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Since 2007, the use of natural gas in China depends on the import, and with an increase in natural gas consumption, gas imports are also constantly growing. In 2018, Chinas natural gas imports approached 100 billion cubic meters, which is 70 times more than in 2006. In recent years, increasing attention has been paid to the use of natural gas in China. Turkmenistan is Chinas main source of pipeline gas imports, and China is Turkmenistans largest exporter of natural gas. In the framework of the traditional model of oil and gas cooperation, China and Turkmenistan are facing such problems as the uniform content of cooperation, lack of close ties in the field of multilateral cooperation and slow progress in the development of the entire industrial chain. Cooperation between China and Central Asia in the field of oil and gas is increasingly affecting the nerves of other countries, except the five countries of Central Asia, but including Russia, Afghanistan, Pakistan, India, Iran and other countries of the Middle East, Japan, South Korea, etc. and even the European Union and the USA. Despite the favorable trading environment for both parties, there are also problems in the domestic market of Turkmenistan and the risks of international competition.

AbstractPrior studies examining humor in romantic relationships often assume that behavioral ratings obtained from different sources (i.e., self, partner, and observer) are interchangeable. However, it is unclear if humor ratings across sources reflect a common construct. The current study (N = 160 couples, Mage = 20 years, Mrelationshiplength = 15 months) investigated whether partner and observer ratings of humor offer unique information beyond actor ratings. Additionally, the relative predictive validity of humor ratings was evaluated. In general, the results showed that ratings tend to converge across sources. In addition, partner and observer ratings generally did not explain unique variance beyond self‐ratings. These results suggest that rating sources for assessing humor are largely interchangeable, with a few exceptions. This study offers recommendations for the methodological consideration of rating sources regarding humor reports in close relationships.

The successful screening for possible learning disabilities (LD) is a crucial first step in the process of identifying signs of LD, gaining assistance and/or accommodations, and obtaining a more complete LD assessment. Although Latino people are the largest ethnic minority in the United States, and more specifically in California, there remains a clear need for a valid LD screening measure that is appropriate for adult Spanish speakers, particularly low-income individuals. This study evaluated the validity of three brief measures to screen for LD among low-income Spanish-speaking adults: Empire State Screen, Welfare-to-Work [WTW] 18, and MATILDA-R. The study also provides an initial estimate of LD risk in the low-income Spanish-speaking population. To estimate the predictive utility of each screening measure, 1,040 Spanish-speaking adults were administered each of the three screens and then assessed for indications of LD using multiple scoring methods (Bateria Discrepancy Diagnosis [BDD], pattern of strengths and weaknesses [PSW], and DSM-5). The translated WTW 18 Screen and the MATILDA-R appeared most promising. A culturally-sensitive, validated LD screen will help ensure that social workers and other helping professionals have access to appropriate and legally required interventions for this marginalized population.

Abstract. With increasing urbanization, particularly in the coastal regions of developing countries, the development of disaster management schemes is needed as the losses from a single event can destroy decades of development and threaten local populations, buildings and infrastructure. Geo-environmental suitability is often evaluated systematically in order to assess the nature of hazards and their potential damage to human life, land, buildings and other property. A suitability assessment will indicate the priorities for geological and environmental hazard management. This paper presents a geological environment suitability assessment that aims to identify grading in a current seaside urban development and develop practices to aid in the identification of hidden geological and environmental hazards. The Laoshan area in the city of Qingdao was used as a case study because it constitutes a good example of a developing city with geological and environmental threats. Also, urban plans have been drawn up here with insufficient or absent information on losses from potential natural hazards.

Background: Compared to commonly-used green space indicators from downward-facing satellite imagery, street view-based green space may capture different types of green space and represent how environments are perceived and experienced by people on the ground, which is important to elucidate the underlying mechanisms linking green space and health. Objectives: This study aimed to evaluate machine learning models that can classify the type of vegetation (i.e., tree, low-lying vegetation, grass) from street view images; and to investigate the associations between street green space and socioeconomic (SES) factors, in Los Angeles County, California. Methods: SES variables were obtained from the CalEnviroScreen3.0 dataset. Microsoft Bing Maps images in conjunction with deep learning were used to measure total and types of street view green space, which were compared to normalized difference vegetation index (NDVI) as commonly-used satellite-based green space measure. Generalized linear mixed model was used to examine associations between green space and census tract SES, adjusting for population density and rural/urban status. Results: The accuracy of the deep learning model was high with 92.5% mean intersection over union. NDVI were moderately correlated with total street view-based green space and tree, and weakly correlated with low-lying vegetation and grass. Total and three types of green space showed significant negative associations with neighborhood SES. The percentage of total green space decreased by 2.62 [95% confidence interval (CI): −3.02, −2.21, p < 0.001] with each interquartile range increase in CalEnviroScreen3.0 score. Disadvantaged communities contained approximately 5% less average street green space than other communities. Conclusion: Street view imagery coupled with deep learning approach can accurately and efficiently measure eye-level street green space and distinguish vegetation types. In Los Angeles County, disadvantaged communities had substantively less street green space. Governments and urban planners need to consider the type and visibility of street green space from pedestrian's perspective.

Funding: HRS and MF are supported by the Medical Research Council [MR/R008345/1]. CJ's salary came through MRC core funding MC_UU_12023/26. SJS is funded by the Wellcome Trust [WT 209560/Z/17/Z]. CRS has received funding from the Medical Research Council [MR/R008345/1], the National Institute for Health Research [11/46/23] and the New Zealand Health Research Council [20/1018] and Ministry for Business, Innovation and Employment. EV is funded by the Medical Research Council [MR/R008345/1] through the EAVE II grant and supported by the Scottish Government. We also acknowledge the support of HDR UK. The views and opinions expressed here are those of the authors and do not necessarily reflect those of the Health Technology Assessment programme, NIHR, NHS, or the UK Department of Health. ; Background Severe Acute Respiratory Syndrome coronavirus-2 (SARS-CoV-2) has challenged public health agencies globally. In order to effectively target government responses, it is critical to identify the individuals most at risk of coronavirus disease-19 (COVID-19), developing severe clinical signs, and mortality. We undertook a systematic review of the literature to present the current status of scientific knowledge in these areas and describe the need for unified global approaches, moving forwards, as well as lessons learnt for future pandemics. Methods Medline, Embase and Global Health were searched to the end of April 2020, as well as the Web of Science. Search terms were specific to the SARS-CoV-2 virus and COVID-19. Comparative studies of risk factors from any setting, population group and in any language were included. Titles, abstracts and full texts were screened by two reviewers and extracted in duplicate into a standardised form. Data were extracted on risk factors for COVID-19 disease, severe disease, or death and were narratively and descriptively synthesised. Results One thousand two hundred and thirty-eight papers were identified post-deduplication. Thirty-three met our inclusion criteria, of which 26 were from ...

BACKGROUND: Severe Acute Respiratory Syndrome coronavirus-2 (SARS-CoV-2) has challenged public health agencies globally. In order to effectively target government responses, it is critical to identify the individuals most at risk of coronavirus disease-19 (COVID-19), developing severe clinical signs, and mortality. We undertook a systematic review of the literature to present the current status of scientific knowledge in these areas and describe the need for unified global approaches, moving forwards, as well as lessons learnt for future pandemics. METHODS: Medline, Embase and Global Health were searched to the end of April 2020, as well as the Web of Science. Search terms were specific to the SARS-CoV-2 virus and COVID-19. Comparative studies of risk factors from any setting, population group and in any language were included. Titles, abstracts and full texts were screened by two reviewers and extracted in duplicate into a standardised form. Data were extracted on risk factors for COVID-19 disease, severe disease, or death and were narratively and descriptively synthesised. RESULTS: One thousand two hundred and thirty-eight papers were identified post-deduplication. Thirty-three met our inclusion criteria, of which 26 were from China. Six assessed the risk of contracting the disease, 20 the risk of having severe disease and ten the risk of dying. Age, gender and co-morbidities were commonly assessed as risk factors. The weight of evidence showed increasing age to be associated with severe disease and mortality, and general comorbidities with mortality. Only seven studies presented multivariable analyses and power was generally limited. A wide range of definitions were used for disease severity. CONCLUSIONS: The volume of literature generated in the short time since the appearance of SARS-CoV-2 has been considerable. Many studies have sought to document the risk factors for COVID-19 disease, disease severity and mortality; age was the only risk factor based on robust studies and with a consistent body of ...

Background: Severe Acute Respiratory Syndrome coronavirus-2 (SARS-CoV-2) has challenged public health agencies globally. In order to effectively target government responses, it is critical to identify the individuals most at risk of coronavirus disease-19 (COVID-19), developing severe clinical signs, and mortality. We undertook a systematic review of the literature to present the current status of scientific knowledge in these areas and describe the need for unified global approaches, moving forwards, as well as lessons learnt for future pandemics. Methods: Medline, Embase and Global Health were searched to the end of April 2020, as well as the Web of Science. Search terms were specific to the SARS-CoV-2 virus and COVID-19. Comparative studies of risk factors from any setting, population group and in any language were included. Titles, abstracts and full texts were screened by two reviewers and extracted in duplicate into a standardised form. Data were extracted on risk factors for COVID-19 disease, severe disease, or death and were narratively and descriptively synthesised. Results: One thousand two hundred and thirty-eight papers were identified post-deduplication. Thirty-three met our inclusion criteria, of which 26 were from China. Six assessed the risk of contracting the disease, 20 the risk of having severe disease and ten the risk of dying. Age, gender and co-morbidities were commonly assessed as risk factors. The weight of evidence showed increasing age to be associated with severe disease and mortality, and general comorbidities with mortality. Only seven studies presented multivariable analyses and power was generally limited. A wide range of definitions were used for disease severity. Conclusions: The volume of literature generated in the short time since the appearance of SARS-CoV-2 has been considerable. Many studies have sought to document the risk factors for COVID-19 disease, disease severity and mortality; age was the only risk factor based on robust studies and with a consistent body of evidence. Mechanistic studies are required to understand why age is such an important risk factor. At the start of pandemics, large, standardised, studies that use multivariable analyses are urgently needed so that the populations most at risk can be rapidly protected. Registration: This review was registered on PROSPERO as CRD42020177714.

Funding HRS and MF are supported by the Medical Research Council [MR/R008345/1]. CJ's salary came through MRC core funding MC_UU_12023/26. SJS is funded by the Wellcome Trust [WT 209560/Z/17/Z]. CRS has received funding from the Medical Research Council [MR/R008345/1], the National Institute for Health Research [11/46/23] and the New Zealand Health Research Council [20/1018] and Ministry for Business, Innovation and Employment. EV is funded by the Medical Research Council [MR/R008345/1] through the EAVE II grant and supported by the Scottish Government. We also acknowledge the support of HDR UK. The views and opinions expressed here are those of the authors and do not necessarily reflect those of the Health Technology Assessment programme, NIHR, NHS, or the UK Department of Health. Our funders had no role in the design of the study and collection, analysis, and interpretation of data, and in writing the manuscript. ; Peer reviewed ; Publisher PDF