National Institute of Technology, Tiruchirappalli

Abstract: Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumoris a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses thestate-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross tota lresection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset.

Abstract: Organic compound, especially aromatic compound is the main pollutant in industrial effluent. Conventional wastewater treatment processes are inefficient for the removal of these types of toxic and hazardous pollutants from wastewater. Electro Fenton is one of the powerful and environmentally friendly emerging technologies for the remediation of wastewaters containing organic, especially aromatic compounds. This paper reviews the fundamentals and recent developments in electro Fenton process. Electro Fenton process utilizes different electrolytic reactors such as bubble reactor, filter press reactor, divided double-electrode electrochemical cell, divided three-electrode electrochemical cell and double compartment cell. Different cathodes as working electrode and anodes as counter electrode used in this process are analyzed. The effects of various operating parameters and their optimum ranges for maximum pollutant removal and mineralization are reviewed. Also various pollutants removed by this process are evaluated. Quick removal and mineralization of pollutants and their intermediate reaction products were reported.

Abstract: Experimental investigations and theoretical determination of effective thermal conductivity and viscosity of Al 2 O 3 /H 2 O nanofluid are reported in this paper. The nanofluid was prepared by synthesizing Al 2 O 3 nanoparticles using microwave assisted chemical precipitation method, and then dispersing them in distilled water using a sonicator. Al 2 O 3 /water nanofluid with a nominal diameter of 43 nm at different volume concentrations (0.33–5%) at room temperature were used for the investigation. The thermal conductivity and viscosity of nanofluids are measured and it is found that the viscosity increase is substantially higher than the increase in thermal conductivity. Both the thermal conductivity and viscosity of nanofluids increase with the nanoparticle volume concentration. Theoretical models are developed to predict thermal conductivity and viscosity of nanofluids without resorting to the well established Maxwell and Einstein models, respectively. The proposed models show reasonably good agreement with our experimental results.

Abstract: Return of used products is becoming an important logistics activity due to government legislation and increasing awareness among the people to protect the environment and reduce waste. For industries, the management of return flow usually requires a specialized infrastructure with special information systems for tracking and dedicated equipment for the processing of returns. Therefore, industries are turning to third-party reverse logistics providers (3PRLPs). In this study, a multi-criteria group decision-making (MCGDM) model in fuzzy environment is developed to guide the selection process of best 3PRLP. The interactions among the criteria are also analyzed before arriving at a decision for the selection of 3PRLP from among 15 alternatives. The analysis is done through Interpretive Structural Modeling (ISM) and fuzzy technique for order preference by similarity to ideal solution (TOPSIS). Finally the effectiveness of the model is illustrated using a case study on battery manufacturing industry in India.

Abstract: As customers are becoming more environmental conscious and governments are making stricter environmental regulations, the industries need to reduce the environmental impact of their supply chain. Indian auto component manufacturing industries especially SMEs (Small and Medium Enterprises) are focused to cleaner production by implementing Green Supply Chain Management (GSCM) in their industries. But they are struggling to implement GSCM concept. The present research analyzes the barriers for the implementation of GSCM concept which has been divided into two phases such as identification of barriers and qualitative analysis. The study has used three different research phases: identification of barriers from the literature, interviews with various department managers and a survey of auto component manufacturing industries. The identification phase led to the selection of twenty-six barriers based on literature and in consultation with industrial experts and academicians. The Interpretive Structural Modeling (ISM) qualitative analysis was used to understand the mutual influences amongst the twenty-six barriers by survey. This study seeks to identify which barrier is acting as the most dominant one for the adoption of green supply chain management and this result is helpful for industries to make easier the adoption of green concept in their supply chain by removing the dominant barrier. It indicates that different Indian auto component manufacturing industries have differing barriers for the implementation of green supply chain management. However, in their GSCM implementation, especially for maintaining the environmental awareness, the supplier barrier is the dominant one. Finally the approach has been applied to ten auto components manufacturing industries in Tamilnadu, South India.