Efficient biodegradation of naphthalene by a newly characterized indigenous Achromobacter sp. FBHYA2 isolated from Tehran Oil Refinery Complex

A bacterial strain, FBHYA2, capable of degrading naphthalene, was isolated from the American Petroleum Institute (API) separator of the Tehran Oil Refinery Complex (TORC). Strain FBHYA2 was identified as Achromobacter sp. based on physiological and biochemical characteristics and also phylogenetic similarity of 16S rRNA gene sequence. The optimal growth conditions for strain FBHYA2 were pH 6.0, 30 °C and 1.0% NaCl. Strain FBHYA2 can utilize naphthalene as the sole source of carbon and energy and was able to degrade naphthalene aerobically very fast, 48 h for 96% removal at 500 mg/L concentration. The physiological response of Achromobacter sp., FBHYA2 to several hydrophobic chemicals (aliphatic and aromatic hydrocarbons) was also investigated. No biosurfactant was detected during bacterial growth on any aliphatic/aromatic hydrocarbons. The results of hydrophobicity measurements showed no significant difference between naphthalene- and LB-grown cells. The capability of the strain FBHYA2 to degrade naphthalene completely and rapidly without the need to secrete biosurfactant may make it an ideal candidate to remediate polycyclic aromatic hydrocarbon (PAH)-contaminated sites.     

Forecasting UK consumer price inflation using inflation forecasts

The inflation rate is a key economic indicator for which forecasters are constantly seeking to improve the accuracy of predictions, so as to enable better macroeconomic decision making. Presented in this paper is a novel approach which seeks to exploit auxiliary information contained within inflation forecasts for developing a new and improved forecast for inflation by modeling with Multivariate Singular Spectrum Analysis (MSSA). Unlike other forecast combination techniques, the key feature of the proposed approach is its use of forecasts, i.e. data into the future, within the modeling process and extracting auxiliary information for generating a new and improved forecast. We consider real data on consumer price inflation in UK, obtained via the Office for National Statistics. A variety of parametric and nonparametric models are then used to generate univariate forecasts of inflation. Thereafter, the best univariate forecast is considered as auxiliary information within the MSSA model alongside historical data for UK consumer price inflation, and a new multivariate forecast is generated. We find compelling evidence which shows the benefits of the proposed approach at generating more accurate medium to long term inflation forecasts for UK in relation to the competing models. Finally, through the discussion, we also consider Google Trends forecasts for inflation within the proposed framework.     

Forecasting the price of gold

This article seeks to evaluate the appropriateness of a variety of existing forecasting techniques (17 methods) at providing accurate and statistically significant forecasts for gold price. We report the results from the nine most competitive techniques. Special consideration is given to the ability of these techniques to provide forecasts which outperforms the random walk (RW) as we noticed that certain multivariate models (which included prices of silver, platinum, palladium and rhodium, besides gold) were also unable to outperform the RW in this case. Interestingly, the results show that none of the forecasting techniques are able to outperform the RW at horizons of 1 and 9 steps ahead, and on average, the exponential smoothing model is seen providing the best forecasts in terms of the lowest root mean squared error over the 24-month forecasting horizons. Moreover, we find that the univariate models used in this article are able to outperform the Bayesian autoregression and Bayesian vector autoregressive models, with exponential smoothing reporting statistically significant results in comparison with the former models, and classical autoregressive and the vector autoregressive models in most cases.     

On the globalization of stock markets: An application of Vector Error Correction Model,Mutual Information and Singular Spectrum Analysis to the G7 countries

This paper analyzes stock market relationships among the G7 countries between 1973 and 2009 using three different approaches: (i) a linear approach based on cointegration, Vector Error Correction (VECM) and Granger Causality; (ii) a nonlinear approach based on Mutual Information and the Global Correlation Coefficient; and (iii) a nonlinear approach based on Singular Spectrum Analysis (SSA). While the cointegration tests are based on regression models and capture linearities in the data, Mutual Information and Singular Spectrum Analysis capture nonlinear relationships in a non-parametric way. The framework of this paper is based on the notion of market integration and uses stock market correlations and linkages both in price levels and returns. The main results show that significant co-movements occur among most of the G7 countries over the period analyzed and that Mutual Information and the Global Correlation Coefficient actually seem to provide more information about the market relationships than the Vector Error Correction Model and Granger Causality. However, unlike the latter, the direction of causality is difficult to distinguish in Mutual Information and the Global Correlation Coefficient. In this respect, the nonlinear Singular Spectrum Analysis technique displays several advantages, since it enabled us to capture nonlinear causality in both directions, while Granger Causality only captures causality in a linear way. The results also show that stock markets are closely linked both in terms of price levels and returns (as well as lagged returns) over the 36 years analyzed.     

Developing a Model for Decision-Makers in Dynamic Modeling of Urban Water System Management

Water managers may modify many components of urban water systems to minimize water shortage. Since each modification activity has its own positive and negative effects, it is necessary to define an appropriate procedure to predict the consequences of each action. As the parameters of urban water supply and demand system have internal relationships in the time domain, a dynamic model is needed to forecast the result of changes and select the best modification activity. Here the Vensim® is applied as a modeling tool to choose the most effective water management activities in Tehran province. It has been found that the annual increase rate of water tariff by 16.4% and assigning 4.5% of revenue on reducing non-revenue water may be the most effective demand management activity to reduce water shortage in Tehran province. It has also been revealed that, even by implementing the most effective demand management activities in Tehran, the amount of required water in the next 10 years is more than the sustainable capacity of its resources and activities like seawater desalination are inevitable to prevent unsustainable use of water sources.

     

Multi-Objective Optimization of Integrated Surface and Groundwater Resources Under the Clean Development Mechanism

Acquiring sustainable water resources for water-based development of countries is the experts? concern in this field, who seek to follow the clean development mechanism (CDM) regulations and overcome water crisis through integrated water resources management (IWRM). The Great Karun River basin is one of the major basins in the Middle East. This basin, containing six of the largest reservoir dams with a cumulative power plant capacity of more than 10,500 MW generates about 93% of hydropower of Iran. The water balance of the aquifer in the study area was simulated using MODFLOW model while water resources and surface water reserves were simulated by the water evaluation and planning (WEAP) model. A separate simulation was performed with each of two models and the results of two models were coupled using a link file. The multi-objective function optimization process including the maximized supply of demands and hydropower and the minimized aquifer drawdown was completed using non-dominated sorting genetic algorithm (NSGA-II). All effective system components, such as inter-basin water transfer, integrated use of water resources, variation of irrigation network efficiencies, and the effect of water shortage were studied and analyzed under the targeted scenarios. Finally, the best scenario, which was capable to supply the future needs until time horizon of 2040 was planned for the basin considering minimization of aquifer drawdown and optimal generation of hydropower resulting in a maximum decrease in emission of greenhouse gases.

     

Calculating Price Elasticity of Water Demand Using Gene Expression Programming Based on Economic,Social and Meteorological Variables

Determining the changing rate of water consumption through altering parameters such as water tariffs can help water companies select appropriate water policies. This paper is intended to find the proper relation between the water price elasticity of demand and some social, economic and climatic variables that are released annually by international organizations like the United Nations Development Program and the World Bank. By using genetic algorithm, different combinations of water price elasticity of demand and variables like gross domestic product, per capita gross domestic product, gross national income, precipitation, human development index, average temperature and household size have been analyzed. It was found that the absolute price elasticity of water demand has positive relationships with precipitation and price of water. It has also been found that the gross national income, average percent of consumers who have secondary education and human development index have a negative relation with the absolute price elasticity of water demand.

     

Evolution of a new surface water quality index for Karoon catchment in Iran

Water quality standards are developed worldwide by national and international agencies for pollution control decision-making. Use-based water quality classification criteria and Water Quality Indices (WQIs) also play an important role in the assessment of the suitability of water resources for various applications. The present study proposes a better overall index for water quality in Iran and its application in Karoon River by exploring the behavior and limitations of conventional methods for quality evaluation. For this purpose, six variables were employed. Water quality determinants of the new index include Dissolved Oxygen, Total Dissolved Solids, Turbidity, Nitrate, Fecal coliform and pH. Besides, the mathematical equations applied to transform the actual concentration values into quality indices have been formulated. This study compares a new index called the Iranian Water Quality Index with other pre-existing indices such as NSFWQI, Oregon, CPCB WQI, MDOE WQI, Kaurish and Younos WQI, and Ahmed Said WQI. Results revealed that the overall quality of the surface water falls under the 'good' class. A case study of Karoon River is made to illustrate the application of this new index system.     

Performance optimization of an online retailer by a unique online resilience engineering algorithm

Online shopping has become more attractive and competitive in electronic markets. Resilience engineering (RE) can help such systems divert to the normal state in case of encountering unexpected events. This study presents a unique online resilience engineering (ORE) approach for online shopping systems and customer service performance. Moreover, this study presents a new ORE algorithm for the performance optimisation of an actual online shopping system. The data are collected by standard questionnaires from both expert employees and customers. The problem is then formulated mathematically using data envelopment analysis (DEA). The results show that the design process which is based on ORE is more efficient than the conventional design approach. Moreover, on-time delivery is the most important factor from the personnel’s perspective. In addition, according to customers’ view, trust, security and good quality assurance are the most effective factors during transactions. This is the first study that introduces ORE for electronic markets. Second, it investigates impact of RE on online shopping through DEA and statistical methods. Third, a practical approach is employed in this study and it may be used for similar online shops. Fourth, the results are verified and validated through complete sensitivity analysis.