluate the performance of mechanical structures.

With the enhanced interoperability of information among vehicles, the demand for collaborative sharing among vehicles increases. Based on blockchain, the classical consensus algorithms in collaborative IoV (Internet of Vehicle), such as PoW (Proof of Work), PoS (Proof of Stake), and DPoS (Delegated Proof of Stake), only consider the node features, which is hard to adapt to the immediacy and flexibility of vehicles. On the other hand, classical consensus algorithms often require mass computing, which undoubtedly increases the communication overhead, resulting in the inability to achieve collaborative IoV under asymmetric networks. Therefore, proposing a low failure rate consensus algorithm that takes into account running time and energy consumption becomes a major challenge in IoV applications. This paper proposes an AI-enabled consensus algorithm with vehicle features, combining vehicle-based metrics and neural networks. First, we introduce vehicle-based metrics such as vehicle online time, performance, and behavior. Then, we propose an integral model and a hierarchical classification method, which combine with a BP neural network to obtain the optimal solution for interconnection. Among them, we also use Informer to predict the future online duration of vehicles, which effectively solves the situation that the primary node vehicle drops off in collaborative IoV. Finally, the experimentations show that the vehicle-based metrics eliminate the problem of the primary node vehicle being offline, which realizes the collaborative IoV considering vehicle features. Meanwhile, it reduces the vehicle network system delay and energy consumption. [ABSTRACT FROM AUTHOR]

Our previous studies have confirmed that the crude tentacle-only extract (cTOE) from the jellyfish Cyanea capillata (Cyaneidae) exhibits hemolytic and cardiovascular toxicities simultaneously. So, it is quite difficult to discern the underlying active component responsible for heart injury caused by cTOE. The inactivation of the hemolytic toxicity from cTOE accompanied with a removal of plenty of precipitates would facilitate the separation of cardiovascular component and the investigation of its cardiovascular injury mechanism. In our research, after the treatment of one-step alkaline denaturation followed by twice dialysis, the protein concentration of the treated tentacle-only extract (tTOE) was about 1/3 of cTOE, and SDS-PAGE showed smaller numbers and lower density of protein bands in tTOE. The hemolytic toxicity of tTOE was completely lost while its cardiovascular toxicity was well retained. The observations of cardiac function, histopathology and ultrastructural pathology all support tTOE with significant cardiovascular toxicity. Blood gas indexes and electrolytes changed far less by tTOE than those by cTOE, though still with significant difference from normal. In summary, the cardiovascular toxicity of cTOE can exist independently of the hemolytic toxicity and tTOE can be employed as a better venom sample for further purification and mechanism research on the jellyfish cardiovascular toxic proteins.

Polyploidy can cause variation in plant functional traits and thereby generate individuals that can adapt to fluctuating environments and exploit new environments. However, few empirical studies have tested for an association between ploidy level and climatic tolerance of invasive cytotypes relative to conspecific native‐range cytotypes. Here, we used an invasive plant Solidago canadensis to test whether invasive populations had a higher proportion of polyploids, greater height and stem‐base diameter, and occupied a wider range of climatic conditions than conspecific native‐range populations. We also tested whether the invasive populations had overcome genetic founder effects. We sampled a total of 80 populations in parts of the invaded range in China and native range in North America for in situ measurements of plant height and stem‐base diameter in the field and for population genetic and cytotype analyses. To examine climatic correlates, we augmented our field‐sampled data with occurrence records obtained from Global Biodiversity Information Facility. All, except one, of the populations that we sampled in China occurred in a humid subtropical climate. In contrast, the North American populations occurred in humid continental, humid subtropical, and semi‐arid climatic zones. All populations of S. canadensis in China were purely hexaploid, while the North American populations were diploid, tetraploid, and hexaploid. The invasive hexaploids were significantly taller and had a larger stem‐base diameter than native hexaploids. Native hexaploids were significantly taller and had larger stem‐base diameter than native diploids. Climatic correlate assessment found that invasive and native populations occupied different climatic envelopes, with invasive populations occurring in warmer and less seasonal climates than native populations. However, there was no significant correlation between ploidy level and climatic envelope of S. canadensis. Molecular phylogeography data suggest reduced genetic founder effects in the invaded range. Overall, these results suggest that polyploidy does not influence S. canadensis climatic tolerance. [ABSTRACT FROM AUTHOR]