Showing papers by "Indian Institute of Technology Kharagpur published in 2017"
ETH Zurich1, University of California, Merced2, University of Hong Kong3, Seoul National University4, The Chinese University of Hong Kong5, Chinese Academy of Sciences6, KAIST7, University of Illinois at Urbana–Champaign8, Harbin Institute of Technology9, Xiamen University10, Peking University11, University of Missouri12, University of Sydney13, Beijing University of Posts and Telecommunications14, Shandong University15, Australian National University16, Sejong University17, Pennsylvania State University18, Tampere University of Technology19, Indian Institute of Technology Kharagpur20, École Polytechnique Fédérale de Lausanne21, University of Electronic Science and Technology of China22
TL;DR: This paper reviews the first challenge on single image super-resolution (restoration of rich details in an low resolution image) with focus on proposed solutions and results and gauges the state-of-the-art in single imagesuper-resolution.
Abstract: This paper reviews the first challenge on single image super-resolution (restoration of rich details in an low resolution image) with focus on proposed solutions and results. A new DIVerse 2K resolution image dataset (DIV2K) was employed. The challenge had 6 competitions divided into 2 tracks with 3 magnification factors each. Track 1 employed the standard bicubic downscaling setup, while Track 2 had unknown downscaling operators (blur kernel and decimation) but learnable through low and high res train images. Each competition had ∽100 registered participants and 20 teams competed in the final testing phase. They gauge the state-of-the-art in single image super-resolution.
1,243 citations
University of Sydney1, Agriculture and Agri-Food Canada2, Institut national de la recherche agronomique3, Natural Resources Conservation Service4, Centre national de la recherche scientifique5, National Taiwan University6, Nanjing Agricultural University7, Indian Institute of Technology Kharagpur8, James Hutton Institute9, Landcare Research10, Rural Development Administration11, Bidhan Chandra Krishi Viswavidyalaya12, British Geological Survey13, Wageningen University and Research Centre14, University College Dublin15, Colorado State University16, World Agroforestry Centre17, Université catholique de Louvain18
TL;DR: In this paper, the authors surveyed the soil organic carbon (SOC) stock estimates and sequestration potentials from 20 regions in the world (New Zealand, Chile, South Africa, Australia, Tanzania, Indonesia, Kenya, Nigeria, India, China Taiwan, South Korea, China Mainland, United States of America, France, Canada, Belgium, England & Wales, Ireland, Scotland, and Russia).
1,171 citations
University of South Carolina1, Los Alamos National Laboratory2, Moscow State University3, Delhi Technological University4, University of Paris5, University of California, Davis6, Indian Institute of Technology (BHU) Varanasi7, University of Moratuwa8, University of Illinois at Urbana–Champaign9, California Polytechnic State University10, Sandia National Laboratories11, Max Planck Society12, Indian Institute of Technology Kharagpur13, French Institute for Research in Computer Science and Automation14, University of New Mexico15, Charles University in Prague16, Birla Institute of Technology and Science17, Indian Institute of Technology Bombay18, University of West Bohemia19
TL;DR: The architecture of SymPy is presented, a description of its features, and a discussion of select domain specific submodules are discussed, to become the standard symbolic library for the scientific Python ecosystem.
Abstract: SymPy is an open source computer algebra system written in pure Python. It is built with a focus on extensibility and ease of use, through both interactive and programmatic applications. These characteristics have led SymPy to become a popular symbolic library for the scientific Python ecosystem. This paper presents the architecture of SymPy, a description of its features, and a discussion of select submodules. The supplementary material provide additional examples and further outline details of the architecture and features of SymPy.
1,126 citations
TL;DR: In this paper, the authors propose a simulator, called iFogSim, to model IoT and fog environments and measure the impact of resource management techniques in latency, network congestion, energy consumption, and cost.
Abstract: Summary
Internet of Things (IoT) aims to bring every object (eg, smart cameras, wearable, environmental sensors, home appliances, and vehicles) online, hence generating massive volume of data that can overwhelm storage systems and data analytics applications. Cloud computing offers services at the infrastructure level that can scale to IoT storage and processing requirements. However, there are applications such as health monitoring and emergency response that require low latency, and delay that is caused by transferring data to the cloud and then back to the application can seriously impact their performances. To overcome this limitation, Fog computing paradigm has been proposed, where cloud services are extended to the edge of the network to decrease the latency and network congestion. To realize the full potential of Fog and IoT paradigms for real-time analytics, several challenges need to be addressed. The first and most critical problem is designing resource management techniques that determine which modules of analytics applications are pushed to each edge device to minimize the latency and maximize the throughput. To this end, we need an evaluation platform that enables the quantification of performance of resource management policies on an IoT or Fog computing infrastructure in a repeatable manner. In this paper we propose a simulator, called iFogSim, to model IoT and Fog environments and measure the impact of resource management techniques in latency, network congestion, energy consumption, and cost. We describe two case studies to demonstrate modeling of an IoT environment and comparison of resource management policies. Moreover, scalability of the simulation toolkit of RAM consumption and execution time is verified under different circumstances.
1,085 citations
TL;DR: This work has demonstrated a bottom-up interfacial crystallization strategy to fabricate these microcrystalline powders as large-scale thin films under ambient conditions, allowing simultaneous control over crystallization and morphology of the framework structure.
Abstract: Exponential interest in the field of covalent organic frameworks (COFs) stems from the direct correlation between their modular design principle and various interesting properties. However, existing synthetic approaches to realize this goal mainly result in insoluble and unprocessable powders, which severely restrict their widespread applicability. Therefore, developing a methodology for easy fabrication of these materials remains an alluring goal and a much desired objective. Herein, we have demonstrated a bottom-up interfacial crystallization strategy to fabricate these microcrystalline powders as large-scale thin films under ambient conditions. This unique design principle exploits liquid–liquid interface as a platform, allowing simultaneous control over crystallization and morphology of the framework structure. The thin films are grown without any support in free-standing form and can be transferred onto any desirable substrate. The porous (with Tp-Bpy showing highest SBET of 1 151 m2 g–1) and crystal...
584 citations
TL;DR: Self-standing, flexible, continuous, and crack-free covalent-organic-framework membranes (COMs) are fabricated via a simple, scalable, and highly cost-effective methodology and show long-term durability, recyclability, and retain their structural integrity in water, organic solvents, and mineral acids.
Abstract: Self-standing, flexible, continuous, and crack-free covalent-organic-framework membranes (COMs) are fabricated via a simple, scalable, and highly cost-effective methodology. The COMs show long-term durability, recyclability, and retain their structural integrity in water, organic solvents, and mineral acids. COMs are successfully used in challenging separation applications and recovery of valuable active pharmaceutical ingredients from organic solvents.
461 citations
TL;DR: DeepFix as mentioned in this paper proposes a fully convolutional neural network (FCN) which models the bottom-up mechanism of visual attention via saliency prediction and predicts the saliency map in an end-to-end manner.
Abstract: Understanding and predicting the human visual attention mechanism is an active area of research in the fields of neuroscience and computer vision. In this paper, we propose DeepFix, a fully convolutional neural network, which models the bottom–up mechanism of visual attention via saliency prediction. Unlike classical works, which characterize the saliency map using various hand-crafted features, our model automatically learns features in a hierarchical fashion and predicts the saliency map in an end-to-end manner. DeepFix is designed to capture semantics at multiple scales while taking global context into account, by using network layers with very large receptive fields. Generally, fully convolutional nets are spatially invariant—this prevents them from modeling location-dependent patterns (e.g., centre-bias). Our network handles this by incorporating a novel location-biased convolutional layer. We evaluate our model on multiple challenging saliency data sets and show that it achieves the state-of-the-art results.
443 citations
TL;DR: A new fully convolutional deep architecture, termed ReLayNet, is proposed for end-to-end segmentation of retinal layers and fluid masses in eye OCT scans, validated on a publicly available benchmark dataset with comparisons against five state-of-the-art segmentation methods.
Abstract: Optical coherence tomography (OCT) is used for non-invasive diagnosis of diabetic macular edema assessing the retinal layers. In this paper, we propose a new fully convolutional deep architecture, termed ReLayNet, for end-to-end segmentation of retinal layers and fluid masses in eye OCT scans. ReLayNet uses a contracting path of convolutional blocks (encoders) to learn a hierarchy of contextual features, followed by an expansive path of convolutional blocks (decoders) for semantic segmentation. ReLayNet is trained to optimize a joint loss function comprising of weighted logistic regression and Dice overlap loss. The framework is validated on a publicly available benchmark dataset with comparisons against five state-of-the-art segmentation methods including two deep learning based approaches to substantiate its effectiveness.
440 citations
TL;DR: A comprehensive survey of different SDN-based technologies, which are useful to fulfill the requirements of IoT, from different networking aspects—edge, access, core, and data center networking.
Abstract: Internet of things (IoT) facilitates billions of devices to be enabled with network connectivity to collect and exchange real-time information for providing intelligent services. Thus, IoT allows connected devices to be controlled and accessed remotely in the presence of adequate network infrastructure. Unfortunately, traditional network technologies such as enterprise networks and classic timeout-based transport protocols are not capable of handling such requirements of IoT in an efficient, scalable, seamless, and cost-effective manner. Besides, the advent of software-defined networking (SDN) introduces features that allow the network operators and users to control and access the network devices remotely, while leveraging the global view of the network. In this respect, we provide a comprehensive survey of different SDN-based technologies, which are useful to fulfill the requirements of IoT, from different networking aspects— edge , access , core , and data center networking. In these areas, the utility of SDN-based technologies is discussed, while presenting different challenges and requirements of the same in the context of IoT applications. We present a synthesized overview of the current state of IoT development. We also highlight some of the future research directions and open research issues based on the limitations of the existing SDN-based technologies.
298 citations
TL;DR: In this paper, the suitability of the energy density to represent the energy transferred to the process parameters was analyzed for the selective laser melting process, and the authors showed that the energy densities can be used to estimate the amount of energy transferred.
Abstract: The effective fabrication of materials using selective laser melting depends on the process parameters. Here, we analyse the suitability of the energy density to represent the energy transferred to...
294 citations
TL;DR: The Compressed Baryonic Matter (CBM) experiment at FAIR will play a unique role in the exploration of the QCD phase diagram in the region of high net-baryon densities, because it is designed to run at unprecedented interaction rates.
Abstract: Substantial experimental and theoretical efforts worldwide are devoted to explore the phase diagram of strongly interacting matter. At LHC and top RHIC energies, QCD matter is studied at very high temperatures and nearly vanishing net-baryon densities. There is evidence that a Quark-Gluon-Plasma (QGP) was created at experiments at RHIC and LHC. The transition from the QGP back to the hadron gas is found to be a smooth cross over. For larger net-baryon densities and lower temperatures, it is expected that the QCD phase diagram exhibits a rich structure, such as a first-order phase transition between hadronic and partonic matter which terminates in a critical point, or exotic phases like quarkyonic matter. The discovery of these landmarks would be a breakthrough in our understanding of the strong interaction and is therefore in the focus of various high-energy heavy-ion research programs. The Compressed Baryonic Matter (CBM) experiment at FAIR will play a unique role in the exploration of the QCD phase diagram in the region of high net-baryon densities, because it is designed to run at unprecedented interaction rates. High-rate operation is the key prerequisite for high-precision measurements of multi-differential observables and of rare diagnostic probes which are sensitive to the dense phase of the nuclear fireball. The goal of the CBM experiment at SIS100 ( $\sqrt{s_{NN}}=$ 2.7--4.9 GeV) is to discover fundamental properties of QCD matter: the phase structure at large baryon-chemical potentials ( $\mu_B > 500$ MeV), effects of chiral symmetry, and the equation of state at high density as it is expected to occur in the core of neutron stars. In this article, we review the motivation for and the physics programme of CBM, including activities before the start of data taking in 2024, in the context of the worldwide efforts to explore high-density QCD matter.
TL;DR: In this article, the authors focus on the significant application based advances in neat and tailored nanostructures of noble metal-metal oxide nanohybrids and touched upon chalcogenides also.
Abstract: The skilful synthesis of nanohybrids composed of noble metals (Au, Ag, Pt and Pd, as well as AuAg alloy) and metal oxides (ZnO, TiO2, Cu2O, MnO2, Fe2O3, WO3 and CeO2) has received considerable attention for applications in photocatalysis, solar cells, drug delivery, surface enhanced Raman spectroscopy and many other important areas. The overall architecture of nanocomposites is one of the most important factors dictating the physical properties of nanohybrids. Noble metals can be coupled to metal oxides and metal chalcogenides to yield diverse nanostructures, including noble metal decorated-metal oxide nanoparticles (NPs), nanoarrays, noble metal/metal oxide core/shell, noble metal/metal oxide yolk/shell and Janus noble metal–metal oxide nanostructures. In this review, we focus on the significant application based advances in neat and tailored nanostructures of noble metal–metal oxide nanohybrids and touched upon chalcogenides also. The improvement in performance in representative energy conversion, electrochemical water splitting, photocatalytic hydrogen generation, photocatalytic CO2 reduction, photocatalytic degradation of organic pollutants and dye-sensitized solar cell (DSSCs) applications is discussed. Finally, we conclude with a perspective on the future direction and prospects of these controllable nanohybrid materials.
TL;DR: A comprehensive review of different approaches used by research community to isolate the impacts of climate change and human activities on streamflow is presented in this paper, where the important issues pertaining to different approaches, to make rational use of methodology, are discussed so that researcher and policymaker can understand the importance of individual methodology and its use in water resources management.
TL;DR: In this article, it was shown that the planar Hall effect (PHE) is present in Dirac and Weyl semimetals and can be directly checked in experiments.
Abstract: In condensed matter physics, the term "chiral anomaly" implies the violation of the separate number conservation laws of Weyl fermions of different chiralities in the presence of parallel electric and magnetic fields. One effect of the chiral anomaly in the recently discovered Dirac and Weyl semimetals is a positive longitudinal magnetoconductance. Here we show that chiral anomaly and nontrivial Berry curvature effects engender another striking effect in Weyl semimetals, the planar Hall effect (PHE). Remarkably, the PHE manifests itself when the applied current, magnetic field, and the induced transverse "Hall" voltage all lie in the same plane, precisely in a configuration in which the conventional Hall effect vanishes. In this work we treat the PHE quasiclassically, and predict specific experimental signatures for type-I and type-II Weyl semimetals that can be directly checked in experiments.
18 Apr 2017
TL;DR: In this paper, the authors formulated the segmentation task as a multi-label inference task and utilized the implicit advantages of the combination of convolutional neural networks and structured prediction.
Abstract: Automatic segmentation of retinal blood vessels from fundus images plays an important role in the computer aided diagnosis of retinal diseases. The task of blood vessel segmentation is challenging due to the extreme variations in morphology of the vessels against noisy background. In this paper, we formulate the segmentation task as a multi-label inference task and utilize the implicit advantages of the combination of convolutional neural networks and structured prediction. Our proposed convolutional neural network based model achieves strong performance and significantly outperforms the state-of-the-art for automatic retinal blood vessel segmentation on DRIVE dataset with 95.33% accuracy and 0.974 AUC score.
TL;DR: An analytical approach for estimation of the thermodynamic parameters from the Langmuir isotherm constant has been introduced in the present paper as discussed by the authors, which unequivocally demonstrated the improper estimations in practice.
TL;DR: The review rationalizes that the choice of silk protein as a biomaterial is not onlyBecause of its natural polymeric nature, mechanical robustness, flexibility and wide range of cell compatibility but also because of its ability to template the growth of hydroxyapatite, the chief inorganic component of bone mineral matrix, resulting in improved osteointegration.
TL;DR: The approach fine-tunes a pre-trained convolutional neural network (CNN), GoogLeNet, to improve its prediction capability and identifies salient responses during prediction to understand learned filter characteristics.
Abstract: We present an algorithm for identifying retinal pathologies given retinal optical coherence tomography (OCT) images. Our approach fine-tunes a pre-trained convolutional neural network (CNN), GoogLeNet, to improve its prediction capability (compared to random initialization training) and identifies salient responses during prediction to understand learned filter characteristics. We considered a data set containing subjects with diabetic macular edema, or dry age-related macular degeneration, or no pathology. The fine-tuned CNN could effectively identify pathologies in comparison to classical learning. Our algorithm aims to demonstrate that models trained on non-medical images can be fine-tuned for classifying OCT images with limited training data.
01 Jan 2017
TL;DR: In this article, the concomitant areas for extending the scope of employee performance as a major domain of human resource (HR) effectiveness are explored, and the authors have interviewed researchers and interviewed experts.
Abstract: The present study explores the concomitant areas for extending the scope of employee performance as a major domain of human resource (HR) effectiveness. We have interviewed researchers and ...
TL;DR: Electrochemical oxidation can be employed as a complementary treatment system with biological process for conventional landfill leachate treatment as well as a standalone system for ammonium nitrogen removal from bioreactor landfill leachesate, according to the conclusion.
TL;DR: A survey of the metrics used for community detection and evaluation can be found in this paper, where the authors also conduct experiments on synthetic and real networks to present a comparative analysis of these metrics in measuring the goodness of the underlying community structure.
Abstract: Detecting and analyzing dense groups or communities from social and information networks has attracted immense attention over the last decade due to its enormous applicability in different domains. Community detection is an ill-defined problem, as the nature of the communities is not known in advance. The problem has turned even more complicated due to the fact that communities emerge in the network in various forms such as disjoint, overlapping, and hierarchical. Various heuristics have been proposed to address these challenges, depending on the application in hand. All these heuristics have been materialized in the form of new metrics, which in most cases are used as optimization functions for detecting the community structure, or provide an indication of the goodness of detected communities during evaluation. Over the last decade, a large number of such metrics have been proposed. Thus, there arises a need for an organized and detailed survey of the metrics proposed for community detection and evaluation. Here, we present a survey of the start-of-the-art metrics used for the detection and the evaluation of community structure. We also conduct experiments on synthetic and real networks to present a comparative analysis of these metrics in measuring the goodness of the underlying community structure.
TL;DR: In this article, the authors focus on the prospects of green solvents and extraction techniques that could improve the commercial viability of biodiesel production, which is a serious concern for a sustainable economy where it has necessitated alternative renewable energy that can have the potential to meet the futuristic needs.
Abstract: Energy security is a serious concern for a sustainable economy where it has necessitated alternative renewable energy that can have the potential to meet the futuristic needs. Among renewable energies, carbon neutral biofuels from microalgae appear to be a promising move towards sustainability and cleaner fuels owing to its attributes of high CO 2 -sequestering capability, high lipid productivity and being easily cultivable in an open pond and waste/marine/brackish water. However, the commercial viability of algae-based fuels suffers mainly from the cost incurred during the process. Among the steps involved in the biodiesel production from microalgae, lipid extraction in particular consumes not only a significant amount of energy and time but also causes environmental contamination by usage of toxic solvents. Conventional solvents used in lipid extraction process may further aggravate the quality of the product by dissolving other compounds like chlorophyll that may lead to erroneous results of lipid content. To circumvent the problem, green solvents and process intensification methods/techniques (green extraction technologies) potentially improve the characteristics of energy reduction, eco-friendliness, non-toxicity and efficient lipid extraction. Hence, this review focuses on the prospects of green solvents and extraction techniques that could improve the commercial viability of biodiesel production.
TL;DR: In this paper, a heterojunction catalyst based on g-C3N4 and BiVO4 was prepared by a hydrothermal synthesis route in the presence of sodium dodecyl sulfate using aqueous NH3 as precipitant.
Abstract: In this study, BiVO4 was prepared by a hydrothermal synthesis route in the presence of sodium dodecyl sulfate using aqueous NH3 as precipitant. g-C3N4 was prepared by a combustion method using melamine. In order to develop highly efficient photocatalyst, a heterojunction catalyst based on g-C3N4 and BiVO4 was prepared. Different amounts of BiVO4 and g-C3N4 were mixed and annealed to obtain heterojunction photocatalysts. FeVO4 and LaVO4 were also prepared for the comparative catalytic investigation. Catalysts were characterized by a series of complementary combinations of powder X-ray diffraction, thermogravimetric analysis, elemental analysis, N2 adsorption–desorption, scanning electron microscopy, transmission electron microscopy, temperature-programmed desorption of NH3 and CO2, diffuse reflectance ultraviolet visible spectroscopy, X-ray photoelectron spectroscopy, photoluminescence spectroscopy, and photoelectrochemical studies. Catalysts were investigated in the visible light driven oxidation of benzy...
TL;DR: In this paper, the basic conduction mechanisms, advantages and disadvantages of different LT oxide ion conducting electrolytes, including bilayer, mixed ion conducting, and proton conducting, are discussed based on the recent research articles.
TL;DR: In this paper, trilaminar core-shell composites of Fe3O4@C@PANI were fabricated by facile hydrothermal method and subsequent high-temperature calcination followed by its encapsulation through oxidative polymerization of aniline.
Abstract: The present work reports fabrication of trilaminar core–shell composites of Fe3O4@C@PANI as efficient lightweight electromagnetic wave absorber by facile hydrothermal method and subsequent high-temperature calcination followed by its encapsulation through oxidative polymerization of aniline. The prepared composite structure was characterized by FTIR, XRD, XPS, TEM, HRTEM, and SQUID. The measurement of reflection loss, complex permittivity, complex permeability, and total shielding efficiency of the composites has been carried out in the frequency range of 2–8 GHz. Our findings showed lowest reflection loss (∼33 dB) in composite comprised of Fe3O4@C:aniline (1:9 wt/wt) corresponding to shielding efficiency predominantly due to absorption (∼47 dB) than reflection (∼15 dB). Such high value of shielding efficiency could be ascribed to the presence of dual interfaces and dielectric–magnetic integration in Fe3O4@C@PANI. In all probability, higher dielectric loss through interface polarization and relaxation eff...
TL;DR: This model will be very beneficial in routine exam, providing pathologists with efficient and effective second opinion for breast cancer grading from whole slide images, and could lead junior and senior pathologists, as medical researchers, to a superior understanding and evaluation of breast cancer stage and genesis.
TL;DR: In this article, the authors used Vector Auto-Regression and Vector Error Correction model to find short-run and long-run causality between transport infrastructure and economic development, and the direction of causality is from economic development to transport infrastructure in most of the cases.
Abstract: Development of transport infrastructure has long been taken as a major tool in promoting economic development and urbanization of a region. However, it is quite debatable whether transport infrastructure promotes economic development and urbanization, or economic development and urbanization create demand first which leads to investment in transport infrastructure. Each of the views has theoretical support. Therefore, apart from theory, empirical evidence is required to establish direction of causality, which bears serious policy implications. This study looks into different sub-sectors of transport infrastructure to find its long-run relationship and direction of causality with economic development and urbanization. It first finds the order of integration of the variables and then tries to find their causal relationship using cointegration and Granger causality test approach for India between 1990 and 2011. It uses Vector Auto-Regression and Vector Error Correction model to find short-run and long-run causality. Results showed existence of long-run relationship between transport infrastructure and economic development, and the direction of causality is from economic development to transport infrastructure in most of the cases, thus drawing support in favor of Wagner’s law.
01 Dec 2017
TL;DR: A new privacy-preserving blockchain architecture for IoT applications based on attribute-based encryption (ABE) techniques is proposed and security, privacy, and numerical analyses are presented to validate the proposed model.
Abstract: The Internet of Things (IoT) has penetrated deeply into our lives and the number of IoT devices per person is expected to increase substantially over the next few years. Due to the characteristics of IoT devices (i.e., low power and low battery), usage of these devices in critical applications requires sophisticated security measures. Researchers from academia and industry now increasingly exploit the concept of blockchains to achieve security in IoT applications. The basic idea of the blockchain is that the data generated by users or devices in the past are verified for correctness and cannot be tampered once it is updated on the blockchain. Even though the blockchain supports integrity and non-repudiation to some extent, confidentiality and privacy of the data or the devices are not preserved. The content of the data can be seen by anyone in the network for verification and mining purposes. In order to address these privacy issues, we propose a new privacy-preserving blockchain architecture for IoT applications based on attribute-based encryption (ABE) techniques. Security, privacy, and numerical analyses are presented to validate the proposed model.
TL;DR: Green solvents and technology like aqueous assisted enzyme extraction are better solution for oil extraction from oilseeds because it can obtain higher yields, cost-effective and aids in obtaining co-products without any damage.
Abstract: Oilseeds are crucial for the nutritional security of the global population. The conventional technology used for oil extraction from oilseeds is by solvent extraction. In solvent extraction, n-hexane is used as a solvent for its attributes such as simple recovery, non-polar nature, low latent heat of vaporization (330 kJ/kg) and high selectivity to solvents. However, usage of hexane as a solvent has lead to several repercussions such as air pollution, toxicity and harmfulness that prompted to look for alternative options. To circumvent the problem, green solvents could be a promising approach to replace solvent extraction. In this review, green solvents and technology like aqueous assisted enzyme extraction are better solution for oil extraction from oilseeds. Enzyme mediated extraction is eco-friendly, can obtain higher yields, cost-effective and aids in obtaining co-products without any damage. Enzyme technology has great potential for oil extraction in oilseed industry. Similarly, green solvents such as terpenes and ionic liquids have tremendous solvent properties that enable to extract the oil in eco-friendly manner. These green solvents and technologies are considered green owing to the attributes of energy reduction, eco-friendliness, non-toxicity and non-harmfulness. Hence, the review is mainly focussed on the prospects and challenges of green solvents and technology as the best option to replace the conventional methods without compromising the quality of the extracted products.
TL;DR: In this paper, the authors present a methodology for evaluating rainwater harvesting potential and identifying suitable sites for RWH and artificial recharge structures using Geographic Information System (GIS)-based multi-criteria decision analysis (MCDA).