A Near-Zero Power Triboelectric Wake-Up System for Autonomous Beaufort Scale of Wind Force Monitoring

Beaufort scale of wind force monitoring is the basic content of meteorological monitoring, which is an important means to ensure the safety of production and life by timely warning of natural disasters. As there is a limited battery life for sensors, determining how to reduce power consumption and extend system life is still an urgent problem. In this work, a near-zero power triboelectric wake-up system for autonomous Beaufort scale of wind force monitoring is proposed, in which a rotary TENG is used to convert wind energy into a stored electric energy capacitor. When the capacitor voltage accumulates to the threshold voltage of a transistor, it turns on as an electronic switch and the system wakes up. In active mode, the Beaufort scale of wind force can be judged according to the electric energy and the signal is sent out wirelessly. In standby mode, when there is no wind, the power consumption of the system is only 14 nW. When the wind scale reaches or exceeds light air, the system can switch to active mode within one second and accurately judge the Beaufort scale of wind force within 10 s. This work provided a triboelectric sensor-based wake-up system with ultralow static power consumption, which has great prospects for unattended environmental monitoring, hurricane warning, and big data acquisition.

An Augmented Warning System for Pedestrians: User Interface Design and Algorithm Development

Warning pedestrians of oncoming vehicles is critical to improving pedestrian safety. Due to the limitations of a pedestrian’s carrying capacity, it is crucial to find an effective solution to provide warnings to pedestrians in real-time. Limited numbers of studies focused on warning pedestrians of oncoming vehicles. Few studies focused on developing visual warning systems for pedestrians through wearable devices. In this study, various real-time projection algorithms were developed to provide accurate warning information in a timely way. A pilot study was completed to test the algorithm and the user interface design. The projection algorithms can update the warning information and correctly fit it into an easy-to-understand interface. By using this system, timely warning information can be sent to those pedestrians who have lower situational awareness or obstructed view to protect them from potential collisions. It can work well when the sightline is blocked by obstructions.

Using dense seismo-acoustic network to provide timely warning of the 2019 paroxysmal Stromboli eruptions

Stromboli Volcano is well known for its persistent explosive activity. On July 3rd and August 28th 2019, two paroxysmal explosions occurred, generating an eruptive column that quickly rose up to 5 km above sea level. Both events were detected by advanced local monitoring networks operated by Istituto Nazionale di Geofisica e Vulcanologia (INGV) and Laboratorio di Geofisica Sperimentale of the University of Firenze (LGS-UNIFI). Signals were also recorded by the Italian national seismic network at a range of hundreds of kilometres and by infrasonic arrays up to distances of 3700 km. Using state-of-the-art propagation modeling, we identify the various seismic and infrasound phases that are used for precise timing of the eruptions. We highlight the advantage of dense regional seismo-acoustic networks to enhance volcanic signal detection in poorly monitored regions, to provide timely warning of eruptions and reliable source amplitude estimate to Volcanic Ash Advisory Centres (VAAC).

Accidental Exposure to Ultraviolet-C (UVC) Germicidal Lights: A Case Report and Recommendations in Orthodontic Clinical Settings

An emergent health crisis, coronavirus disease 2019 (COVID-19), has demanded strict disinfection and sterilization protocols to limit the spread. One such approach is the use of ultravioletC (UVC) radiation for surface decontamination in hospital settings. UVC is the most powerful of the UV wavelengths and, therefore, the most damaging. The radiation is used for sterilization of workplaces and objects, and although there is no current evidence, perhaps it may eradicate airborne coronavirus. Humans should not be subjected to this form of irradiation. This form of radiation comes with a health warning, and humans should not be in the vicinity when UVC sterilization is occurring. This case report deals with the unfortunate incidence of an operator affected by UVC radiation while working on a patient in an orthodontic clinic. The article will serve as a timely warning to clinicians that awareness of the safety protocols should be observed. Besides, we present some recommendations for the use of UVC depuration of dental operatories.

Long-Term and Emergency Monitoring of Zhongbao Landslide Using Space-Borne and Ground-Based InSAR

This work presents the ideal combination of space-borne and ground-based (GB) Interferometric Synthetic Aperture Radar (InSAR) applications. In the absence of early investigation reporting and specialized monitoring, the Zhongbao landslide unexpectedly occurred on 25 July 2020, forming a barrier lake that caused an emergency. As an emergency measure, the GB-InSAR system was installed 1.8 km opposite the landslide to assess real-time cumulative deformation with a monitoring frequency of 3 min. A zone of strong deformation was detected, with 178 mm deformation accumulated within 15 h, and then a successful emergency warning was issued to evacuate on-site personnel. Post-event InSAR analysis of 19 images acquired by the ESA Sentinel-1 from December 2019 to August 2020 revealed that the landslide started in March 2020. However, the deformation time series obtained from satellite InSAR did not show any signs that the landslide had occurred. The results suggest that satellite InSAR is effective for mapping unstable areas but is not qualified for rapid landslide monitoring and timely warning. The GB-InSAR system performs well in monitoring and providing early warning, even with dense vegetation on the landslide. The results show the shortcomings of satellite InSAR and GB-InSAR and a clearer understanding of the necessity of combining multiple monitoring methods.

Ranking Icelandic volcanoes by threat and prioritizing their monitoring requirements

<p>How well are our volcanoes monitored? When and why should we review and enhance the monitoring setup for volcano surveillance? These questions are often raised at Volcano Observatories or at those Institutions in charge of monitoring volcanoes and their associated hazards. The Icelandic Meteorological Office (IMO) is responsible for monitoring natural hazards in Iceland, including volcanoes and volcanic eruptions. IMO operates an extended multidisciplinary monitoring network which comprises seismometers, cGPS, gas sensors, MultiGAS and DOASes, hydrological stations, strainmeters and tiltmeters, infrasound networks and webcams, with the aim of detecting in a timely manner potential unrest at any of the 32 active volcanoes in the country. Limited resources and funding opportunities often pose limitations on how extensive (in terms of number of sensors and their variety) a volcano monitoring network can be. Therefore, the Volcano Observatories are often required to decide how to prioritize the monitoring needs and find a balance in sensitivity, reliability, and efficacy of the network.  </p><p>In this contribution, we will present the results of the analysis performed at the IMO to rank the Icelandic active volcanoes by their threat and, consequently, to prioritize their monitoring needs. Some criteria (based on eruption frequency, potential hazards, infrastructure exposure and current status) are defined as guidelines and they are used to drive decisions regarding when and how to alter the monitoring setup. The specific case of Hekla volcano is used here to evaluate the validity of such criteria and to perform an analysis of the current capability of issuing a timely warning for one of the most dangerous volcanoes in Iceland. </p>

Secure Incident & Evidence Management Framework (SIEMF) for Internet of Vehicles using Deep Learning and Blockchain

Even though there is continuous improvement in road and vehicle safety, road traffic incidents have been increasing over last few decades. There is a need to reduce traffic incidents like accidents through predictive analysis and timely warnings while at the same time data related to accidents and traffic violations need to be maintained in a tamper proof storage system that can be retrieved for forensic analysis and law enforcement at a later stage. The Secure Incident and Evidence Management Framework (SIEMF) proposed in this work address these two challenges of predictive modeling for timely warning and secure evidence management for forensics analysis in case of accidents and traffic violations. The system proposes a deep learning based predictive incident modeling with blockchain and CP-ABE based access control for the incident data stored in blockchain.

Mitigating the Twin Threats of Climate-Driven Atlantic Hurricanes and COVID-19 Transmission

ABSTRACTThe co-occurrence of the 2020 Atlantic hurricane season and the ongoing coronavirus disease 2019 (COVID-19) pandemic creates complex dilemmas for protecting populations from these intersecting threats. Climate change is likely contributing to stronger, wetter, slower-moving, and more dangerous hurricanes. Climate-driven hazards underscore the imperative for timely warning, evacuation, and sheltering of storm-threatened populations – proven life-saving protective measures that gather evacuees together inside durable, enclosed spaces when a hurricane approaches. Meanwhile, the rapid acquisition of scientific knowledge regarding how COVID-19 spreads has guided mass anti-contagion strategies, including lockdowns, sheltering at home, physical distancing, donning personal protective equipment, conscientious handwashing, and hygiene practices. These life-saving strategies, credited with preventing millions of COVID-19 cases, separate and move people apart. Enforcement coupled with fear of contracting COVID-19 have motivated high levels of adherence to these stringent regulations. How will populations react when warned to shelter from an oncoming Atlantic hurricane while COVID-19 is actively circulating in the community? Emergency managers, health care providers, and public health preparedness professionals must create viable solutions to confront these potential scenarios: elevated rates of hurricane-related injury and mortality among persons who refuse to evacuate due to fear of COVID-19, and the resurgence of COVID-19 cases among hurricane evacuees who shelter together.