Estimation of Rainfall from Climatology Data Using Artificial Neural Networks in Palembang City South Sumatera

Estimation of climatological parameters, especially rainfall is a data requirement for all regions of Indonesia. The availability of rainfall data is used for early warning of flood or drought disasters. The study location is in Palembang City, South Sumatra Province, where floods and droughts often occur and lack of availability of rainfall data. This study aims to obtain the best model in estimating rainfall from climatological data. The analysis was carried out to estimate the rainfall from the climatological data using the Artificial Neural Networks method. The Artificial Neural Networks were applied and showed some results with the best calibration was at 16 years using TRAINLM with 1500 epochs that is the performances NSE = 0.54, RMSE = 99.37, and R = 0.74. Whereas the best validation was at 1 year that is the performances NSE = 0.41, RMSE = 87.32, and R = 0.65.

Factors Related to Utilization of Maternal Child Health Handbook Data by Midwives

Midwives are one of the health workers utilizing the MCH (Maternal Child Health) handbook which records the data used to prevent infant, under-five, and maternal mortality. This study aimed to investigate factors related to utilization of the MCH handbook data, namely individual, organizational, and operational techniques. Quantitative approach was used in a cross-sectional survey with a structured questionnaire. Thirty-one midwives were selected as the sample from Active Alert Village in Temanggung district with rural and urban area representative consideration. Besides, descriptive data were tabulated, and inferential analysis was conducted by Pearson product-moment correlation. Result showed the rate of MCH Handbook data utilization was 78.54%. Also, data requirement perception, data filling skill, motivation, attitudes of midwives and facilities availablity (p < 0.05) are significantly related. The MCH handbook data utilization was determined by the individual factor, but with no significant relationship to operational and organizational factors except facilities availability. Therefore, efforts are required to improve data utilization by increasing data requirement perception, data filling skills, motivation and midwives attitudes.

Split Ways: An Efficient Replacement Policy for Larger Sized Cache Memory

As the data requirement of today’s application is increasing the size of the cache memory in a multicore processor is also increasing. A multicore processor has many levels of cache memory. The Last Level Cache (LLC) is normally shared by all the cores and its size must be large enough to handle today’s data intensive applications. Such larger sized set-associative LLC facing major challenges to efficiently implement the replacement policy of its sets. As the number of ways are increasing in the LLC, the replacement policy becomes a bottleneck of the system. To improve the performance of the system and to reduce the hardware overhead, in this paper we propose a simple but hardware efficient replacement policy for the larger sized LLCs. We call the techniques as SplitWays as it divides (splits) the ways of a set into multiple groups called wayGroups. Each wayGroup maintains its own replacement policy. Experimental analysis using full-system simulator found that the proposed technique reduces the hardware overhead by up to 66% without any performance degradation..