The dynamic insulin sensitivity and secretion test (DISST) is a dynamic test that can quantify participant-specific insulin sensitivity$(SI)$values and endogenous insulin$(U_{N})$profiles. It is claimed that the DISST$SI$value is highly correlated to the Euglycemic Hyper-Insulinaemic Clamp EIC (R = 0.81), and the test can also contrast UNcharacteristics across participant groups with varying degrees of insulin resistance$(IR)$based on deconvolution of measured C-peptide data. The DISST, however, was only implemented on the New Zealand. In order to provide further insight and modelling capability for various ethnic cohorts, this study reports the first clinical trial employing the DISST model for a Malaysian cohort. Results show the inversely proportional relationship of BMI towards$SI$values and match expected outcomes for this cohort. In addition, results also show the relationship between area under the curve (AUC)$U_{1}$, AUC$U_{2}$VS$SI$values. As the metabolic states of the individuals move from Normal to Type 2 Diabetes (T2D), the AUC$Uz$for Pre-diabetes and$\mathrm{T}2\mathrm{D}$are higher than the Normal group (in the range of 400$-1400\ \mathbf{mU}\cdot\min^{-1}$). As expected, the loss of U1and reduced$Uz$define the UNcharacteristics of observed in individuals with$\mathrm{T}2\mathrm{D}$. Furthermore, higher AUC U11400 - 2000$\mathbf{mU}\cdot\mathbf{min}^{-1})$could define the participant potentially has$IR$, while lower AUC U1$(100\ -700\mathbf{mU}\cdot\min^{-1})$could potentially denote that the participant could inherit insulin deficiency. This research clearly demonstrates the applicability of the DISST test to Malaysian, and potentially SE Asian who have different distributions of body fat and metabolic responses than the European-derived cohorts usually studied.

Asynchrony breathing (AB) refers to a situation where the patient's breathing does not align with the mechanical ventilator (MV), which can have a detrimental effect on the patient's recovery. A few types of AB make it difficult for clinicians to identify and manage MV properly. Hence, there is a need to develop a method that can classify the type of AB in MV patients. In this study, a 2- dimensional (2D) convolutional neural network (CNN) method is presented to classify the type of AB based on the input image of the airway pressure. A total of 866 images of airway pressure were analysed in this study, and 4 types of AB were classified: 1) double triggering (DT); 2) reverse triggering (RT); 3) delayed triggering (DC); and 4) premature cycling (PC). Two types of activation functions for classification purposes, SoftMax and Sigmoid, were compared based on performances. Results show SoftMax produced a higher accuracy of 98.5% with a training dataset of 70% and a testing dataset of 30% of the data. In contrast, the Sigmoid function produced an accuracy of 98.1 % when trained and tested with the same dataset. Furthermore, this 2D-CNN model produced a range of accuracy between 89% and 96% in classifying the type of AB, with the highest accuracy of 96% in classifying DT. Overall, the developed CNN model, based on the input image of airway pressure, accurately extracts critical and unique features to precisely classify various types of AB, which could help clinicians in managing MV patients.

Plant classification systems, in general, could be a beneficial tool in the agricultural industry, especially when it comes to recognising plant types in a systematic and manageable manner. Previously, plant growers used to rely on observation and experienced personnel to distinguish between plant varieties. However, some plants, such as leaves and branches, have nearly identical traits, making identification difficult. Hence, there is a need for a system capable of resolving this issue. Thus, the focus of this research is on classifying plant leaves using a convolutional neural network (CNN) technique. Coriander and parsley were chosen as test subjects for this study because their leaves have comparable structures. The input image was subjected to a number of filter layers using CNN. A total of 100 coriander and parsley leaf photos are collected for this research. These photos were filtered using kernels. These kernels have a set size and extract features from the input photos to create a feature map. These extracted features will then be used to classify plant leaves according to its classes type. With the use of the Graphical User Interface (GUI), the end user will be able to determine the type of leaf. Results show that, using the ReLu activation layer with 15 layers of network design and a 70–30 training-testing proportion, this plant leaf classification system was able to attain a coriander and parsley classification accuracy of 90% with an error rate of 0.1. In addition, due to its great accuracy, this system can be extended for additional uses such as recognising plant diseases and species.

Most mathematical models were developed to guide clinicians in managing patients who are mechanically ventilated (MV) in intensive care unit (ICU). However, asynchrony events (AE) could occur when a patient's breathing is not synchronized with the MV support, which is caused by spontaneously breathing (SB) effort or mismatch of inspiratory and expiratory timings of ventilator support even though the patients are fully sedated. One of the real metrics that can detect AEs in MV patients is through time varying elastance estimation. Previous studies found that SB patients developed a negative elastance as a result of the SB effort put forth by these patients. Hence, this study aims to estimate the AEs of MV patients by adding negative elastance (AUC Edrs_negative) in the model. Data were obtained from nine mechanically ventilated respiratory failure patients from the International Islamic University Malaysia (IIUM) Hospital. Asynchrony index (AInew) represents a total estimation of AEs and the negative elastance in MV patients. Patients’ data were classified by ventilation mode, and AInewwas computed for each of the patients and compared with the previous methods in calculating the AI. The results show that the new modelbased technique in estimating the value of AInewhas produced a higher value as compared to previous measurements of AIorias expected. Hence, this new measurement of AI has successfully shown that by adding AEs and AUC Edrs negativetogether, this model is more sensitive and precisely measures the AI especially during the synchronized intermittent mandatory ventilation (SIMV) mode. Thus, the estimation of AEs with negative elastance may aid clinicians in selecting the appropriate MV ventilation mode and allow for precise respiratory mechanics monitoring, especially in SB patients.

Short T1 Inversion Recovery (STIR) is a fat suppressed technique commonly used in Magnetic Resonance Imaging (MRI) to suppress fat signals from tissues. The technique is to improve visual inspection during diagnosis. Suspected fluids will appear bright in STIR to identify the abnormality. Due to hardware limitation, tissue contrast and signal-to-noise ratio are reduced. We propose a framework of image fusion system which mimics the MRI machine to produce a fused ‘STIR’ image. The resultant fused ‘STIR’ image has high similarity index (0.989971), small mean square error (0.1092), high peak signal-to-noise ratio (106.9173) and good Pearson correlation coefficient (0.696).

The purpose of this paper is to study a mathematical formulation of a water level and product liquid temperature control loop in a boiler drum and heat exchanger process control training system. Mathematical modeling was carried out to understand the dynamics of the liquid product temperature for different settings of PID controller mode. The modeling objective is to address the prediction of the plant performance with sufficient accuracy so that experimental data can be verified with the simulation results. The experimental data was obtained from the mini plant and the simulation results by model developed by using Simulink toolbox in Matlab software. The experimental and simulation results that were verified exhibited fair agreement.