Characterizing features of outbreak duration for novel SARS-CoV-2 variants of concern

Characterizing the dynamics of epidemic trajectories is critical to understanding the potential impacts of emerging outbreaks and to designing appropriate mitigation strategies. As the COVID-19 pandemic evolves, however, the emergence of SARS-CoV-2 variants of concern has complicated our ability to assess in real-time the potential effects of imminent outbreaks, such as those presently caused by the Omicron variant. Here, we report that SARS-CoV-2 outbreaks across regions exhibit strain-specific times from onset to peak, specifically for Delta and Omicron variants. Our findings may facilitate real-time identification of peak medical demand and may help fine-tune ongoing and future outbreak mitigation deployment efforts.

Automatic Fungi Recognition: Deep Learning Meets Mycology

The article presents an AI-based fungi species recognition system for a citizen-science community. The system’s real-time identification too — FungiVision — with a mobile application front-end, led to increased public interest in fungi, quadrupling the number of citizens collecting data. FungiVision, deployed with a human-in-the-loop, reaches nearly 93% accuracy. Using the collected data, we developed a novel fine-grained classification dataset — Danish Fungi 2020 (DF20) — with several unique characteristics: species-level labels, a small number of errors, and rich observation metadata. The dataset enables the testing of the ability to improve classification using metadata, e.g., time, location, habitat and substrate, facilitates classifier calibration testing and finally allows the study of the impact of the device settings on the classification performance. The continual flow of labelled data supports improvements of the online recognition system. Finally, we present a novel method for the fungi recognition service, based on a Vision Transformer architecture. Trained on DF20 and exploiting available metadata, it achieves a recognition error that is 46.75% lower than the current system. By providing a stream of labeled data in one direction, and an accuracy increase in the other, the collaboration creates a virtuous cycle helping both communities.

Implementation of a COVID-19 Screening Testing Program in a Rural, Tribal Nation: Experience of the San Carlos Apache Tribe, January–February 2021

The COVID-19 pandemic has disproportionately affected tribal populations, including the San Carlos Apache Tribe. Universal screening testing in a community using rapid antigen tests could allow for near–real-time identification of COVID-19 cases and result in reduced SARS-CoV-2 transmission. Published experiences of such testing strategies in tribal communities are lacking. Accordingly, tribal partners, with support from the Centers for Disease Control and Prevention, implemented a serial testing program using the Abbott BinaxNOW rapid antigen test in 2 tribal casinos and 1 detention center on the San Carlos Apache Indian Reservation for a 4-week pilot period from January to February 2021. Staff members at each setting, and incarcerated adults at the detention center, were tested every 3 or 4 days with BinaxNOW. During the 4-week period, 3834 tests were performed among 716 participants at the sites. Lessons learned from implementing this program included demonstrating (1) the plausibility of screening testing programs in casino and prison settings, (2) the utility of training non–laboratory personnel in rapid testing protocols that allow task shifting and reduce the workload on public health employees and laboratory staff, (3) the importance of building and strengthening partnerships with representatives from the community and public and private sectors, and (4) the need to implement systems that ensure confidentiality of test results and promote compliance among participants. Our experience and the lessons learned demonstrate that a serial rapid antigen testing strategy may be useful in work settings during the COVID-19 pandemic as schools and businesses are open for service.

Weigh-in-Motion: Lightweight Real-Time Identification of Gbps Wireless Traffic

Distinguishing between wireless and wired traffic in a network middlebox is an essential ingredient for numerous applications including security monitoring and quality-of-service (QoS) provisioning. The majority of existing approaches have exploited the greater delay statistics, such as round-trip-time and inter-packet arrival time, observed in wireless traffic to infer whether the traffic is originated from Ethernet (i.e., wired) or Wi-Fi (i.e., wireless) based on the assumption that the capacity of the wireless link is much slower than that of the wired link. However, this underlying assumption is no longer valid due to increases in wireless data rates over Gbps enabled by recent Wi-Fi technologies such as 802.11ac/ax. In this paper, we revisit the problem of identifying Wi-Fi traffic in network middleboxes as the wireless link capacity approaches the capacity of the wired. We present Weigh-in-Motion, a lightweight online detection scheme, that analyzes the traffic patterns observed at the middleboxes and infers whether the traffic is originated from high-speed Wi-Fi devices. To this end, we introduce the concept of ACKBunch that captures the unique characteristics of high-speed Wi-Fi, which is further utilized to distinguish whether the observed traffic is originated from a wired or wireless device. The effectiveness of the proposed scheme is evaluated via extensive real experiments, demonstrating its capability of accurately identifying wireless traffic from/to Gigabit 802.11 devices.

The 2021 Bitcoin Bubbles and Crashes—Detection and Classification

In this study, the Log-Periodic Power Law Singularity (LPPLS) model is adopted for real-time identification and monitoring of Bitcoin bubbles and crashes using different time scale data, and the modified Lagrange regularization method is proposed to alleviate the impact of potential LPPLS model over-fitting to better estimate bubble start time and market regime change. The goal here is to determine the nature of the bubbles and crashes (i.e., whether they are endogenous due to their own price evolution or exogenous due to external market and/or policy influences). A systematic market event analysis is performed and correlated to the Bitcoin bubbles detected. Based on the daily LPPLS confidence indictor from 1 December 1, 2019 to 24 June 2021, this analysis has disclosed that the Bitcoin boom from November 2020 to mid-January 2021 is an endogenous bubble, stemming from the self-reinforcement of cooperative herding and imitative behaviors of market players, while the price spike from mid-January 2021 to mid-April 2021 is likely an exogenous bubble driven by extrinsic events including a series of large-scale acquisitions and adoptions by well-known institutions such as Visa and Tesla. Finally, the utilities of multi-resolution LPPLS analysis in revealing both short-term changes and long-term states have also been demonstrated in this study.

Multispectral Active Infrared Thermo-Reflectometry for Absolute Temperature Measurement by Contactless IR Thermography

Knowledge of material emissivity maps and their true temperatures is of great interest for contactless process monitoring and control with infrared cameras when strong heat transfer and temperature change are involved.In this work, we describe the development of a contactless infrared and multispectral imaging technique based on the pyro-reflectometry approach and a specular model of the material reflection.This approach enables in situ and real-time identification of emissivity fields and autocalibration of the radiative intensity leaving the sample by using a black body equivalent ratio.This is done to obtain the absolute temperature field of any specular material using the infrared wavelength.The proposed method is evaluated at room temperature with several heterogeneous samples covering a large range of emissivity values. From these emissivity fields, raw and heterogeneous measured radiative fluxes are transformed into complete absolute temperature fields.

Digital Identification Algorithms for Primary Frequency Control in Unified Power System

The article studies and develops the methods for assessing the degree of participation of power plants in the general primary frequency control in a unified energy system (UES) of Russia based on time series analysis of frequency and power. To identify the processes under study, methods of associative search are proposed. The methods are based on process knowledgebase development, data mining, associative research, and inductive learning. Real-time identification models generated using these algorithms can be used in automatic control and decision support systems. Evaluation of the behavior of individual UES members enables timely prevention of abnormal and emergency situations. Methods for predictive diagnostics of generating equipment in terms of their readiness to participate in the primary frequency control are also proposed. In view of the non-stationarity of the load in electrical networks, the algorithms have been developed using wavelet analysis. Case studies are given showing the operating of the proposed methods.

Real-Time Identification of Rice Weeds by UAV Low-Altitude Remote Sensing Based on Improved Semantic Segmentation Model

Real-time analysis of UAV low-altitude remote sensing images at airborne terminals facilitates the timely monitoring of weeds in the farmland. Aiming at the real-time identification of rice weeds by UAV low-altitude remote sensing, two improved identification models, MobileNetV2-UNet and FFB-BiSeNetV2, were proposed based on the semantic segmentation models U-Net and BiSeNetV2, respectively. The MobileNetV2-UNet model focuses on reducing the amount of calculation of the original model parameters, and the FFB-BiSeNetV2 model focuses on improving the segmentation accuracy of the original model. In this study, we first tested and compared the segmentation accuracy and operating efficiency of the models before and after the improvement on the computer platform, and then transplanted the improved models to the embedded hardware platform Jetson AGX Xavier, and used TensorRT to optimize the model structure to improve the inference speed. Finally, the real-time segmentation effect of the two improved models on rice weeds was further verified through the collected low-altitude remote sensing video data. The results show that on the computer platform, the MobileNetV2-UNet model reduced the amount of network parameters, model size, and floating point calculations by 89.12%, 86.16%, and 92.6%, and the inference speed also increased by 2.77 times, when compared with the U-Net model. The FFB-BiSeNetV2 model improved the segmentation accuracy compared with the BiSeNetV2 model and achieved the highest pixel accuracy and mean Intersection over Union ratio of 93.09% and 80.28%. On the embedded hardware platform, the optimized MobileNetV2-UNet model and FFB-BiSeNetV2 model inferred 45.05 FPS and 40.16 FPS for a single image under the weight accuracy of FP16, respectively, both meeting the performance requirements of real-time identification. The two methods proposed in this study realize the real-time identification of rice weeds under low-altitude remote sensing by UAV, which provide a reference for the subsequent integrated operation of plant protection drones in real-time rice weed identification and precision spraying.