Expectation of Rice Pod Production in Iraq by Using Time Series

The research aims to shed light on the reality of the production of Rice pods  in Iraq during the period of time (1943-2019) and its development with time, then predict the production of Rice pods based on three Models of prediction Models, which are the time regression Model on production, in addition to studying the effect of harvested area on production quantities. Then forecasting the production of the Rice pods  according to the Model of the regression of the harvested area on the production, the Autoregression Model, and the integrative moving averages (Box Jenkins Models), and in the end the comparison between the expected values ​​of production through the three Models to know the best Model to represent the time series of production of the Rice pods , through the use of the statistical program (SPSS (, Based on annual secondary data represented by the quantities of Rice pods, and the size of the harvested areas of this material in Iraq for the period from 1945 until 2019 obtained from (Central Statistical Organization, Iraq, 2020

A CASE STUDY CORRELATING CARDIOMETABOLIC MARKERS AND T HELPER INFLAMMATORY PATHWAYS IN COVID-19 PATIENTS

COVID-19 is one of the deadliest pandemics in the history of mankind and the infection and mortality rate to this date has been immense. There is several information and data available to analyze patient data regarding both deceased and non-severe patients. A vast amount of research and study is ongoing to understand the implications and cure for this disease. The main purpose of this paper is to find the correlation between inflammatory pathway markers and cardiometabolic markers of COVID-positive patients and to see which T-helper pathway markers are enriched and which markers have reduced count amongst three categories of patients

Bayesian analysis of factors affecting crash frequency and severity during winter seasons in Iowa

Traffic safety during winter seasons has been a serious concern in Iowa as hundreds of people are injured on Iowa\u27s highways each winter. As the goal of the state transportation agency is to ensure the mobility of road users without compromising the safety during winter periods, it is important to understand the factors affecting winter-weather crash frequency and occupant injury risk through quantitative prediction models. It is of utmost importance to identify locations prone to winter-weather crashes to utilize the limited resources efficiently for improving safety during winter conditions. This research intended to develop a systematic prioritization technique to identify winter-weather crash hotspots by using Empirical Bayes technique that addresses the serious limitations of the traditional methods to screen road networks for identifying high crash locations. This research also addresses the issue of hierarchical structure in the crash data by developing quantitative models to predict occupant injury risk for crashes occurring during winter seasons to obtain unbiased and accurate estimation of the parameters for better management of road safety during winter seasons. Along with developing site prioritization techniques for identifying roadway segments with potential for safety improvement through traditional statistical methods using raw crash data, Empirical Bayes technique is used to screen roadway segment through developing safety performance functions for winter-weather crashes. A novel approach is adopted to extract weather data from information reported by winter maintenance crew members to incorporate weather related factors in developing safety performance functions at network level for three roadway types in Iowa. Weather factors such as visibility, wind velocity, air temperature are found to have statistically significant effects on winter-weather crash frequency. The ranking of roadway segments based on Potential for Safety Improvement (PSI) by employing Empirical Bayes technique differs from the ranking produced by simple crash frequency. Safety Performance Functions developed in this research can be used to produce ranking based on PSI by using crash observations made over a specific number of years for winter-weather crashes. Models predicting occupant injury risk with binomial logit formulation are developed considering the hierarchical structure of the crash data in a Bayesian framework in this research for weather-related crashes, non-weather related crashes, and all crashes occurring during the four winter seasons (2008/09 to 2011/12) in Iowa. These models are developed using disaggregate crash data with occupants nested within crashes. High values of between-crash variance for the three models underscore the justification of considering the hierarchical nature of the crash data due to the natural crash data collection process. Factors related to occupants (gender, seating position, trap status, ejection status, airbag deployment, safety equipment used) had statistically significant effects on occupant injury risk for all the models. Weather-related variables such as visibility and air temperature were found significant predictors of all crashes and weather-related crashes during the winter seasons. The variable representing road surface condition is also found to be a significant factor in all three models developed to predict occupant injury risk during the winter seasons

Statistical inference with normal-compound gamma priors in regression models

Scale-mixture shrinkage priors have recently been shown to possess robust empirical performance and excellent theoretical properties such as model selection consistency and (near) minimax posterior contraction rates. In this paper, the normal-compound gamma prior (NCG) resulting from compounding on the respective inverse-scale parameters with gamma distribution is used as a prior for the scale parameter. Attractiveness of this model becomes apparent due to its relationship to various useful models. The tuning of the hyperparameters gives the same shrinkage properties exhibited by some other models. Using different sets of conditions, the posterior is shown to be both strongly consistent and have nearly-optimal contraction rates depending on the set of assumptions. Furthermore, the Monte Carlo Markov Chain (MCMC) and Variational Bayes algorithms are derived, then a method is proposed for updating the hyperparameters and is incorporated into the MCMC and Variational Bayes algorithms. Finally, empirical evidence of the attractiveness of this model is demonstrated using both real and simulated data, to compare the predicted results with previous models

Therapeutic use of plants by local communities in and around Rema-Kalenga Wildlife Sanctuary: implications for protected area management in Bangladesh

Traditional systems of medicine have become a topic of global importance recently. Increased commercialization of economically important medicinal plants has resulted in overharvesting, threatening their survival. The present study was carried out to document the indigenous uses of medicinal plants by the local communities in and around Rema-Kalenga Wildlife Sanctuary, Bangladesh. Data collection was predominantly qualitative recording the species use, identifying their relative importance (RI) and assessing the informants' consensus factor (F-ic) on associated knowledge. We interviewed 140 households of the local community and 5 local herbal practitioners. A total of 44 plant species were in use to treat 33 ailments under 10 broad disease categories. Five species were found to have high use versatility (RI > 1), Emblica officinale L. being the most versatile. Respiratory problems scored the highest F-ic value (0.56) involving the use of 30% of the species recorded. Terminalia bellerica Roxb., Sterculia villosa Roxb., Dillenia pentagyna Roxb. and Terminalia arjuna Bedd. were being harvested commercially. Use by the community, particularly for subsistence consumption, seemed to be sustainable, but commercial extraction of some species appeared unsustainable. Buffer zone-based commercial farming of medicinal plants with a commercial value could serve a dual purpose of assuring sustainable alternative income generation for local communities as well as conserving the natural resources in protected areas.ArticleAGROFORESTRY SYSTEMS. 80(2):241-257 (2010)journal articl

Towards exploration of plant-based ethno-medicinal knowledge of rural community: basis for biodiversity conservation in Bangladesh

Because lack of data impedes the assessment of the conservation of medicinal plants, ethno-medicinal studies are important to fill this gap. This study considered the traditional use of plants for health care by the rural communities in two forested and non-forested regions of Bangladesh. A total of 230 respondents were interviewed accompanied by field observation and voucher specimen collection. Altogether, 68 species of medicinal plants belonging to 38 families distributing over 58 genera were recorded, of which 22 species were common in both regions. Trees were the most commonly utilized growth form and leaves were the most commonly used plant part. Forests and homesteads were the major sources of medicinal plants in forested and non-forested regions, respectively. High use versatility (Relative Importance > 1) was represented by 14 species; Emblica officinale L. and Allium sativum L. were the most versatile species. Forty-one individual ailments were treated with the medicinal plants recorded. The ailment categories 'respiratory problems' and 'sexual problems' received the highest score from the calculation of informants' consensus factor (F (ic)) in forested and non-forested regions, respectively. The findings could contribute in the pharmaceutical sector by directing further investigation of bio-active compounds in medicinal plants. Secondly, results could inform the clues for conservation strategies of forest resources in that region.NEW FORESTS. 40(2):243-260 (2010)journal articl

Motorcycle conspicuity: what factors have the greatest impact

The objective of this project was to determine the effect of headlight configuration (daytime running lights, high beam, modulating) and rider color (bright yellow, blue denim, and black torso and helmet) on the conspicuity of a motorcycle to a driver of a passenger vehicle in a simulated environment. To achieve this, 36 participants completed three drives on a National Advanced Driving Simulator (NADS)-2 driving simulator. During two of the drives, participants were presented with six oncoming motorcycles and three leading parked motorcycles, each with a different combination of rider color and headlight configuration. Each of the nine motorcycles was present in either the urban or rural driving environment. Participants indicated when each motorcycle was first visible to them by pressing a button on the steering wheel of the driving simulator. The detection distances from the motorcycles to the participant vehicles were then recorded. Participants were within one of two groups: younger drivers (25 to 55) or older drivers (65 and older). This research applied repeated measures analysis of variance to investigate the impact of headlight configurations and rider color on motorcycle conspicuity in urban and rural environments. The researchers found that oncoming motorcycles with modulating headlights were detected at the greatest distance compared to motorcycles with high beam or daytime running lights. Participant ability to detect an oncoming motorcycle was also significantly influenced by the combination of headlight configurations with black or bright yellow rider colors. Leading motorcycles in urban environments were detected at a greater distance compared to those in rural environments. Leading motorcycles with riders having bright yellow clothing and helmet were detected at the greatest distance, followed by motorcycles with riders having blue denim and black rider colors. A significant interaction effect among the driving environment, rider color, and age group was also found for the detection distance of leading motorcycles

Maximum power point tracking (MPPT) control of pressure retarded osmosis (PRO) salinity power plant : development and comparison of different techniques

This paper presents two new methods for the maximum power point tracking (MPPT) control of a pressure retarded osmosis (PRO) salinity power plant, including mass feedback control (MFC) and fuzzy logic control (FLC). First, a brief overview of perturb & observe (P&O) and incremental mass resistance (IMR) control is given as those two methods have already demonstrated their merit in good control performance. Then, two new methods employing variable-step strategy, MFC and FLC, are proposed to address the trade-off relationship between rise-time and oscillation of P&O and IMR. Genetic algorithm (GA) is used for finding the optimum parameters of membership functions of FLC. From the case-study of start-up of the PRO adopting MPPT control, MFC and FLC have shown faster convergence to the target performance without oscillation compared with P&O and IMR. These four MPPT techniques are further evaluated in case-studies of state transitions of the PRO due to operational fluctuations. It is proven that the MPPT using FLC and modified MFC has better performance than the other two methods. Finally, the paper reports a comparison of major characteristics of the four MPPT methods, which could be considered as guidance for selecting a MPPT technique for the PRO in practice

Biomass fuel use, burning technique and reasons for the denial of improved cooking stoves by Forest User Groups of Rema-Kalenga Wildlife Sanctuary, Bangladesh

This is an electronic version of an article published in International Journal of Sustainable Development & World Ecology, 1745-2627, 18(1) 2011, 88-97. International Journal of Sustainable Development & World Ecology is available online at: http://www.informaworld.com/smpp/content~db=all~content=a933218896~frm=titlelinkUse of biomass fuel in traditional cooking stoves (TCS) is a long-established practice that has incomplete combustion and generates substances with global warming potential (GWP). Improved cooking stoves (ICS) have been developed worldwide as an alternative household fuel burning device, as well as a climate change mitigation. A study was conducted among female Forest User Groups (FUGs) of Rema-Kalenga Wildlife Sanctuary, Bangladesh, to assess the status of ICS disseminated by the Forest Department (FD) under the Nishorgo (2009) Support Project, along with the community's biomass fuel consumption pattern. Wood consumption was highest (345kg month-1 household-1) followed by agricultural residues (60kg month-1 household-1), tree leaves (51kg month-1 household-1) and cow dung (25kg month-1 household-1). Neighbouring forests of the sanctuary was the core source for wood fuel, with little or no reduction in the extraction even after joining the FUG. Twenty-two species, both indigenous and introduced, were preferred as wood fuel. None of the respondents were found willing to use ICS although 43% owned one; either as a status symbol or to meet the conditions of the FD for membership in FUG. Seven negative features of the disseminated ICS were identified by households, which made them unwilling to use them further. Manufacturing faults may be responsible for some ICS demerits, while the FD failed to convince the community of the benefits. A proper examination of the disseminated ICS efficacy is crucial, with active involvement of community members. The Sustainable Energy Triangle Strategy (SETS) could be implemented for this purpose. Findings of the study are of immense importance in designing a strategy for the introduction of ICS into Bangladesh.ArticleINTERNATIONAL JOURNAL OF SUSTAINABLE DEVELOPMENT AND WORLD ECOLOGY. 18(1):88-97 (2011)journal articl

Safety and Mobility Impacts of Winter Weather --Phase 3

Highway agencies spend millions of dollars to ensure safe and efficient winter travel. However, the effectiveness of winter-weather maintenance practices on safety and mobility are somewhat difficult to quantify. Safety and Mobility Impacts of Winter Weather - Phase 1 investigated opportunities for improving traffic safety on state-maintained roads in Iowa during winter-weather conditions. In Phase 2, three Iowa Department of Transportation (DOT) high-priority sites were evaluated and realistic maintenance and operations mitigation strategies were also identified. In this project, site prioritization techniques for identifying roadway segments with the potential for safety improvements related to winter-weather crashes, were developed through traditional naïve statistical methods by using raw crash data for seven winter seasons and previously developed metrics. Additionally, crash frequency models were developed using integrated crash data for four winter seasons, with the objective of identifying factors that affect crash frequency during winter seasons and screening roadway segments using the empirical Bayes technique. Based on these prioritization techniques, 11 sites were identified and analyzed in conjunction with input from Iowa DOT district maintenance managers and snowplow operators and the Iowa DOT Road Weather Information System (RWIS) coordinator