The popularity of big data analytics (BDA) has boosted the interest of organisations into exploiting their large scale data. This technology can become a strategic stimulation for organisations to achieve competitive advantage and sustainable growth. Previous BDA research, however, has focused more on introducing more traits, known as Vs for big data traits, while ignoring the quality of data when examining the application of BDA. Therefore, this study aims to explore the effect of big data traits and data quality dimensions on BDA application. This study has formulated 10 hypotheses that comprised of the relationships of big data traits, accuracy, believability, completeness, timeliness, ease of operation, and BDA application constructs. This study conducted a survey using a questionnaire as a data collection instrument. Then, the partial least squares structural equation modelling technique was used to analyse the hypothesised relationships between the constructs. The findings revealed that big data traits can significantly affect all constructs for data quality dimensions and that the ease of operation construct has a significant effect on BDA application. This study contributes to the literature by bringing new insights to the field of BDA and may serve as a guideline for future researchers and practitioners when studying BDA application.

Recycling is one of the most efficient methods for environmental friendly waste management. Among municipal wastes, plastics are the most common material that can be easily recycled and polyethylene terephthalate (PET) is one of its major types. PET material is used in consumer goods packaging such as drinking bottles, toiletry containers, food packaging and many more. Usually, a recycling process is tailored to a specific material for optimal purification and decontamination to obtain high grade recyclable material. The quantity and quality of the sorting process are limited by the capacity of human workers that suffer from fatigue and boredom. Several automated sorting systems have been proposed in the literature that include using chemical, proximity and vision sensors. The main advantages of vision based sensors are its environmentally friendly approach, non-intrusive detection and capability of high throughput. However, the existing methods rely heavily on deterministic approaches that make them less accurate as the variations in PET plastic waste appearance are too high. We proposed a probabilistic approach of modeling the PET material by analyzing the reflection region and its surrounding. Three parameters are modeled by Gaussian and exponential distributions: color, size and distance of the reflection region. The final classification is made through a supervised training method of likelihood ratio test. The main novelty of the proposed method is the probabilistic approach in integrating various PET material signatures that are contaminated by stains under constant lighting changes. The system is evaluated by using four performance metrics: precision, recall, accuracy and error. Our system performed the best in all evaluation metrics compared to the benchmark methods. The system can be further improved by fusing all neighborhood information in decision making and by implementing the system in a graphics processing unit for faster processing speed.

Currently, many recycling activities adopt manual sorting for plastic recycling that relies on plant personnel who visually identify and pick plastic bottles as they travel along the conveyor belt. These bottles are then sorted into the respective containers. Manual sorting may not be a suitable option for recycling facilities of high throughput. It has also been noted that the high turnover among sorting line workers had caused difficulties in achieving consistency in the plastic separation process. As a result, an intelligent system for automated sorting is greatly needed to replace manual sorting system. The core components of machine vision for this intelligent sorting system is the image recognition and classification. In this research, the overall plastic bottle sorting system is described. Additionally, the feature extraction algorithm used is discussed in detail since it is the core component of the overall system that determines the success rate. The performance of the proposed feature extractions were evaluated in terms of classification accuracy and result obtained showed an accuracy of more than 80%

Background. Pre-school children are at a higher risk to acquire infectious diseases such as hand, foot and mouth disease due to their immature immune system. Good hand hygiene prevents transmission of infectious diseases. This study aimed to determine the knowledge and practices of hand hygiene among pre-schoolers. Methods. In this prospective, multi-center study, the pre-schools were selected according to the selection criteria. A questionnaire consisting of socio-demographics, knowledge and practices of hand hygiene were administered via face-to-face interview during the pre- and post-intervention period. A total of 435 pre-schoolers aged 5 and 6 years old from 2 pre-schools within Klang Valley, School P (test group) and School C (control group) were involved in this study. The test group was provided with comprehensive hand hygiene education including video on proper handwashing technique during the 2 months intervention period, whereas the control group did not receive any form of intervention. The data were statistically analyzed using descriptive analysis and independent t-test. Results. Majority of pre-schoolers gained knowledge of handwashing from their parents. However, only 63% demonstrated good handwashing technique. Test group were significantly better ( P < 0.05) in handwashing technique and hand hygiene routine score. Conclusion. A comprehensive hand hygiene education program should include proper handwashing facilities, resources, and awareness of the care-givers in instilling and sustaining good hand hygiene behavior.

Background Absenteeism amongst preschool children is often due to illnesses such as hand, foot, and mouth disease, acute gastroenteritis, cold and flu, which are easily spread amongst them. This is because of weak immunity and lack of knowledge on proper hand hygiene. This quasi-experimental study assessed the efficacy of an intervention consisting of a hand hygiene education programme, along with digital tools in bringing about a change in behaviour and health conditions amongst preschool children in Klang Valley, Malaysia. Methods A total of 377 school children, male and female, aged 5-6 years old, participated and were assigned to either the intervention or a control group. During the 2 months intervention period, children in the test group were trained on proper hand hygiene practices and techniques with the aid of the interactive android-based tablets. The numbers of absent days of all the children were recorded for 2 months before the intervention and during the intervention. Results In the test group, there was a 25% increase in the total number of absent days from the pre-intervention period to the intervention period, a much lesser increment observed as compared to that of control group in which the increase was much higher at 89%. Results showed a significant difference (P < 0•05) between the absenteeism rates for the test and control group during the intervention period. Conclusion These results suggest that proper education and intervention increase hand hygiene compliance, which may help decrease school absenteeism due to illness; however, a longer study duration may be necessary to evaluate the benefit further.