Objectives First, to assess the impact of scientific and technical training on midwives' abilities in collecting and recording the results of routine antenatal care examinations. Second, to explore midwives' views with regard to factors affecting their abilities to successfully complete the data documentation tasks. Methods The study was conducted in South Kalimantan, Indonesia (April 2016-October 2017). Nineteen urban and rural midwives were selected. Access to antenatal care information on 4,946 women (retrospective cohort study) and 381 women (prospective cohort study) was granted. A descriptive and exploratory design was used to describe midwives' abilities and challenges pertaining to timely collection and recording of results concerning antenatal care examinations. Results Scientific and technical training has significantly improved the average amount of recorded antenatal care data (from 17.5% to 62.1%, p-value < 0.0005). Lack of awareness, high workload, and insufficient skills and facilities are the main reasons for the database gaps. Conclusions The training has equipped midwives with scientific knowledge and technical abilities to allow routine collection of antenatal care data. Provision and adequate use of this information during different stages of pregnancy is crucial as an evidence-based guideline to assess maternal and foetal risk factors to ending preventable mortality.

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BackgroundBirth weight is one of the most important indicators of neonatal survival. A reliable estimate of foetal weight at different stages of pregnancy would facilitate intervention plans for medical practitioners to prevent the risk of low birth weight delivery. This study has developed reliable models to more accurately predict estimated foetal weight at a given gestation age in the absence of ultrasound facilities.MethodsA primary health care centre was involved in collecting retrospective non-identified Indonesian data. The best subset model selection criteria, coefficient of determination, standard deviation, variance inflation factor, Mallows Cp, and diagnostic tests of residuals were deployed to select the most significant independent variables. Simple and multivariate linear regressions were used to develop the proposed models. The efficacy of models for predicting foetal weight at a given gestational age was assessed using multi-prediction accuracy measures.ResultsFour weight prediction models based on fundal height and its combinations with gestational age (between 32 and 41 weeks) and ultrasonic estimates of foetal head circumference and foetal abdominal circumference have been developed. Multiple comparison criteria show that the proposed models were more accurate than the existing models (mean prediction errors between − 0.2 and 2.4 g and median absolute percentage errors between 4.1 and 4.2%) in predicting foetal weight at a given gestational age (between 35 and 41 weeks).ConclusionsThis research has developed models to more accurately predict estimated foetal weight at a given gestational age in the absence of ultrasound machines and trained ultra-sonographers. The efficacy of the models was assessed using retrospective data. The results show that the proposed models produced less error than the existing clinical and ultrasonic models. This research has resulted in the development of models where ultrasound facilities do not exist, to predict the estimated foetal weight at varying gestational age. This would promote the development of foetal inter growth charts, which are currently unavailable in Indonesian primary health care systems. Consistent monitoring of foetal growth would alleviate the risk of having inter growth abnormalities, such as low birth weight that is the most leading factor of neonatal mortality.Electronic supplementary materialThe online version of this article (10.1186/s12884-018-2047-z) contains supplementary material, which is available to authorized users.

In this paper, we proposed a home energy management system (HEMS) that includes photovoltaic (PV), electric vehicle (EV), and energy storage systems (ESS). The proposed HEMS fully utilizes the PV power in operating domestic appliances and charging EV/ESS. The surplus power is fed back to the grid to achieve economic benefits. A novel charging and discharging scheme of EV/ESS is presented to minimize the energy cost, control the maximum load demand, increase the battery life, and satisfy the user’s-traveling needs. The EV/ESS charges during low pricing periods and discharges in high pricing periods. In the proposed method, a multi-objective problem is formulated, which simultaneously minimizes the energy cost, peak to average ratio (PAR), and customer dissatisfaction. The multi-objective optimization is solved using binary particle swarm optimization (BPSO). The results clearly show that it minimizes the operating cost from 402.89 cents to 191.46 cents, so that a reduction of 52.47% is obtained. Moreover, it reduces the PAR and discomfort index by 15.11% and 16.67%, respectively, in a 24 h time span. Furthermore, the home has home to grid (H2G) capability as it sells the surplus energy, and the total cost is further reduced by 29.41%.

Objectives. To assess the impact of scientific and technical training on midwives’ abilities in collecting and recording the key performance indicators for fetal growth chart development in limited-resource settings. Methods. A descriptive design was used to describe midwives’ abilities in timely collecting and recording the minimum data required to estimate fetal weight and develop fetal growth chart. The study was conducted among 19 urban and rural midwives in South Kalimantan, Indonesia, between April 2016 and October 2017. The training provided access to antenatal care information on 4,946 women (retrospective cohort study) and 381 women (prospective cohort study). Results. The average amount of recorded antenatal care data on the key performance indicators of fetal growth assessment has been significantly improved (from 33.4% to 89.1%, p-value < 0.0005) through scientific and technical training. Conclusions. Scientific knowledge and technical abilities have enabled midwives to timely record routine data of the key performance indicators for fetal growth surveillance. Access to this information is vital during different stages of pregnancy. The information can be utilised as evidence-based guidelines to assess fetal risks through fetal weight estimation and to develop fetal growth chart that is currently not available in Indonesian primary healthcare systems.