Hands off : a handshake interaction detection and localization model for COVID-19 threat control

A handshake interaction localization model in real-time that may help mitigate the threat for transmitting COVID-19, is presented using computer vision in a non-intrusive technique. A real-time detection model (using YOLO/you only look once) is proposed to identify handshake interactions in realistic scenarios. YOLO can detect multiple interactions in a single frame. The model can be applied to public spaces to identify handshake interactions. The study is the first to use a human interaction localization model in a multi-person setting. YOLO is a convolutional neural network (CNN) for object detection in real-time.Lewis Power, Singapor

Holistic interpretation of public scenes using computer vision and temporal graphs to identify social distancing violations

Social distancing measures are proposed as the primary strategy to curb the spread of the COVID-19 pandemic. Therefore, identifying situations where these protocols are violated has implications for curtailing the spread of the disease and promoting a sustainable lifestyle. This paper proposes a novel computer vision-based system to analyze CCTV footage to provide a threat level assessment of COVID-19 spread. The system strives to holistically interpret the information in CCTV footage spanning multiple frames to recognize instances of various violations of social distancing protocols, across time and space, as well as identification of group behaviors. This functionality is achieved primarily by utilizing a temporal graph-based structure to represent the information of the CCTV footage and a strategy to holistically interpret the graph and quantify the threat level of the given scene. The individual components are evaluated in a range of scenarios, and the complete system is tested against human expert opinion. The results reflect the dependence of the threat level on people, their physical proximity, interactions, protective clothing, and group dynamics, with a system performance of 76% accuracy

Holistic interpretation of public scenes using computer vision and temporal graphs to identify social distancing violations

Social distancing measures are proposed as the primary strategy to curb the spread of the COVID-19 pandemic. Therefore, identifying situations where these protocols are violated has implications for curtailing the spread of the disease and promoting a sustainable lifestyle. This paper proposes a novel computer vision-based system to analyze CCTV footage to provide a threat level assessment of COVID-19 spread. The system strives to holistically interpret the information in CCTV footage spanning multiple frames to recognize instances of various violations of social distancing protocols, across time and space, as well as identification of group behaviors. This functionality is achieved primarily by utilizing a temporal graph-based structure to represent the information of the CCTV footage and a strategy to holistically interpret the graph and quantify the threat level of the given scene. The individual components are evaluated in a range of scenarios, and the complete system is tested against human expert opinion. The results reflect the dependence of the threat level on people, their physical proximity, interactions, protective clothing, and group dynamics, with a system performance of 76% accuracy

A sensitivity matrix approach using two-stage optimization for voltage regulation of LV networks with high PV penetration

The occurrence of voltage violations is a major deterrent for absorbing more rooftop solar power into smart Low-Voltage Distribution Grids (LVDGs). Recent studies have focused on decentralized control methods to solve this problem due to the high computational time in performing load flows in centralized control techniques. To address this issue, a novel sensitivity matrix was developed to estimate the voltages of the network by replacing load flow simulations. In this paper, a Centralized Active, Reactive Power Management System (CARPMS) is proposed to optimally utilize the reactive power capability of smart Photovoltaic (PV) inverters with minimal active power curtailment to mitigate the voltage violation problem. The developed sensitivity matrix is able to reduce the time consumed by 55.1% compared to load flow simulations, enabling near-real-time control optimization. Given the large solution space of power systems, a novel two-stage optimization is proposed, where the solution space is narrowed down by a Feasible Region Search (FRS) step, followed by Particle Swarm Optimization (PSO). The failure of standalone PSO to converge to a feasible solution for 34% of the scenarios evaluated further validates the necessity of the two-stage optimization using FRS. The performance of the proposed methodology was analysed in comparison to the load flow method to demonstrate the accuracy and the capability of the optimization algorithm to mitigate voltage violations in near-real time. The deviations of the mean voltages of the proposed methodology from the load flow method were: 6.5×10−3 p.u for reactive power control using Q-injection, 1.02×10−2 p.u for reactive power control using Q-absorption, and 0 p.u for active power curtailment case

Blockchain-based digital contact tracing apps for COVID-19 pandemic management: Issues, challenges, solutions, and future directions

The COVID-19 pandemic has caused substantial global disturbance by affecting more than 42 million people (as of the end of October 2020). Since there is no medication or vaccine available, the only way to combat it is to minimize transmission. Digital contact tracing is an effective technique that can be utilized for this purpose, as it eliminates the manual contact tracing process and could help in identifying and isolating affected people. However, users are reluctant to share their location and contact details due to concerns related to the privacy and security of their personal information, which affects its implementation and extensive adoption. Blockchain technology has been applied in various domains and has been proven to be an effective approach for handling data transactions securely, which makes it an ideal choice for digital contact tracing apps. The properties of blockchain such as time stamping and immutability of data may facilitate the retrieval of accurate information on the trail of the virus in a transparent manner, while data encryption assures the integrity of the information being provided. Furthermore, the anonymity of the user’s identity alleviates some of the risks related to privacy and confidentiality concerns. In this paper, we provide readers with a detailed discussion on the digital contact tracing mechanism and outline the apps developed so far to combat the COVID-19 pandemic. Moreover, we present the possible risks, issues, and challenges associated with the available contact tracing apps and analyze how the adoption of a blockchain-based decentralized network for handling the app could provide users with privacy-preserving contact tracing without compromising performance and efficiency

Antifungal efficacy of botanicals against major postharvest pathogens of Kinnow mandarin and their use to maintain postharvest quality

Introduction. Kinnow is an important citrus crop grown in India, which suffers from several postharvest diseases during storage. Hence, an attempt was made to combat such diseases with the botanicals Aloe vera, Eucalyptus and Ocimum on Kinnow mandarin to prolong its availability for a longer time. Materials and methods. For this, in vitro and in vivo studies were conducted. The poisoned food technique was used for in vitro studies, and, for in vivo studies, Kinnow fruit were pre-inoculated with pathogens (Penicillium digitatum and P. italicum), treated with different botanicals, then stored at (5 ± 1) °C temperature and 85–90% RH. Results and discussion. Our results indicated that all botanicals inhibited the growth (colony diameter) of both pathogens over untreated PDA plates, but the inhibition was the strongest by Aloe vera extracts. Similarly, under in vivo conditions, all botanicals influenced the decay incidence, decay loss, lesion diameter, respiration rate, ethylene evolution and physiological loss in weight, but Aloe vera was the most effective. All the botanicals were able to retain postharvest quality of Kinnow fruits without any adverse effect on quality parameters such as TSS, TA and ascorbic acid. Under in vivo conditions, the incidence of Penicillium italicum was higher than P. digitatum; however, it was the reverse under in vitro conditions. Conclusion. Thus, it is evident from our studies that botanicals have the potential to control green and blue mold without causing any injury or harmful effects on Kinnow mandarin; botanicals can be recommended as a safe method for extending its storage life while maintaining fruit quality at the same time

Exploring the Blockchain Technology: Issues, Applications and Research Potential

The blockchain is revolutionizing the current IT industry by increasing its integration with other prominent technologies like Artificial Intelligence, Internet of Things, Big Data and Cloud Computing to name a few. It works as a distributed network with no central authority, where the data is continuously added in the form of blocks. The blocks are validated by the network itself, which makes it transparent and secure. It was first used as a ledger for the transactions of a crypto currency named bitcoin, but with time, innumerable industries have started to implement it. And because of its promising future, it would not take much time to spread in all the IT related areas globally. The blockchain technology is contemplated to be a technology that is still in its infancy and needs a thorough research so that it can be utilized to its full capacity. Consequently, in order to provide a thorough analysis, this paper gives a systematic and comprehensive review of architecture and working of the blockchain along with its types and important characteristics. This paper also discusses about the prominent blockchain platforms available today along with the description of diverse blockchain supported application areas. The issues related to the blockchain technology and the areas in which the work can be done in future are also pointed in this paper, which would be helpful for practitioners and researchers who are willing to work on this fast growing platform

Security Aspects of Blockchain Technology Intended for Industrial Applications

Blockchain technology plays a significant role in the industrial development. Many industries can potentially benefit from the innovations blockchain decentralization technology and privacy protocols offer with regard to securing, data access, auditing and managing transactions within digital platforms. Blockchain is based on distributed and secure decentralized protocols in which there is no single authority, and no single point of control; the data blocks are generated, added, and validated by the nodes of the network themselves. This article provides insights into the current developments within blockchain technology and explores its ability to revolutionize the multiple industrial application areas such as supply chain industry, Internet of Things (IoT), healthcare, governance, finance and manufacturing. It investigates and provides insights into the security issues and threats related to the blockchain implementations by assessing the research through a systematic literature review. This article proposes possible solutions in detail for enhancing the security of the blockchain for industrial applications along with significant directions for future explorations. The study further suggests how in recent years the adoption of blockchain technology by multiple industrial sectors has gained momentum while in the finance sector it is touching new heights day by day