AI Prediction of Brain Signals for Human Gait Using BCI Device and FBG Based Sensorial Platform for Plantar Pressure Measurements.

Artificial intelligence (AI) in developing modern solutions for biomedical problems such as the prediction of human gait for human rehabilitation is gaining ground. An attempt was made to use plantar pressure information through fiber Bragg grating (FBG) sensors mounted on an in-sole, in tandem with a brain-computer interface (BCI) device to predict brain signals corresponding to sitting, standing and walking postures of a person. Posture classification was attained with an accuracy range between 87-93% from FBG and BCI signals using machine learning models such as K-nearest neighbor (KNN), logistic regression (LR), support vector machine (SVM), and naïve Bayes (NB). These models were used to identify electrodes responding to sitting, standing and walking activities of four users from a 16 channel BCI device. Six electrode positions based on the 10-20 system for electroencephalography (EEG) were identified as the most sensitive to plantar activities and found to be consistent with clinical investigations of the sensorimotor cortex during foot movement. A prediction of brain EEG corresponding to given FBG data with lowest mean square error (MSE) values (0.065-0.109) was made with the selection of a long-short term memory (LSTM) machine learning model when compared to the recurrent neural network (RNN) and gated recurrent unit (GRU) models.

A comprehensive optimum integrated water resources management approach for multidisciplinary water resources management problems.

A holistic Integrated Water Resources Management (IWRM) model can be difficult to implement and the associated high-dimension optimization problems' complexity often forces the decision makers to downscale such problems. These challenges however have motivated this research to develop a comprehensive Optimum IWRM approach (OP-IWRM) using a many-objective optimization algorithm to solve complex and large-scale problems. The approach employs the social, economic, and environmental objectives; ground and surface water resources; and water infrastructure for river basin management to: (1) improve the relevant revenues, (2) enhance community welfare, and (3) pave the road for the decision makers to set better investment policy. The results demonstrate comprehensive improvement of all considered targets. The decision makers may reconsider implementing complex integrated water resources management of large-scale regions. The OP-IWRM may extend for country-scale approach as a pathway towards a national sustainable development plan. The large-scale Diyala river basin, Iraq, was adopted to evaluate the approach using seventeen objectives and more than 1500 decision variables.

Optimum socio-environmental flows approach for reservoir operation strategy using many-objectives evolutionary optimization algorithm.

Water resource system complexity, high-dimension modelling difficulty and computational efficiency challenges often limit decision makers' strategies to combine environmental flow objectives (e.g. water quality, ecosystem) with social flow objectives (e.g. hydropower, water supply and agriculture). Hence, a novel Optimum Social-Environmental Flows (OSEF) with Auto-Adaptive Constraints (AAC) approach introduced as a river basin management decision support tool. The OSEF-AAC approach integrates Socio-Environmental (SE) objectives with convergence booster support to soften any computational challenges. Nine SE objectives and 396 decision variables modelled for Iraq's Diyala river basin. The approach's effectiveness evaluated using two non-environmental models and two inflows' scenarios. The advantage of OSEF-AAC approved, and other decision support alternatives highlighted that could enhance river basin SE sectors' revenues, as river basin economic benefits will improve as well. However, advanced land use and water exploitation policy would need adoption to secure the basin's SE sectors.