Breakthrough Infections with SARS-CoV-2 in Vaccinated Students After a Festive Event - Identification of Risk Factors

We report an outbreak with SARS-CoV-2 breakthrough infections related to a festive event in Northern Bavaria, Germany in October 2021, with 24 of 95 participants infected. Correlation analyses among 16 interrogated variables revealed that duration at the event and conversation with the supposed index person were significant risk factors.

Event Reliability in Wireless Sensor Networks

<p>Ensuring reliable transport of data in resource-constrained Wireless Sensor Networks (WSNs) is one of the primary concerns to achieve a high degree of efficiency in monitoring and control systems. The two reliability mechanisms typically used in WSNs are packet reliability and event reliability. Packet reliability, which requires all packets from all the sensor nodes to reach the sink, can result in wastage of the sensors' limited energy resources. Event reliability, which only requires that one packet related to each event reaches the sink, exploits the overlap of the sensing regions of densely deployed sensor nodes to eliminate redundant packets from nodes in close proximity that contain duplicate information about an event. The majority of previous research in this area focuses on packet reliability rather than event reliability. Moreover, the research that does focus on event reliability relies on the sink to impose some form of control over the flow of data in the network. The sinks' centralized control and decision-making increases the transmission of unnecessary packets, which degrades overall network performance in terms of energy, congestion and data flow. This thesis proposes a distributed approach to the control of the flow of data in which each node makes in-node decisions using data readily available to it. This reduces the transmission of unnecessary packets, which reduces the network cost in terms of energy, congestion, and data flow. The major challenges involved in this research are to: (i) accurately identify that multiple packets are carrying information about the same event, (ii) reliably deliver the packets carrying information about the unique event, (iii) ensure that enough information about the area of interest is reliably delivered to the sink, and (iv) maintain the event coverage throughout the network. This thesis presents the Event Reliability Protocol (ERP) and its extension, the Enhanced Event Reliability Protocol (EERP). The protocols aim for the reliable transmission of a packet containing information about each unique event to the sink while identifying and minimizing the unnecessary transmission of similar redundant packets from nodes in the region of the event. In this way, the sensor nodes consume less energy and increase the overall network lifetime. EERP uses a multilateration technique to identify multiple packets containing similar event information and thus is able to filter redundant packets of the same event. It also makes use of implicit acknowledgment (iACKs) for reliable delivery of the packets to the sink node. The process is based on the hop-by-hop mechanism where the decisions are made locally by the intermediate nodes. The thesis reports on simulations in QualNet 5.2 for verifying the accuracy of our event identification and event reliability mechanisms employed in the ERP and EERP. The results show that EERP performs better in terms of minimizing overall packet transmission and hence the energy consumption at the sensor nodes in a WSN. Also, the results for event identification mechanism and reliable event delivery show that EERP considerably improves upon other protocols in terms of unique events delivery.</p>

Event Reliability in Wireless Sensor Networks

<p>Ensuring reliable transport of data in resource-constrained Wireless Sensor Networks (WSNs) is one of the primary concerns to achieve a high degree of efficiency in monitoring and control systems. The two reliability mechanisms typically used in WSNs are packet reliability and event reliability. Packet reliability, which requires all packets from all the sensor nodes to reach the sink, can result in wastage of the sensors' limited energy resources. Event reliability, which only requires that one packet related to each event reaches the sink, exploits the overlap of the sensing regions of densely deployed sensor nodes to eliminate redundant packets from nodes in close proximity that contain duplicate information about an event. The majority of previous research in this area focuses on packet reliability rather than event reliability. Moreover, the research that does focus on event reliability relies on the sink to impose some form of control over the flow of data in the network. The sinks' centralized control and decision-making increases the transmission of unnecessary packets, which degrades overall network performance in terms of energy, congestion and data flow. This thesis proposes a distributed approach to the control of the flow of data in which each node makes in-node decisions using data readily available to it. This reduces the transmission of unnecessary packets, which reduces the network cost in terms of energy, congestion, and data flow. The major challenges involved in this research are to: (i) accurately identify that multiple packets are carrying information about the same event, (ii) reliably deliver the packets carrying information about the unique event, (iii) ensure that enough information about the area of interest is reliably delivered to the sink, and (iv) maintain the event coverage throughout the network. This thesis presents the Event Reliability Protocol (ERP) and its extension, the Enhanced Event Reliability Protocol (EERP). The protocols aim for the reliable transmission of a packet containing information about each unique event to the sink while identifying and minimizing the unnecessary transmission of similar redundant packets from nodes in the region of the event. In this way, the sensor nodes consume less energy and increase the overall network lifetime. EERP uses a multilateration technique to identify multiple packets containing similar event information and thus is able to filter redundant packets of the same event. It also makes use of implicit acknowledgment (iACKs) for reliable delivery of the packets to the sink node. The process is based on the hop-by-hop mechanism where the decisions are made locally by the intermediate nodes. The thesis reports on simulations in QualNet 5.2 for verifying the accuracy of our event identification and event reliability mechanisms employed in the ERP and EERP. The results show that EERP performs better in terms of minimizing overall packet transmission and hence the energy consumption at the sensor nodes in a WSN. Also, the results for event identification mechanism and reliable event delivery show that EERP considerably improves upon other protocols in terms of unique events delivery.</p>

Locomotion Mode Transition Prediction Based on Gait-Event Identification Using Wearable Sensors and Multilayer Perceptrons

People walk on different types of terrain daily; for instance, level-ground walking, ramp and stair ascent and descent, and stepping over obstacles are common activities in daily life. Movement patterns change as people move from one terrain to another. The prediction of transitions between locomotion modes is important for developing assistive devices, such as exoskeletons, as the optimal assistive strategies may differ for different locomotion modes. The prediction of locomotion mode transitions is often accompanied by gait-event detection that provides important information during locomotion about critical events, such as foot contact (FC) and toe off (TO). In this study, we introduce a method to integrate locomotion mode prediction and gait-event identification into one machine learning framework, comprised of two multilayer perceptrons (MLP). Input features to the framework were from fused data from wearable sensors—specifically, electromyography sensors and inertial measurement units. The first MLP successfully identified FC and TO, FC events were identified accurately, and a small number of misclassifications only occurred near TO events. A small time difference (2.5 ms and −5.3 ms for FC and TO, respectively) was found between predicted and true gait events. The second MLP correctly identified walking, ramp ascent, and ramp descent transitions with the best aggregate accuracy of 96.3%, 90.1%, and 90.6%, respectively, with sufficient prediction time prior to the critical events. The models in this study demonstrate high accuracy in predicting transitions between different locomotion modes in the same side’s mid- to late stance of the stride prior to the step into the new mode using data from EMG and IMU sensors. Our results may help assistive devices achieve smooth and seamless transitions in different locomotion modes for those with motor disorders.

Full Moment Tensor Solutions of U.S. Underground Nuclear Tests for Event Screening and Yield Estimation

ABSTRACT Moment tensor (MT) solutions are proving increasingly valuable in explosion monitoring, especially now that they are more routinely calculated for the unconstrained, full (six component) MT. In this study, we have calculated MTs for U.S. underground nuclear tests conducted at the Nevada National Security Site using seismic recordings primarily from the Livermore Nevada Network. We are able to determine them for 130 nuclear explosions from 1970 to 1996 for a range of yields and under a variety of material conditions, which we have supplemented with 10 additional chemical explosions at the test site. The result is an extensive database of MTs that can be used to assess the performance of important monitoring tasks such as event identification and yield determination. We test the explosion event screening on the fundamental lune of the MT eigensphere and find MT screening to be a robust discriminant between earthquakes and explosions. We then explore the estimation of moment-derived yield, in which we find that material properties are the largest contributor to differences in the estimated moment-to-yield ratio. Further research conducted on this dataset can be used to develop, test, and improve various explosion monitoring methodologies.

New Particle Formation Events Detection with Deep Learning

Atmospheric new particle formation (NPF) is an important source of climate-relevant aerosol particles which has been observed at many locations globally. To study this phenomenon, the first step is to identify whether an NPF event occurs or not on a given day. In practice, NPF event identification is performed visually by classifying the NPF event or non-event days from the particle number size distribution surface plots. Unfortunately, this day-by-day visual classification is time-consuming, labor-intensive, and the identification process renders subjective results. To detect NPF events automatically, we regard the visual signature (banana shape) which has been observed all over the world in NPF surface plots as a special kind of object, and a deep learning model called Mask R-CNN is applied to localize the spatial layouts of NPF events in their surface plots. Utilizing only 358 human-annotated masks on data from the Station for Measuring Ecosystem and Atmospheric Relations (SMEAR) II station (Hyytiälä, Finland), the Mask R-CNN model was successfully generalized for three SMEAR stations in Finland and the San Pietro Capofiume (SPC) station in Italy. In addition to the detection of NPF events (especially the strongest events), the presented method can determine the growth rates, start times, and end times for NPF events automatically. The automatically determined growth rates agree with the growth rates determined by the maximum concentration and mode fitting methods. The statistical results valid the potential of applying the proposed method on different sites, which will improve the automatic level for NPF events detection and analysis. Furthermore, the proposed automatic NPF event analysis method provides more consistent results compared with human-made analysis, especially when long-term data series are analyzed and statistically comparisons between different sites are needed for event characteristics such as the start and end times, thereby saving time and effort of scientists studying NPF events.

Spillover as Movement Agenda Setting: Using Computational and Network Techniques for Improved Rare Event Identification

The increasing availability of data, along with sophisticated computational methods for analyzing them, presents researchers with new opportunities and challenges. In this article, we address both by describing computational and network methods that can be used to identify cases of rare phenomena. We evaluate each method’s relative utility in the identification of a specific rare phenomenon of interest to social movement researchers: the spillover of social movement claims from one movement to another. We identify and test five different approaches to detecting cases of spillover in the largest data set of protest events currently available, finding that an ensemble approach that combines clique and correspondence analysis and an ensemble approach combining all methods perform considerably better than others. Our approach is preferable to other ways of analyzing such cases; compared to qualitative approaches, our computational process identifies many more cases of spillover—some of which are surprising and would likely not be otherwise investigated. At the same time, compared to crude quantitative measures, our approach substantially reduces the “noise,” or identification of false-positive cases, of movement spillover. We argue that this technique, which can be adapted to other research topics, is a good illustration of how the thoughtful implementation of computational methods can allow for the efficient identification of rare events and also bridge deductive and inductive approaches to scientific inquiry.