Bangladesh remains one of the most vulnerable countries in the world to the effects of climate change. Given the reliance of a large segment of the population on the agricultural sector for both their livelihoods as well as national food security, climate change adaptation in the agricultural sector is crucial for continued national food security and economic growth. Using household data from lowland rice farmers of selected haor areas in Sylhet, the current work presents an analysis of the determinants behind the implementation of different climate change adaptation strategies by lowland rice farmers. The first objective of this study was to explore the extent of awareness of climate change within this population as well as the type of opinions held by lowland rice farmers with respect to climate change. To serve this purpose, a severity index (SI) was developed and subsequently employed to evaluate the perceptions and attitudes of 378 farmers with respect to climate change vulnerability. Respondents were interviewed with respect to climate change related circumstances they faced in their daily lives. Attained SI index values ranged from 69.18% to 93.52%. The SI for the perception “Climate change affects rice production” was measured as 93.52%. Using data collected from the same 378 farmers, a logistic regression was carried out to investigate the impact of socio-economic and institutional factors on adaptation. The results show that credit from non-government organizations is highly statistically significant for adaptation, and that rural market structure also has a positive effect on adaptation. Among the studied factors, credit from non-governmental organizations (NGOs) was found to be the most important factor for adaptation. The results of this work further indicate that marginal farmers would benefit from government (GoB) funded seasonal training activities that cover pertinent information regarding adaptation after flash floods. Additionally, the authors of this piece recommend timely issuance of government-assisted credit during early flash floods to afflicted farmers, as such an initiative can aid farmers in adapting different strategies to mitigate losses and enhance their productivity as well as livelihood.

Background: The aim of this study was to assess the commitments of food companies in Malaysia to improving population nutrition using the Business Impact Assessment on population nutrition and obesity (BIA-Obesity) tool and process, and proposing recommendations for industry action in line with government priorities and international norms. Methods: BIA-Obesity good practice indicators for food industry commitments across a range of domains (n = 6) were adapted to the Malaysian context. Euromonitor market share data was used to identify major food and nonalcoholic beverage manufacturers (n = 22), quick service restaurants (5), and retailers (6) for inclusion in the assessment. Evidence of commitments, including from national and international entities, were compiled from publicly available information for each company published between 2014 and 2017. Companies were invited to review their gathered evidence and provide further information wherever available. A qualified Expert Panel (≥5 members for each domain) assessed commitments and disclosures collected against the BIA-Obesity scoring criteria. Weighted scores across domains were added and the derived percentage was used to rank companies. A Review Panel, comprising of the Expert Panel and additional government officials (n = 13), then formulated recommendations.

After the East Asian crisis in 1997, the issue of whether stock prices and exchange rates are related or not have received much attention. This is due to realization that during the crisis the countries affected saw turmoil in both their currencies and stock markets. This paper studies the non-linear interactions between stock price and exchange rate in Malaysia using a two regimes multivariate Markov switching vector autoregression (MS-VAR) model with regime shifts in both the mean and the variance. In the study, the Kuala Lumpur Composite Index (KLCI) and the exchange rates of Malaysia ringgit against four other countries namely the Singapore dollar, the Japanese yen, the British pound sterling and the Australian dollar between 1990 and 2005 are used. The empirical results show that all the series are not cointegrated but the MS-VAR model with two regimes manage to detect common regime shifts behavior in all the series. The estimated MS-VAR model reveals that as the stock price index falls the exchange rates depreciate and when the stock price index gains the exchange rates appreciate. In addition, the MS-VAR model fitted the data better than the linear vector autoregressive model (VAR).

Is the prediction accuracy affected by the method used in the ensemble of the classifiers? This paper is a sequel of our experiment in order to find an answer for such question. Previously, we had conducted an experiment by using single classifiers in the machine learning against traditional statistical methods. The results showed that single classifiers in machine learning perform well compared to the traditional statistical methods. Still, we believe that there is another way to increase the prediction accuracy of these classifiers. In this paper, we conducted another experiment by combining these classifiers in predicting currency crisis of 25 countries. The combined classifiers are support vector machine with k-nearest neighbor, logistic regression with k-nearest neighbor and finally LADTree with knearest neighbor. These three combined classifiers are tested on 13 chosen macroeconomic indicators which the data is taken from first quarter 1980 to third quarter 2012. The results of this experiment showed that these three different combined classifiers averagely have same higher accuracy and quite comparable. Our proposed method, nearest neighbor tree has the highest area under ROC curve number among these three combined classifiers although in terms of computational time it took longer running times than the others.

Many researchers documented that the stock market data are nonstationary and nonlinear time series data. In this study, we use EMD-HW bagging method for nonstationary and nonlinear time series forecasting. The EMD-HW bagging method is based on the empirical mode decomposition (EMD), the moving block bootstrap and the Holt-Winter. The stock market time series of six countries are used to compare EMD-HW bagging method. This comparison is based on five forecasting error measurements. The comparison shows that the forecasting results of EMD-HW bagging are more accurate than the forecasting results of the fourteen selected methods.