Pandemic Asteroid Defense: DART Integration and Test in the time of COVID-19

NASA's Double Asteroid Redirection Test (DART) successfully intercepted the asteroid Didymos on September 26th of 2022, potentially changing its orbital period with a deflection by kinetic impact. The spacecraft launched aboard a SpaceX Falcon 9 rocket on November 24th, 2021. DART's Integration and Test (I&T) campaign was scheduled to commence in April of 2020 at the Johns Hopkins University Applied Physics Laboratory (JHU/APL) in Laurel, Maryland. In March of that year, one month prior to beginning I&T, the rapid spread of the Coronavirus (COVID-19) forced JHU/APL to rethink how to assemble, test and deliver a spacecraft on schedule during a very challenging period of time. This paper will discuss the details of the successful I&T strategy used by the DART team during the COVID-19 lockdown and subsequent return to post-lockdown life. The team learned how to effectively meet virtually, how to integrate hardware, and how to operate the spacecraft with the least amount of people required. Communication was key in keeping the various DART team members, who were located across the country, connected and safe. The team had also moved documentation online for the first time, which turned out to be very instrumental in keeping everyone on track. A variety of tools to collaborate and document test procedures and results proved valuable for record keeping. Creative solutions were implemented during the test campaign for scheduling both remote and in person monitoring. This paper will conclude with DART lessons learned and recommendations for future I&T programs. © 2023 IEEE.

Is self-testing the next paradigm for diagnostics?

The study estimates the usability and attitude assessment of users for India's first approved rapid antigen self-test kit;the CoviSelf™. India approved its first AI-powered self-test for Covid-19 in April 2021 a few weeks after the first approval in the US. We present here a study on usability and attitude assessment of users of India's first approved rapid antigen self-test kit;the CoviSelf™. The study evaluates participants' understanding of and performance of test procedure and interprets the results. Analysis revealed that more than 90% study participants followed steps correctly as illustrated in the user's manual. Age group and gender-based analysis showed comparable scores for usability of the test kit suggesting users of different age groups has same ease in using the test kit. What we learnt from this study could be start of self-test revolution, where rapid tests could expand the access of diagnostics for hundreds of diseases including HIV, HPV, and dengue to millions of people who could not get access to diagnostics because we lacked manpower or facility to conduct tests. Self-testing could break the barriers for diagnostics that Internet did for information.

An Adaptive-to-Model Test for Parametric Functional Single-Index Model

Model checking methods based on non-parametric estimation are widely used because of their tractable limiting null distributions and being sensitive to high-frequency oscillation alternative models. However, this kind of test suffers from the curse of dimensionality, resulting in slow convergence, especially for functional data with infinite dimensional features. In this paper, we propose an adaptive-to-model test for a parametric functional single-index model by using the orthogonality of residual and its conditional expectation. The test achieves model adaptation by sufficient dimension reduction which utilizes functional sliced inverse regression. This test procedure can be easily extended to other non-parametric test methods. Under certain conditions, we prove the asymptotic properties of the test statistic under the null hypothesis, fixed alternative hypothesis and local alternative hypothesis. Simulations show that our test has better performance than the method that does not use functional sufficient dimension reduction. An analysis of COVID-19 data verifies our conclusion.

CRISPR‐Cas Biochemistry and CRISPR‐Based Molecular Diagnostics

Polymerase chain reaction (PCR)‐based nucleic acid testing has played a critical role in disease diagnostics, pathogen surveillance, and many more. However, this method requires a long turnaround time, expensive equipment, and trained personnel, limiting its widespread availability and diagnostic capacity. On the other hand, the clustered regularly interspaced short palindromic repeats (CRISPR) technology has recently demonstrated capability for nucleic acid detection with high sensitivity and specificity. CRISPR‐mediated biosensing holds great promise for revolutionizing nucleic acid testing procedures and developing point‐of‐care diagnostics. This review focuses on recent developments in both fundamental CRISPR biochemistry and CRISPR‐based nucleic acid detection techniques. Four ongoing research hotspots in molecular diagnostics‐target preamplification‐free detection, microRNA (miRNA) testing, non‐nucleic‐acid detection, and SARS‐CoV‐2 detection‐are also covered.

COVID-19 diagnosis in CXR Images using Deep Transfer Learning Approach

The COVID-19 virus is a novel pathogen that has genetic similarities with SARS and several cold viruses. The utilization of traditional test procedures including polymerase chain reactions, serology tests, and antigen assays is common. The patient may not receive the test results for several days or it may take just several hours. Hence, the implementation of an autonomous diagnosis system as a quick novel diagnostic system is required to prevent the spread of COVID-19 between the population. Although a number of researchers had excellent success in the detection of COVID-19, the majority of them had lower accuracy and overfitting problems that make advance screening of this is challenging. The best method for more accurately resolving this issue is transfer learning. In this study, four convnets supported by pre-trained neural networks (ResNet50, DenseNet101, Inception-V3 and CapsNet) are presented for identifying patients with pneumonia due to bacterial or coronavirus or any other virus or normal using Chest X-Rays (CXR). The proposed study implements multi class classifications using cross-validation. When results are taken into account, the pre-trained ResNet50 model offers the best classification efficiency (97.77% accuracy, 100% sensitivity, 93.33% specificity, and 98.00% F1-score) over 6259 images of the other four models that were utilized. © 2022 IEEE.

Semiparametric Permutation-Based Change Point Detection with an Application on Chicago Cardiovascular Mortality Data

Climate change has several negative effects on health, including cardiovascular disease. Many studies have considered the effect of temperature on cardiovascular disease and found that there is an association between extreme levels of temperature, cold and hot, and cardiovascular disease. However, the number of articles that have studied the change point or the threshold in temperature is very limited. To the best of our knowledge, there have been no studies focusing on detecting and testing the significance of the change point in the temperature–cardiovascular relationship. Identifying the change point in cities may help to design better adaptive strategies in view of predicted weather changes in the future. Knowing the change points of temperature may prevent further mortality associated with the weather changes. Therefore, in this paper, we propose a unified approach that simultaneously estimates the semiparametric relationship and detects the significant point. A semiparametric generalized change point single index model is introduced as our unified approach by adjusting for several weather variables. A permutation-based testing procedure to detect the change point is introduced as well. A simulation study is conducted to evaluate the proposed algorithm. The advantage of our proposed approach is demonstrated using the cardiovascular mortality data of the city of Chicago, USA.

Experimental Tests on a Spoke-Type Permanent Magnets Synchronous Machine for Light Electric Vehicle Application

Featured ApplicationLight electric vehicles propulsion.In an attempt to limit the effects of global warming, virtually all car manufacturers have introduced in the last years Hybrid or full Electric Vehicles. The current study shows the experimental testing of a spoke-type PMSM that was developed based on the requirements of the L6e European light vehicle class. A test bench was developed for this purpose, using a DC machine fed by a bidirectional DC Power Supply that allowed the testing of the PMSM prototype both in motor and generator/brake regimes. The Worldwide Harmonized Light-Duty Vehicles Test Procedure (WLTP) was implemented on the control stage of the testing set-up, allowing an accurate estimation of the PMSM-based E-drivetrain performance. The test results validated the FEM-simulated results and provided an insight on the efficiency of the entire drive system (battery–inverter–PMSM) and the autonomy of the L6e light EV. The electric drive system was integrated and tested on a prototype vehicle in normal operating conditions, validating the results obtained on the developed test bench.

Sensory Panel Performance Evaluation—Comprehensive Review of Practical Approaches

Featured ApplicationAuthors are encouraged to provide a concise description of the specific application or a potential application of the work. This section is not mandatory.Sensory assessors determine the result of sensory analysis;therefore, investigation of panel performance is inevitable to obtain well-established results. In the last few decades, numerous publications examine the performance of both panelists and panels. The initial point of any panelist measures are the applied selection methods, which are chosen according to the purpose (general suitability or product-specific skills). A practical overview is given on the available solutions, methods, protocols and software relating to all major panelist and panel measure indices (agreement, discrimination, repeatability, reproducibility and scale usage), with special focus on the utilized statistical methods. The novel approach of the presented methods is multi-faceted, concerning time factor (measuring performance at a given moment or over a period), the level of integration in the sensory testing procedure and the target of the measurements (panelist versus panel). The present paper supports the choice of the performance parameter and its related statistical procedure. Available software platforms, their accessibility (open-source status) and their functions are thoroughly analyzed concerning panelist or whole panel evaluation. The applied sensory test method strongly defines the applicable performance evaluation tools;therefore, these aspects are also discussed. A special field is related to proficiency testing. With the focus on special activities (product competitions, expert panels, food and horticultural goods), practical examples are given. In our research, special attention was given to sensory activity in companies and product experts or product-specific panels. Emerging future trends in this field will involve meta-analyses, application of AI and integration of psychophysics.

Characterization of Existing Steel Racks via Dynamic Identification

Steel storage racks are widely used in logistics for storing materials and goods. Rack design is carried out by adopting the so-called design-assisted-by-testing procedure. In particular, experimental analyses must be carried out by rack producers on the key structural components in order to adopt the design approach proposed for the more traditional carpentry frames. For existing racks, i.e., those in-service for decades, it is required to evaluate the load carrying capacity in accordance with the design provisions currently in use. The main problem in several cases should be the appraisal of the key component performance, owing to the impossibility to obtain specimens from in-service racks without reduction or interruption of the logistic flows. To overcome this problem, a quite innovative procedure for the identification of the structural unknowns of existing racks has been proposed in the paper. The method is based on in-situ modal identification tests combined with extensive numerical analyses. To develop the procedure, cheap measurement systems are required, and they could be immediately applied to existing racks. A real case study is discussed, showing the efficiency of the procedure in the evaluation of the effective elastic stiffness of beam-to-column joints and base plate connections, that are parameters which remarkably affect the rack performance. The structural unknowns have been determined based on four sets of modal tests (two configurations on the longitudinal direction and two in the transversal direction) plus 9079 iterative structural analyses. The results obtained were then directly compared with experimental component tests, showing differences lower than 9%.