The perturbation bound of the extended vertical linear complementarity problem

In this paper, we discuss the perturbation analysis of the extended vertical linear complementarity problem (EVLCP). Under the assumption of the row $\mathcal{W}$-property, several absolute and relative perturbation bounds of EVLCP are given, which can be reduced to some existing results. Some numerical examples are given to show the proposed bounds

Characterization of ultrathin InSb nanocrystals film deposited on SiO2/Si substrate

Recently, solid-phase recrystallization of ultrathin indium antimonide nanocrystals (InSb NCs (films grown on SiO2/Si substrate is very attractive, because of the rapid development of thermal annealing technique. In this study, the recrystallization behavior of 35 nm indium antimonide film was studied. Through X-ray diffraction (XRD) analysis, it is demonstrated that the InSb film is composed of nanocrystals after high temperature rapid thermal annealing. Scanning electron microscopy shows that the film has a smooth surface and is composed of tightly packed spherical grains, the average grain size is about 12.3 nm according to XRD results. The optical bandgap of the InSb NCs film analyzed by Fourier Transform infrared spectroscopy measurement is around 0.26 eV. According to the current-voltage characteristics of the InSb NCs/SiO2/p-Si heterojunction, the film has the rectifying behavior and the turn-on voltage value is near 1 V

Embracing Safe Contacts with Contact-aware Planning and Control

Unlike human beings that can employ the entire surface of their limbs as a means to establish contact with their environment, robots are typically programmed to interact with their environments via their end-effectors, in a collision-free fashion, to avoid damaging their environment. In a departure from such a traditional approach, this work presents a contact-aware controller for reference tracking that maintains interaction forces on the surface of the robot below a safety threshold in the presence of both rigid and soft contacts. Furthermore, we leveraged the proposed controller to extend the BiTRRT sample-based planning method to be contact-aware, using a simplified contact model. The effectiveness of our framework is demonstrated in hardware experiments using a Franka robot in a setup inspired by the Amazon stowing task. A demo video of our results can be seen here: https://youtu.be/2WeYytauhNgComment: RSS 2023. Workshop: Experiment-oriented Locomotion and Manipulation Researc

Intrinsic Cerebro-Cerebellar Functional Connectivity Reveals the Function of Cerebellum VI in Reading-Related Skills

Funding This work was supported by grants from the National Natural Science Foundation of China (NSFC: 31971036, 31971039, and 31571158).Peer reviewedPublisher PD

Can annual land use plan control and regulate construction land growth in China?

Annual land use plan (ALUP) stands for the quota allocation of land converted to non-agricultural use, which has been in place since 1987 in China. Although the ALUP has been implemented for more than 30 years and has played an important role in China’s non-agricultural land growth management, little has been done to assess the effect of its implementation. The purpose of this research is to evaluate the effect of the ALUP on controlling the growth of construction land in China. The province-level data on land use in China during the period of 2006–2013 were collected and then analyzed using panel data model. The results show that (1) the ALUP can effectively curb construction land growth, and the construction land decreased about 1721 ha with a 1% increment of the intensity of growth management. Construction land growth is closely related to the implementation intensity of the ALUP, which decreases 30,892 ha under strict implementation but increases an extra 181,451 ha with relaxed implementation; (2) the implementation effect of the ALUP shows significant differences between provinces, and the provinces of northwestern and eastern China show better performance than provinces from the southwest and central regions. National development strategy and region’s differentiation land policy may have contributed to this phenomenon; (3) taking both the goal achievement and effectiveness into account, the implementation of the ALUP is described as effective though not completely successful; and (4) for more efficiency and success, the study proposes some suggestions on improving policy implementation in the future

Digital Modeling and Algorithms for Series Topological Mechanisms Based on POC Set

International audienc

Modeling and Preliminary Analysis of the Impact of Meteorological Conditions on the COVID-19 Epidemic

Since the COVID-19 epidemic outbreak at the end of 2019, many studies regarding the impact of meteorological factors on the attack have been carried out, and inconsistent conclusions have been reached, indicating the issue’s complexity. To more accurately identify the effects and patterns of meteorological factors on the epidemic, we used a combination of logistic regression (LgR) and partial least squares regression (PLSR) modeling to investigate the possible effects of common meteorological factors, including air temperature, relative humidity, wind speed, and surface pressure, on the transmission of the COVID-19 epidemic. Our analysis shows that: (1) Different countries and regions show spatial heterogeneity in the number of diagnosed patients of the epidemic, but this can be roughly classified into three types: “continuous growth”, “staged shock”, and “finished”; (2) Air temperature is the most significant meteorological factor influencing the transmission of the COVID-19 epidemic. Except for a few areas, regional air temperature changes and the transmission of the epidemic show a significant positive correlation, i.e., an increase in air temperature is conducive to the spread of the epidemic; (3) In different countries and regions studied, wind speed, relative humidity, and surface pressure show inconsistent correlation (and significance) with the number of diagnosed cases but show some regularity

Deep reinforcement learning based electric taxi service optimization

Electric taxis have been demonstrated with the promotion of electric vehicles. Compared with internal combustion engine vehicles, electric taxis spend more time in recharging, which reduces the taxi drivers’ intention to use. Reinforcement learning is applicable to the sequential decision-making process of taxis driver. This paper presents the double deep Q-learning network (DDQN) model to simulate the operation of electric taxis. According to the real-time state of taxis, DDQN will choose the optimal actions to execute. After training, we obtain a global optimal electric taxi service strategy, and finally optimize the taxi service. Using real-world taxi travel data, an experiment is conducted in Manhattan Island in New York City, USA. Results show that, comparing with the baseline methods, DDQN reduces the waiting time for charging and the rejection rate by 70% and 53%, respectively. Taxi drives’ income are finally increased by about 7%. Moreover, the results of model parameter sensitivity analysis indicate that the charge speed and the number of vehicles have greater impact on drives’ income than the battery capacity. When the charging rate reaches 120 kW, electric taxis achieve the best performance. The government should build more fast charging station to improve the revenue of electric taxis