A research paper by scientists at Chinese Academy of Sciences proposed a dual-task learning framework, the “Twin Brother” model, which fuses convolutional neural network (CNN), long short-term memory (LSTM), neural networks (NNs), and the squeezing-elicited attention mechanism to classify the lateral gait stage and estimate the hip angle from electromyography (EMG) signals.

The new research paper, published on May. 1 in the journal Cyborg and Bionic Systems, provide a “Twin Brother” model. The model is a dual-task learning framework designed for simultaneous gait phases recognition for lateral walking and continuous hip angle prediction.

Korea Institute of Civil Engineering and Building Technology (KICT, President Park, Sun-Kyu) has successfully developed a real-time, low-cost algal bloom monitoring system utilizing inexpensive optical sensors and a novel labeling logic.

Lead contamination in municipal water sources is a consistent threat to public health. Ingesting even tiny amounts of lead can harm the human brain and nervous system — especially in young children. To empower people to detect lead contamination in their own homes, a team of researchers developed an accessible, handheld water-testing system called the E-Tongue. This device, described in ACS Omega, was tested through a citizen science project across four Massachusetts towns.