Falling is one of the causes of accidental death of elderly people over 65 years old in Taiwan. If the fall incidents are not detected in a timely manner, it could lead to serious injury or even death of those who fell. General fall detection approaches require the users to wear sensors, which could be cumbersome for the users to put on, and misalignment of sensors could lead to erroneous readings. In this paper, we propose using computer vision and applied machine-learning algorithms to detect fall without any sensors. We applied OpenPose real-time multi-person 2D pose estimation to detect movement of a subject using two datasets of 570 × 30 frames recorded in five different rooms from eight different viewing angles. The system retrieves the locations of 25 joint points of the human body and detects human movement through detecting the joint point location changes. The system is able to effectively identify the joints of the human body as well as filtering ambient environmental noise for an improved accuracy. The use of joint points instead of images improves the training time effectively as well as eliminating the effects of traditional image-based approaches such as blurriness, light, and shadows. This paper uses single-view images to reduce equipment costs. We experimented with time series recurrent neural network, long- and short-term memory, and gated recurrent unit models to learn the changes in human joint points in continuous time. The experimental results show that the fall detection accuracy of the proposed model is 98.2%, which outperforms the baseline 88.9% with 9.3% improvement.

The utilization of electronic voting systems for the election of public offices is becoming widespread globally. This trend can be attributed to the benefits provided by these systems, including remote voting capabilities and accelerated vote counting. Furthermore, electronic voting systems offer improved privacy and enhanced protection against voting bias. Blockchain technology enhances the robustness of the voting process through its immutable vote storage mechanism, thereby reducing the threat of vote tampering and safeguarding the legitimacy of elections. This technology has been adopted by countries such as Germany, Russia, Estonia, and Switzerland for use in their e-voting systems. This study provides a comprehensive overview of the blockchain-based e-voting systems currently being implemented by various countries and companies and proposed for academic research. Additionally, this study analyzes the challenges faced by blockchain e-voting systems and identifies areas for future research to enhance the trustworthiness of such systems.

In this paper, we introduce a model of task scheduling for a cloud-computing data center to analyze energy-efficient task scheduling. We formulate the assignments of tasks to servers as an integer-programming problem with the objective of minimizing the energy consumed by the servers of the data center. We prove that the use of a greedy task scheduler bounds the constraint service time whilst minimizing the number of active servers. As a practical approach, we propose the most-efficient-server-first task-scheduling scheme to minimize energy consumption of servers in a data center. Most-efficient-server-first schedules tasks to a minimum number of servers while keeping the data-center response time within a maximum constraint. We also prove the stability of most-efficient-server-first scheme for tasks with exponentially distributed, independent, and identically distributed arrivals. Simulation results show that the server energy consumption of the proposed most-efficient-server-first scheduling scheme is 70 times lower than that of a random-based task-scheduling scheme.

Supply chains are challenged with providing transparency and trust among participants and stakeholders and ensuring an efficient operation. However, some of these challenges are difficult to resolve because the records of a supply chain can be altered by its participants. The emergence of blockchain technology has been identified as a promising solution to resolve these challenges. Blockchain is an immutable distributed ledger. Here, we introduce blockchain and survey different blockchain frameworks that have been recently proposed to address some of the supply chain challenges. We describe the operation of these blockchain frameworks and their components. We identify the objectives and motivation in each of the surveyed use cases and highlight the advantages and disadvantages of each adopted framework by different industries. We analyze how the reported blockchain frameworks address supply chain challenges. We present a comparative summary of existing literature on blockchain for supply chain. We also summarize the properties of a blockchain framework for its successful adoption in future supply chains and discuss several challenges and opportunities that remain for future research.

Intersections of flight paths in multidrone missions are indications of a high likelihood of in-flight drone collisions. This likelihood can be proactively minimized during path planning. This article proposes two offline collision-avoidance multidrone path-planning algorithms: 1) DETACH and 2) STEER. Large drone tasks can be divided into smaller ones and carried out by multiple drones. Each drone follows a planned flight path that is optimized to efficiently perform the task. The path planning of the set of drones can then be optimized to complete the task in a short time, with minimum energy expenditure, or with maximum waypoint coverage. Here, we focus on maximizing waypoint coverage. Different from existing schemes, our proposed offline path-planning algorithms detect and remove possible in-flight collisions. They are based on a constrained nearest-neighbor search algorithm that aims to cover a large number of waypoints per flight path. DETACH and STEER perform vector intersection check for flight path analysis, but each at different stages of path planning. We evaluate the waypoint coverage of the proposed algorithms through a novel profit model and compare their performance on a work area with different waypoint densities. Our results show that STEER covers 40% more waypoints and generates 20% more profit than DETACH in high-density waypoint scenarios.

The United States Centers for Disease Control and Prevention considers saliva contact the lead transmission mean of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes the coronavirus disease 2019 (COVID-19). Saliva droplets or aerosols expelled by sneezing, coughing, breathing, and talking may carry this virus. People in close distance may be exposed directly to these droplets or indirectly when touching the droplets that fall on surrounding surfaces and ending up contracting COVID-19 after touching the mucosa tissue of their faces. It is of great interest to quickly and effectively detect the presence of SARS-CoV-2 in an environment, but the existing methods only work in laboratory settings, to the best of our knowledge. However, it may be possible to detect the presence of saliva in the environment and proceed with prevention measures. However, detecting saliva itself has not been documented in the literature. On the other hand, many sensors that detect different organic components in saliva to monitor a person’s health and diagnose different diseases, ranging from diabetes to dental health, have been proposed and they may be used to detect the presence of saliva. This paper surveys sensors that detect organic and inorganic components of human saliva. Humidity sensors are also considered in the detection of saliva because a large portion of saliva is water. Moreover, sensors that detect infectious viruses are also included as they may also be embedded into saliva sensors for a confirmation of the presence of the virus. A classification of sensors by their working principles and the substances they detect is presented, including the sensors’ specifications, sample size, and sensitivity. Indications of which sensors are portable and suitable for field application are presented. This paper also discusses future research and challenges that must be resolved to realize practical saliva sensors. Such sensors may help minimize the spread of not only COVID-19 but also other infectious diseases.