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Abstract
The omnipresence of microplastics (MPs) in marine and terrestrial environments as a pollutant of concern is well established and widely discussed in the literature. However, studies on MP contamination in commercial food sources like salts from the terrestrial environment are scarce. Thus, this is the first study to investigate various varieties of Australian commercial salts (both terrestrial and marine salts) as a source of MPs in the human diet, and the first to detect MPs in black salt. Using Nile red dye, the MPs were detected and counted under light microscopy, further characterised using attenuated total reflectance Fourier transformed infrared spectroscopy (ATR-FTIR) and scanning electron microscopy and energy-dispersive X-ray spectroscopy (SEM–EDS). Of all the 90 suspected particles, 78.8% were identified as MPs with a size ranging between 23.2 µm and 3.9 mm. The fibres and fragments constituted 75.78% and 24.22% respectively. Among the tested samples, Himalayan pink salt (coarse) from terrestrial sources was found to have the highest MP load, i.e. 174.04 ± 25.05 (SD) particle/kg, followed by black salt at 157.41 ± 23.13 particle/kg. The average concentration of detected MPs in Australian commercial salts is 85.19 ± 63.04 (SD) per kg. Polyamide (33.8%) and polyurethane (30.98%) were the dominant MP types. Considering the maximum recommended (World Health Organization) salt uptake by adults daily at 5 g, we interpret that an average person living in Australia may be ingesting approximately 155.47 MPs/year from salt uptake. Overall, MP contamination was higher in terrestrial salts (such as black and Himalayan salt) than the marine salt. In conclusion, we highlight those commercial salts used in our daily lives serve as sources of MPs in the diet, with unknown effects on human health.

With the emergence of Low-Cost Sensor (LCS) devices, measuring real-time data on a large scale has become a feasible alternative approach to more costly devices. Over the years, sensor technologies have evolved which has provided the opportunity to have diversity in LCS selection for the same task. However, this diversity in sensor types adds complexity to appropriate sensor selection for monitoring tasks. In addition, LCS devices are often associated with low confidence in terms of sensing accuracy because of the complexities in sensing principles and the interpretation of monitored data. From the data analytics point of view, data quality is a major concern as low-quality data more often leads to low confidence in the monitoring systems. Therefore, any applications on building monitoring systems using LCS devices need to focus on two main techniques: sensor selection and calibration to improve data quality. In this paper, data-driven techniques were presented for sensor calibration techniques. To validate our methodology and techniques, an air quality monitoring case study from the Bradford district, UK, as part of two European Union (EU) funded projects was used. For this case study, the candidate sensors were selected based on the literature and market availability. The candidate sensors were narrowed down into the selected sensors after analysing their consistency. To address data quality issues, four different calibration methods were compared to derive the best-suited calibration method for the LCS devices in our use case system. In the calibration, meteorological parameters temperature and humidity were used in addition to the observed readings. Moreover, we uniquely considered Absolute Humidity (AH) and Relative Humidity (RH) as part of the calibration process. To validate the result of experimentation, the Coefficient of Determination (R2), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) were compared for both AH and RH. The experimental results showed that calibration with AH has better performance as compared with RH. The experimental results showed the selection and calibration techniques that can be used in designing similar LCS based monitoring systems.

With the emergence of Low-Cost Sensor (LCS) devices, measuring real-time data on a large scale has become a feasible alternative approach to more costly devices. Over the years, sensor technologies have evolved which has provided the opportunity to have diversity in LCS selection for the same task. However, this diversity in sensor types adds complexity to appropriate sensor selection for monitoring tasks. In addition, LCS devices are often associated with low confidence in terms of sensing accuracy because of the complexities in sensing principles and the interpretation of monitored data. From the data analytics point of view, data quality is a major concern as low-quality data more often leads to low confidence in the monitoring systems. Therefore, any applications on building monitoring systems using LCS devices need to focus on two main techniques: sensor selection and calibration to improve data quality. In this paper, data-driven techniques were presented for sensor calibration techniques. To validate our methodology and techniques, an air quality monitoring case study from the Bradford district, UK, as part of two European Union (EU) funded projects was used. For this case study, the candidate sensors were selected based on the literature and market availability. The candidate sensors were narrowed down into the selected sensors after analysing their consistency. To address data quality issues, four different calibration methods were compared to derive the best-suited calibration method for the LCS devices in our use case system. In the calibration, meteorological parameters temperature and humidity were used in addition to the observed readings. Moreover, we uniquely considered Absolute Humidity (AH) and Relative Humidity (RH) as part of the calibration process. To validate the result of experimentation, the Coefficient of Determination (R(2)), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) were compared for both AH and RH. The experimental results showed that calibration with AH has better ...

Background: Diabetes mellitus is a widespread disease, associated with rapid social and cultural changes, such as aging of population, urbanization, dietary changes, reduced physical activity, and unhealthy behaviours, leading to lower quality of life and decreased survival of affected individuals. This study aims to evaluate the sleep quality in patients with type 2 diabetes mellitus (T2DM), and to assess the relevance of other factors to sleep quality. Methods: A cross-sectional study was carried out at the Government general hospital, Ananthapuramu, during the period from December 2020 to May, 2021. A total of 384 patients with T2DM were recruited. Data were collected using the Pittsburgh sleep quality index (PSQI) and ESS to assess the sleep quality with a cutoff point of PSQI ≥ 8. Participants' demographic background data were also recorded. Statistical analysis was conducted by using graph pad prism. Results& discussion: Using Scale scores with cutoff point global PSQI ≥ 8 for sleep evaluationin our study, we found that 77.6% of T2DM patients suffer from poorsleep quality.Our study found that poor sleep quality was higher in employed diabeticpatients, as compared to unemployed patients.This study showed that diabetic patients on insulin treatment were 2.17times more likely to complain of poor sleep quality compared to patients receiving OHA only. Conclusions: Effectiveness of patient counselling by clinical pharmacist which improves the sleep quality. Thus patients reporting with sleep difficulties should be screened for diabetes. Type 2 diabetes patients with poor glycaemic control should be assessed for sleep disorders and if present it should be corrected to achieve optimum control of blood sugar levels. Keywords: Daytime dysfunction, Diabetes mellitus, ESS, Glycaemic control, PSQI, Sleep quality

Crowdsourcing has been known as developing industry that able to be used as a platform to get more income and provide opportunity for busineses to conduct their operation in more innovative ways. Crowdsourcing has become an effective way for the companies to offer work opportunity for crowd outside organization to apply their abilities and skills, and receive more money. Under the Malaysian government initiatives called Digital economy, various crowdsourcing efforts and programs have been introduced to catch up with the global development. The ecosystem of crowdsourcing which consists of job provider, platform, micro worker and industry is considered still in a formative stage. Thus, the integration of all these components is not fully discovered and understood yet which, can cause confusion among the crowdsourcing industry players. In order to understand the complex integration of multi perspective micro sourcing ecosystem, the components and its significance priority level need to be identified. In this study, the components that involved in crowdsourcing ecosystem were identified and ranked using analytical hierarchy process (AHP) method. The results can be later used by crowdsourcing industry players to plan more proper crowd sourcing strategic development in Malaysia