The Uptake of Integrated Perinatal Prevention of Mother-to-Child HIV Transmission Programs in Low- and Middle-Income Countries: A Systematic Review

BACKGROUND: The objective of this review was to assess the uptake of WHO recommended integrated perinatal prevention of mother-to-child transmission (PMTCT) of HIV interventions in low- and middle-income countries. METHODS AND FINDINGS: We searched 21 databases for observational studies presenting uptake of integrated PMTCT programs in low- and middle-income countries. Forty-one studies on programs implemented between 1997 and 2006, met inclusion criteria. The proportion of women attending antenatal care who were counseled and who were tested was high; 96% (range 30-100%) and 81% (range 26-100%), respectively. However, the overall median proportion of HIV positive women provided with antiretroviral prophylaxis in antenatal care and attending labor ward was 55% (range 22-99%) and 60% (range 19-100%), respectively. The proportion of women with unknown HIV status, tested for HIV at labor ward was 70%. Overall, 79% (range 44-100%) of infants were tested for HIV and 11% (range 3-18%) of them were HIV positive. We designed two PMTCT cascades using studies with outcomes for all perinatal PMTCT interventions which showed that an estimated 22% of all HIV positive women attending antenatal care and 11% of all HIV positive women delivering at labor ward were not notified about their HIV status and did not participate in PMTCT program. Only 17% of HIV positive antenatal care attendees and their infants are known to have taken antiretroviral prophylaxis. CONCLUSION: The existing evidence provides information only about the initial PMTCT programs which were based on the old WHO PMTCT guidelines. The uptake of counseling and HIV testing among pregnant women attending antenatal care was high, but their retention in PMTCT programs was low. The majority of women in the included studies did not receive ARV prophylaxis in antenatal care; nor did they attend labor ward. More studies evaluating the uptake in current PMTCT programs are urgently needed

The Impact of eHealth on the Quality and Safety of Health Care: A Systematic Overview

Aziz Sheikh and colleagues report the findings of their systematic overview that assessed the impact of eHealth solutions on the quality and safety of health care

Application of adaptive neuro-fuzzy inference system and artificial neural network in inventory level forecasting

Copyright © 2015 Inderscience Enterprises Ltd. Determining optimum level of inventory is very important for any organisation which depends on various factors. In this research, six main factors have been considered as input parameters and the inventory level has been considered as the single output for this inventory management problem. Price of raw material, demand of raw material, holding cost, setup cost, supplier's reliability and lead time are considered as input parameters. An adaptive neuro-fuzzy inference system (ANFIS) has been applied as the artificial intelligence technique for modelling the inventory problem. ANFIS results have been compared with results from another artificial intelligence technique, artificial neural network (ANN), to validate the output results. Performance of both methods has been shown regarding different error measures. Comparison clearly shows the superiority of ANFIS results over ANN results and thus makes ANFIS a better choice for inventory level forecasting

Creep behaviour of short fibre reinforced QE22 magnesium alloy using impression creep test

Creep properties of QE22 magnesium based alloy and composites reinforced with 20 volume percent of short-fibers - Maftech (R), Saffil (R) or Supertech (R), were evaluated using the impression creep test. In the impression creep test, a load is applied with the help of a cylindrical tungsten carbide indenter of 1 mm diameter. This has advantages over conventional creep testing in terms of small specimen size requirement and simple machining. Depth of impression is recorded with time and steady state strain rate is obtained from the slope of the secondary strain (depth of impression divided by indenter diameter) vs. time plot. The results are compared with the creep obtained from conventional creep performed in tension on the same materials earlier. Microstructural examination of the plastically deformed regions is carried out to explain creep behaviour of these composites

Mitigating partial-disruption risk: A joint facility location and inventory model considering customers’ preferences and the role of substitute products and backorder offers

© 2020 This paper studies a joint facility location and inventory model from the viewpoint of partial-disruption risk—i.e., when manufacturing facilities meet the demands of third-party distribution centers with a portion of their capacity, free from any disruptions—while considering substitute products as a disruption risk mitigation strategy. We considered these third-party distribution centers as the customers of the manufacturing facilities. We used a multinomial logit model to rank-order the facilities according to customers’ preferences. Then, a non-linear integer programming model was developed which attempted to assign a sequence of facilities to each customer based on their preferences while at the same time, minimizing the total supply-chain cost. We also considered customers’ decisions for backorders while developing the model. Due to the NP-hard nature of the problem, we developed a particle swarm optimization-based metaheuristic algorithm to solve the model. The efficiency of the modified particle swarm optimization (MPSO) was illustrated through computational tests and systematic comparison with the exact method, a hybrid meta-heuristic algorithm including tabu search (TS) and variable neighborhood search (VNS) from the literature, and its modified form (Modified TS-VNS). A numerical example was used to show the applicability of the model. Finally, we gained useful insight into the role of substitute products and customers’ decisions for backorders through scenario-based analysis. We found that the total supply chain cost could increase in disruption scenarios when customers were more likely to refuse backorder offers. However, the cost-saving from producing a substitute for key products could be significant

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Not AvailableSoil, the skin of the Earth is one of the fundamental natural resource and important component that contributes to the ecosystem. Soil performs a wide range of ecosystem services like food production, climate and water regulation, provision of energy and inhabiting various life forms. This fundamental natural resource of ecosystem is a home of diverse ranges of microbes (beneficial and pathogenic) known as soil microbiome, which are grouped into three domains i.e. archaea, bacteria, eukarya (fungi, algae and nematodes) of life. Diversity of soil microbiome varies with environment and their existence. They exist in bulk soil as well as root influenced soil. Soil microbes also show their existence in the different extreme environments. The microbial genera such as Achromobacter, Arthrobacter, Azospirillum, Azotobacter, Bacillus, Burkholderia, Exiguobacterium, Flavobacterium, Herbaspirillum, Methylobacterium, Paenibacillus, Pseudomonas, Rhizobium, Serratia and Staphylococcus have been reported as predominant in all the different conditions of soil. All the different groups of microbes present in the soil naturally plays a several significant roles like nutrients cycling, recycling of ground water, maintenance of soil fertility, decomposition of organic matter and formation of fossil fuels. As their role in environment these microbes may have several applications and can be used as in agriculture as plant protector and plant growth enhancer. Soil microbes can also be used in the environment for pollutants remediation and decomposition purposes. Present review deals with the biodiversity of beneficial soil microbiomes and their potential biotechnological contribution for nutrient cycling, plant growth improvement and nutrient uptake.Not Availabl

Metaheurestic algorithm based hybrid model for identification of building sale prices

The overall cost of a building depends on several variables such as economical, project physical and financial variables. The CCB (construction cost of building) also depends on deviations of several indices which are not control in an easy way. Therefore, the overall sales prices of a building may not be controlled due to these indices. In this chapter, a metaheuristic algorithm based hybrid model for identification of building's sales prices is presented, which is developed by using conventional Feedforward Neural Network (FNN).table The identification accuracy of FNN is varies with respect to the number of input variables and its modal parameters such as weight (w) and bias (b). In this chapter, the number of most relevant input variables are selected by using Relief F Attribute evaluator (RFAE) with the help of ranker search method. After selecting most appropriate variables, the FNN parameters are optimized by using particle swarm optimization (PSO) based metaheuristic algorithm (MA). The total 208 intelligent models have been designed and validated using 372 real side construction cost dataset of three to nine story buildings. The validated results by FNN and PSO-FNN show that selected variables gives better results as compared with other models.Scopu

Data-Driven Intelligent Model for Sale Price Prediction and Monitoring of a Building

The construction cost forecasting and monitoring plays an important role in a building condition assessment. The construction cost of a building (CCB) not only depends on the method of construction, equipment, labor, and material but also depends on type, scheduling, project locality, and project duration, etc. Moreover, abrupt variations in economic indices and attributes (i.e., WPI: wholesale price, liquidity, building services, etc.) are reasons for cost variation and deviate the CCB, which are not possible to monitor and/or identify in easy way during the current economic scenario. So, these indices may be snubbed in CCB. In this chapter, a data-driven intelligent model for sale price monitoring and detection of a building is presented which may be utilized in hospital planning. For the implementation of the proposed approach, the cost's data of construction for 372 buildings of three to nine stories have been utilized. The recorded dataset has physical, financial, and economic variables and indices of real sites. The proposed approach includes the comparative analysis of conventional statistical and advanced soft computing techniques. The obtained results show that monitor and/or identification of CCB is higher in case of soft computing technique than statistical method.Scopu