Design and Assessment of an Electric Vehicle Powertrain Model Based on Real-World Driving and Charging Cycles

In this paper, an advanced analytical model for an electric vehicle (EV) powertrain has been developed to illustrate the vehicular dynamics by combining electrical and mechanical models in the analysis. This study is based on a Nissan Leaf EV. In the electrical system, the powertrain has various components including a battery pack, a battery management system, a dc/dc converter, a dc/ac inverter, a permanent magnet synchronous motor, and a control system. In the mechanical system, it consists of power transmissions, axial shaft, and vehicle wheels. Furthermore, the driving performance of the Nissan Leaf is studied through the real-world driving tests and simulation tests in MATLAB/Simulink. In the analytical model, the vehicular dynamics is evaluated against changes in the vehicle velocity and acceleration, state of charge of the battery, and the motor power. Finally, a number of EVs involved in the power dispatch is studied. The greenhouse gas emissions of the EV are analyzed according to various energy power and driving features, and compared with the conventional internal combustion engine vehicle. In this case, Nissan Leaf is a pure EV. For a given drive cycle, Nissan Leaf can reduce CO2 emissions by 70%, depending on the way electricity is generated and duty cycles

Multi-label classification for biomedical literature: an overview of the BioCreative VII LitCovid Track for COVID-19 literature topic annotations

The coronavirus disease 2019 (COVID-19) pandemic has been severely impacting global society since December 2019. The related findings such as vaccine and drug development have been reported in biomedical literature—at a rate of about 10 000 articles on COVID-19 per month. Such rapid growth significantly challenges manual curation and interpretation. For instance, LitCovid is a literature database of COVID-19-related articles in PubMed, which has accumulated more than 200 000 articles with millions of accesses each month by users worldwide. One primary curation task is to assign up to eight topics (e.g. Diagnosis and Treatment) to the articles in LitCovid. The annotated topics have been widely used for navigating the COVID literature, rapidly locating articles of interest and other downstream studies. However, annotating the topics has been the bottleneck of manual curation. Despite the continuing advances in biomedical text-mining methods, few have been dedicated to topic annotations in COVID-19 literature. To close the gap, we organized the BioCreative LitCovid track to call for a community effort to tackle automated topic annotation for COVID-19 literature. The BioCreative LitCovid dataset—consisting of over 30 000 articles with manually reviewed topics—was created for training and testing. It is one of the largest multi-label classification datasets in biomedical scientific literature. Nineteen teams worldwide participated and made 80 submissions in total. Most teams used hybrid systems based on transformers. The highest performing submissions achieved 0.8875, 0.9181 and 0.9394 for macro-F1-score, micro-F1-score and instance-based F1-score, respectively. Notably, these scores are substantially higher (e.g. 12%, higher for macro F1-score) than the corresponding scores of the state-of-art multi-label classification method. The level of participation and results demonstrate a successful track and help close the gap between dataset curation and method development. The dataset is publicly available via https://ftp.ncbi.nlm.nih.gov/pub/lu/LitCovid/biocreative/ for benchmarking and further development

INTRACELLULAR BIOMOLECULE DELIVERY FOR THERAPEUTIC EFFECTS AND ANALYSIS OF CELLULAR METABOLIC ACTIVITIES

Ph.DDOCTOR OF PHILOSOPHY (NGS

(Comparison of delayed passenger flow forecasting methods for urban rail transit based on ARIMA and LSTM)

With the transition of the development of the urban rail transit from expansion stage to operation stage in many Chinese cities,improving the operational efficiency has been considered as the development theme of the next stage.With the increasing demand for extending operation time in Chinese first-tier cities such as Beijing,Shanghai,Guangzhou and Shenzhen,how to balance the duration,cost and operational efficiency of time-extended operation of the urban rail transit has become a great challenge to refined operation.By using the data from Shanghai Metro and pre-processing the metro card data,delayed passenger flow forecast models for urban rail transit based on ARIMA and LSTM are developed.After conducting the predictive analysis for the 5 minutes intervals and 15 minute intervals by using full-day data and half-day data separately,this research finds that:1)the half-day data generally has a smaller root mean square deviation than the full-day data,which indicates that the model has a high fitting degree;2)LSTM has a smaller root mean square deviation than the ARIMA method and LSTM has a better prediction effect.The findings of this research can provide technical support for passenger flow prediction in the time extended operation of urban rail transit

The effect of heat treatment on microstructure of the melt-spun Mg–7Y–4Gd–5Zn–0.4Zr alloy

The microstructure evolution of the melt-spun Mg–7Y–4Gd–5Zn–0.4Zr alloy during annealing treatment has been investigated by using X-ray diffraction (XRD), optical microscope (OM), differential scanning calorimetry (DSC) and transmission electron microscope (TEM). The results indicated that two kinds of primary grains were contained in the melt-spun alloy. One was the supersaturated magnesium matrix, and the other was the 18R-LPSO phase. The 18R-LPSO phase transformed into the 14H-LPSO phase during annealing treatment at 300 °C for 0.5 h. The new precipitate of the 14H-LPSO phase was found at 300 °C for 5 h. Lots of linear precipitates formed as well as some precipitate with quadrangular morphology in matrix at 500 °C for 0.5 h. The melt-spun alloy displayed the highest hardness of 103 NHV after annealing treatment at 300 °C for 5 h

(Ecological-adaptive reuse and post-evaluation on energy-efficiency of industrial heritage)

The protection and adaptation of industrial heritage has been increasing in domestic attention, and the number of cases is increasing. The traditional functional implantation can no longer meet the existing recycling requirements. How to ensure dozens or hundreds of years of industrial heritage no longer be urban scars, and integrate with the city as an organic whole, and meet or approach the energy and comfort requirements of new buildings, has become a challenge for architects. The Australian Geelong wool warehouse building was reconstructed to educational building, which is as the analysis case. The energy-saving transformation strategy in the adaptive transformation of industrial heritage is expounded, evaluating the status quo of its transformation through energy consumption data, to provide a reference for energy-saving transformation of existing industrial buildings in China