Study on nutritional status of children under 5 years in palpa district, nepal: speacial reference to baal vita

Background: Malnutrition is a pathological state resulting from a relative or absolute deficiency or excess of one or more essential nutrients. Malnutrition is a major underlying cause of the child morbidity and mortality in Nepal. Adequate nutrition is a fundamental right for every human being. Malnourished child is depriving from physical and mental development. Objectives: To assess the nutritional status of children under 5 years and to find out the knowledge and practice regarding micronutrient powder “Baal vita” Materials and Methods: Descriptive cross sectional community based study was conducted in Palpa district, total of 390 respondents at the age of 6-59 months were selected with the help of multistage sampling. Through anthropometry, prevalence of underweight, stunting and wasting was determined. Results: Prevalence of underweight, stunting and wasting was 25.9%, 27.2% and 7.3% respectively. The association between age of the mother at the birth of the children and nutritional status of children is not statistically significant. Majority of the children (80.5%) used to take junk foods sometimes, followed by 16.7% very often, 2.8% children never used to take. Majority of the children (52.6%) were taken the micronutrient powder (first course) but the coverage of second course of micronutrient powder was 29.5% and followed by third course coverage was only 18.9%. Conclusion: The nutritional status of children in this study were found to be satisfactory because compared to the Millennium Development Goals (MDGs) target but the coverage of micronutrient powder is low

Methyl Mercury Induced Changes in the Serum Proteins of Bluegills – Lepomis Macrochirus (Teleostei)

Author Institution: Department of Biological Sciences, Kent State UniversityThere were qualitative and quantitative changes in the serum proteins of bluegills, Lepomis macrochirus, exposed to 8.728 X 10~4 ppb (W/V) of methyl mercury (MeHg) for 24, 48, and 72 hr. Sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) of serum proteins revealed significant changes in the qualitative and quantitative profiles at 24 and 48 hr. However, at 72 hr a trend to return to control levels was noted. The data suggest that at the dose tested, MeHg produced repairable lesions in certain tissues or organs of bluegills

Local polynomial method for ensemble forecast of time series

We present a nonparametric approach based on local polynomial regression for ensemble forecast of time series. The state space is first reconstructed by embedding the univariate time series of the response variable in a space of dimension (<i>D</i>) with a delay time (&tau;). To obtain a forecast from a given time point <i>t</i>, three steps are involved: (i) the current state of the system is mapped on to the state space, known as the feature vector, (ii) a small number (<i>K</i>=&alpha;*<i>n</i>, &alpha;=fraction (0,1] of the data, <i>n</i>=data length) of neighbors (and their future evolution) to the feature vector are identified in the state space, and (iii) a polynomial of order <i>p</i> is fitted to the identified neighbors, which is then used for prediction. A suite of parameter combinations (<i>D</i>, &tau;, &alpha;, <i>p</i>) is selected based on an objective criterion, called the Generalized Cross Validation (GCV). All of the selected parameter combinations are then used to issue a T-step iterated forecast starting from the current time <i>t</i>, thus generating an ensemble forecast which can be used to obtain the forecast probability density function (PDF). The ensemble approach improves upon the traditional method of providing a single mean forecast by providing the forecast uncertainty. Further, for short noisy data it can provide better forecasts. We demonstrate the utility of this approach on two synthetic (Henon and Lorenz attractors) and two real data sets (Great Salt Lake bi-weekly volume and NINO3 index). This framework can also be used to forecast a vector of response variables based on a vector of predictors

Local polynomial method for ensemble forecast of time series

International audienceWe present a nonparametric approach based on local polynomial regression for ensemble forecast of time series. The state space is first reconstructed by embedding the univariate time series of the response variable in a space of dimension (D) with a delay time (?). To obtain a forecast from a given time point t, three steps are involved: (i) the current state of the system is mapped on to the state space, known as the feature vector, (ii) a small number (K=?*n, ?=fraction (0,1] of the data, n=data length) of neighbors (and their future evolution) to the feature vector are identified in the state space, and (iii) a polynomial of order p is fitted to the identified neighbors, which is then used for prediction. A suite of parameter combinations (D, ?, ?, p) is selected based on an objective criterion, called the Generalized Cross Validation (GCV). All of the selected parameter combinations are then used to issue a T-step iterated forecast starting from the current time t, thus generating an ensemble forecast which can be used to obtain the forecast probability density function (PDF). The ensemble approach improves upon the traditional method of providing a single mean forecast by providing the forecast uncertainty. Further, for short noisy data it can provide better forecasts. We demonstrate the utility of this approach on two synthetic (Henon and Lorenz attractors) and two real data sets (Great Salt Lake bi-weekly volume and NINO3 index). This framework can also be used to forecast a vector of response variables based on a vector of predictors

Development of Mountain Climate Generator and Snowpack Model for Erosion Predictions in the Western United States Using WEPP: Phase IV

Executive Summary: Introduction: This report summarizes work conducted during the funding period (December 1, 1991 through September 30, 1992) of a Research Joint Venture Agreement between the Intermountain Research Station, Forest Service, U. S. Department of Agriculture and the Utah Water Research Laboratory (UWRL), Utah State University (USU). The purpose of the agreement is to develop a Western Mountain Cilmate Generator (MCLIGEN) similar in function to the existing (non-orographic area) Climate Generator (CLIGEN), which is part of the Water Erosion Prediciton Project (WEPP) procedure. Aso, we are developing a Western U.S. Snowpack Simulation Model for includsion in WEPP. In the western U.S., topographic influences on climate make the climate too variable to be captured by one representatbie station per 100 km, as is done in CLIGEN. Also, few meteorological observations exist in high-elevation areas where Forest Service properties are located. Therefore, a procedure for estimating climatological variables in mountainous areas is needed to apply WEPP in these regions. A physically based approach, using an expanded and improved orographic precipitation model, is being utilized. It will use radiosonde lightning data to estimate historical weather sequences. Climatological sequences estimated at ungaged locations will be represented using stochastic models, similar to the approach used in the existing CLIGEN. By using these stochastic models, WEPP users will be able to synthesize climate sequences for input to WEPP. MCLIGEN will depend on historically based, physically interpolated weather sequences from a mesoscale-climate modeling system which is comprised of four nested layers: 1. an existing synoptic scale forecast model (200 x 300 km) 2. a regional scale slimate model (60 x60 km) 3. a local scale climate model (10 x 10 km); and 4. a specific point climate predictor, referred to as ZOOM. Two additional MCLIGEN components are: 5. a local scalses stochastic climate generator; and 6. a point energy balance snowmelt model Progress made during the reporting period in developing the physically based interpolation climate modeling system stochastic models, and snowpack models is summareized below

Foreign Direct Investments in Business Services: Transforming the Visegrád Four Region into a Knowledge-based Economy?

Foreign direct investments (FDIs) in the service sector are widely attributed an important role in bringing more skill-intensive activities into the Visegrad Four (V4). This region—comprising Poland, the Czech Republic, Hungary and Slovakia—relied heavily on FDIs in manufacturing, which was often found to generate activities with limited skill content. This contribution deconstructs the chaotic concept of “business services” by analysing the actual nature of service sector activities outsourced and offshored to the V4. Using the knowledge-based economy (KBE) as a benchmark, the paper assesses the potential of service sector outsourcing in contributing to regional competitiveness by increasing the innovative capacity. It also discusses the role of state policies towards service sector FDI (SFDI). The analysis combines data obtained from case studies undertaken in service sector outsourcing projects in V4 countries. Moreover, it draws on interviews with senior employees of investment promotion agencies and publicly available data and statistics on activities within the service sector in the region. It argues that the recent inward investments in business services in the V4 mainly utilize existing local human capital resources, and their contribution to the development of the KBE is limited to employment creation and demand for skilled labour

Transcatheter and surgical intervention for secondary mitral regurgitation

Objectives: This is a protocol for a Cochrane Review (intervention). The objectives are as follows:. To assess the effects in secondary mitral regurgitation of:. Surgical mitral valve intervention and coronary artery bypass graft versus coronary artery bypass graft alone; and Transcatheter mitral valve intervention and medical therapy versus medical therapy alone

Treatment compliance and effectiveness of a cognitive behavioural intervention for low back pain : a complier average causal effect approach to the BeST data set

Background: Group cognitive behavioural intervention (CBI) is effective in reducing low-back pain and disability in comparison to advice in primary care. The aim of this analysis was to investigate the impact of compliance on estimates of treatment effect and to identify factors associated with compliance. Methods: In this multicentre trial, 701 adults with troublesome sub-acute or chronic low-back pain were recruited from 56 general practices. Participants were randomised to advice (control n = 233) or advice plus CBI (n = 468). Compliance was specified a priori as attending a minimum of three group sessions and the individual assessment. We estimated the complier average causal effect (CACE) of treatment. Results: Comparison of the CACE estimate of the mean treatment difference to the intention-to-treat (ITT) estimate at 12 months showed a greater benefit of CBI amongst participants compliant with treatment on the Roland Morris Questionnaire (CACE: 1.6 points, 95% CI 0.51 to 2.74; ITT: 1.3 points, 95% CI 0.55 to 2.07), the Modified Von Korff disability score (CACE: 12.1 points, 95% CI 6.07 to 18.17; ITT: 8.6 points, 95% CI 4.58 to 12.64) and the Modified von Korff pain score (CACE: 10.4 points, 95% CI 4.64 to 16.10; ITT: 7.0 points, 95% CI 3.26 to 10.74). People who were non-compliant were younger and had higher pain scores at randomisation. Conclusions: Treatment compliance is important in the effectiveness of group CBI. Younger people and those with more pain are at greater risk of non-compliance