Area-Based Medicine

Japan’s health insurance system has reached a critical turning point owing to a decreasing birthrate, increasing longevity, and changes in disease trends. The Japanese government is promoting the establishment of a community-based integrated care system aimed at maintaining the dignity of elderly individuals and supporting independent living. This care system will ensure medical and nursing care, preventive measures, and independent living support. This type of care system should be based on the characteristics of individual geographical areas, as there are marked regional variations in patterns of aging, lifestyle, and the adequacy of local medical care. Therefore, it is important that medical services are tailored to fit the kind of medical care needed by residents of each geographical area and to provide medical services accordingly. In this paper, we propose a need for area-based medicine, whereby medical care is provided according to the characteristics of individual geographical areas in super-ageing societies such as that of Japan

Implementing genetic algorithms to CUDA environment using data parallelization

Računarske metode rješavanja paralelnih problema korištenjem grafičkih obradnih jedinica (GPUs) zadnjih su godina pobudile veliki interes. Paralelno izračunavanje može se primijeniti na genetske algoritme (GAs) u odnosu na proces evaluacije jedinki u populaciji. Ovaj rad opisuje još jednu metodu primjene GAs na CUDA okruženje gdje je CUDA računarsko okruženje opće namjene za GPUs koje daje NVIDIA. Osnovna karakteristika ovog istraživanja leži u tome da se paralelna obrada koristi ne samo za jedinke nego i za gene u jedinki. Predložena implementacija se procjenjuje kroz osam ispitnih funkcija. Ustanovili smo da predložena metoda implementacije daje 7,6-18,4 puta brže rezultate od onih kod primjene CPU.Computation methods of parallel problem solving using graphic processing units (GPUs) have attracted much research interests in recent years. Parallel computation can be applied to genetic algorithms (GAs) in terms of the evaluation process of individuals in a population. This paper describes yet another implementation method of GAs to the CUDA environment where CUDA is a general-purpose computation environment for GPUs provided by NVIDIA. The major characteristic point of this study is that the parallel processing is adopted not only for individuals but also for the genes in an individual. The proposed implementation is evaluated through eight test functions. We found that the proposed implementation method yields 7,6-18,4 times faster results than those of a CPU implementation

Implementing genetic algorithms to CUDA environment using data parallelization

Računarske metode rješavanja paralelnih problema korištenjem grafičkih obradnih jedinica (GPUs) zadnjih su godina pobudile veliki interes. Paralelno izračunavanje može se primijeniti na genetske algoritme (GAs) u odnosu na proces evaluacije jedinki u populaciji. Ovaj rad opisuje još jednu metodu primjene GAs na CUDA okruženje gdje je CUDA računarsko okruženje opće namjene za GPUs koje daje NVIDIA. Osnovna karakteristika ovog istraživanja leži u tome da se paralelna obrada koristi ne samo za jedinke nego i za gene u jedinki. Predložena implementacija se procjenjuje kroz osam ispitnih funkcija. Ustanovili smo da predložena metoda implementacije daje 7,6-18,4 puta brže rezultate od onih kod primjene CPU.Computation methods of parallel problem solving using graphic processing units (GPUs) have attracted much research interests in recent years. Parallel computation can be applied to genetic algorithms (GAs) in terms of the evaluation process of individuals in a population. This paper describes yet another implementation method of GAs to the CUDA environment where CUDA is a general-purpose computation environment for GPUs provided by NVIDIA. The major characteristic point of this study is that the parallel processing is adopted not only for individuals but also for the genes in an individual. The proposed implementation is evaluated through eight test functions. We found that the proposed implementation method yields 7,6-18,4 times faster results than those of a CPU implementation

Factors related with low back pain and pelvic pain at the early stage of pregnancy in Japanese women

The aim of this study was to clarify the proportion of women with low back and/or pelvic pain (LBPP) and LBPP-related factors at the early stage of pregnancy and to clarify the differences between LBPP-related factors in primiparous women and multiparous women in Japan. 157 pregnant women were recruited. Information about the presence of LBPP, degree of pain by using a visual analog scale (VAS), location of pain, past history of LBPP and background characteristics were collected. Physical status was assessed by the pregnancy mobility index (PMI). The Ethics Committee of Tokushima University Hospital approved the study. The proportion of women who complained of LBPP was 65.6%. PMI score in women with LBPP was significantly higher than that in women without LBPP (p<0.001). The proportions of women with a past history of LBPP before pregnancy and with a past history of LBPP in the previous pregnancy were significantly higher in women with LBPP (p<0.001 and p=0.002, respectively). In women with LBPP, the score of VAS in multiparous women was significantly higher than that in primiparous women (p=0.019). Early management for women with a past history of LBPP before pregnancy and with a past history of LBPP in the previous pregnancy is important. Management for lumbar pain according to parity is needed for health guidance at the early stage of pregnancy