This study aims to develop a deep learning model to monitor rice serving amounts in institutional foodservice, enhancing personalized nutrition management. The goal is to identify the best convolutional neural network (CNN) for detecting rice quantities on serving trays, addressing balanced dietary intake challenges. Both a vanilla CNN and 12 pre-trained CNNs were tested, using features extracted from images of varying rice quantities on white trays. Configurations included optimizers, image generation, dropout, feature extraction, and fine-tuning, with top-1 validation accuracy as the evaluation metric. The vanilla CNN achieved 60% top-1 validation accuracy, while pre-trained CNNs significantly improved performance, reaching up to 90% accuracy. MobileNetV2, suitable for mobile devices, achieved a minimum 76% accuracy. These results suggest the model can effectively monitor rice servings, with potential for improvement through ongoing data collection and training. This development represents a significant advancement in personalized nutrition management, with high validation accuracy indicating its potential utility in dietary management. Continuous improvement based on expanding datasets promises enhanced precision and reliability, contributing to better health outcomes.

The water uptake, ionic conductivity, vanadium (VO2+) permeability and stability of polysulfone (PSF) based AEMs in alkaline media and in strongly oxidizing solutions were assessed. The highest ion conductivity was obtained with PSF-trimethylammonium (TMA)+. PSF-TMA+ also had better alkaline stability in comparison to PSF-AEM with different bases. PSF-TMA+ was demonstrated to show fuel cell performance. PSF-TMA+ demonstrated a 40-fold reduction in vanadium (VO2+) permeability when compared to Nafion® membrane. Comprehensive 2D NMR studies verified that PSF-TMA+ remained chemically stable even after exposure to a 1.5 M vanadium(V) solution for 90 days. Excellent energy efficiencies (85%) were attained and sustained over several charge–discharge cycles for a vanadium redox flow battery prepared using the PSF-TMA+ separator.

This work deals with a 4-DOF flexible continuum robot that employs a spring as its backbone. The mechanism consists of two modules and each module has 2 DOF. The special features of the proposed mechanism are the flexibility and the backdrivability of the whole body by using a spring backbone. Thus, even in the case of collision with human body, this device can ensure safety. The design and the kinematics for this continuum mechanism are introduced. The performance of this continuum mechanism was shown through simulation and experiment.

This work proposes structure of spring backbone micro endoscope. For effective surgery in narrow and limited space, many manipulators are developing that different to existed structure. This device can move like elephant nose or snake unlike the existing robots. For this motion, a mechanism that uses spring backbone and wires has been developed. The new type endoscope that has Z axis motion for spring structure, therefore it has 3 degree of freedom, two rotations and one linear motion. And new kinematics for backbone structure is proposed using simple geographic analysis. The Jacobian and stiffness modeling are also derived. Exact actuator sizing is determined using stiffness model. Finally, the proposed kinematics are verified by simulation and experiments.