The mortality of oral cancer attributable to tobacco in China, the US, and India.

PURPOSE: Assessing the mortality rates associated with tobacco-related oral cancer (OC) is crucial for effective allocation of resources within healthcare and economic systems. METHODS: In this study, data from the Global Burden of Disease Study (GBD) 2019 were utilized to analyze the burden of tobacco-attributable OC in China, the United States (US), and India from 1990 to 2019. Descriptive statistics and an age-period-cohort model were employed to examine and compare the effects on OC mortality. RESULTS: 1. Attributable to tobacco, the deaths remained stable in the US, but increased in China and India. The trend of age-standardized mortality rate of OC increased in China, and decreased in the US and India, whereas the rate in India was the highest. 2. According to the APC model, the risk of death increased with age in all three countries. The period and later birth cohort effects were identified as risk factors in China and India, while in the US, the previous cohorts were identified as a risk factor. Except for India, males faced higher death risk than females in China and the US. CONCLUSIONS: The burden of OC attributable to tobacco remains substantial in China and India. Public health officials in these countries should implement prevention and treatment strategies for OC, and interventions aimed at regulating the tobacco industry. The elderly is at an elevated risk for OC, and medical resources and policies should be directed toward this population. The successes experience in tobacco control and OC prevention in the US may serve as a model for other countries.

Skeleton-Based Abnormal Behavior Detection Using Secure Partitioned Convolutional Neural Network Model.

Theabnormal behavior detection is the vital for evaluation of daily-life health status of the patient with cognitive impairment. Previous studies about abnormal behavior detection indicate that convolution neural network (CNN)-based computer vision owns the high robustness and accuracy for detection. However, executing CNN model on the cloud possible incurs a privacy disclosure problem during data transmission, and the high computation overhead makes difficult to execute the model on edge-end IoT devices with a well real-time performance. In this paper, we realize a skeleton-based abnormal behavior detection, and propose a secure partitioned CNN model (SP-CNN) to extract human skeleton keypoints and achieve safely collaborative computing by deploying different CNN model layers on the cloud and the IoT device. Because, the data outputted from the IoT device are processed by the several CNN layers instead of transmitting the sensitive video data, objectively it reduces the risk of privacy disclosure. Moreover, we also design an encryption method based on channel state information (CSI) to guarantee the sensitive data security. At last, we apply SP-CNN in abnormal behavior detection to evaluate its effectiveness. The experiment results illustrate that the efficiency of the abnormal behavior detection based on SP-CNN is at least 33.2% higher than the state-of-the-art methods, and its detection accuracy arrives to 97.54%.

Research on Secure Debugging Interaction of Sensor Nodes Based on Visible Light Communication.

When a wireless sensor node's wireless communication fails after being deployed in an inaccessible area, the lost node cannot be repaired through a debugging interaction that relies on that communication. Visible light communication (VLC) as a supplement of radio wave communication can improve the transmission security at the physical layer due to its unidirectional propagation characteristic. Therefore, we implemented a VLC-based hybrid communication debugging system (HCDS) based on VLC using smartphone and sensor node. For the system's downlink, the smartphone is taken as the VLC gateway and sends the debugging codes to the sensor node by the flashlight. To improve the transmission efficiency of the downlink, we also propose a new coding method for source coding and channel coding, respectively. For the source coding, we analyze the binary instructions and compress the operands using bitmask techniques. The average compression rate of the binary structure reaches 84.11%. For the channel coding, we optimize dual-header pulse interval (DH-PIM) and propose overlapped DH-PIM (ODH-PIM) by introducing a flashlight half-on state. The flashlight half-on state can improve the representation capability of individual symbols. For the uplink of HCDS, we use the onboard LED of the sensor node to transmit feedback debugging information to the smartphone. At the same time, we design a novel encoding format of DH-PIM to optimize uplink transmission. Experimental results show that the optimized uplink transmission time and BER are reduced by 10.71% and 22%, compared with the original DH-PIM.