Nikon has developed a system that uses artificial intelligence to alert farmers if a cow is about to give birth, analyzing their movements with cameras installed on farms. The technology, which is going on sale in Japan this month, aims to increase efficiency and ease the burden on farmers who need to conduct regular checks on pregnant cows in the weeks leading up to giving birth. The system is estimated to cost approximately 900,000 ( 6,200) per year for a farm with 100 cows. A dedicated smartphone application is used to alert farmers when a calf is due. According to Nikon, a pregnant cow exhibits typical signs around five hours before going into labor, such as increased movement and the start of the release of the amniotic sac containing the calf.

How much gaming is too much gaming? This is the question Japan's pre-eminent addiction expert, Dr. Susumu Higuchi, is trying to answer as he treats people whose lives have been destroyed by video game addiction. Online gaming addiction has become the fastest-growing form of addiction in the 21st century, and it's the most vulnerable people -- children -- who mainly fall prey to its psychoactive effects, Higuchi says. As head of the Kurihama Medical and Addiction Center in Kanagawa Prefecture, which started the country's first program for internet addition in 2011, Higuchi is rolling up his sleeves to tackle a scourge that has eaten into the vitals of our society. "This isn't just about Japan, it's happening all over the world," Higuchi said in a recent interview.

Reliable diagnosis of depressive disorder is essential for both optimal treatment and prevention of fatal outcomes. In this study, we aimed to elucidate the effectiveness of two non-linear measures, Higuchi Fractal Dimension (HFD) and Sample Entropy (SampEn), in detecting depressive disorders when applied on EEG. HFD and SampEn of EEG signals were used as features for seven machine learning algorithms including Multilayer Perceptron, Logistic Regression, Support Vector Machines with the linear and polynomial kernel, Decision Tree, Random Forest, and Naive Bayes classifier, discriminating EEG between healthy control subjects and patients diagnosed with depression. We confirmed earlier observations that both non-linear measures can discriminate EEG signals of patients from healthy control subjects. The results suggest that good classification is possible even with a small number of principal components. Average accuracy among classifiers ranged from 90.24% to 97.56%. Among the two measures, SampEn had better performance. Using HFD and SampEn and a variety of machine learning techniques we can accurately discriminate patients diagnosed with depression vs controls which can serve as a highly sensitive, clinically relevant marker for the diagnosis of depressive disorders.