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Computational thinking (CT) is one of the systematic tools in problem solving and widely accepted as an important skill in the 21st century. This study aimed to identify the effectiveness of the Chemistry Computational Thinking (CT-CHEM) Module on achievement in chemistry. This study also employed a quasi-experimental design with the participation of 85 form four students in Malaysia. The three types of teaching approaches, namely CT-CHEM Module Plugged-in (CTMP), CT-CHEM Module Unplugged + Plugged-in (CTMUP) and conventional method (CM), were systematically designed and implemented. The achievement of students was measured using an achievement test, where validity and reliability were justified and two-way ANCOVA was used to analyse the data. Findings confirmed that the achievement of students in chemistry is significantly higher in the CTMP group as compared with the CTMUP and CM groups. Instead, gender had no significant effect on students' chemistry achievement. This study concludes that when students were exposed to teaching and learning strategies by integrated CT through plugged-in strategy more effective than a combination of plugged-in and unplugged. Plugged-in visualisation activities are more effective in increasing the understanding and achievement of students compared with the combination of plugged-in and unplugged activities. Plugged-in through visualisation activities is more effective than the combination of plugged-in and unplugged. This is because, the abstract concept in electrochemistry is easier to understand by students through the visualisation activity approach using a computer in explaining the important concepts in the topic and because the whole content is interrelated.

Computational thinking (CT) is often associated with computer science and mathematics in general. However, the involvement of computer science according to gender is seen to be imbalanced. Therefore, this study aimed to identify the level of students' CT skills by gender and their relationship to achievement in mathematics. The study employed survey research design and purposive sampling that involved 128 participants from four science stream students in one of the states in Malaysia (n = 128). The measurement of CT skills was done using a modified CT test adapted from the previous studies. The results of the study were analyzed using descriptive and inferential statistics. Spearman correlational analysis was applied to examine the relationship between CT skill and mathematical achievement and students' t-test analysis was used to determine the difference in CT skills across gender. The study found that the students' CT skills were at the honors level. The relationship between CT skills and mathematics achievement was statistically significant, whereas there exist no significant gender differences in CT skills. This study suggests that the teaching of mathematical logic needs to be considered to improve students' CT skills.

This study investigated the influence of CThink4CS2 Module on computational thinking (CT) skills of form four chemistry students. The CThink4CS2 Module integrated CT with the Engineering Design Process (EDP) in chemistry class. This study utilized quantitative research methods and quasi-experimental design. Quantitative data were collected using the Computational Thinking Skill Test (CTST) which consisted of algorithmic reasoning, abstraction, decomposition, and pattern recognition constructs. A total of 73 students were in the treatment group (n=39) and control group (n=34). Experimental data were described by means of descriptive analysis and inferential analysis employing two-way MANOVA analysis. The results of the analysis indicated significant differences in CT skills between groups; students in the treatment group demonstrated better results compared to those in the control group. The paper provides insight into the integration of CT and EDP as effective pedagogical strategies for inculcating CT skills. [ABSTRACT FROM AUTHOR]

An All-Digital Phase-Locked Loop (ADPLL) is an architecture that is widely employed in the communication system due to the advancement of the Complementary Metal-Oxide-Semiconductor (CMOS) technology process. A 2.4GHz Radio Frequency Identification (RFID) system needs a frequency synthesizer in the local oscillator architecture of the transceiver to generate a stable frequency tuning range Therefore, in this paper, a Digital Phase-Frequency Detector (DPFD) is designed to achieve the phase and frequency acquisition in the ADPLL system. The proposed DPFD is divided into two main parts, the first is the Phase Detector (PD) and the second is the Frequency Detector (FD). The PD has managed to detect the presence of the phase difference by recognizing two different input signals. The FD, on the other hand, is capable to detect the higher frequency by identifying the output signals from the PD in digital formation. In addition, a control unit module is developed to control and adjust the Preset Word (PW) for the system by using a binary search scheme. Comparison results show that the final value of the PW from the simulation is the same as from the manual calculation (theoretical values). The digital PFD and the PW control modules are designed and simulated by using Verilog HDL code. These two designed modules will be integrated into the targeted ADPLL to achieve fast locking performance and ultra-low power for Ultra-High Frequency (UHF) RFID applications. [ABSTRACT FROM AUTHOR]

Hyperuricemia is an alarming issue that contributes to cardiovascular disease. Uric acid (UA) level was proven to be related to pulse wave velocity, a marker of arterial stiffness. A hyperuricemia prediction method utilizing photoplethysmogram (PPG) and arteriograph by using machine learning (ML) is proposed. From the literature search, there is no available papers found that relates PPG with UA level even though PPG is highly associated with vessel condition. The five phases in this research are data collection, signal preprocessing including denoising and signal quality indexes, features extraction for PPG and SDPPG waveform, statistical analysis for feature selection and classification of UA levels using ML. Adding PPG to the current arteriograph able to reduce cost and increase the prediction performance. PPG and arteriograph data were measured from 113 subjects, and 226 sets of data were collected from the left and right hands of the subjects. The performance of four types of ML, namely, artificial neural network (ANN), linear discriminant analysis (LDA), k-nearest neighbor (kNN), and support vector machine (SVM) in predicting hyperuricemia was compared. From the total of 98 features extracted, 16 features of which showed statistical significance for hyper and normouricemia. ANN gives the best performance compared to the other three ML techniques with 91.67%, 95.45%, and 94.12% for sensitivity, specificity, and accuracy, respectively. Features from PPG and arteriograph able to be used to predict hyperuricemia accurately and noninvasively. This study is the first to find the relationship of PPG with hyperuricemia. It shows a significant relation between PPG signals and arteriograph data toward the UA level. The proposed method of UA prediction shows its potential for noninvasive preliminary assessment. [ABSTRACT FROM AUTHOR]

A comprehensive study was conducted to differentiate cardiovascular disease (CVD) subjects from non-CVD subjects using short recording electrocardiogram (ECG) of 244 Malaysian adults in The Malaysian Cohort project. An automated peak detection algorithm to detect nine fiducial points of electrocardiogram (ECG) was developed. Forty-eight features were extracted in both time and frequency domains, including statistical features obtained from heart rate variability and Poincare plot analysis. These include five new features derived from spectrum counts of five different frequency ranges. Feature selection was then made based on p-value and correlation matrix. Selected features were used as input for five classifiers of artificial neural network (ANN), k-nearest neighbors (kNN), support vector machine (SVM), discriminant analysis (DA), and decision tree (DT). Results showed that six features related to T wave were statistically significant in distinguishing CVD and non-CVD groups. ANN had performed the best with 94.44% specificity and 86.3% accuracy, followed by kNN with 80.56% specificity, 86.49% sensitivity and 83.56% accuracy. The novelties of this study were in providing alternative solutions to detect P-onset, P-offset, T-offset as well as QRS-onset points using discrete wavelet transform method. Additionally, two out of the five newly proposed spectral features were significant in differentiating both groups, at frequency ranges of 1-10 Hz and 5-10 Hz. The prediction outcomes were also comparable to previous related studies and significantly important in using ECG to predict cardiac-related events among CVD and non-CVD subjects in the Malaysian population. [ABSTRACT FROM AUTHOR]

This paper presents a bibliographical survey of recently published research on different techniques for extracting features from photoplethysmograms (PPG). These techniques and approaches have been implemented to increase the accuracy of disease detection. Moreover, several aspects of PPG waveform analysis, including techniques for feature extraction, parameters involved, and performance comparisons, are discussed. This review will serve as a comparative study and reference for researches working on PPG waveform in healthcare applications. [ABSTRACT FROM AUTHOR]

The demand for small, thin, and lightweight electronic devices is increasing. More advanced design and assembly processes of electronic packaging technology have developed to fulfill this need. The critical processes in semiconductor packaging involved in meeting the ever increasing demands of technology include wafer back grinding, dicing, and die attachment. With low die thickness, the risk of die failure, which can cause functional damage, is high. In the die attachment process, the pin ejector causes an impact during the pick and place process. Those effects can result in a micro indentation or micro crack under the die and would be the weak point throughout the entire process. This study designed and evaluated an ejector system for the die attachment process. The proposed method uses a static pole heated inside the cavity for the platform to die before being ejected. Vacuum stabilizes the die suction. Moreover, heat softens the sawing tape and weakens the die adhesion. For die selection during the die attachment process, the results show that the critical die crack problem for a thin and rectangular die is solved using the proposed method. In summary, the packaging of semiconductors has advanced to accommodate the pick-up technology solution in relation to the challenging material needed for the current miniaturization market trend and demand. [ABSTRACT FROM AUTHOR]

The demand for producing small, thin, and light electronic devices is increasing. As a result, the design and assembly of electronic packaging technology have been developed. To meet the ever-increasing technology requirements, the critical process in the semiconductor packaging include wafer back grinding, sawing, and die attach. Given that the die thickness is lower than the previous ones, the risk of die cracking failures, which can lead to device malfunction, becomes high. In the die attach process, the ejector pin has an effect during the pick and place processes. Such impact may result in micro dented mark or micro crack underneath the die, which becomes the weakened point throughout the entire process. In this study, an ejectorless system for the die pick and place during the die attach process has been designed and evaluated. The methodology of using ejector pin is replaced by heated static pillar inside cavity for die platform before being picked up. Vacuum is used to stabilize the die, and heat is applied to soften the sawing tape and weaken the adhesion of the die to the sawing tape. Results show that the critical issues of die crack for thin high aspect ratio die are resolved by using the proposed method for the die pick during the die attach process. In conclusion, the semiconductor packaging advances the pick-up technology solution for the challenging material, which is needed for the current miniaturization market trend and demand. [ABSTRACT FROM AUTHOR]

Cardiovascular disease (CVD) is the leading cause of deaths worldwide. In 2017, CVD contributed to 13,503 deaths in Malaysia. The current approaches for CVD prediction are usually invasive and costly. Machine learning (ML) techniques allow an accurate prediction by utilizing the complex interactions among relevant risk factors. This study presents a case-control study involving 60 participants from The Malaysian Cohort, which is a prospective population-based project. Five parameters, namely, the R-R interval and root mean square of successive differences extracted from electrocardiogram (ECG), systolic and diastolic blood pressures, and total cholesterol level, were statistically significant in predicting CVD. Six ML algorithms, namely, linear discriminant analysis, linear and quadratic support vector machines, decision tree, knearest neighbor, and artificial neural network (ANN), were evaluated to determine the most accurate classifier in predicting CVD risk. ANN, which achieved 90% specificity, 90% sensitivity, and 90% accuracy, demonstrated the highest prediction performance among the six algorithms. In summary, by utilizing ML techniques, ECG data can serve as a good parameter for CVD prediction among the Malaysian multiethnic population. [ABSTRACT FROM AUTHOR]

Shannon energy-based algorithm has been implemented in peak detection method of various physiological signals including electrocardiogram, which is used to enhance significant peaks for accurate peak detection. Two significant methods of R-peak detection that apply Shannon energy are identified. However, direct comparison cannot be made due to the differences in database used, number of beat analysed, frequency range selected, and signal processing technique applied. This paper aimed to properly evaluate the performance of Shannon energy-based algorithms for R-peak detection on two methods of bandpass filter and Stockwell transform. Simple enveloping technique using moving average filter is proposed, and a threshold is set to localize R-peak at a selected frequency range of 7-15 Hz. Performance of both methods were then evaluated using all 48 data from MIT-BIH Arrhythmia database. Result showed that both methods are equivalently useful in reducing P and T waves interference and produced similar output of Shannon energy envelope. However, Shannon energy application on bandpass filter offered 99.71% sensitivity, 99.80% positive predictivity and 99.52% accuracy, slightly better than that of the Stockwell transform method that only produced 99.65% sensitivity, 99.68% positive predictivity and 99.33% accuracy. [ABSTRACT FROM AUTHOR]