An exoskeleton suit helps people sprint faster, according to the results of a small study. Elite athletes could one day use the suit in training sessions to improve their running performance, although one expert says it may increase the risk of injuries. Researchers have previously developed exoskeleton devices that help people to walk or jog more efficiently. Now, Giuk Lee at Chung-Ang University in Seoul, South Korea, and his colleagues have created an "exosuit" that enables people to sprint faster. The exosuit, which weighs 4.4 kilograms, has electrical motors on its back that control the length of two steel cables that attach to the wearer's hips and thighs, says Lee. The length of the cable running between each hip and its corresponding thigh shortens as a wearer extends their legs backwards, helping them to complete this motion.

An exoskeleton suit helps people sprint faster, according to the results of a small study. Elite athletes could one day use the suit in training sessions to improve their running performance, although one expert says it may increase the risk of injuries. Researchers have previously developed exoskeleton devices that help people to walk or jog more efficiently. Now, Giuk Lee at Chung-Ang University in Seoul, South Korea, and his colleagues have created an "exosuit" that enables people to sprint faster. The exosuit, which weighs 4.4 kilograms, has electrical motors on its back that control the length of two steel cables that attach to the wearer's hips and thighs, says Lee. The length of the cable running between each hip and its corresponding thigh shortens as a wearer extends their legs backwards, helping them to complete this motion.

Deep neural networks are popularly used for object recognition, detection, and segmentation across different avenues. Of these, generative adversarial networks (GANs) are a superior class of neural networks whose performance exceeds that of conventional neural networks. They are meant to minimize the inconsistencies between real and fake data, and have proven successful for image detection, medical imaging, video prediction, 3D image reconstruction, and more. Despite their growth over the last few years, they are not devoid of limitations. Training conventional GANs is difficult and involves very high computational costs, making them unreliable for complex computer vision problems.

However, the Korean Dementia Screening Questionnaire (KDSQ) is an easier and more reliable screening method. Instead, other clinical variables and raw data were used for this study without the consideration of a cutoff value. The objective of this study was to develop a machine-learning algorithm for the detection of cognitive impairment (CI) based on the KDSQ and the MMSE. Patients and methods: The original dataset from the Clinical Research Center for Dementia of South Korea study was obtained. In total, 9,885 and 300 patients were randomly allocated to the training and test datasets, respectively.

An artificial synaptic device has been developed which can mimic the function of nerve cells and synapses that are responsible for memory in human brains. The research team, led by Director Dr Myoung-Jae Lee from the Intelligent Devices and Systems Research Group at DGIST, included joint research teams led by Professor Gyeong-Su Park from Seoul National University; Professor Sung Kyu Park from Chung-ang University; and Professor Hyunsang Hwang from POSTEC. The teams developed a highly reliable, artificial synaptic device with multiple values by structuring tantalum oxide – a trans-metallic material – into two layers of Ta2O5-x and TaO2-x, and by controlling its surface. This electrical synaptic device stimulates the function of synapses in the brain, as the resistance of the tantalum oxide gradually increases or decreases depending on the strength of the electric signals. It has succeeded in overcoming durability limitations of current devices by allowing current control only on one layer of Ta2O5-x.