The effect of geographic distance on independent directors’ performance from the perspective of inefficient investment

Geoeconomics has attracted sustained attention in recent years, but the role of independent directors’ geographic distance in investment efficiency remains unexplored. We explore the governance effects of independent directors from a geographic location perspective. Specifically, the Great Circle Distance Formula is employed to calculate the geographic distance between the independent directors and the enterprise. Then, we measure the inefficient investment. Using a detailed sample in the Chinese market from 2009 to 2018, we find that geographic distance is not conducive to the functioning of independent directors and that there is a positive relationship between independent directors’ geographic distance and inefficient investment. The coefficients are robust to multiple robustness checks. In addition, the positive effect of independent directors’ geographic distance on inefficient investment will increase (become more positive) when there is no high-speed rail and the marketisation process is low in the enterprise’s location. Mechanism tests show that geographic distance does affect inefficient investment by inhibiting independent directors’ access to information as well as their reputation. Our results have important implications for investment policy and corporate governance

Scientometric research and critical analysis of battery state-of-charge estimation

With the advent of lithium-ion batteries (LIBs) and electric vehicle (EV) technology, the research on the battery State-of-Charge (SoC) estimation has begun to rise and develop rapidly. In order to objectively understand the current research status and development trends in the field of battery SoC estimation, this work uses an advanced search method to analyse the literature in the field of battery SoC estimation from 2004 to 2020 in the Web of Science (WoS) database. We employed bibliometrics analysis methods to make statistics on the publication year, the number of publications, discipline distribution, journal distribution, research institutions, application fields, test methods, analysis theories, and influencing factors in the field of battery SoC estimation. With using the Citespace software, a total of 2946 relevant research literature in the field of battery SoC estimation are analyzed. The research results show that the publication of relevant research documents keeps increasing from 2004 to 2020 in the field of battery SoC estimation. The research topics focus on battery model, management system, LIB, and EV. The research contents mainly involve Kalman filtering, wavelet neural network, impedance, and model predictive control. The main research approaches include model simulation, charging and discharging data recording, algorithm improvement, and environmental test. The research direction is shown to be more and more closely related to computer science and even artificial intelligence (AI). Intelligence, visualization, and multi-method collaboration are the future research trends of battery SoC estimation

Origami-inspired soft twisting actuator

Soft actuators have shown great advantages in compliance and morphology matched for manipulation of delicate objects and inspection in a confined space. There is an unmet need for a soft actuator that can provide torsional motion to e.g. enlarge working space and increase degrees of freedom. Towards this goal, we present origami-inspired soft pneumatic actuators (OSPAs) made from silicone. The prototype can output a rotation of more than one revolution (up to 435{\deg}), more significant than its counterparts. Its rotation ratio (=rotation angle/ aspect ratio) is more than 136{\deg}, about twice the largest one in other literature. We describe the design and fabrication method, build the analytical model and simulation model, and analyze and optimize the parameters. Finally, we demonstrate the potentially extensive utility of the OSPAs through their integration into a gripper capable of simultaneously grasping and lifting fragile or flat objects, a versatile robot arm capable of picking and placing items at the right angle with the twisting actuators, and a soft snake robot capable of changing attitude and directions by torsion of the twisting actuators.Comment: 9 figures. Soft Robotics (2022

Auto-diagnosis of time-of-flight for ultrasonic signal based on defect peaks tracking model

With the popularization of humans working in tandem with robots and artificial intelligence (AI) by Industry 5.0, ultrasonic non-destructive testing (NDT)) technology has been increasingly used in quality inspections in the industry. As a crucial part of handling ultrasonic testing results–signal processing, the current approach focuses on professional training to perform signal discrimination but automatic and intelligent signal optimization and estimation lack systematic research. Though the automated and intelligent framework for ultrasonic echo signal processing has already exhibited essential research significance for diagnosing defect locations, the real-time applicability of the algorithm for the time-of-flight (ToF) estimation is rarely considered, which is a very important indicator for intelligent detection. This paper conducts a systematic comparison among different ToF algorithms for the first time and presents the auto-diagnosis of the ToF approach based on the Defect Peaks Tracking Model (DPTM). The proposed DPTM is used for ultrasonic echo signal processing and recognition for the first time. The DPTM using the Hilbert transform was verified to locate the defect with the size of 2–10 mm, in which the wavelet denoising method was adopted. With the designed mechanical fixture through 3D printing technology on the pipeline to inspect defects, the difficulty of collecting sufficient data could be conquered. The maximum auto-diagnosis error could be reduced to 0.25% and 1.25% for steel plate and pipeline under constant pressure, respectively, which were much smaller than those with the DPTM adopting the cross-correlation. The real-time auto-diagnosis identification feature of DPTM has the potential to be combined with AI in future work, such as machine learning and deep learning, to achieve more intelligent approaches for industrial health inspection

Integrated metabolome and transcriptome analyses provide insight into the effect of red and blue LEDs on the quality of sweet potato leaves

Red and blue light-emitting diodes (LEDs) affect the quality of sweet potato leaves and their nutritional profile. Vines cultivated under blue LEDs had higher soluble protein contents, total phenolic compounds, flavonoids, and total antioxidant activity. Conversely, chlorophyll, soluble sugar, protein, and vitamin C contents were higher in leaves grown under red LEDs. Red and blue light increased the accumulation of 77 and 18 metabolites, respectively. Alpha-linoleic and linolenic acid metabolism were the most significantly enriched pathways based on Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. A total of 615 genes were differentially expressed between sweet potato leaves exposed to red and blue LEDs. Among these, 510 differentially expressed genes were upregulated in leaves grown under blue light compared with those grown under red light, while the remaining 105 genes were expressed at higher levels in the latter than in the former. Among the KEGG enrichment pathways, blue light significantly induced anthocyanin and carotenoid biosynthesis structural genes. This study provides a scientific reference basis for using light to alter metabolites to improve the quality of edible sweet potato leaves

Willingness to Accept HIV Pre-Exposure Prophylaxis among Chinese Men Who Have Sex with Men

OBJECTIVE: We investigated the awareness and acceptability of pre-exposure prophylaxis (PrEP) among men who have sex with men (MSM) and potential predicting factors. METHODS: This study was conducted among MSM in Beijing, China. Study participants, randomly selected from an MSM cohort, completed a structured questionnaire, and provided their blood samples to test for HIV infection and syphilis. Univariate logistic regression analyses were performed to evaluate the factors associated with willingness to accept (WTA) PrEP. Factors independently associated with willingness to accept were identified by entering variables into stepwise logistic regression analysis. RESULTS: A total of 152 MSM completed the survey; 11.2% had ever heard of PrEP and 67.8% were willing to accept it. Univariate analysis showed that age, years of education, consistent condom use in the past 6 months, heterosexual behavior in the past 6 months, having ever heard of PrEP and the side effects of antiretroviral drugs, and worry about antiretroviral drugs cost were significantly associated with willingness to accept PrEP. In the multivariate logistic regression model, only consistent condom use in the past 6 months (odds ratio [OR]: 0.31; 95% confidence interval [CI]: 0.13-0.70) and having ever heard of the side effects of antiretroviral drugs (OR: 0.30; 95% CI: 0.14-0.67) were independently associated with willingness to accept PrEP. CONCLUSIONS: The awareness of PrEP in the MSM population was low. Sexual behavioral characteristics and knowledge about ART drugs may have effects on willingness to accept PrEP. Comprehensive prevention strategies should be recommended in the MSM community