BackgroundThere are limited data on TB among prison inmates in Bangladesh. The aim of the study was to determine the prevalence of pulmonary tuberculosis (TB), its drug resistance and risk factors in Dhaka Central Jail, the largest prison in Bangladesh.MethodsCross sectional survey with, active screening of a total number of 11,001 inmates over a period of 2 years. Sputum samples from TB suspects were taken for acid- fast bacilli (AFB) microscopy, culture and drug susceptibility testing.ResultsAmong 1,781 TB suspects 245 (13.8%) were positive for AFB on microscopy and/or culture. The prevalence rate of sputum- positive pulmonary TB was 2,227/100,000. Fifty three cases (21.6% of 245 cases) were AFB- negative on microscopy but were found positive on culture. Resistance to isoniazid, rifampicin, streptomycin and ethambutol was 11.4%, 0.8%, 22.4% and 6.5% respectively. No multidrug resistance was observed. The main risk factors of TB in prison were exposure to TB patients (adjusted odds ratio 3.16, 95% CI 2.36–4.21), previous imprisonment (1.86, 1.38–2.50), longer duration of stay in prison (17.5 months for TB cases; 1.004, 1.001–1.006) and low body mass index which is less than 18.5 kg/m2 (5.37, 4.02–7.16).ConclusionsThe study results revealed a very high prevalence of TB in the prison population in Dhaka Central Jail. Entry examinations and active symptom screening among inmates are important to control TB transmission inside the prison. Identifying undiagnosed smear-negative TB cases remains a challenge to combat this deadly disease in this difficult setting.

Support vector machines (SVMs) are new semi-parametric tool for regression estimation. This paper introduced a new class of hybrid models, the nonlinear support vector machines heterogeneous autoregressive (SVM-HAR) models and aimed to compare the forecasting performance with the classical heterogeneous autoregressive (HAR) models to forecast financial volatilities. It was observed through empirical experiment that the newly proposed hybrid (SVM-HAR) models produced higher predicting ability than the classical HAR model.

Soil salinity has become a major constraint to rice productivity in the coastal region of Bangladesh, which threatened food security. Therefore, field experiment was conducted at salt stressed Shyamnagor Upazilla of Satkhira district to improve the soil salinity status, sustainable rice production and suppression of global warming potentials. Selected soil amendments viz. trichocompost, tea waste compost, azolla compost and phospho-gypsum (PG) were applied in the field plots one week prior to rice transplanting. In addition, proline solution (25 mM) was applied on the transplanted rice plants at active vegetative stage. Gas samples from the paddy field were collected by Closed Chamber technique and analyzed in by Gas Chromatograph. The 25% replacement of chemical fertilizer (i.e., 75% NPKS) with trichocompost, tea waste compost, Azolla compost and Phospho-gypsum amendments increased grain yield by 4.7% -7.0%, 2.3% -7.1% 11.9% -16.6% and 9.5% -14.2% during dry boro rice cultivation, while grain yield increments of 5.0% -7.6%, 2.3% -10.2%, 12.8% -15.3% and 10.2% -15.3% were recorded in wet Aman season respectively, compared to chemically fertilized (100% NPKS) field plot. The least GWPs 3575 and 3650 kg CO 2 eq./ha were found in PG Cyanobacterial mixture with proline (T10) and tea waste compost with proline (T8) amended rice field, while the maximum GWPs 4725 and 4500 kg CO 2 eq./ha were recorded in NPKS fertilized (100%, T2) and NPKS (75%) with Azolla compost (T5) amended plots during dry boro rice cultivation. The overall soil properties improved significantly with the selected soil amendments, while soil electrical conductivity (EC), soil pH and Na + cation in the amended soil decreased, eventually improved the soil salinity status. Conclusively, phos-How to cite this paper:

Stock market plays the crucial role to move funds from the surplus units to the deficit units, therefore, it accelerate the economic development of a country. Noise trading is one of the important issues that influence the stock market volatility dynamics. Noise traders are very sensitive in having well or bad news rather use standard data/stock index for decision to buy or sell share. This sensitivity makes market more volatile. The aim of this study is to collect opinions from the market practitioners on their thinking about noise trading and profit from stock market.