Optimal Cost-Effective Maintenance Policy for a Helicopter Gearbox Early Fault Detection under Varying Load

Most of the existing fault detection methods rarely consider the cost-optimal maintenance policy. A novel multivariate Bayesian control approach is proposed, which enables the implementation of early fault detection for a helicopter gearbox with cost minimization maintenance policy under varying load. A continuous time hidden semi-Markov model (HSMM) is employed to describe the stochastic relationship between the unobservable states and observable observations of the gear system. Explicit expressions for the remaining useful life prediction are derived using HSMM. Considering the maintenance cost in fault detection, the multivariate Bayesian control scheme based on HSMM is developed; the objective is to minimize the long-run expected average cost per unit time. An effective computational algorithm in the semi-Markov decision process (SMDP) framework is designed to obtain the optimal control limit. A comparison with the multivariate Bayesian control chart based on hidden Markov model (HMM) and the traditional age-based replacement policy is given, which illustrates the effectiveness of the proposed approach

W-procer: Weighted Prototypical Contrastive Learning for Medical Few-Shot Named Entity Recognition

Contrastive learning has become a popular solution for few-shot Name Entity Recognization (NER). The conventional configuration strives to reduce the distance between tokens with the same labels and increase the distance between tokens with different labels. The effect of this setup may, however, in the medical domain, there are a lot of entities annotated as OUTSIDE (O), and they are undesirably pushed apart to other entities that are not labeled as OUTSIDE (O) by the current contrastive learning method end up with a noisy prototype for the semantic representation of the label, though there are many OUTSIDE (O) labeled entities are relevant to the labeled entities. To address this challenge, we propose a novel method named Weighted Prototypical Contrastive Learning for Medical Few Shot Named Entity Recognization (W-PROCER). Our approach primarily revolves around constructing the prototype-based contractive loss and weighting network. These components play a crucial role in assisting the model in differentiating the negative samples from OUTSIDE (O) tokens and enhancing the discrimination ability of contrastive learning. Experimental results show that our proposed W-PROCER framework significantly outperforms the strong baselines on the three medical benchmark datasets

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)

Transjugular Intrahepatic Portosystemic Shunt for the Treatment of Portal Hypertension in Noncirrhotic Patients with Portal Cavernoma

Background. The purpose of this study was to evaluate the safety and efficacy of transjugular intrahepatic portosystemic shunt (TIPS) placement in the management of portal hypertension in noncirrhotic patients with portal cavernoma. Methods. We conducted a single institution retrospective analysis of 15 noncirrhotic patients with portal cavernoma treated with TIPS placement. 15 patients (4 women and 11 men) were evaluated via the technical success of TIPS placement, procedural complications, and follow-up shunt patency. Results. TIPS placement was technically successful in 11 out of 15 patients (73.3%). Procedure-related complications were limited to a single instance of hepatic encephalopathy in one patient. In patients with successful shunt placement, the portal pressure gradient decreased from 25.8±5.7 to 9.5±4.2 mmHg (P<0.001). TIPS dysfunction occurred in two patients during a median follow-up time of 45.2 months. Revision was not performed in one patient due to inadequate inflow. The other patient died of massive gastrointestinal bleeding in a local hospital. The remaining nine patients maintained functioning shunts through their last evaluation. Conclusions. TIPS is a safe and effective therapeutic treatment for noncirrhotic patients with symptomatic portal hypertension secondary to portal cavernoma

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