THE RELATIONSHIP BETWEEN ECONOMIC GROWTH, OIL PRODUCTION, ENERGY CONSUMPTION AND CARBON DIOXIDE EMISSIONS: EVIDENCE FROM SELECTED OPEC COUNTRIES

The article aims to investigate the relationship between economic growth, oil production, energy consumption and CO2 emissions in five OPEC countries for the last four decades (1978–2017). We found that per capita energy consumption has a negative relationship with per capita GDP while per capita CO2 emissions positively affect per capita GDP. Per capita GDP negatively affects oil production, but per capita energy consumption has a positive relationship with oil production. Further, per capita CO2 emissions have a positive relationship with oil production. Per capita energy consumption negatively influences per capita CO2 emissions. We also found that there is a directional relationship running from per capita GDP to oil production, per capita energy consumption and per capita CO2 emissions and from per capita CO2 emissions to per capita energy consumption. The Johansen co-integration test shows that there is a long run relationship among variables. Our finding supports the conservation hypothesis that means the growth of GDP in these countries is found as a result of increasing energy consumption

THE RELATIONSHIP BETWEEN ECONOMIC GROWTH, FOREIGN DIRECT INVESTMENT AND UNEMPLOYMENT IN CENTRAL ASIA: A VECTOR AUTOREGRESSIVE MODEL APPROACH

The article investigates the relationship between economic growth, foreign direct investment and unemployment in five Central Asian countries between 1997 and 2016. We found that GDP has a positive relationship with FDI and this implies that the growth of GDP in Central Asian countries has attracted more FDI inflows. However, GDP negatively affects FDI in lag 4 and this reflects the fact that the growth of GDP in this region depends on other variables rather than FDI. Moreover, this result also suggests that the use of FDI to boost economic growth of Central Asian countries is ineffective. Results also indicate that FDI has a negative influence on unemployment and this implies that an increase of FDI can contribute to reducing the unemployment rate in Central Asia. We also found that GDP and unemployment do not cause other variables, while FDI causes other variables. Lastly, results indicate that there is a co-integration among variables

A DYNAMIC LANDSCAPE OF FEAR: HUMAN IMPACTS ON CARNIVORE COMMUNITIES

Mammalian carnivores are elusive, enigmatic species that often play keystone roles in ecosystems through direct (i.e., predation) and indirect (i.e., perceived predation risk) effects. Worldwide many carnivore species are experiencing rapid human-mediated population declines due to landscape change and habitat disturbance. For researchers, carnivores present unique challenges due to their large home ranges, low population densities, sensitivity to human disturbance, and direct persecution. Further, growing evidence shows that human activity can impact carnivore behavior and community structure by altering predator-prey interactions, shifting diel activity patterns, and altering wildlife movement leading to increased sightings, nuisance reports, and harvests. To investigate how human activity influences U.S. carnivore communities, I explored variation in spatiotemporal activity of American black bear and bobcat, and assessed carnivore co-occurrence using camera trap data. I constructed diel activity density curves, applied multispecies occupancy models, and calculated attraction-avoidance ratios to describe relationships among members of the carnivore guild relative to various types of human activity. My results suggested the bobcat can function as a dominant carnivore dependent on community structure, with dominant carnivores (i.e., wolves, pumas) influenced primarily by human-related factors, and subordinate carnivores (i.e., foxes) impacted by environmental factors. Further, American black bear activity did not vary with different types of human activity, yet protected areas were positively associated with black bear presence during the annual hunting season along with increased nocturnal activity. Understanding the influence human activity has on carnivore community dynamics is critical for establishing successful management practices to promote the persistence of carnivore guilds

Automated Type 2 Diabetes Case and Control Identification from the MIMIC-IV Database

Phenotyping for Type 2 Diabetes (T2DM) is needed due to the increasing demand for T2DM research on electronic health records (EHRs). eMERGE is a reliable and interpretable rule-based algorithm for the identification of T2DM cases and controls in EHRs. MIMIC-IV, an extension of MIMIC-III, contains more than 520,000 hospital admissions and has become a valuable EHR database for secondary medical research. However, there was no prior work to extract T2DM cases and controls from MIMIC-IV, which requires a comprehensive knowledge of the database. Our work provided insight into the structure and data elements in MIMIC-IV and adapted eMERGE to accomplish the task. The results included MIMIC-IV\u27s data tables and elements used, 12,735 cases and 9,828 controls of T2DM, and summary statistics of the cohorts in comparison with those on other EHR databases. They could be used for the development of statistical and machine learning models in future studies about the disease