A Self-attention Knowledge Domain Adaptation Network for Commercial Lithium-ion Batteries State-of-health Estimation under Shallow Cycles

Accurate state-of-health (SOH) estimation is critical to guarantee the safety, efficiency and reliability of battery-powered applications. Most SOH estimation methods focus on the 0-100\% full state-of-charge (SOC) range that has similar distributions. However, the batteries in real-world applications usually work in the partial SOC range under shallow-cycle conditions and follow different degradation profiles with no labeled data available, thus making SOH estimation challenging. To estimate shallow-cycle battery SOH, a novel unsupervised deep transfer learning method is proposed to bridge different domains using self-attention distillation module and multi-kernel maximum mean discrepancy technique. The proposed method automatically extracts domain-variant features from charge curves to transfer knowledge from the large-scale labeled full cycles to the unlabeled shallow cycles. The CALCE and SNL battery datasets are employed to verify the effectiveness of the proposed method to estimate the battery SOH for different SOC ranges, temperatures, and discharge rates. The proposed method achieves a root-mean-square error within 2\% and outperforms other transfer learning methods for different SOC ranges. When applied to batteries with different operating conditions and from different manufacturers, the proposed method still exhibits superior SOH estimation performance. The proposed method is the first attempt at accurately estimating battery SOH under shallow-cycle conditions without needing a full-cycle characteristic test

Comparison of T Helper Cell Patterns in Primary Open-Angle Glaucoma and Normal-Pressure Glaucoma

Background: HSP60-related immunological activities are found in normal-pressure glaucoma (NPG) patients, in whom an elevated intraocular pressure (IOP) found in primary open-angle glaucoma (POAG) is not observed. HSP60 was found in POAG and NPG patients, while anti-HSP60 level was mainly found to be higher in NPG patients. The purpose of this study was to compare the percentages of Th cells and levels of related cytokines, attempting to provide evidence to explain this discrepancy. Material/Methods Blood samples from POAG, NPG, and normal control (NC) groups were collected and peripheral blood monocytes were isolated and cultured with or without the stimulation of HSP60. Flow cytometry and enzyme-linked immunosorbent assay were used to assess the percentages of Th1, Th2, Th17, and Treg cells, as well as HSP60 antibody levels and related cytokine levels, before and after culture. Results: Significantly higher titers of anti-HSP60 were observed only in NPG patients. Comparable Th1 and Th2 cell frequencies, IL-4 level, and IFN-γ level were found in POAG and NPG patients, while higher Treg cell frequency was only found in POAG patients. After culturing with HSP60, increased Th2 frequencies and decreased Th1 frequencies were observed in the POAG, NPG, and NC groups, while increased Treg frequency was only identified in the POAG and NC groups. Conclusions: Different Th cell patterns were observed among POAG, NPG, and NC groups. Lack of induction of Treg cells and imbalance of the pro-inflammatory and anti-inflammatory response patterns of Th cells exist in some NPG patients

Biomass-based carbon materials for CO2 capture:A review

Carbon capture and sequestration technologies are essential to reduce CO2 emissions which are responsible for global warming. Carbon-based materials can play an important role in the reduction of CO2 emissions. These materials are normally produced from biomass through technologies such as pyrolysis and hydrothermal carbonization. The type of biomass feedstock and biomass conversion conditions can significantly affect the textual properties and surface chemistry of the carbon materials. Various modification methods such as material activation or N-doping can improve the properties of carbon materials to obtain better CO2 capture effects. This review summarizes recently reported research in the areas of using biomass-based materials for CO2 capture. The technologies of biomass conversion to carbon materials and modification of the carbon materials are critically analyzed. Meanwhile, the mechanisms of the CO2 capture process and research of different modification carbon materials for CO2 capture are also discussed. Finally, potential future research directions are suggested to promote carbon capture using biomass-based materials

The Potential Geographical Distribution of Bactrocera cucurbitae (Diptera: Tephritidae) in China Based on Eclosion Rate Model and ArcGIS

Abstract. The melon fruit fly, Bactrocera cucurbitae Coquillett (Diptera: Tephritidae), is one of the important insect pests of fruits and vegetables. In order to monitor and control it effectively, it is necessary to know the potential geographical distribution of this pest. The ER (Eclosion rate) model was constructed from empirical biological data, and analyzed with ArcGIS. Based on the soil temperature and moisture data of Chinese meteorological stations, the potential geographical distribution of B. cucurbitae from January to December in China was predicted. Six categories were used to describe different levels of suitability for B. cucurbitae in China. The potential geographical distribution and suitable levels for every month in China were obtained and showed that almost all locations were suitable from May to September. Further analysis showed that monitoring measures should be taken in Guangdong, Guangxi, Yunnan, and Hainan provinces throughout the year

Research on CVDs Prediction and Early Warning Techniques in Healthcare Monitoring System

Abstract-Chronic diseases are gradually becoming the principal factors of harm to people's health. Fortunately, the development of e-health provides a novel thought for chronic disease prevention and treatment. This paper focuses on the research of cardiovascular disease (CVDs) prevention and early warning techniques using e-health and data mining. In this paper, we will use weighted associative classification algorithm to model the data in healthcare database to determine the level of cardiovascular risk. Besides, on the basis of data mining and knowledge discovery, intelligent warning mechanisms are proposed to provide different services to patients with different levels of risk. The experimental results show that the used classification algorithm is a more effective mining algorithm in the field of healthcare with higher accuracy and better comprehension. Our study is of definite significance to help control risk level of CVDs patients

A novel plantar pressure analysis method to signify gait dynamics in Parkinson's disease

Plantar pressure can signify the gait performance of patients with Parkinson's disease (PD). This study proposed a plantar pressure analysis method with the dynamics feature of the sub-regions plantar pressure signals. Specifically, each side's plantar pressure signals were divided into five sub-regions. Moreover, a dynamics feature extractor (DFE) was designed to extract features of the sub-regions signals. The radial basis function neural network (RBFNN) was used to learn and store gait dynamics. And a classification mechanism based on the output error in RBFNN was proposed. The classification accuracy of the proposed method achieved 100.00% in PD diagnosis and 95.89% in severity assessment on the online dataset, and 96.00% in severity assessment on our dataset. The experimental results suggested that the proposed method had the capability to signify the gait dynamics of PD patients

Artemisia pollen allergy in China : Component-resolved diagnosis reveals allergic asthma patients have significant multiple allergen sensitization

Background: Artemisia pollen allergy is a major cause of asthma in Northern China. Possible associations between IgE responses to Artemisia allergen components and clinical phenotypes have not yet been evaluated. This study was to establish sensitization patterns of four Artemisia allergens and possible associations with demographic characteristics and clinical phenotypes in three areas of China. Methods: Two hundred and forty patients allergic to Artemisia pollen were examined, 178 from Shanxi and 30 from Shandong Provinces in Northern China, and 32 from Yunnan Province in Southwestern China. Allergic asthma, rhinitis, conjunctivitis, and eczema symptoms were diagnosed. All patients sera were tested by ImmunoCAP with mugwort pollen extract and the natural components nArt v 1, nArt ar 2, nArt v 3, and nArt an 7. Results: The frequency of sensitization and the IgE levels of the four components in Artemisia allergic patients from Southwestern China were significantly lower than in those from the North. Art v 1 and Art an 7 were the most frequently recognized allergens (84% and 87%, respectively), followed by Art v 3 (66%) and Art ar 2 (48%). Patients from Northern China were more likely to have allergic asthma (50%) than patients from Southwestern China (3%), and being sensitized to more than two allergens increased the risk of allergic asthma, in which cosensitization to three major allergens Art v 1, Art v 3, and Art an 7 is prominent. Conclusions: Componentresolved diagnosis of Chinese Artemisia pollenallergic patients helps assess the potential risk of mugwortassociated allergic asthma.(VLID)329956

Evaluating how lodging affects maize yield estimation based on UAV observations

Timely and accurate pre-harvest estimates of maize yield are vital for agricultural management. Although many remote sensing approaches have been developed to estimate maize yields, few have been tested under lodging conditions. Thus, the feasibility of existing approaches under lodging conditions and the influence of lodging on maize yield estimates both remain unclear. To address this situation, this study develops a lodging index to quantify the degree of lodging. The index is based on RGB and multispectral images obtained from a low-altitude unmanned aerial vehicle and proves to be an important predictor variable in a random forest regression (RFR) model for accurately estimating maize yield after lodging. The results show that (1) the lodging index accurately describes the degree of lodging of each maize plot, (2) the yield-estimation model that incorporates the lodging index provides slightly more accurate yield estimates than without the lodging index at three important growth stages of maize (tasseling, milking, denting), and (3) the RFR model with lodging index applied at the denting (R5) stage yields the best performance of the three growth stages, with R2 = 0.859, a root mean square error (RMSE) of 1086.412 kg/ha, and a relative RMSE of 13.1%. This study thus provides valuable insight into the precise estimation of crop yield and demonstra\tes that incorporating a lodging stress-related variable into the model leads to accurate and robust estimates of crop grain yield