Deep segmentation of the liver and the hepatic tumors from abdomen tomography images

A pipelined framework is proposed for accurate, automated, simultaneous segmentation of the liver as well as the hepatic tumors from computed tomography (CT) images. The introduced framework composed of three pipelined levels. First, two different transfers deep convolutional neural networks (CNN) are applied to get high-level compact features of CT images. Second, a pixel-wise classifier is used to obtain two output-classified maps for each CNN model. Finally, a fusion neural network (FNN) is used to integrate the two maps. Experimentations performed on the MICCAI’2017 database of the liver tumor segmentation (LITS) challenge, result in a dice similarity coefficient (DSC) of 93.5% for the segmentation of the liver and of 74.40% for the segmentation of the lesion, using a 5-fold cross-validation scheme. Comparative results with the state-of-the-art techniques on the same data show the competing performance of the proposed framework for simultaneous liver and tumor segmentation.

Nationwide CoViD-19 lockdown impact on air quality in India

This paper discusses the comparative results of surface and satellite measurements made during the Phase1 (25 March to 14 April), Phase2 (15 April to 3 May) and Phase3 (3 May to 17May) of Covid-19 imposed lockdown periods of 2020 and those of the same locations and periods during 2019 over India. These comparative analyses are performed for Indian states and Tier 1 megacities where economic activities have been severely affected with the nationwide lockdown. The focus is on changes in the surface concentration of sulfur dioxide (SO2), carbon monoxide (CO), PM2.5 and PM10, Ozone (O3), Nitrogen dioxide (NO2) and retrieved columnar NO2 from TROPOMI and Aerosol Optical Depth (AOD) from MODIS satellite. Surface concentrations of PM2.5 were reduced by 30.59%, 31.64% and 37.06%, PM10 by 40.64%, 44.95% and 46.58%, SO2 by 16.73%, 12.13% and 6.71%, columnar NO2 by 46.34%, 45.82% and 39.58% and CO by 45.08%, 41.51% and 60.45% during lockdown periods of Phase1, Phase2 and Phase3 respectively as compared to those of 2019 periods over India. During 1st phase of lockdown, model simulated PM2.5 shows overestimations to those of observed PM2.5 mass concentrations. The model underestimates the PM2.5 to those of without reduction before lockdown and 1st phase of lockdown periods. The reduction in emissions of PM2.5, PM10, CO and columnar NO2 are discussed with the surface transportation mobility maps during the study periods. Reduction in the emissions based on the observed reduction in the surface mobility data, the model showed excellent skills in capturing the observed PM2.5 concentrations. Nevertheless, during the 1st & 3rd phases of lockdown periods AOD reduced by 5 to 40%. Surface O3 was increased by 1.52% and 5.91% during 1st and 3rd Phases of lockdown periods respectively, while decreased by -8.29% during 2nd Phase of lockdown period.

Fullerene-Tetrabenzofluorene (C60-TBF) in Multivalent Photosensitisation for Enhanced ZnO-based Photo-catalysts: Mono vs. Hexakis Adduct

Photo-catalysts offer a simple catalytic method with widespread applications like degradation of polluting dyes, hydrogen generation from water, etc., in the presence of a photon source like sunlight. The development of a second-generation photo-catalyst in the form of a nanocomposite is an integral part of research to improve the practical usefulness and efficiency of the process. A systematic study using the active material with controlled functional groups is required to understand the process in detail as well as to develop efficient photocatalytic systems. In this paper, we report the design, synthesis, detailed physicochemical studies, and self-assembly of interesting materials where fullerenes have been functionalized with polycyclic, aromatic, conjugated, butterfly-shaped molecules like Tetrabenzofluorene (TBF) using a well-known click chemistry approach. Detailed analyses using spectroscopic, electrochemical, and microscopic or X-ray diffraction (single crystal) techniques were undertaken for a clear understanding of their photophysical or self-assembly behavior. The functionalized fullerene material was mainly used so that comparative results could be presented where two units (mono adduct) or twelve units (hexakis adduct) of TBF molecules were attached separately. These comparative studies were beneficial for unambiguous interpretation of results and drawing definitive conclusions regarding the energy transfer with cascade-type systems. Finally, those results were useful for the logical understanding of photo-catalytic experiments using those designer fullerene materials.

Design of Optimal Deep Learning-Based Pancreatic Tumor and Nontumor Classification Model Using Computed Tomography Scans

Pancreatic tumor is a lethal kind of tumor and its prediction is really poor in the current scenario. Automated pancreatic tumor classification using computer-aided diagnosis (CAD) model is necessary to track, predict, and classify the existence of pancreatic tumors. Artificial intelligence (AI) can offer extensive diagnostic expertise and accurate interventional image interpretation. With this motivation, this study designs an optimal deep learning based pancreatic tumor and nontumor classification (ODL-PTNTC) model using CT images. The goal of the ODL-PTNTC technique is to detect and classify the existence of pancreatic tumors and nontumor. The proposed ODL-PTNTC technique includes adaptive window filtering (AWF) technique to remove noise existing in it. In addition, sailfish optimizer based Kapur’s Thresholding (SFO-KT) technique is employed for image segmentation process. Moreover, feature extraction using Capsule Network (CapsNet) is derived to generate a set of feature vectors. Furthermore, Political Optimizer (PO) with Cascade Forward Neural Network (CFNN) is employed for classification purposes. In order to validate the enhanced performance of the ODL-PTNTC technique, a series of simulations take place and the results are investigated under several aspects. A comprehensive comparative results analysis stated the promising performance of the ODL-PTNTC technique over the recent approaches.

Modeling and Profiling of Aggregated Industrial Network Traffic

The industrial network infrastructures are transforming to a horizontal architecture to enable data availability for advanced applications and enhance flexibility for integrating new technologies. The uninterrupted operation of the legacy systems needs to be ensured by safeguarding their requirements in network configuration and resource management. Network traffic modeling is essential in understanding the ongoing communication for resource estimation and configuration management. The presented work proposes a two-step approach for modeling aggregated traffic classes of brownfield installation. It first detects the repeated work-cycles and then aims to identify the operational states to profile their characteristics. The performance and influence of the approach are evaluated and validated in two experimental setups with data collected from an industrial plant in operation. The comparative results show that the proposed method successfully captures the temporal and spatial dynamics of the network traffic for characterization of various communication states in the operational work-cycles.

Pumping test analysis for assessment of hydraulic parameters and aquifer system formation in hilly terrain

Groundwater conditions (GWCs) of an area depends on aquifer hydraulic parameters such as storativity () or storage coefficient (), transmissivity () and hydraulic conductivity (). It plays a key role concerning- groundwater flow modeling, well performance, solute and contaminants transports assessment and also for identification of areas for additional hydrologic testing. Specifically, the geologic formation of a regions control the porosity and permeability, however, in hilly terrain prospecting ground water potential is more challenging due to its limited extent and its occurrences that are usually confined to fractures and weathered rocks. The present study, aims at estimating the hydraulic parameters through pumping test analysis to assess aquifer system formation on hilly terrain from 16 bore wells. The aforesaid parameters were examined through a case study in some selective regions of Hamirpur district of Himachal Pradesh, India. The study area is controlled under two main geological horizons that is the post-tertiary and tertiary. The papers end with comparative results of hydraulic parameters and the aquifers system formation on different GWCs which may be helpful in the outlook of sustainable groundwater resource in the regions.

Cementation of Sand With Enzyme-Induced Carbonate Precipitation (EICP) Using Concrete-Extracted Calcium

Calcium carbonate precipitation and crystallization induced by urease enzyme to solidify soil is known as biocement technology. The uses of waste and cheap materials can make this technology more cost-effective and practical for applications. In this study, calcium ions were obtained by dissolving waste concretes in acidic liquid. Sand columns were treated by enzyme-induced carbonate precipitation (EICP) with either concrete-extracted calcium or reagent calcium for comparison. Compressive strengths, calcium carbonate contents, and microscopic analysis on the treated sand were carried out. It was found that the compressive strength of the former could reach 833 kPa in the dry state and 204 kPa in the wet state after 5 times of EICP treatment, both of which were higher than that of the latter. The calcium carbonate contents could reach 2–3% after 3–5 times of treatment. Based on the scanning electron microscope (SEM) and X-ray diffractometer (XRD) analyses, the crystal type of calcium carbonate produced in sand was calcite. The comparative results showed that the treatment effect using concrete-extracted calcium was similar or better than that using reagent calcium.