A novel autonomous wireless sensor node for IoT applications

A novel wireless sensor network node (WSNN) is presented in this paper where the solar energy harvester system is used as an autonomous power solution for endless battery lifetime. In this sensor node, the meander-line Inverted-F-Antenna (MIFA) is proposed and integrated in a single -CC2650 chip of Texas Instrument. The simple structure, low cost, compact size, high efficiency and low power consumption are advantages of this single-chip WSNN. The experimental results show that MIFA antenna is promising solution to enhance communication performance in WSN. In addition, the investigated single-chip WSNN with multi-wireless technologies including Bluetooth Low Energy and Zigbee as well as 6LowPAN is an attractive device for internet of thing (IoT) applications

Musculoskeletal Pain and Work-related Risk Factors among Waste Collectors in Hanoi, Vietnam: A Cross-sectional Study

BACKGROUND: Musculoskeletal disorders (MSDs) are prevalent among waste collectors (WCs) in developing countries. AIM: This study aimed to investigate the prevalence of MSDs and the factors associated with the risk of persistent musculoskeletal pain among WCs in Hanoi, Vietnam. METHODS: A cross-sectional survey was utilized to study 468 WCs in 2017. The Örebro Musculoskeletal Pain Questionnaire and a questionnaire on demographic and work conditions were used to collect data. Descriptive and multivariate logistics regression analyzes were applied at a significance level of 0.05 to examine the factors related to the risk of persistent pain. FINDINGS: About 74.4% of the participants of this study experienced MSDs in at least one body region and 9.4% reported MSDs in all 10 body sites. The lower back was reported to be the most affected followed by the neck and shoulders. The risk of persistent musculoskeletal pain was significantly associated with age (odds ratio (OR) = 2.31, confidence interval (CI) = 1.05–5.09), gender (OR = 3.29, CI = 1.28–8.44), work hours (OR = 2.35, CI = 1.12–4.92), work shift (OR = 0.48, CI = 0.26–0.92), duration of poor postures of the neck (OR = 0.31, CI = 0.13–0.76), bent back (OR = 0.4 CI = 0.18–0.92) and for medial rotation (OR = 3.01, CI = 1.42–6.36), carrying heavy objects (OR = 2.94, CI = 1.15–7.48), and experience of work dissatisfaction (OR = 3.31, CI = 1.46-7.52), stress (OR = 7.14, CI = 3.14–16.24), or anxiety (OR = 6.37, CI = 3.07–13.21). CONCLUSIONS: High prevalence of MSDs among WCs and its association with self-assessed unfavorable work postures and work-related stress implies the need of mechanical and social support at work for WC to prevent the development of MSDs and persistent pain

BARTPhoBEiT: Pre-trained Sequence-to-Sequence and Image Transformers Models for Vietnamese Visual Question Answering

Visual Question Answering (VQA) is an intricate and demanding task that integrates natural language processing (NLP) and computer vision (CV), capturing the interest of researchers. The English language, renowned for its wealth of resources, has witnessed notable advancements in both datasets and models designed for VQA. However, there is a lack of models that target specific countries such as Vietnam. To address this limitation, we introduce a transformer-based Vietnamese model named BARTPhoBEiT. This model includes pre-trained Sequence-to-Sequence and bidirectional encoder representation from Image Transformers in Vietnamese and evaluates Vietnamese VQA datasets. Experimental results demonstrate that our proposed model outperforms the strong baseline and improves the state-of-the-art in six metrics: Accuracy, Precision, Recall, F1-score, WUPS 0.0, and WUPS 0.9