TIME SERIES ANALYSIS OF ONION PRODUCTION IN BANGLADESH

Onion is one of the most important spices in Bangladesh. It's rank top in respect of production and second in terms of area among the spices crops grown in Bangladesh. The main purpose of this research is to identify the Auto-Regressive Integrated Moving Average (ARIMA) model that could be used to forecast the production of onion in Bangladesh. This study considered the published secondary data of yearly onion production in Bangladesh over the period 1971 to 2013. The best selected Box-Jenkins ARIMA model for forecasting the onion productions in Bangladesh is ARIMA (0,2,1). From the comparison between the original series and forecasted series shows the same manner indicating fitted model are statistically well behaved to forecast onion productions in Bangladesh i.e., the models forecast well during and beyond the estimation period to a satisfactory level

On the Selection of Samples in Probability Proportional to Size Sampling: Cumulative Relative Frequency Method

Generally in the sense that, the unit with large size contain more ancillary information than the unit with smaller size. So when samples from different sized subgroups or units are used and sampling is taken with the same probability, the chances of selecting a member from a large group are less than selecting a member from a smaller group although here the chances of selecting a member from a large group will be greater than selecting a member from a smaller group. That is it is clear that, the probability of selecting a unit is positively proportional to its size. The aim of this paper is to propose a method of selecting samples in probability proportional to size. This method uses relative frequency to select samples in probability proportional to size. Comparatively it takes less time and easy to apply than Cumulative Total Method and Lahiri’s Method.   Keywords: Probability Proportional to Size (PPS) Sampling, Cumulative Total Method, Lahiri’s Method, Cumulative Relative Frequency Method.

A New (Proposed) Formula for Interpolation and Comparison with Existing Formula of Interpolation

The word “interpolation” originates from the Latin verb interpolare, a contraction of “inter,” meaning “between,” and “polare,” meaning “to polish.” That is to say, to smooth in between given pieces of information. A number of different methods have been developed to construct useful interpolation formulas for evenly and unevenly spaced points. The aim of this paper is to develop a central difference interpolation formula which is derived from Gauss’s Backward Formula and another formula in which we retreat the subscripts in Gauss’s Forward Formula by one unit and replacing by . Also, we make the comparisons of the developed interpolation formula with the existing interpolation formulas based on differences. Results show that the new formula is very efficient and posses good accuracy for evaluating functional values between given data. Keywords: Interpolation, Central Difference, Gauss’s Formula

Forecasting the Sugarcane Production in Bangladesh by ARIMA Model

Around 70% of the world’s sugar is produced from sugarcane. The production of sugarcane is fluctuated from year to year due to fluctuation of area under sugarcane cultivation. According to FAO, sugar requirement per capita/day is 29g and Bangladesh requires 1.0-1.2 million tonnes of sugar/year to meet the demand of domestic consumption. To meet the demand of domestic consumption of sugar, it is too much essential to estimate the production of sugar since sugar is produced mainly from sugarcane in Bangladesh which leads us to do this research. The main purpose of this research is to identify the Auto-Regressive Integrated Moving Average (ARIMA) model that could be used to forecast the production of sugarcane in Bangladesh. This study considered the published secondary data of yearly sugarcane production in Bangladesh over the period 1971 to 2013. The best selected Box-Jenkins ARIMA model for forecasting the sugarcane productions in Bangladesh is ARIMA (0,2,1). The comparison between the original series and forecasted series shows the same manner indicating fitted model are statistically well behaved to forecast sugarcane productions in Bangladesh i.e., the models forecast well during and beyond the estimation period to a satisfactory level

Forecasting of Wheat Production in Kushtia District & Bangladesh by ARIMA Model: An Application of Box-Jenkin’s Method

After independence, in 1971, Bangladesh faced an acute food shortage. Production of rice, the main crop, declined because of the disruption of virtually all agricultural activities during the War of Liberation, and also due to various natural calamities, such as floods, droughts, cyclones, and rapid population growth. It was realized that rice alone could not meet the food requirement of the country. Wheat was therefore chosen as an alternate food crop in the winter season, which remains mostly free from natural calamities. Trend of wheat consumption is increasing over the three decades due to rapid expansion of green revolution technologies, irrigation in dry season, government subsidies in agriculture, improved seeds, increase of arable land, appropriate pesticides use and sufficient fertilizer use. One of the main aims of the Millennium Development Goals (MDG) of Bangladesh by the year 2015 is to eradicate hunger, chronic food insecurity, and extreme destitution. Thus it is essential to estimate the production of food-grains. The main purpose of this paper is to identify the Auto-Regressive Integrated Moving Average (ARIMA) model by Box-Jenkin’s methods that could be used to forecast the production of wheat in Kushtia district as well as Bangladesh. The best selected ARIMA model for forecasting the wheat productions in Kushtia district is ARIMA (1,2,1), and, for whole Bangladesh it is ARIMA (0,2,1). This paper makes a comparison between the original series and forecasted series which also shows the same trend in wheat productions, indicating the fitted model are statistically well behaved to forecast wheat productions in Kushtia district as well as Bangladesh

Exploring T & B-cell epitopes and designing multi-epitope subunit vaccine targeting integration step of HIV-1 lifecycle using immunoinformatics approach

Till now, AIDS, caused by the human immunodeficiency virus (HIV) is still a severe health problem worldwide. It weakens the immune system by targeting the T-helper cells. Specifically, the severity of the pandemic HIV-1 makes the emergence of an enduring effective vaccine against HIV-1. Therefore, we have applied a series of immunoinformatics approaches to the four conserved domains of HIV-1 integrase (IN) proteins to design an effective multi-epitope based subunit vaccine which might induce a competent immunity against HIV-1. Therefore, we have selected three peptide fragments that contained all overlapping epitopes (35 CD4+, 8 CD8+ T-cell epitopes, and 3 B-cell epitopes) where the epitopes had a high conservancy score. The cumulative population coverage for combined CD8+ and CD4+ T-cell epitopes and their respective HLA-alleles were found as 98.03% in the world which is also followed by East Asia (96.24%), South Asia (96.31%), North Africa (96.14%), North America (98.99%), and Europe (98.80%). The proposed vaccine composed by an adjuvant (β-defensin) at the N-terminal site of the vaccine constructs and three peptide fragments where the adjuvant was fused by EAAAK linker and the peptide fragments were fused by GPGPG linker. The designed final vaccine construct (length: 159 amino acid) was found to be antigenic and non-allergic, which indicates its safety. The vaccine construct was found as good antigenic, stable, higher thermostable, and hydrophilic in nature. The codon adaptation and in silico cloning ensured the high expression rate of the vaccine constructs in E. coli K12 with CAI value of 1.0. Finally, the binding affinity of the vaccine constructs with the immune receptor TLR3 was confirmed by the lowest energy score of −1026.8 evaluated by molecular docking. However, the proposed in silico vaccine construct needs experimental validation for assuring the safety and immunogenicity profile which will ensure an active immunity against HIV-1

Prevalence and determinants of wife-beating in Bangladesh: evidence from a nationwide survey

Background Intimate partner violence (IPV) is a global public health concern, with women in low- and middle-income countries (LMICs) bearing a disproportionately high burden. This study investigates the prevalence and factors correlated with attitudes regarding wife-beating among Bangladeshi women in urban–rural contexts. Methods A sample of 13,033 urban women and 51,344 rural women data from the Bangladesh Multiple Indicator Cluster Survey (MICS) 2019 were analyzed using the Chi-square test and ordinal logistic regression model. Results The findings reveal that arguing with her husband is the widespread reason for wife-beating in Bangladesh (urban: 17.3%, rural: 21.9%), followed by neglecting the children (urban: 12.7%, rural: 15.8%). About 8% of urban women and 10% of rural women favoured the opinion that refusing to involve sexual intercourse is a legitimate justification for wife-beating. In comparison, around 5% feel that a husband has a right to beat his wife due to burning food. The respondents’ age, education, marital status, number of children, socioeconomic level, any health or physical difficulty, having problems becoming pregnant, and the husband’s age are all significant factors in justifying wife-beating. Conclusions Bangladesh has a massive challenge in eliminating IPV. Women from lower socioeconomic classes, low levels of education, other challenges, and residents of rural areas are particularly more vulnerable than their urban counterparts. Therefore, it is vital to develop a proper action plan that considers women’s education and occupation to raise awareness of the various implications of wife-beating in women, particularly in Bangladesh’s rural areas

A Non-Linear Biostatistical Graphical Modeling of Preventive Actions and Healthcare Factors in Controlling COVID-19 Pandemic

Background: With the insurgence of the COVID-19 pandemic, many people died in the past several months, and the situation is ongoing with increasing health, social, and economic panic and vulnerability. As most of the countries relying on different preventive actions to control the outcomes of COVID-19, it is necessary to boost the knowledge about the effectiveness of such actions so that the policymakers take their country-based appropriate actions. This study generates evidence of taking the most impactful actions to combat COVID-19. Objective: In order to generate community-based scientific evidence, this study analyzed the outcome of COVID-19 in response to different control measures, healthcare facilities, life expectancy, and prevalent diseases. Methods: It used more than a hundred countries’ data collected from different databases. We performed a comparative graphical analysis with non-linear correlation estimation using R. Results: The reduction of COVID-19 cases is strongly correlated with the earliness of preventive initiation. The apathy of taking nationwide immediate precaution measures has been identified as one of the critical reasons to make the circumstances worse. There is significant non-linear relationship between COVID-19 case fatality and number of physicians (NCC = 0.22; p-value ≤ 0.001), nurses and midwives (NCC = 0.17; p-value ≤ 0.001), hospital beds (NCC = 0.20; p-value ≤ 0.001), life expectancy of both sexes (NCC = 0.22; p-value ≤ 0.001), life expectancy of female (NCC = 0.27; p-value ≤ 0.001), and life expectancy of male (NCC = 0.19; p-value ≤ 0.001). COVID-19 deaths were found to be reduced with increased medical personnel and hospital beds. Interestingly, no association between the comorbidities and severity of COVID-19 was found excluding asthma, cancer, Alzheimer’s, and smoking. Conclusions: Enhancing healthcare facilities and early imposing the control measures could be valuable to prevent the COVID-19 pandemic. No association between COVID-19 and other comorbidities warranted further investigation at the pathobiological level

New understandings of how dielectric properties of fruits and vegetables are affected by heat-induced dehydration: a review

Dielectric heating (DH) is an alternative to traditional conductive heating. Preservation of fruits and vegetables through drying based on DH is faster than conventional heating systems, needing less processing time, and delivering a better dehydrated product as well as reduced treatment costs. Dielectric properties (DPs) are significant physical qualities that are affected by microwave (MW) and radio frequency (RF) heating systems; these attributes directly affect the electromagnetic arrangement and currents surrounding the materials. In other words, DPs define the response of materials in electric fields at the desired frequency and temperature. Thus, DPs of materials are of increasing interest in the agricultural and food-processing fields. The principles of the DPs of fruits and vegetables according to frequency, temperature, and composition are crucial in the designing and handling of MW and RF heating operations. A consideration of DPs is required to ensure the quality of fruits and vegetables is enhanced throughout the drying process for better quality final products. This review aimed to provide a comprehensive update on the prospects of DH for drying of fruits and vegetables. The factors that affect the DPs during the dehydration process of fruits and vegetables and discussions about the correlation among these factors were also provided. In addition, the fundamentals of DPs and their measurement techniques were also discussed. This study is an update on the state-of-the-art DH system and illustrates the important influence of DPs on the radiative heating of fruits and vegetables

Exploring Lassa virus proteome to design a multi-epitope vaccine through immunoinformatics and immune simulation analyses

Lassa virus (LASV) is responsible for a type of acute viral haemorrhagic fever referred to as Lassa fever. Lack of adequate treatment and preventive measures against LASV resulted in a high mortality rate in its endemic regions. In this study, a multi-epitope vaccine was designed using immunoinformatics as a prophylactic agent against the virus. Following a rigorous assessment, the vaccine was built using T-cell (NCTL = 8 and NHTL = 6) and B-cell (NLBL = 4) epitopes from each LASV-derived protein in addition with suitable linkers and adjuvant. The physicochemistry, immunogenic potency and safeness of the designed vaccine (~ 68 kDa) were assessed. In addition, chosen CTL and HTL epitopes of our vaccine showed 97.37% worldwide population coverage. Besides, disulphide engineering also improved the stability of the chimeric vaccine. Molecular docking of our vaccine protein with toll-like receptor 2 (TLR2) showed binding efficiency followed by dynamics simulation for stable interaction. Furthermore, higher levels of cell-mediated immunity and rapid antigen clearance were suggested by immune simulation and repeated-exposure simulation, respectively. Finally, the optimized codons were used in in silico cloning to ensure higher expression within E. coli K12 bacterium. With further assessment both in vitro and in vivo, we believe that our proposed peptide-vaccine would be potential immunogen against Lassa fever