Critical insights into the effects of plastic pyrolysis oil on emission and performance characteristics of CI engine.

Pyrolysis is an encouraging solution considering the facts of energy demand and waste plastic management as it produces liquid fuel for compression ignition engine application. This study provides critical insights into the effects of waste plastic oil on the emission and performance characteristics of compression ignition engines. Though most of the studies have shown a negative influence, promising outcomes have been noticed in a few specific cases. A maximum of 71%, 80%, 76%, 71%, 21%, and 13% decrease in nitrogen oxide emission, carbon monoxide emission, unburnt hydrocarbon emission, smoke emission, exhaust gas temperature, and brake-specific fuel consumption, respectively, have been noticed with waste plastic oil or its blends at certain operating conditions. Nevertheless, the presence of long carbon chains, higher aromatic content, and non-homogeneous air-fuel mixture owing to the wide product distribution in plastic oil are the few reasons which affected the emission and performance characteristics of the engines. More rigorous investigations are needed to improve the quality of the fuel and to establish correlations between the fuel properties and pyrolysis parameters. In addition, the effects of incorporating exhaust gas recirculation, emulsification process, and use of additives with waste plastic oil need to be explored more for reducing the emissions with satisfactory engine performance, and in this regard, the use of bio-additives with waste plastic oil can provide a new direction to this research field. Further, studies on the economic feasibility and the impact of waste plastic oil on engine materials are also required.

A computational modeling for the detection of diabetic retinopathy severity.

Prolonged diabetes ultimately leads to Diabetic Retinopathy (DR) which is one of the leading causes of preventable blindness in the world. Through advanced image analysis techniques are used for abnormalities detection in retina that define and correlate the severity of DR. A thorough study is done in this area in recent past years and on the basis of these studies we have developed a computer based prediction model that is used to determine the severity of DR. To identify severity DR, we have analyzed the human eye image. We have extracted some important features from human eye image i.e. Blood Artery, Optical disc, Exudates. Based on these image and data we have designed an automated system for the determination of DR severity. This automated DR severity assessment methods can be used to predict the clinical case and conditions when young clinicians would agree or disagree with their more experienced fellow members. The algorithms described in this study may be used in clinical practice to validate or invalidate the diagnoses. Algorithms or method developed here may also be used for pooling diagnostic knowledge for serving mankind. Here we have described a computational based low cost retinal diagnostic approach which can aid an ophthalmologist to quickly diagnose the various stages of DR. This system can accept retinal images and can successfully detect any pathological condition associated with DR.

Joint classification and pairing of human chromosomes.

We reexamine the problems of computer-aided classification and pairing of human chromosomes, and propose to jointly optimize the solutions of these two related problems. The combined problem is formulated into one of optimal three-dimensional assignment with an objective function of maximum likelihood. This formulation poses two technical challenges: 1) estimation of the posterior probability that two chromosomes form a pair and the pair belongs to a class and 2) good heuristic algorithms to solve the three-dimensional assignment problem which is NP-hard. We present various techniques to solve these problems. We also generalize our algorithms to cases where the cell data are incomplete as often encountered in practice.