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.

In industrial robots, a performance issue is backlash, which is the clearance between mating gears of its joints. Over time, backlash grows through wear and tear, causing inaccuracies in robot positioning. Current methods in backlash detection are performed in low-speed and laboratory settings, or require offline diagnostics. These methods are impractical in actual manufacturing environments, where industrial robots operate continuously at high speeds. Other methods require additional sensors unavailable in typical industrial robots. In this article, we present an online method to quantify backlash and predict the remaining useful life (RUL) in an industrial robot performing cyclic production tasks, using only standard available sensors. To achieve the robot's target position, the input torque oscillates; these oscillations grow as the backlash becomes more severe. We modeled the oscillations as an unknown input, and used an unknown input observer to estimate them and detect/quantify the backlash. Then, a health indicator (HI) is plotted over time and a failure threshold is set based on historical data. Finally, an exponential degradation model is used to predict the RUL of the robot joint. The UIO successfully detected and quantified the backlash through the HI. The degradation model gave a good estimate of the RUL with an accuracy of 20 days after 250 days of operation.

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.

Accurate and robust three‐dimensional reconstruction of objects allows for applications in many aspects of modern life. Yet, it typically suffers from outliers and noise which often need to be post‐processed. Although many algorithms are able to effectively remove the outliers, most require a certain amount of manual tuning of the parameter(s) or to have a parameter(s) set based on the rule of thumb. New machine learning and artificial intelligence‐based methods have also been introduced but may require vast parallel computing resources as well as training data. In the present study, a novel combinatory‐distance‐based method capable of high accuracy outlier detection named as the sorted distance divergence point (SDDP) is introduced. Results show that SDDP is able to achieve an average accuracy of 98% in outlier detection. Moreover, the introduced distance function and outlier percentage allow clear labelling of inliers and outliers cloud points. Therefore, SDDP presents an attractive enhancement to existing methods; namely, the manual parameter(s) tuning may not be necessary. The adaptability and utility of SDDP is further demonstrated by incorporating SDDP with current methods, to produce a high accuracy outlier detector. When tested with 17 objects with 20–50% outliers, attain F 1 and F 2 scores averaging 0.960 and 0.968, respectively.