Single-Point Mutations in the N Gene of SARS-CoV-2 Adversely Impact Detection by a Commercial Dual Target Diagnostic Assay

This paper reports the identification of single-point mutations in the N gene of SARS-CoV-2 associated with a gene target failure by the Cepheid Xpert commercial system. In order to determine the mutation(s) responsible for the N gene detection failures, the genomic products from the Cepheid Xpert system were sequenced and compared to whole genomes of SARS-CoV-2 from clinical cases.

Design and Analysis of Electronic Wearables for Taekwondo Sports

<div>Taekwondo is a combat sport that is based on striking and involves full body contact. Initially, it a referee-exclusive sport and that led to increased controversy on referee/judges’ accuracy, bias and fairness. To address these concerns, point scoring systems (PSS) were introduced in 2012 only consisting of a chest protector and in 2016 head protectors were added. Constant improvements have been made on these PSS and new impact classification algorithms and hardware were developed for a system made by 20/20 Armor. The work achieved 90% accuracy for illegal vs legal classification on the head protector and 94.4% accuracy between legal impacts to the chest protector. This work proved to be a great step forward since reliance is increasing on these PSS as they are now the “final decision” for impact detection in Taekwondo. Furthermore, our algorithms use edge computing that allow for real time application and at-home training.</div>

Design and Analysis of Electronic Wearables for Taekwondo Sports

<div>Taekwondo is a combat sport that is based on striking and involves full body contact. Initially, it a referee-exclusive sport and that led to increased controversy on referee/judges’ accuracy, bias and fairness. To address these concerns, point scoring systems (PSS) were introduced in 2012 only consisting of a chest protector and in 2016 head protectors were added. Constant improvements have been made on these PSS and new impact classification algorithms and hardware were developed for a system made by 20/20 Armor. The work achieved 90% accuracy for illegal vs legal classification on the head protector and 94.4% accuracy between legal impacts to the chest protector. This work proved to be a great step forward since reliance is increasing on these PSS as they are now the “final decision” for impact detection in Taekwondo. Furthermore, our algorithms use edge computing that allow for real time application and at-home training.</div>

Reformist Framework for Improving Human Security for Mobile Robots in Industry 4.0

In this paper, the cooperation between human and robot companies plays a significant role in factories, contributing to greater productivity and efficiency. However, this development breaches established safety procedures when the workspaces are separated from the robot and the human being. These changes have been reflected in industrial robotic safety standards for the last 20 years. We have directed the expansion of a broad field of examination, which focuses on avoiding robotic humans’ effects and minimizing associated risks and consequences. The paper depicts an analysis of prominent safety systems projected and implemented in engineering robotic surroundings that contribute to safe, collective work between humans and robots. Besides, the current regulation has introduced a review and new concepts. The discussion includes multidisciplinary approaches such as estimating and evaluating human-robot collision injuries, mechanical equipment and software to minimize human-robot impacts, impact detection systems, and collision prevention strategies and minimizing their impact to proposed approach for Human Security with Mobile Robots in Industry 4.0 using SDN and CPS with GMM-GM machine learning model.

New Downhole Sand Entry Detection Technology Leads Directly to Successful Remedial Work and Additional Oil Production

The consequences of sand production are often disadvantageous to the short and long-term productivity of the well. Although some wells routinely experience controllable sand production, these are the exception rather than the rule. Sand production and its management over the life of the well is not an attractive situation but is often essential to extract the resource. Knowing the root cause behind sand inflow in a well and the possible results can inform an appropriate strategy to safely extract as much of the resource as possible. The sands in such reservoir units often have high permeability and are mechanically weak and prone to sand production. The producing wells are often completed with gravel-packed completions for efficient sand control. Most of the wells have multi-zone completions for better productivity but this further complicates reservoir characterization. This paper describes the first use of downhole sand impact detection technology in such fields. The sand detection technology integrates the fully digitized high-resolution acquisition with signal processing and interpretation algorithm to enhance the sand particle detections as small as 0.1 mm in diameter and up to 1,500 impacts per second. The tool is designed to immune the sensors from any background noise and gas/liquid jetting effect. A combination of production logging tools (PLT) and the sand impact detection tool, was used to understand four phase zonal contributions (gas, oil, water and sand) and pinpoint sand entry in these cases. Results exceeded expectations and the ability for the sand detection tool to accurately detect the point of sand entry enabled immediate intervention to eliminate sand production in these case studies. One of them also resulted in increased production of 7.4kb/d oil without any sand flow and with greatly reduced gas-oil ratio as compared to pre-intervention production. The work clearly demonstrates the practical and effective use of downhole sand impact detection with new sand detection technology to identify and isolate sand production in wells. The innovative tool design makes it feasible to detect even small sand particles in adverse wellbore conditions and varied production rates, thus adding a detection of the fourth phase in an otherwise three phase production log.

Vibration Parameters for Impact Detection of Composite Panel: A Neural Network Based Approach

The need for reliable methodologies for structural monitoring is certainly a current line of research in many engineering sectors. The detection of the impact on composite materials is in fact a recent subject of study, aimed at safeguarding the mechanical integrity and improving the useful life of structural components. In such a context, the work deals with evaluation of the use of neural algorithms for localizing the position of the impacts on composite structures. Starting from FE (finite element) simulations, representative of the dynamic response of a CFRP (Carbon Fiber Reinforced Polymer) panel as a benchmark, the approach has been finally validated experimentally by modal parameters identification.