Modeling and forecasting long-term natural gas (NG) consumption in Iran, using particle swarm optimization (PSO)

The gradual changes in the world development have brought energy issues back into high profile. An ongoing challenge for countries around the world is to balance the development gains against its effects on the environment. The energy management is the key factor of any sustainable development program. All the aspects of development in agriculture, power generation, social welfare and industry in Iran are crucially related to the energy and its revenue. Forecasting end-use natural gas consumption is an important Factor for efficient system operation and a basis for planning decisions. In this thesis, particle swarm optimization (PSO) used to forecast long run natural gas consumption in Iran. Gas consumption data in Iran for the previous 34 years is used to predict the consumption for the coming years. Four linear and nonlinear models proposed and six factors such as Gross Domestic Product (GDP), Population, National Income (NI), Temperature, Consumer Price Index (CPI) and yearly Natural Gas (NG) demand investigated.

A Study on China's Income Inequality and the Relationship with Economic Growth

The purpose of this paper is to study China’s income inequality under rapid economic growth.Does the relationship between economic growth and income inequality in China follow theKuznets hypothesis? What is the main cause and trend of China’s income inequality? We usedata which covers the period 1980-2005 to analyze the overall inequality, and data coveringthe period 1980-2002 to analyze the inequality inside rural and urban areas. The derivedresults doubt the validity of Kuznets hypothesis on explaining the relationship betweeneconomic growth and income inequality in China. Also we derive the trend of China’sincreased income inequality and find that the urban-rural income disparity is the main causeof China’s income inequality.

MODELLING AND FORECASTING INFLATION RATES IN GHANA: AN APPLICATION OF SARIMA MODELS

Ghana faces a macroeconomic problem of inflation for a long period of time. The problem in somehow slows the economic growth in this country. As we all know, inflation is one of the major economic challenges facing most countries in the world especially those in African including Ghana. Therefore, forecasting inflation rates in Ghana becomes very important for its government to design economic strategies or effective monetary policies to combat any unexpected high inflation in this country. This paper studies seasonal autoregressive integrated moving average model to forecast inflation rates in Ghana. Using monthly inflation data from July 1991 to December 2009, we find that ARIMA (1,1,1)(0,0,1)12 can represent the data behavior of inflation rate in Ghana well. Based on the selected model, we forecast seven (7) months inflation rates of Ghana outside the sample period (i.e. from January 2010 to July 2010). The observed inflation rate from January to April which was published by Ghana Statistical Service Department fall within the 95% confidence interval obtained from the designed model. The forecasted results show a decreasing pattern and a turning point of Ghana inflation in the month of July.

Daily Calls Volume Forecasting

A massive amount has been written about forecasting but few articles are written about the development of time series models of call volumes for emergency services. In this study, we use different techniques for forecasting and make the comparison of the techniques for the call volume of the emergency service Rescue 1122 Lahore, Pakistan. For the purpose of this study data is taken from emergency calls of Rescue 1122 from 1st January 2008 to 31 December 2009 and 731 observations are used. Our goal is to develop a simple model that could be used for forecasting the daily call volume. Two different approaches are used for forecasting the daily call volume Box and Jenkins (ARIMA) methodology and Smoothing methodology. We generate the models for forecasting of call volume and present a comparison of the two different techniques.

Forecasting with Vector Nonlinear Time Series Models

This work concerns forecasting with vector nonlinear time series models when errorsare correlated. Point forecasts are numerically obtained using bootstrap methods andillustrated by two examples. Evaluation concentrates on studying forecast equality andencompassing. Nonlinear impulse responses are further considered and graphically sum-marized by highest density region. Finally, two macroeconomic data sets are used toillustrate our work. The forecasts from linear or nonlinear model could contribute usefulinformation absent in the forecasts form the other model.

Extended local labor markets due to high speed trains : Visualization of estimations in the Swedish national travel demand forecasting tool, SAMPERS

Wider economic benefits resulting from extended geographical mobility is one argument for investments in high-speed rail. More specifically, the argument for high-speed trains in Sweden has been that they can help to further spatially extend labor market regions which in turn has a positive effect on growth and development. In this paper the aim is to cartographically visualize the potential size of the labor markets in areas that could be affected by possible future high-speed trains. The visualization is based on the forecasts of labor mobility with public transport made by the Swedish national mobility transport forecasting tool, SAMPERS, for two alternative high-speed rail scenarios. The analysis, not surprisingly, suggests that the largest impact of high-speed trains results in the area where the future high speed rail tracks are planned to be built. This expected effect on local labor market regions of high-speed trains could mean that possible regional economic development effects also are to be expected in this area. However, the results, in general, from the SAMPERS forecasts indicaterelatively small increases in local labor market potentials.

Health system context and implementation of evidence-based practices-development and validation of the Context Assessment for Community Health (COACH) tool for low- and middle-income settings

Background: The gap between what is known and what is practiced results in health service users not benefitting from advances in healthcare, and in unnecessary costs. A supportive context is considered a key element for successful implementation of evidence-based practices (EBP). There were no tools available for the systematic mapping of aspects of organizational context influencing the implementation of EBPs in low- and middle-income countries (LMICs). Thus, this project aimed to develop and psychometrically validate a tool for this purpose. Methods: The development of the Context Assessment for Community Health (COACH) tool was premised on the context dimension in the Promoting Action on Research Implementation in Health Services framework, and is a derivative product of the Alberta Context Tool. Its development was undertaken in Bangladesh, Vietnam, Uganda, South Africa and Nicaragua in six phases: (1) defining dimensions and draft tool development, (2) content validity amongst in-country expert panels, (3) content validity amongst international experts, (4) response process validity, (5) translation and (6) evaluation of psychometric properties amongst 690 health workers in the five countries. Results: The tool was validated for use amongst physicians, nurse/midwives and community health workers. The six phases of development resulted in a good fit between the theoretical dimensions of the COACH tool and its psychometric properties. The tool has 49 items measuring eight aspects of context: Resources, Community engagement, Commitment to work, Informal payment, Leadership, Work culture, Monitoring services for action and Sources of knowledge. Conclusions: Aspects of organizational context that were identified as influencing the implementation of EBPs in high-income settings were also found to be relevant in LMICs. However, there were additional aspects of context of relevance in LMICs specifically Resources, Community engagement, Commitment to work and Informal payment. Use of the COACH tool will allow for systematic description of the local healthcare context prior implementing healthcare interventions to allow for tailoring implementation strategies or as part of the evaluation of implementing healthcare interventions and thus allow for deeper insights into the process of implementing EBPs in LMICs.