Bi2O2Se nanowires presenting high mobility and strong spin-orbit coupling

Systematic electrical transport characterizations were performed on high-quality Bi2O2Se nanowires to illustrate its great transport properties and further application potentials in spintronics. Bi2O2Se nanowires synthesized by chemical vapor deposition method presented a high field-effect mobility up to 1.34*104 cm2V-1s-1, and exhibited ballistic transport in the low back-gate voltage (Vg) regime where conductance plateaus were observed. When further increasing the electron density by increasing Vg, we entered the phase coherent regime and weak antilocalization (WAL) was observed. The spin relaxation length extracted from the WAL was found to be gate tunable, ranging from ~100 nm to ~250 nm and reaching a stronger spin-obit coupling (SOC) than the two-dimensional counterpart (flakes). We attribute the strong SOC and the gate tunability to the presence of a surface accumulation layer which induces a strong inversion asymmetry on the surface. Such scenario was supported by the observation of two Shubnikov-de Haas oscillation frequencies that correspond to two types of carriers, one on the surface, and the other in the bulk. The high-quality Bi2O2Se nanowires with a high mobility and a strong SOC can act as a very prospective material in future spintronics.Comment: 22 pages, 7 figure

COVID-19 and Computer Audition: An Overview on What Speech & Sound Analysis Could Contribute in the SARS-CoV-2 Corona Crisis

At the time of writing, the world population is suffering from more than 10,000 registered COVID-19 disease epidemic induced deaths since the outbreak of the Corona virus more than three months ago now officially known as SARS-CoV-2. Since, tremendous efforts have been made worldwide to counter-steer and control the epidemic by now labelled as pandemic. In this contribution, we provide an overview on the potential for computer audition (CA), i.e., the usage of speech and sound analysis by artificial intelligence to help in this scenario. We first survey which types of related or contextually significant phenomena can be automatically assessed from speech or sound. These include the automatic recognition and monitoring of breathing, dry and wet coughing or sneezing sounds, speech under cold, eating behaviour, sleepiness, or pain to name but a few. Then, we consider potential use-cases for exploitation. These include risk assessment and diagnosis based on symptom histograms and their development over time, as well as monitoring of spread, social distancing and its effects, treatment and recovery, and patient wellbeing. We quickly guide further through challenges that need to be faced for real-life usage. We come to the conclusion that CA appears ready for implementation of (pre-)diagnosis and monitoring tools, and more generally provides rich and significant, yet so far untapped potential in the fight against COVID-19 spread

Discovery of genes and proteins possibly regulating mean wool fibre diameter using cDNA microarray and proteomic approaches

Wool fibre diameter (WFD) is one of the wool traits with higher economic impact. However, the main genes specifically regulating WFD remain unidentified. In this current work we have used Agilent Sheep Gene Expression Microarray and proteomic technology to investigate the gene expression patterns of body side skin, bearing more wool, in Aohan fine wool sheep, a Chinese indigenous breed, and compared them with that of small tail Han sheep, a sheep bread with coarse wool. Microarray analyses showed that most of the genes likely determining wool diameter could be classified into a few categories, including immune response, regulation of receptor binding and growth factor activity. Certain gene families might play a role in hair growth regulation. These include growth factors, immune cytokines, solute carrier families, cellular respiration and glucose transport amongst others. Proteomic analyses also identified scores of differentially expressed proteins.This project was funded by First Class Grassland Science Discipline Program of Shandong Province (China), National Natural Science Foundation of China (31572383, 31301936), National Hair Sheep Industry Technology System (CARS-40), the Special Fund for Agro-scientific Research in the Public Interest (201403071) and Projects of Qingdao People’s Livelihood Science and Technology (13-1-3-88-nsh, 14-2-3-45-nsh, 19-6-1-68-nsh)

Ecological Effects of Oasis Shelterbelts in Ulan Buh Desert

In arid region, shelterbelt is the ecological barrier for oasis. Understanding its ecological effects can provide theoretical supports for its long-term management and sustainable development. Two standard meteorological stations were used to monitor climatic factors continuously for 7 years, and two 50 m dust monitoring towers were used to continuously monitor sandstorm for 10 times, which were located inside and outside oasis shelterbelts in the northeastern edge of Ulan Buh Desert. The microclimate differences were analyzed, as well as the ecological effects of oasis shelterbelts was clarified inside and outside oasis. In the present study, under the influence of a large-scale shelterbelts, air temperature, land ground temperature and evaporation respectively decreased 5.13% ~ 24.74%, 2.38% ~ 20.09% and 7.06% ~ 17.68%, whereas the relative humidity and precipitation respectively increased 6.93% ~ 25.53% and 4.30% ~ 50.15%. During the occurrence of sandstorms, the wind speed inside and outside shelterbelt showed an increasing trend with the increase in height. The relationship between wind speed and height was expressed as a power function. The wind direction was mainly W, WNW and NE, but the proportion of each direction was different inside and outside shelterbelt. When the sandstorm passed through oasis shelterbelts, the wind speed was significantly weakened, with an average reduction of 30.68%. The horizontal aeolian sediment flux decreased 414.44 g·m−2 and the aeolian deposition flux decreased 0.81 g·m−2. The results revealed that the microclimate was improved by oasis shelterbelts, especially in the growing season. Therefore, oasis shelterbelts help to maintain the sustainable development of oasis

An Early Study on Intelligent Analysis of Speech under COVID-19: Severity, Sleep Quality, Fatigue, and Anxiety

The COVID-19 outbreak was announced as a global pandemic by the World Health Organisation in March 2020 and has affected a growing number of people in the past few weeks. In this context, advanced artificial intelligence techniques are brought to the fore in responding to fight against and reduce the impact of this global health crisis. In this study, we focus on developing some potential use-cases of intelligent speech analysis for COVID-19 diagnosed patients. In particular, by analysing speech recordings from these patients, we construct audio-only-based models to automatically categorise the health state of patients from four aspects, including the severity of illness, sleep quality, fatigue, and anxiety. For this purpose, two established acoustic feature sets and support vector machines are utilised. Our experiments show that an average accuracy of .69 obtained estimating the severity of illness, which is derived from the number of days in hospitalisation. We hope that this study can foster an extremely fast, low-cost, and convenient way to automatically detect the COVID-19 disease

The Human Activity Radar Challenge: benchmarking based on the ‘Radar signatures of human activities’ dataset from Glasgow University

Radar is an extremely valuable sensing technology for detecting moving targets and measuring their range, velocity, and angular positions. When people are monitored at home, radar is more likely to be accepted by end-users, as they already use WiFi, is perceived as privacy-preserving compared to cameras, and does not require user compliance as wearable sensors do. Furthermore, it is not affected by lighting condi-tions nor requires artificial lights that could cause discomfort in the home environment. So, radar-based human activities classification in the context of assisted living can empower an aging society to live at home independently longer. However, challenges remain as to the formulation of the most effective algorithms for radar-based human activities classification and their validation. To promote the exploration and cross-evaluation of different algorithms, our dataset released in 2019 was used to benchmark various classification approaches. The challenge was open from February 2020 to December 2020. A total of 23 organizations worldwide, forming 12 teams from academia and industry, participated in the inaugural Radar Challenge, and submitted 188 valid entries to the challenge. This paper presents an overview and evaluation of the approaches used for all primary contributions in this inaugural challenge. The proposed algorithms are summarized, and the main parameters affecting their performances are analyzed

The Human Activity Radar Challenge: benchmarking based on the ‘Radar signatures of human activities’ dataset from Glasgow University

Radar is an extremely valuable sensing technology for detecting moving targets and measuring their range, velocity, and angular positions. When people are monitored at home, radar is more likely to be accepted by end-users, as they already use WiFi, is perceived as privacy-preserving compared to cameras, and does not require user compliance as wearable sensors do. Furthermore, it is not affected by lighting condi-tions nor requires artificial lights that could cause discomfort in the home environment. So, radar-based human activities classification in the context of assisted living can empower an aging society to live at home independently longer. However, challenges remain as to the formulation of the most effective algorithms for radar-based human activities classification and their validation. To promote the exploration and cross-evaluation of different algorithms, our dataset released in 2019 was used to benchmark various classification approaches. The challenge was open from February 2020 to December 2020. A total of 23 organizations worldwide, forming 12 teams from academia and industry, participated in the inaugural Radar Challenge, and submitted 188 valid entries to the challenge. This paper presents an overview and evaluation of the approaches used for all primary contributions in this inaugural challenge. The proposed algorithms are summarized, and the main parameters affecting their performances are analyzed

Evolution of the Shadow Effect with Film Thickness and Substrate Conductivity on a Hemispherical Workpiece during Magnetron Sputtering

When depositing films on a complex workpiece surface by magnetron sputtering, the shadow effect occurs and causes the columnar structure to tilt toward the substrate owing to the oblique incident angle of the plasma flux, affecting the microstructure and properties of the films. Improving the surface diffusion could alleviate the shadow effect, whereas changing the energy of the deposited particles could improve surface diffusion. Different substrate conductivities could affect the energy of the deposited particles when they reach the substrate. In this study, Si (semiconductor) and SiO2 (insulator) sheets are mounted on the inner surface of a hemispherical workpiece, and Ti films with different thicknesses (adjusted by the deposition time) are deposited on the inner surface of the hemispherical workpiece by direct current magnetron sputtering. The results show that there is a threshold thickness and incident angle before the films are affected by the shadow effect. The threshold could be affected by the film thickness, the incident angle, and the conductivity of the substrate. The threshold would decrease as the film thickness or incidence angle increased or the conductivity of the substrate decreased. When the film thickness or incident angle does not reach the threshold, the film would not be affected by the shadow effect. In addition, the film deposited later would tilt the vertical columnar structure of the film deposited earlier. Owing to the different conductivities, the shadow effect manifest earlier for Ti films deposited on the insulator SiO2 than for films deposited on the semiconductor Si when the film thickness is >500 nm