Generalized Eigenvalue Decomposition Applied to Estimation of Spatial rPPG Distribution of Skin

Journal of Mathematical Imaging and Vision - Remote photoplethysmography (rPPG) has been at the forefront recently, thanks to its capacity in estimating non-contact physiological parameters such as...     

Role of Random and Coincidence Site Lattice Grain Boundaries in Liquid Metal Embrittlement of Iron (FCC)-Zn Couple

Metallurgical and Materials Transactions A - Liquid metal embrittlement (LME) has frequently been reported in various systems: Cu-Bi, Ni-Bi, Al-Ga, and Fe-Zn for more than 60 years. Although...     

A quantitative measure for degree of automation and its relation to system performance and mental load

In this paper we quantitatively model degree of automation (DofA) in supervisory control as a function of the number and nature of tasks to be performed by the operator and automation. This model uses a task weighting scheme in which weighting factors are obtained from task demand load, task mental load, and task effect on system performance. The computation of DofA is demonstrated using an experimental system. Based on controlled experiments using operators, analyses of the task effect on system performance, the prediction and assessment of task demand load, and the prediction of mental load were performed. Each experiment had a different DofA. The effect of a change in DofA on system performance and mental load was investigated. It was found that system performance became less sensitive to changes in DofA at higher levels of DofA. The experimental data showed that when the operator controlled a partly automated system, perceived mental load could be predicted from the task mental load for each task component, as calculated by analyzing a situation in which all tasks were manually controlled. Actual or potential applications of this research include a methodology to balance and optimize the automation of complex industrial systems.     

A review on nano-TiO<Subscript>2</Subscript> sol–gel type syntheses and its applications

Nanomaterials, defined as particles ranging from 1 to 100 nm diameter, have become widely utilized because of their unique physicochemical properties. Among those nanoparticles, titanium dioxide (TiO₂) is frequently used in the production of paints, paper, plastics, welding rod-coating material, cosmetics, etc. TiO₂ is the most commonly used semiconductor photocatalyst. Among the different nanomaterials, it is the most studied. Activated by UV-A irradiation, its photocatalytic properties have been utilized in various applications. A wealth of information on TiO₂ photocatalytic in activation of bacteria has been acquired over the last 20 years. Hence, in this review article we have described synthesis methods mainly sol–gel type method like sol–gel method, ultrasonic-assisted sol–gel method, microemulsion method, colloidal synthesis, and also other method are discussed like solvo-thermal method, thermal plasma process, supersonically expanded plasma jet method, induction plasma torch, reactive plasma processing, plasma electrolytic oxidation, hydrolysis method, thermohydrolysis method, coprecipitation method, citrate–nitrate autocombustion method, etc. Also applications of TiO₂ like medical applications, environmental application, sensor application, photocatalytic applications, and also its health impact for long-term exposure are discussed.