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Objectives: This study took Fuzhou city as a case, described how the public health insurance coverage policy in 2016 of novel anti-lung cancer medicines benefited patients, and who benefited the most from the policy in China. Methods: This was a retrospective study based on health insurance claim data with a longitudinal analysis of the level and trend changes of the monthly number of patients to initiate treatment with the novel targeted anti-lung cancer medicines gefitinib and icotinib before and after health insurance coverage. The study also conducted a multivariate linear regression analysis to predict the potential determinants of the share of patient out-of-pocket (OOP) expenditure for lung cancer treatment with the study medicines. Results: The monthly number of the insured patients in Fuzhou who initiated the treatment with the studied novel targeted anti-lung cancer medication abruptly increased by 26 in the month of the health insurance coverage (95% CI: 14–37, p < 0.01) and kept at an increasing level afterward (p < 0.01). By controlling the other factors, the shares of OOP expenditure for lung cancer treatment of the patients who were formal employee program enrollees not entitled to government-funded supplementary health insurance coverage and resident program enrollees were 18.3% (95% CI: 14.1–22.6) and 26.7% (95% CI: 21.0–32.4) higher than that of the patients who were formal employee program enrollees with government-funded supplementary health insurance coverage. Conclusion: The public health insurance coverage of novel anti-lung cancer medicines benefited patients generally. To enable that patients benefit from this policy more equally and thoroughly, in order to achieve the policy goal of not to leave anyone behind, it is necessary to strengthen the benefits package of the resident program and to optimize the current financing mechanism of the public health insurance system.

Most of the existing research on shared autonomous vehicles (SAVs) and road congestion pricing have studied the short-term impact on traffic flow. These types of studies focused on the influences on mobility and ignored the long-term impacts on regional job accessibility. Given this, from the perspective of land use and transportation integration, this study explored the long-term effects of SAVs and cordon-based congestion pricing on regional land use, transportation, and job accessibility. The contributions of this study have been summarized by the following three purposes. First, to the best of the authors' knowledge, this study was the first attempt to identify the long-term impact of the combination of these two technologies on regional job accessibility. Second, compared to the previous research methodology, this study adopted the land use and transportation integrated model (TRANUS model) and scenario planning to ensure the comprehensiveness and validity of the results. Third, this study analyzed the spatial heterogeneity of the impact of the combination of the two technologies on regional job accessibility in different areas with different built-environment attributes. To realize this purpose, this study quantitatively classified traffic analysis zones (TAZs) using data mining technology, i.e., factor analysis and clustering analysis. Results showed that the introduction of SAVs will contribute to job and population development in the charging zone and reduce the negative effect of road congestion pricing. From the perspective of reducing the average travel time between TAZs, the best strategy will be to implement SAVs alone, followed by integrated use of SAVs and road congestion pricing, while the worst strategy will be to implement the cordon-based congestion pricing policy alone. By comparison, from the perspective of improving regional job accessibility, the effect of introducing SAVs was better than that of road congestion pricing, while the combination of these two technologies was not superior to implementing SAVs alone.

Intro -- Contents -- 1 Applications of UAVs and Machine Learning in Agriculture -- 1.1 Introduction -- 1.2 Types of UAVs -- 1.3 Examples of UAV-Based Agricultural Applications -- 1.4 Artificial Intelligence and Machine Learning -- 1.5 Conclusion -- References -- 2 Robot Operating System Powered Data Acquisition for Unmanned Aircraft Systems in Digital Agriculture -- 2.1 Introduction -- 2.2 ROS-Based Data Acquisition System -- 2.2.1 Basic Concepts and Components in ROS -- 2.2.2 Connecting with Other UAS Components -- 2.2.3 Examples for Representative Sensors -- 2.3 A Case Study for Industrial Hemp Phenotyping -- 2.3.1 UAS Data Acquisition System -- 2.3.2 Plant Materials and Experimental Design -- 2.3.3 Data Acquisition and Ground-Truth Measurements -- 2.3.4 Data Processing Pipeline for Extracting Morphological and Vegetation Traits -- 2.3.5 Measurement Accuracy -- 2.4 Discussion -- 2.5 Summary -- References -- 3 Unmanned Aerial Vehicle (UAV) Applications in Cotton Production -- 3.1 Introduction -- 3.1.1 Precision Agriculture Technology in Agricultural Production -- 3.1.2 UAV-Based Remote Sensing (RS) for Crop Monitoring -- 3.1.3 UAV Imagery Data Processing Pipeline -- 3.2 UAV Systems in Cotton Production -- 3.2.1 Field Management for Cotton Production -- 3.2.2 Cotton Emergence Assessment -- 3.2.3 Cotton Growth Monitoring Using UAV-Based RS -- 3.2.4 Cotton Yield Estimation -- 3.3 Summary -- References -- 4 Time Effect After Initial Wheat Lodging on Plot Lodging Ratio Detection Using UAV Imagery and Deep Learning -- 4.1 Introduction -- 4.2 Materials and Methods -- 4.2.1 Experimental Field and Data Collection -- 4.2.2 Data Pre-Processing and Auto Dataset Generation -- 4.2.3 Handcrafted Features -- 4.2.4 Deep Features -- 4.2.5 Classifier -- 4.3 Results and Discussion -- 4.3.1 Deep Learning Model Selection for Deep Feature Extraction.

Purpose To identify predictive factors associated with US adolescents' transition through the stages of change for potentially quitting e-cigarettes using the Trans-theoretical model of behavior change. Design Prospective cohort study. Setting United States. Subjects We utilized data from adolescents (12-17 years) in Wave 3 of the Population Assessment of Tobacco and Health study who used e-cigarettes exclusively over the past 30 days (n = 177) and were followed up with in Wave 4. Measures Outcome variables were 3 transition categories: those who remained stagnant, those who progressed, and those who regressed in their stage of quitting e-cigarettes. Predictor variables were socio-demographics, e-cigarette harm perception, e-cigarette use at home or by important people, social norms, e-cigarette and anti-tobacco advertisements, and e-cigarette health warnings. Analysis Weighted-adjusted multinomial regression analysis was performed to determine the association between predictor and outcome variables. Results From Wave 3 to Wave 4, 19% of adolescents remained stagnant; 73.3% progressed; and 7.7% regressed. Adolescents were less likely to progress in their stage of change if they perceived nicotine in e-cigarettes to be "not at all/slightly harmful" (AOR = .26 [95% CI: .25, .27], P < .001); reported important people's use of e-cigarettes (AOR = .18 [95% CI: .05, .65, P = .009); and "rarely" noticed e-cigarette health warnings (AOR = .28 [95% CI: .08, .98, P = .054). Conclusion Intervention efforts must target specific predictive factors that may help adolescents quit e-cigarettes.