Dairy feeding causes significant water pollution. By controlling the proper amount of feed, reducing the waste to minimum will effectively reduce the problem of water contamination. In this project, a Sustainable Aquaculture Feed System (SAFS) has been designed and developed. It can automatically feed the fishes by estimating fishes' appetite through machine vision. The discussion includes design and optimization of the vision system using Labview as well as the integration of various components in the SAFS. With the developed algorithm, the system is able to detect the presence of fishes and count the number of fishes. The outcome is able to estimate and infer the fish appetite. Therefore, the feeding time can be planned ahead. In addition, the system includes a Graphical User Interface (GUI) for monitoring, display the feeding status and sensors reading such as pH, turbidity and temperature.
Falls are common and often lead to serious physical and psychological consequences for older persons. The occurrence of falls are usually attributed to the interaction between multiple risk factors. The clinical evaluation of falls risks is time-consuming as a result, hence limiting its availability. The purpose of this study was, therefore, to develop a clustering-based algorithm to determine falls risk. Data from the Malaysian Elders Longitudinal Research (MELoR), comprising 1411 subjects aged ≥55 years, were utilized. The proposed algorithm was developed through the stages of: data pre-processing, feature identification and extraction with either t-Distributed Stochastic Neighbour Embedding (t-SNE) or principal component analysis (PCA)), clustering (K-means clustering, Hierarchical clustering, and Fuzzy C-means clustering) and characteristics interpretation with statistical analysis. A total of 1279 subjects and 9 variables were selected for clustering after the data pre-possessing stage. Using feature extraction with the t-SNE and the K-means clustering algorithm, subjects were clustered into low, intermediate A, intermediate B and high fall risk groups which corresponded with fall occurrence of 13%, 19%, 21% and 31% respectively. Slower gait, poorer balance, weaker muscle strength, presence of cardiovascular disorder, poorer cognitive performance, and advancing age were the key variables identified. The proposed fall risk clustering algorithm grouped the subjects according to features. Such a tool could serve as a case identification or clinical decision support tool for clinical practice to enhance access to falls prevention efforts.
The agriculture industry is facing labour shortage problem due to the difficulty to employ workers for “3D” (dangerous, dirty and difficult) jobs. This will reduce the number of labours needed and minimize the threats associated with the traditional agricultural system. Cyber-physical system (CPS) can provide farmers with a framework to remotely monitor and control the various factors that affect plant growth. The aim of this project is to implement CPS into a vertical farming system in which an Android-based remote monitoring and control (M&C) system is developed based on CPS for optimal production of the plantation system using Arduino ESP32 and sensors through the Internet. Experiments were carried to evaluate the practicality of implementing CPS in vertical farming process. Growth factors that were monitored and controlled in the experiments include ambient temperature, water temperature, humidity, pH level, light and carbon dioxide. In this study, an android application was developed and evaluated. The controlled parameters were display on the phone. The evaluation results shown that, the CPS system is practical to be used in plant growth monitoring (sensors and feedback system) and plant growth optimisation (sensors, feedback system and actuators). In addition, the automation of plant growth monitoring process can overcome the shortage of manpower in agriculture industry.
Solid Oxide Fuel Cells (SOFCs) are emerging as an advanced and efficient energy conversion technology that could be a solution to some of the environmental issues. SOFCs are able to produce clean electricity and heat from hydrogen energy. Due to their high ionic conductivity in the intermediate temperature range of 600 °C to 800 °C, scandia stabilized zirconia is a very promising electrolyte material for SOFCs. However, the long term damage caused by cyclic heating and cooling during the stages of start-up and shut down of SOFC will greatly affect the performance of the fuel cell. The structural damages will result in poor mechanical properties which directly influence the durability of the ceramic electrolyte. Therefore, it is essential to investigate the thermal degradation behavior of various zirconia ceramic systems. In this work, the thermal degradation behavior of the zirconia ceramics are investigated by adopting thermal cycling test at two different heating rates (10°C/min and 20°C/min) to examine the durability of the zirconia electrolyte, in particular the structure degradation caused in thermal cycling. Ordered array of convex meniscus were observed on the surface of the undoped zirconia ceramics and the grain growth was suppressed during thermal cycling. The effect of thermal cycling on the mechanical stability of zirconia based ceramics were affected by the addition of 1 wt% MnO2 which showed a reduction in the hardness and fracture toughness.
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