Paper-Based Point-of-Care Testing of SARS-CoV-2

The COVID-19 pandemic has resulted in significant global social and economic disruption. The highly transmissive nature of the disease makes rapid and reliable detection critically important. Point-of-care (POC) tests involve performing diagnostic tests outside of a laboratory that produce a rapid and reliable result. It therefore allows the diagnostics of diseases at or near the patient site. Paper-based POC tests have been gaining interest in recent years as they allow rapid, low-cost detection without the need for external instruments. In this review, we focus on the development of paper-based POC devices for the detection of SARS-CoV-2. The review first introduces the principles of detection methods that are available to paper-based devices. It then summarizes the state-of-the-art paper devices and their analytical performances. The advantages and drawbacks among methods are also discussed. Finally, limitations of the existing devices are discussed, and prospects are given with the hope to identify research opportunities and directions in the field. We hope this review will be helpful for researchers to develop a clinically useful and economically efficient paper-based platform that can be used for rapid, accurate on-site diagnosis to aid in identifying acute infections and eventually contain the COVID-19 pandemic.

Performance Variance Evaluation on Mozilla Firefox

<p>In order to evaluate software performance and find regressions, many developers use automated performance tests. However, the test results often contain a certain amount of noise that is not caused by actual performance changes in the programs. They are instead caused by external factors like operating system decisions or unexpected non-determinisms inside the programs. This makes interpreting the test results hard since results that differ from previous results cannot easily be attributed to either genuine changes or noise. In this thesis we use Mozilla Firefox as an example to try to find the causes for this performance variance, develop ways to reduce the noise and present a statistical technique that makes identifying genuine performance changes more reliable. Our results show that a significant amount of noise is caused by memory randomization and other external factors, that there is variance in Firefox internals that does not seem to be correlated with test result variance, and that our suggested statistical forecasting technique can give more reliable detection of genuine performance changes than the one currently in use by Mozilla.</p>

Performance Variance Evaluation on Mozilla Firefox

<p>In order to evaluate software performance and find regressions, many developers use automated performance tests. However, the test results often contain a certain amount of noise that is not caused by actual performance changes in the programs. They are instead caused by external factors like operating system decisions or unexpected non-determinisms inside the programs. This makes interpreting the test results hard since results that differ from previous results cannot easily be attributed to either genuine changes or noise. In this thesis we use Mozilla Firefox as an example to try to find the causes for this performance variance, develop ways to reduce the noise and present a statistical technique that makes identifying genuine performance changes more reliable. Our results show that a significant amount of noise is caused by memory randomization and other external factors, that there is variance in Firefox internals that does not seem to be correlated with test result variance, and that our suggested statistical forecasting technique can give more reliable detection of genuine performance changes than the one currently in use by Mozilla.</p>