Nanocrystalline SnO gas sensors in view of surface reactions and modifications

The gas-sensing mechanism of an n-type semiconductor (tin dioxide) gas sensor is reviewed in this paper. It is demonstrated that very high sensitivity can be obtained only when the crystallite size is less than -10 nm. Various mechanisms involving the surface and the bulk modifications of the semiconductor oxide gas sensors are discussed to improve the gas sensitivity. Current challenges and problems in nanocrystalline oxide gas sensors are also presented.     

Macrophage Polarization in Cardiac Tissue Repair Following Myocardial Infarction

Cardiovascular disease is the leading cause of mortality and morbidity around the globe, creating a substantial socio-economic burden as a result. Myocardial infarction is a significant contributor to the detrimental impact of cardiovascular disease. The death of cardiomyocytes following myocardial infarction causes an immune response which leads to further destruction of tissue, and subsequently, results in the formation of non-contractile scar tissue. Macrophages have been recognized as important regulators and participants of inflammation and fibrosis following myocardial infarction. Macrophages are generally classified into two distinct groups, namely, classically activated, or M1 macrophages, and alternatively activated, or M2 macrophages. The phenotypic profile of cardiac macrophages, however, is much more diverse and should not be reduced to these two subsets. In this review, we describe the phenotypes and functions of macrophages which are present in the healthy, as well as the infarcted heart, and analyze them with respect to M1 and M2 polarization states. Furthermore, we discuss therapeutic strategies which utilize macrophage polarization towards an anti-inflammatory or reparative phenotype for the treatment of myocardial infarction.     

Automated contact tracing: a game of big numbers in the time of COVID-19

One of the more widely advocated solutions for slowing down the spread of COVID-19 has been automated contact tracing. Since proximity data can be collected by personal mobile devices, the natural proposal has been to use this for automated contact tracing providing a major gain over a manual implementation. In this work, we study the characteristics of voluntary and automated contact tracing and its effectiveness for mapping the spread of a pandemic due to the spread of SARS-CoV-2. We highlight the infrastructure and social structures required for automated contact tracing to work. We display the vulnerabilities of the strategy to inadequate sampling of the population, which results in the inability to sufficiently determine significant contact with infected individuals. Of crucial importance will be the participation of a significant fraction of the population for which we derive a minimum threshold. We conclude that relying largely on automated contact tracing without population-wide participation to contain the spread of the SARS-CoV-2 pandemic can be counterproductive and allow the pandemic to spread unchecked. The simultaneous implementation of various mitigation methods along with automated contact tracing is necessary for reaching an optimal solution to contain the pandemic.     

Pipelined VLSI Architecture using CORDIC for Transform Domain Equalizer

In this paper, a pipelined architecture using CORDIC for realization of transform domain equalizer is presented. Transform domain equalizer has much faster convergence than its time domain counterpart for practical hardware realization having nonzero adaptation delay. Here running DFT is employed as the transform, and CORDIC is used for realization of running DFT. Pipelining is applied throughout the architecture, thus limiting the critical path delay to the propagation delay of a single 16 bit adder for 16 bit arithmetic. For N tap equalizer, primary clock speed is N times of the sample clock speed, so that on arrival of each sample, the computation of whole transform and weight update is possible. In the proposed architecture, hardware complexity is reduced by fully utilizing the pipeline without using parallel structures. The adaptation delay is only 2 sample clock periods resulting in fast convergence. The proposed architecture is suitable for VLSI implementation with primary clock speed limited by the binary adder propagation delay which could be as low as 2 ns in the present state-of-the-art technology.     

Automobile seat comfort prediction: statistical model vs. artificial neural network

The current automobile seat comfort development process, which is executed in a trial and error fashion, is expensive and outdated. The prevailing thought is that process improvements are contingent upon the implementation of empirical/prediction models. In this context, seat-interface pressure measures, anthropometric characteristics, demographic information, and perceptions of seat appearance were related to an overall comfort index (which was a single score derived from a previously published 10-item survey with demonstrated levels of reliability and validity) using two distinct modeling approaches-stepwise, linear regression and artificial neural network. The purpose of this paper was to compare and contrast the resulting models. While both models could be used to adequately predict subjective perceptions of comfort, the neural network was deemed superior because it produced higher r2 values (0.832 vs. 0.713) and lower average error values (1.192 vs. 1.779).