Despite theoretical advantages of DEX to improve overall patient satisfaction, the two agents provide similar responses to amnesia and pain control. According to our findings, DEX does not seem to have any advantage compared with propofol for short-term sedation after coronary artery bypass graft surgery.

We report the controlled synthesis of AlN/GaN multi-quantum well (MQW) radial nanowire heterostructures by metal-organic chemical vapor deposition. The structure consists of a single-crystal GaN nanowire core and an epitaxially grown (AlN/GaN)(m) (m = 3, 13) MQW shell. Optical excitation of individual MQW nanowires yielded strong, blue-shifted photoluminescence in the range 340-360 nm, with respect to the GaN near band-edge emission at 368.8 nm. Cathodoluminescence analysis on the cross-sectional MQW nanowire samples showed that the blue-shifted ultraviolet luminescence originated from the GaN quantum wells, while the defect-associated yellow luminescence was emitted from the GaN core. Computational simulation provided a quantitative analysis of the mini-band energies in the AlN/GaN superlattices and suggested the observed blue-shifted emission corresponds to the interband transitions between the second subbands of GaN, as a result of quantum confinement and strain effect in these AlN/GaN MQW nanowire structures.

With the emergence of the Hospital Readmission Reduction Program of the Center for Medicare and Medicaid Services on October 1, 2012, forecasting unplanned patient readmission risk became crucial to the healthcare domain. There are tangible works in the literature emphasizing on developing readmission risk prediction models; However, the models are not accurate enough to be deployed in an actual clinical setting. Our study considers patient readmission risk as the objective for optimization and develops a useful risk prediction model to address unplanned readmissions. Furthermore, Genetic Algorithm and Greedy Ensemble is used to optimize the developed model constraints.

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Autism spectrum condition (ASC) or autism spectrum disorder (ASD) is primarily identified with the help of behavioral indications encompassing social, sensory and motor characteristics. Although categorized, recurring motor actions are measured during diagnosis, quantifiable measures that ascertain kinematic physiognomies in the movement configurations of autistic persons are not adequately studied, hindering the advances in understanding the etiology of motor mutilation. Subject aspects such as behavioral characters that influences ASD need further exploration. Presently, limited autism datasets concomitant with screening ASD are available, and a majority of them are genetic. Hence, in this study, we used a dataset related to autism screening enveloping ten behavioral and ten personal attributes that have been effective in diagnosing ASD cases from controls in behavior science. ASD diagnosis is time exhaustive and uneconomical. The burgeoning ASD cases worldwide mandate a need for the fast and economical screening tool. Our study aimed to implement an artificial neural network with the Levenberg-Marquardt algorithm to detect ASD and examine its predictive accuracy. Consecutively, develop a clinical decision support system for early ASD identification.

The inelastic deformation properties of sintered metal nanoparticle joints are complicated by the inherent nanocrystalline and nanoporous structures as well as by dislocation networks formed in sintering or under cyclic loading. Creep rates of sintered nanocopper structures were found to be dominated by the diffusion of individual atoms or vacancies, while dislocation motion remained negligible up to stresses far above those of practical interest. Rapid sintering of one material led to unstable structures the creep of which could be strongly reduced by subsequent annealing or aging. Longer sintering of another material led to more stable structures, but creep rates could still be strongly enhanced by subsequent work hardening in mild cycling.

Objective: In this paper, we have summarized the influence of human age and gender on acceptance of humanoid robot and discuss the effect of robot's appearance, physical movement, and usability on human behavior and their perspective towards a robot. The paper also studies the factors that affect a user's trust towards a humanoid robot and observes the possible application and users of humanoid robots in diverse domains.Background: Application of humanoid robot has been well established in the field on healthcare and education. It has been frequently used to treat autistic children and has been able to reduce distress level in children who have cancer and cerebral palsy. In the domain of education, humanoid robots have been able to enhance student participation but not learning.Methodology: Systematic literature review was conducted to gather information about humanoid robot application and its influence on human behavior and productivity.Results: Effect of gender and age were found to be the most influential factor that defines a user's perspective towards the humanoid robot. Moreover, robot's appearance and gaze determine users' acceptance and trust towards robots. Conclusion:Children and Older adults prefer humanoid robots. The deterministic behavior of humanoid robots improves autistic condition among children. People with less social networks treat robots as their companion. Humanoids have influenced human productivity in the field of education and healthcare.

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