OpenDriver: an open-road driver state detection dataset

In modern society, road safety relies heavily on the psychological and physiological state of drivers. Negative factors such as fatigue, drowsiness, and stress can impair drivers' reaction time and decision making abilities, leading to an increased incidence of traffic accidents. Among the numerous studies for impaired driving detection, wearable physiological measurement is a real-time approach to monitoring a driver's state. However, currently, there are few driver physiological datasets in open road scenarios and the existing datasets suffer from issues such as poor signal quality, small sample sizes, and short data collection periods. Therefore, in this paper, a large-scale multimodal driving dataset for driver impairment detection and biometric data recognition is designed and described. The dataset contains two modalities of driving signals: six-axis inertial signals and electrocardiogram (ECG) signals, which were recorded while over one hundred drivers were following the same route through open roads during several months. Both the ECG signal sensor and the six-axis inertial signal sensor are installed on a specially designed steering wheel cover, allowing for data collection without disturbing the driver. Additionally, electrodermal activity (EDA) signals were also recorded during the driving process and will be integrated into the presented dataset soon. Future work can build upon this dataset to advance the field of driver impairment detection. New methods can be explored for integrating other types of biometric signals, such as eye tracking, to further enhance the understanding of driver states. The insights gained from this dataset can also inform the development of new driver assistance systems, promoting safer driving practices and reducing the risk of traffic accidents. The OpenDriver dataset will be publicly available soon

Z Distance Function for KNN Classification

This paper proposes a new distance metric function, called Z distance, for KNN classification. The Z distance function is not a geometric direct-line distance between two data points. It gives a consideration to the class attribute of a training dataset when measuring the affinity between data points. Concretely speaking, the Z distance of two data points includes their class center distance and real distance. And its shape looks like "Z". In this way, the affinity of two data points in the same class is always stronger than that in different classes. Or, the intraclass data points are always closer than those interclass data points. We evaluated the Z distance with experiments, and demonstrated that the proposed distance function achieved better performance in KNN classification

Characteristics of the Evolution of China’s Regional Economic Differences Based on the Perspective of Urban Agglomerations

Urban agglomerations are becoming an important spatial carrier for China’s industrialisation and new-type urbanisation, and an important engine to drive the national economic development, and regional economic differences based on urban agglomerations have also become a new feature of China’s regional differences in the new period. Based on the urban agglomeration perspective, we quantitatively measure China’s regional economic differences from 2001 to 2020 through the two-stage nested decomposition method of the Theil Index, and analyse the evolutionary characteristics of the economic differences within the 19 urban agglomerations in the country by decomposing them into four major regions: East, Central, West and Northeast, and the differences between the urban agglomerations within the regions and the economic differences within the urban agglomerations. The study found that: (1) Overall the overall differences of the region with urban agglomerations as the core were gradually shrinking. In terms of the trend of changes in the differences at all levels, the economic differences between regions and within urban agglomerations were significantly reduced, and the changes in the economic differences between urban agglomerations were not obvious. In terms of contribution rate, the contribution rate of intra-urban agglomeration differences had always remained at a high level, reaching about half of the overall economic differences in the country, and was the primary contributing factor to the overall economic differences. (2) In terms of absolute differences, the economic differences among urban agglomerations in the four regions showed a clear pattern of “East > West > Central ≈ Northeast”. (3) The intra-cluster economic differences of each urban agglomeration in 20 years were at different levels and showed a differentiated trend of evolution, and the areas with relatively more prominent intra-cluster economic differences were the Pearl River Delta urban agglomeration in the east, and the Lan-xi urban agglomeration and the North Slope of Tianshan Mountain urban agglomeration in the northwestern region. The economic differences of urban agglomerations are the result of a variety of factors, and the reasons for the formation of regional economic differences are analysed from the factors of economic location, national regional development policies, human resource factors and high-speed traffic differences within the urban agglomerations

Research on Wellbore Quality Control Technology for Coalbed Methane Deviated Wells

AbstractDeviated well is one of the main well types to develop coalbed methane in China. It's been widely used by various coalbed methane companies in recent years. Compared with traditional oil and gas wells, coalbed methane wells are characterized by shallow depth and strict well spacing, which means higher wellbore quality control technology. Furthermore, coalbed methane wells are apt to produce coal dust during dewatering and coal dust causes serious abrasion of pipe and rod. It's required to improve wellbore quality. This paper analyzes the weakness of current wellbore quality control standard and technology for coalbed methane wells on the basis of the characterization of coaled methane wells and finds out the key factors that control wellbore quality of coalbed methane deviated wells. The technology is developed to improve wellbore quality of coalbed methane wells from trajectory design and real-time control. Through the on-site test in Baode block, Shanxi Province, the technology is further improved and it's confirmed by the test result that this technology can improve the wellbore quality of coalbed methane deviated wells. In addition, this paper also presents some good suggestions for compiling standard about wellbore quality control for coalbed methane deviated wells

Biomimetic Layer-by-Layer Self-Assembly of Nanofilms, Nanocoatings, and 3D Scaffolds for Tissue Engineering

Achieving surface design and control of biomaterial scaffolds with nanometer- or micrometer-scaled functional films is critical to mimic the unique features of native extracellular matrices, which has significant technological implications for tissue engineering including cell-seeded scaffolds, microbioreactors, cell assembly, tissue regeneration, etc. Compared with other techniques available for surface design, layer-by-layer (LbL) self-assembly technology has attracted extensive attention because of its integrated features of simplicity, versatility, and nanoscale control. Here we present a brief overview of current state-of-the-art research related to the LbL self-assembly technique and its assembled biomaterials as scaffolds for tissue engineering. An overview of the LbL self-assembly technique, with a focus on issues associated with distinct routes and driving forces of self-assembly, is described briefly. Then, we highlight the controllable fabrication, properties, and applications of LbL self-assembly biomaterials in the forms of multilayer nanofilms, scaffold nanocoatings, and three-dimensional scaffolds to systematically demonstrate advances in LbL self-assembly in the field of tissue engineering. LbL self-assembly not only provides advances for molecular deposition but also opens avenues for the design and development of innovative biomaterials for tissue engineering