Lossless Data Compression for Time-Series Sensor Data Based on Dynamic Bit Packing.

In this paper, we propose a bit depth compression (BDC) technique, which performs bit packing by dynamically determining the pack size based on the pattern of the bit depth level of the sensor data, thereby maximally reducing the space wastage that may occur during the bit packing process. The proposed technique can dynamically perform bit packing according to the data's characteristics, which may have many outliers or several multidimensional variations, and therefore has a high compression ratio. Furthermore, the proposed method is a lossless compression technique, which is especially useful as training data in the field of artificial intelligence or in the predictive analysis of data science. The proposed method effectively addresses the spatial inefficiency caused by unpredictable outliers during time-series data compression. Additionally, it offers high compression efficiency, allowing for storage space savings and optimizing network bandwidth utilization while transmitting large volumes of data. In the experiment, the BDC method demonstrated an improvement in the compression ratio of up to 247%, with 30% on average, compared with other compression algorithms. In terms of energy consumption, the proposed BDC also improves data transmission using Bluetooth up to 34%, with 18% on average, compared with other compression algorithms.

Correction: Digital Device Exposure and Cognition Levels of Children in Low- and Middle-Income Countries: Cross-sectional Study in Cambodia.

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Digital Device Exposure and Cognition Levels of Children in Low- and Middle-Income Countries: Cross-sectional Study in Cambodia.

BACKGROUND: Policy makers and practitioners in low- and middle-income countries (LMICs) are increasingly focusing on the effectiveness of digital devices in the delivery of medical and educational services to children under resource constraints. It is widely known that digital literacy can be fostered through exposure to and education regarding digital devices, which can improve children's academic performance as well as their search and communication skills in the digital era. However, the correlation between the cognitive function of children and exposure and intensity of the exposure to digital devices has rarely been studied, and the association between digital device exposure and the socioeconomic characteristics and cognitive development of children in LMICs is unknown. OBJECTIVE: This study examines the association among exposure to digital devices, socioeconomic status, and cognitive function in children aged 3 to 9 years in Cambodia. METHODS: We used a survey of 232 children that gathered data on familiarity with digital devices, demographic characteristics, and socioeconomic status, as well as a Cambridge Neuropsychological Test Automated Battery test for cognitive function, to examine the association between possible barriers and factors that may influence the cognitive function of children in 2 Cambodian schools from April 22, 2019, to May 4, 2019. A comparative analysis was performed with and without digital exposure, and an association analysis was performed among the variables from the survey and cognitive function. RESULTS: Significant differences were observed in demographic and socioeconomic characteristics such as school location, family type, and family income according to digital device exposure. The results of the Cambridge Neuropsychological Test Automated Battery tests, except for 1 test related to executive function, indicated no significant differences (P>.05) between group A and group B or among the 4 subgroups. Pretest digital device experience and amount of time spent using digital devices during the test had no significant impacts on the cognitive development of the children. Conversely, the multivariate analyses showed that cognitive function was associated with educational expenses per child, school (location), family type, and family income. CONCLUSIONS: These results provide evidence to policy makers and practitioners on the importance of improving socioeconomic conditions, leading to investment in education by implementing programs for children's cognitive development through digital devices in LMICs.

Measuring the cumulative effect of G force on aviator neck pain.

INTRODUCTION: In many studies, high G force is thought to be the most important causal factor of aviator neck pain. However, most of the studies performed to date have compared neck pain of aviators exposed to high G force with that of aviators not so exposed or with that of the general population. As a result, neck pain for aviators exposed to high G force as it relates to G level is not yet well-known. METHODS: The subjects were 1003 male aviators who were sent an anonymous questionnaire. Subject variables were information on physical characteristics, lifestyle, and flight characteristics, including information about exposed maximum G (Gmax). There were three dependent variables: whether the aviator had experienced neck pain, frequency of neck pain, and subjective severity of neck pain. RESULT: With the multivariate analysis, amount of Gmax exposure (frequency or monthly duration of Gmax exposure) had a statistically significant positive relationship with all three dependent variable. BMI, flight hours in current aircraft, and posterior seat type had a meaningful positive relationship with experience of neck pain and subjective severity of neck pain. Other characteristics, including Gmax level, were not significantly related with any dependent variables. DISCUSSION: These results suggest that G level could have a ceiling effect; in other words, above a certain G level, aviator neck pain risk does not increase. This means that amount of Gmax exposure becomes more influential on aviator neck pain than the level of Gmax.