Contribution of infrastructure to the township's sustainable development in Southwest China

Townships in Southwest China are usually located in mountainous regions, which are abundant in natural and cultural landscape resources. There are additional requirements for the township’s sustainable development in these areas. However, insufficient infrastructures, due to limited resources, constrain the sustainable development of these townships. Sustainable contribution of infrastructure (SCOI) in this study is defined as the performance of infrastructure as a contribution to the coordinated development among economic, social, and environmental dimensions of township’s sustainable development. It is necessary to assess these infrastructures according to SCOI and provide choices for investment to maximize resource utilization. Therefore, an assessing model of SCOI with 26 general indicators was developed, which covers five most urgently needed infrastructures of these townships in Southwest China, including road transport, sewage treatment, waste disposal, water supply, and gas. In this model, quantitative and qualitative methods are combined to acquire different SCOI of each infrastructure. The result of the SCOI would be an important reference for infrastructure investment. A case study of Jiansheng Town, that is located in the Dadukou district of Chongqing, demonstrates the applicability of the model. It shows the assessing model of SCOI is efficient to identify the most valuable infrastructure that is appropriate for investment with the goal of township’s sustainable development. This study can provide insights for infrastructure investment and management in townships or areas

Spectral analysis: money, income and price, 1962-1987

Ankara : The Department of Management and Graduate School of Business Administration of Bilkent Univ., 1990.Thesis (Master's) -- Bilkent University, 199Includes bibliographical references leaves 37-39In this study, the influence of monetary policy upon the price level and the real income over the business cycle is analyzed. The cross-spectral analysis, which is utilised in this study, minimises the effects of differential goverment policies. The observation period is from 1962 to 1987. The findings of the study show that the monetary policy has a significant influence upon the price level and so on the inflation as well.Özyıldırım, SezginM.S

New trends of HCV infection in China revealed by genetic analysis of viral sequences determined from first-time volunteer blood donors

Recently, we studied hepatitis C virus (HCV) sera-prevalence among 559 890 first-time volunteer blood donors in China. From randomly selected 450 anti-HCV positive donors, we detected HCV RNA in 270 donors. In this study, we amplified HCV E1 and/or NS5B sequences from 236 of these donors followed by DNA sequencing and phylogenetic analysis. The results indicate new trends of HCV infection in China. The HCV genotype distribution differed according to the donors’ region of origin. Among donors from Guangdong province, we detected subtypes 6a, 1b, 3a, 3b, 2a, and 1a at frequencies of 49.7%, 31.0%, 7.6%, 5.5%, 4.1%, and 2.1%, respectively. Among donors from outside Guangdong, we detected 1b, 2a, 6a, 3b, 3a, 6e, and 6n at frequencies 57.1%, 13.2%, 11.0%, 9.9%, 4.4%, 2.2%, and 2.2%, respectively. Although we found no significant differences among regions in age or gender, subtype 6a was more common (P< 0.001) in donors from Guangdong than those from elsewhere, whilst subtypes 1b (P< 0.02) and 2a (P < 0.001) were more frequent outside Guangdong. Disregarding origins, the male/female ratio was higher for subtype 6a-infected donors (P < 0.05) than for subtype 1b donors, whilst the mean age of subtype 2a donors was 8–10 years older (P < 0.05) than that for all other subtypes. Detailed phylogenetic analysis of our sequence data provides further insight into the transmission of HCV within China, and between China and other countries. The predominance of HCV 6a among blood donors in Guangdong is striking and mandates studies into risk factors for its acquisition

Profiling Beyond Race: Characteristics Associated with Traffic Stop Outcomes

Research related to profiling and the outcome of traffic stops has generally focused on the race of the individuals involved. Little research has examined other characteristics, such as age and socioeconomic status, that may also play a role in traffic stop outcomes. The current study sought to address this limitation in two ways: (1) determine whether the characteristics of age, sex, race, social class, and demeanor are profiled during traffic stops and (2) whether these characteristics influenced the outcome of the traffic stops with regard to tickets and vehicle searches. Secondary data were utilized from the 2015 Police-Public Contact Survey. Findings revealed that not only race, but age, sex, social class, and demeanor of both the officer and the driver had an affect on the outcome of a traffic stops

Extreme fire weather is the major driver of severe bushfires in southeast Australia

In Australia, the proportion of forest area that burns in a typical fire season is less than for other vegetation types. However, the 2019–2020 austral spring-summer was an exception, with over four times the previous maximum area burnt in southeast Australian temperate forests. Temperate forest fires have extensive socio-economic, human health, greenhouse gas emissions, and biodiversity impacts due to high fire intensities. A robust model that identifies driving factors of forest fires and relates impact thresholds to fire activity at regional scales would help land managers and fire-fighting agencies prepare for potentially hazardous fire in Australia. Here, we developed a machine-learning diagnostic model to quantify nonlinear relationships between monthly burnt area and biophysical factors in southeast Australian forests for 2001–2020 on a 0.25° grid based on several biophysical parameters, notably fire weather and vegetation productivity. Our model explained over 80% of the variation in the burnt area. We identified that burnt area dynamics in southeast Australian forest were primarily controlled by extreme fire weather, which mainly linked to fluctuations in the Southern Annular Mode (SAM) and Indian Ocean Dipole (IOD), with a relatively smaller contribution from the central Pacific El Nino Southern Oscillation (ENSO). Our fire diagnostic model and the non-linear relationships between burnt area and environmental covariates can provide useful guidance to decision-makers who manage preparations for an upcoming fire season, and model developers working on improved early warning systems for forest fires

Extreme fire weather is the major driver of severe bushfires in southeast Australia

In Australia, the proportion of forest area that burns in a typical fire season is less than for other vegetation types. However, the 2019−2020 austral spring-summer was an exception, with over four times the previous maximum area burnt in southeast Australian temperate forests. Temperate forest fires have extensive socio-economic, human health, greenhouse gas emissions, and biodiversity impacts due to high fire intensities. A robust model that identifies driving factors of forest fires and relates impact thresholds to fire activity at regional scales would help land managers and fire-fighting agencies prepare for potentially hazardous fire in Australia. Here, we developed a machine-learning diagnostic model to quantify nonlinear relationships between monthly burnt area and biophysical factors in southeast Australian forests for 2001−2020 on a 0.25° grid based on several biophysical parameters, notably fire weather and vegetation productivity. Our model explained over 80% of the variation in the burnt area. We identified that burnt area dynamics in southeast Australian forest were primarily controlled by extreme fire weather, which linked to fluctuations in the Southern Annular Mode (SAM) and Indian Ocean Dipole (IOD), with a relatively smaller contribution from the Central Pacific El Niño Southern Oscillation (ENSO). Our fire diagnostic model and the non-linear relationships between burnt area and environmental covariates can provide useful guidance to decision-makers who manage preparations for an upcoming fire season, and to model developers working on improved early warning systems for forest fires

Process Safety Management Expert System (PSMES)

Unexpected releases of toxic, reactive, or flammable liquids and gases in processes involving highly hazardous chemicals have been reported for many years in various industries that use chemicals with such properties. Regardless of the industry that uses these highly hazardous chemicals, there is a potential for an accidental release any time they are not properly controlled, creating the possibility of disaster. To help ensure safe and healthful workplaces, OSHA has issued the Process Safety Management of Highly Hazardous Chemicals standard (29 CFR 1910.119), which contains requirements for the management of hazards associated with processes using highly hazardous chemicals. The objective of this project is to develop Process Safety Management Expert System (PSMES) (1). PSMES treats seven out of total of fourteen elements of Process Safety Management. PSMES is intended to serve as a tool to assist employers and employees in complying with the safety requirements. The tool is built using Visual Basic 2005 (Vb. net programming language). The project begin with the literature search on PSM. An expert system is software that attempts to provide an answer to a problem, or clarify uncertainties where normally one or more human experts would need to be consulted (2). This expert system will be a solution to the current Process Safety Management (PSM) weak point which its elements are treated separately, inconsistent, disintegrated and uncorrelated between one element to another. Early stage of the developing this tool starts with preparation of framework of each elements that are covered in scope of the project and to prepare all documents needed under each PSM elements to comply with OSHA requirements. The module is relevant to the current industry needs because it create a systematic approach to manage PSM in controlling the process hazards in the workplace especially in oil and gas industry like PETRONAS. The field test for this expert system is Natural Gas Dehydration Unit of Universiti Teknologi PETRONAS. This tool will be at its best fit for use, conformance to requirements and will able to satisfy the PSM implied needs. The expected outcome of this Process Safety Management Expert System (PSMES) is to replace the `paper based' conventional way in managing the PS

The Summer 2019-2020 Wildfires in East Coast Australia and Their Impacts on Air Quality and Health in New South Wales, Australia.

The 2019–2020 summer wildfire event on the east coast of Australia was a series of major wildfires occurring from November 2019 to end of January 2020 across the states of Queensland, New South Wales (NSW), Victoria and South Australia. The wildfires were unprecedent in scope and the extensive character of the wildfires caused smoke pollutants to be transported not only to New Zealand, but also across the Pacific Ocean to South America. At the peak of the wildfires, smoke plumes were injected into the stratosphere at a height of up to 25 km and hence transported across the globe. The meteorological and air quality Weather Research and Forecasting with Chemistry (WRF-Chem) model is used together with the air quality monitoring data collected during the bushfire period and remote sensing data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellites to determine the extent of the wildfires, the pollutant transport and their impacts on air quality and health of the exposed population in NSW. The results showed that the WRF-Chem model using Fire Emission Inventory (FINN) from National Center for Atmospheric Research (NCAR) to simulate the dispersion and transport of pollutants from wildfires predicted the daily concentration of PM2.5 having the correlation (R2) and index of agreement (IOA) from 0.6 to 0.75 and 0.61 to 0.86, respectively, when compared with the ground-based data. The impact on health endpoints such as mortality and respiratory and cardiovascular diseases hospitalizations across the modelling domain was then estimated. The estimated health impact on each of the Australian Bureau of Statistics (ABS) census districts (SA4) of New South Wales was calculated based on epidemiological assumptions of the impact function and incidence rate data from the 2016 ABS and NSW Department of Health statistical health records. Summing up all SA4 census district results over NSW, we estimated that there were 247 (CI: 89, 409) premature deaths, 437 (CI: 81, 984) cardiovascular diseases hospitalizations and 1535 (CI: 493, 2087) respiratory diseases hospitalizations in NSW over the period from 1 November 2019 to 8 January 2020. The results are comparable with a previous study based only on observation data, but the results in this study provide much more spatially and temporally detailed data with regard to the health impact from the summer 2019–2020 wildfire

Early Childhood Exposure to Anesthesia and Risk of Developmental and Behavioral Disorders in a Sibling Birth Cohort

Background: In vitro and in vivo studies of anesthetics have demonstrated serious neurotoxic effects on the developing brain. However, the clinical relevance of these findings to children undergoing anesthesia remains unclear. Using data from a sibling birth cohort, we assessed the association between exposure to anesthesia in the setting of surgery in patients younger than 3 years and the risk of developmental and behavioral disorders. Methods: We constructed a retrospective cohort of 10,450 siblings who were born between 1999 and 2005 and who were enrolled in the New York State Medicaid program. The exposed group was 304 children without a history of developmental or behavioral disorders who underwent surgery when they were younger than 3 years. The unexposed group was 10,146 children who did not receive any surgical procedures when they were younger than 3 years. Exposed children were entered into analysis at the date of surgery. Unexposed children were entered into analysis at age 10 months (the mean age at which exposed children underwent surgery). Both exposed and unexposed children were followed until diagnosis with a developmental or behavioral disorder, loss to follow-up, or the end of 2005. The association of exposure to anesthesia with subsequent developmental and behavioral disorders was assessed with both proportional hazards modeling, and pair-matched analysis. Results: The incidence of developmental and behavioral disorders was 128.2 diagnoses per 1000 person-years for the exposed cohort and 56.3 diagnoses per 1000 person-years for the unexposed cohort. With adjustment for sex and history of birth-related medical complications, and clustering by sibling status, the estimated hazard ratio of developmental or behavioral disorders associated with any exposure to anesthesia when they were younger than 3 years was 1.6 (95% confidence interval [CI]: 1.4, 1.8). The risk increased from 1.1 (95% CI: 0.8, 1.4) for 1 operation to 2.9 (94% CI: 2.5, 3.1) for 2 operations and 4.0 (95% CI: 3.5, 4.5) for ≥3 operations. The relative risk in a matched analysis of 138 sibling pairs was 0.9 (95% CI: 0.6, 1.4). Conclusion: The risk of being subsequently diagnosed with developmental and behavioral disorders in children who were enrolled in a state Medicaid program and who had surgery when they were younger than 3 years was 60% greater than that of a similar group of siblings who did not undergo surgery. More tightly matched pairwise analyses indicate that the extent to which the excess risk is causally attributable to anesthesia or mediated by unmeasured factors remains to be determined

Commissioning and equipment assessment of a semi-industrial bioreactor

Tasks regarding the commission and equipment assessment on the fermenter situated in PILOT PLANT Research Center were carried out. The bioreactor consists in a pilotscale fermenter with a capacity of 150L. Piping and Instrumentation Diagrams were elaborated, in which a posterior Sterilization Standard Operating Procedure was designed. In such SOP, Steam In Place method was implemented. The sterilization was designed as a batch process, in which the reaction medium is sterilized simultaneously to the fermentation vessel. This way, the use of additional equipment was avoided and the risk of contamination between sterilization and fermentation start was minimized. Optimal saturated vapor temperature was taken as 121ºC. The required holding time for equipment sterilization (spare parts) was determined to be 10 minutes, whereas a holding time of 20 minutes was considered for medium sterilization. Other key factors taken into account for the design of the procedure were condensate removal, air evacuation and post-sterilization integrity, The SOP is still in need of validation. A sealing test revealed the inoperability of the safety device (burst disk). This device was then replaced and tested with success. Gas-liquid mass transfer capacity of the fermenter was assessed, applying the hydrogen peroxide method. A KLa of 25±3.3 h-1was determined when the stirrer run at 450 rpm. However, due to supersaturation effect, this value could be an overestimation of the actual value. Comparison with other typical KLa values for pilot-scale bioreactors led to the conclusion that the aeration capacity of the fermenter is low and might be insufficient for some biochemical processes. A change in the impeller type is proposed in order to address this problem