Practices of anti-malaria pharmaceuticals inventory control system and associated challenges in public health facilities of Oromiya special zone, Amhara region, Ethiopia

Background Anti-malaria pharmaceuticals inventory control system helps to maintain an appropriate stock level using logistics management information system records and reports. Antimalaria pharmaceuticals are highly influenced by seasonality and demand variation. Thus, to compensate the seasonality, resupply quantities should be adjusted by multiplying the historical consumption with the Look-ahead seasonality indexes (LSI) to minimize stock-outs during the peak transmission season and overstocks (possible expiries) during off-peak seasons The purpose of this study was to assess anti-malaria pharmaceuticals inventory control practice and associated challenges in public health facilities of the Oromiya special zone, Amhara region, Ethiopia. Methodology Facility-based cross-sectional study design employing both quantitative and qualitative methods, explanatory sequential mixed method, of data collection and analysis was used in all public health facilities in the Oromia special zone from September 1 to September 30, 2019. The study was conducted in 27 health centers and 2 hospitals, the dispensing units managing anti-malaria pharmaceuticals and data was collected using observation checklists The quantitative data were analyzed by Statistical package for social sciences using linear regression. Purposive sampling was used to select key informants and 12 in-depth interviews were conducted by the principal investigator. Thematic analysis was performed using Nvivo 11 plus and interpretation by narrative strategies. Results The quantitative finding in this study revealed that none of the health facilities surveyed calculated months of stock and multiplied the historical consumption with look ahead seasonal indices (LSI) to forecast the upcoming year consumptions.. Average months of stock of anti-malaria pharmaceuticals were 5.32 months with the annual wastage rate of 11.32%. The point and periodic availability of anti-malaria pharmaceuticals was 72.38 and 77.03% respectively. The number of stocks out days within the previous 6 months was 41.34 days. The study also reported bin card usage (β = − 3.5, p = 0.04) and availability of daily dispensing register (β = − 2.7, p = 0.005) had statistically significant effect on anti-malaria pharmaceuticals inventory control practice. The perceived challenges attributed to the poor anti-malaria pharmaceuticals inventory control practice were lack of integrated pharmaceutical logistics system training, management support, inadequate and near expiry supply from pharmaceuticals supply agency, job dissatisfaction, and staff turnover. Conclusion Inventory control practices for anti-malaria pharmaceuticals was poor as indicated by maximum stock level and none of the health facilities calculated months of stock and the previous consumption was not multiplied by look ahead seasonal indices to compensate the seasonal and demand variation. Efforts should be under-taken by concerned bodies to improve inventory control practice; such as training and regular follow up have to be provided to the health professionals managing anti-malaria pharmaceuticals.

Effects of the COVID-19 Pandemic on the Spot Price of Colombian Electricity

COVID-19 disease shocked global economic activity and affected the electricity markets due to lockdown and work-from-home policies. Therefore, this study proposes an empirical analysis to identify the electricity spot price response during the preventive and mandatory insulation in Colombia, where the economic contraction caused the largest decrease in the electricity demand, especially in the industrial sector. The methodology applied was quantile regression to quantify the non-linear effect on the spot price returns, and two sample periods were selected to contrast the results: 2018 and 2019. The main findings showed that regulated demand variation caused the highest variability on the spot price dynamic during the strict quarantine. However, the price could not fully capture the effects of the demand change due to the short duration of the shock and, also, the price variability in 2019 was higher than 2020 by an El Niño shock.

Stochastic Generation of District Heat Load

Modelling heat load is a crucial challenge for the proper management of heat production and distribution. Several studies have tackled this issue at building and urban levels, however, the current scale of interest is shifting to the district level due to the new paradigm of the smart system. This study presents a stochastic procedure to model district heat load with a different number of buildings aggregation. The proposed method is based on a superimposition approach by analysing the seasonal component using a linear regression model on the outdoor temperature and the intra-daily component through a bi-parametric distribution of different times of the day. Moreover, an empirical relationship, that estimates the demand variation given the average demand together with a user aggregation coefficient, is proposed. To assess the effectiveness of the proposed methodology, the study of a group of residential users connected to the district heating system of Bozen-Bolzano is carried out. In addition, an application on a three-day prevision shows the suitability of this approach. The final purpose is to provide a flexible tool for district heat load characterisation and prevision based on a sample of time series data and summary information about the buildings belonging to the analysed district.

Using Transformations to Predict and Smooth Time Series

Time series has a leading position in statistical Analysis. Nowadays, many economic and industrial operations have been built based on time series. These operations include predicting the product demand variation, the future product prices oscillation, the stock storing control etc. This paper presents a study to show the effect of transformation and smoothing on the performance of the time series. The research results have shown a significant improvement in time-series operation can be noticed when the principles of transformation and smoothing are applied on time series.

Data-Driven Leak Localization in Urban Water Distribution Networks Using Big Data for Random Forest Classifier

In the present paper, a Random Forest classifier is used to detect leak locations on two different sized water distribution networks with sparse sensor placement. A great number of leak scenarios were simulated with Monte Carlo determined leak parameters (leak location and emitter coefficient). In order to account for demand variations that occur on a daily basis and to obtain a larger dataset, scenarios were simulated with random base demand increments or reductions for each network node. Classifier accuracy was assessed for different sensor layouts and numbers of sensors. Multiple prediction models were constructed for differently sized leakage and demand range variations in order to investigate model accuracy under various conditions. Results indicate that the prediction model provides the greatest accuracy for the largest leaks, with the smallest variation in base demand (62% accuracy for greater- and 82% for smaller-sized networks, for the largest considered leak size and a base demand variation of ±2.5%). However, even for small leaks and the greatest base demand variations, the prediction model provided considerable accuracy, especially when localizing the sources of leaks when the true leak node and neighbor nodes were considered (for a smaller-sized network and a base demand of variation ±20% the model accuracy increased from 44% to 89% when top five nodes with greatest probability were considered, and for a greater-sized network with a base demand variation of ±10% the accuracy increased from 36% to 77%).

Plant Hydraulic Transport Controls Transpiration Response to Soil Water Stress

Plant transpiration downregulation in the presence of soil water stress is a critical mechanism for predicting global water, carbon, and energy cycles. Currently, many terrestrial biosphere models (TBMs) represent this mechanism with an empirical correction function (β) of soil moisture – a convenient approach that can produce large prediction uncertainties. To reduce this uncertainty, TBMs have increasingly incorporated physically-based Plant Hydraulic Models (PHMs). However, PHMs introduce additional parameter uncertainty and computational demands. Therefore, understanding why and when PHM and β predictions diverge would usefully inform model selection within TBMs. Here, we use a minimalist PHM to demonstrate that coupling the effects of soil water stress and atmospheric moisture demand leads to a spectrum of transpiration response controlled by soil-plant hydraulic transport (conductance). Within this transport-limitation spectrum, β emerges as an end-member scenario of PHMs with infinite conductance, completely decoupling the effects of soil water stress and atmospheric moisture demand on transpiration. As a result, PHM and β transpiration predictions diverge most when conductance is low (transport-limited), atmospheric moisture demand variation is high, and soil moisture is moderately available to plants. We apply these minimalist model results to land surface modeling of an Ameriflux site. At this transport-limited site, a PHM downregulation scheme outperforms the β scheme due to its sensitivity to variations in atmospheric moisture demand. Based on this observation, we develop a new dynamic β that varies with atmospheric moisture demand – an approach that balances realism with parsimony and overcomes existing biases within β schemes.