Recognizing human physical activities from streaming smartphone sensor readings is essential for the successful realization of a smart environment. Physical activity recognition is one of the active research topics to provide users the adaptive services using smart devices. Existing physical activity recognition methods lack in providing fast and accurate recognition of activities. This paper proposes an approach to recognize physical activities using only2-axes of the smartphone accelerometer sensor. It also investigates the effectiveness and contribution of each axis of the accelerometer in the recognition of physical activities. To implement our approach, data of daily life activities are collected labeled using the accelerometer from 12 participants. Furthermore, three machine learning classifiers are implemented to train the model on the collected dataset and in predicting the activities. Our proposed approach provides more promising results compared to the existing techniques and presents a strong rationale behind the effectiveness and contribution of each axis of an accelerometer for activity recognition. To ensure the reliability of the model, we evaluate the proposed approach and observations on standard publicly available dataset WISDM also and provide a comparative analysis with state-of-the-art studies. The proposed approach achieved 93% weighted accuracy with Multilayer Perceptron (MLP) classifier, which is almost 13% higher than the existing methods.

Unmanned aerial vehicles (UAVs), when interconnected in a multi-hop ad-hoc fashion, or as a flying ad-hoc network (FANET), can efficiently accomplish mission-critical tasks. However, UAVs usually suffer from the issues of shorter lifespan and limited computational resources. Therefore, the existing security approaches, being fragile, are not capable of countering the attacks, whether known or unknown. Such a security lapse can result in a debilitated FANET system. In order to cope up with such attacks, various efficient signature schemes have been proposed. Unfortunately, none of the solutions work effectively because of incurred computational and communication costs. We aimed to resolve such issues by proposing a blind signature scheme in a certificateless setting. The scheme does not require public-key certificates, nor does it suffer from the key escrow problem. Moreover, the data that are aggregated from the platform that monitors the UAVs might be too huge to be processed by the same UAVs engaged in the monitoring task. Due to being latency-sensitive, it demands high computational capability. Luckily, the envisioned fifth generation (5G) mobile communication introduces multi-access edge computing (MEC) in its architecture. MEC, when incorporated in a UAV environment, in our proposed model, divides the workload between UAVs and the on-board microcomputer. Thus, our proposed model extends FANET to the 5G mobile network and enables a secure communication between UAVs and the base station (BS).

Assessment of children's behavior in the dental setting and its correlation with thumb print and lip print. The behavior pattern, lip print pattern, and thumb print pattern of 518 children aged 5–12 years were recorded in their first dental visit. The behavior pattern, lip print pattern, and thumb print pattern were determined using Frankl's Behavior Rating Scale, Tsuchihashi's classification, and Cummin's classification, respectively. The data were tabulated according to the classified criteria and statistically analyzed. It was observed that positive and definitely positive Frankl Behavior was associated with the children bearing an arch pattern of thumb print, whereas their lip pattern had no association with their behavior pattern, particularly in dental sittings. Thumb print pattern can aid as a behavior marker before a child's first dental appointment and this can prove beneficial for both the child and the clinician in providing a high quality of dental care and modify behavior for future dental appointments. Navit S, Pramanik S, Khan SA, et al. Cheiloscopy and Dactyloscopy as Behavior Assessment Tool in Dental Settings: A Cross-sectional Study. Int J Clin Pediatr Dent 2021;14(2):238–242.

The term “Mucocele” originates from Latin words ‘mucus’ which means ‘fluid’ and ‘coele’ which means ‘cavity’. It is defined as the accumulation of mucus in the oral cavity’s subepithelial tissue secreted from salivary glands and their ducts. It is a frequently occurring salivary gland lesion which is seen in the oral cavity. They occur as mucus extravasation and mucus retention type. They may be obstructive in nature and occur due to trauma to a minor salivary gland duct which results in pooling in the epithelial tissue. Clinically, a mucocele appears as a typical blue colored dome shaped swelling which is soft in consistency and contains salivary fluid. It lies superficial or deeply attached to the tissues. Management of mucocele includes surgical excision, marsupialization, micro marsupialization, cryosurgery, laser vaporization, and laser excision. Recently, high - intensity lasers have been proven to be more beneficial than conventional surgery as it causes prompt hemostasis, minimal blood loss and reduced healing time of the site.