Production of lignocellulosic ethanol from Lantana camara by bacterial cellulase of termite symbionts

Due to the rapid growth in population and industrialization, worldwide ethanol demand is increasing continuously. Lignocellulosic biomasses are most abundant and renewable sources of the world and they can act as a promising source for bioethanol production. The major objective of the work was to evaluate the effect of acid and steam pretreatment on Lantana camara for improved yield of bioethanol production by using cellulase production by bacteria isolated from termite gut and optimization of conditions required for maximum activity of cellulase enzyme. Cellulase producing bacteria isolated from termite’s gut screened by congored test. Cellulase activity was measured by DNS method. From the present study it is concluded that bacteria isolated from termite gut was producing maximum amount of cellulase enzyme after 40 hours (TCDB1, 2) and after 60 hours (TCDB 3). Cellulase enzyme produced by bacteria isolated from termite gut was found to have pH around 5, temperature 50°C for TCDB 1 and 70°C for TCDB 2 and 3 as optimum conditions .Activity of Cellulase enzyme produced by bacteria isolated from termite gut was found to be increased by the addition of 5 mM MnSO4 (all three TCDB) and MgSO4 (only TCDB3). It Possible to produce lignocellulosic bioethanol (11.66%) from Lantana camara after steam and acid pretreatment by using Cellulase for Saccharification (72 hours) and Saccharomyces cerevisiae for fermentation (72 hours). Bioethanol from lignocellulosic biomass is a globally accepted alternative fuel. The production of ethanol from Lantana camara would have the dual advantage of producing energy and serving as an effective method of weed management

Deep Quality: A Deep No-reference Quality Assessment System

Image quality assessment (IQA) continues to garner great interestin the research community, particularly given the tremendousrise in consumer video capture and streaming. Despite significantresearch effort in IQA in the past few decades, the area of noreferenceimage quality assessment remains a great challenge andis largely unsolved. In this paper, we propose a novel no-referenceimage quality assessment system called Deep Quality, which leveragesthe power of deep learning to model the complex relationshipbetween visual content and the perceived quality. Deep Qualityconsists of a novel multi-scale deep convolutional neural network,trained to learn to assess image quality based on training samplesconsisting of different distortions and degradations such as blur,Gaussian noise, and compression artifacts. Preliminary results usingthe CSIQ benchmark image quality dataset showed that DeepQuality was able to achieve strong quality prediction performance(89% patch-level and 98% image-level prediction accuracy), beingable to achieve similar performance as full-reference IQA methods

Deep Quality: A Deep No-reference Quality Assessment System

Image quality assessment (IQA) continues to garner great interestin the research community, particularly given the tremendousrise in consumer video capture and streaming. Despite significantresearch effort in IQA in the past few decades, the area of noreferenceimage quality assessment remains a great challenge andis largely unsolved. In this paper, we propose a novel no-referenceimage quality assessment system called Deep Quality, which leveragesthe power of deep learning to model the complex relationshipbetween visual content and the perceived quality. Deep Qualityconsists of a novel multi-scale deep convolutional neural network,trained to learn to assess image quality based on training samplesconsisting of different distortions and degradations such as blur,Gaussian noise, and compression artifacts. Preliminary results usingthe CSIQ benchmark image quality dataset showed that DeepQuality was able to achieve strong quality prediction performance(89% patch-level and 98% image-level prediction accuracy), beingable to achieve similar performance as full-reference IQA methods

Identification of Real-Time Maglev Plant using Long-Short Term Memory network based Deep learning Technique

Deep neural network has emerged as one of the most effective networks for modeling of highly non-linear complex real-time systems. The Long-Short Term Memory network (LSTM) which is a one of the variants of Recurrent Neural Network (RNN) has been proposed for the identification of a highly nonlinear Maglev plant. The comparative analysis of its performance is carried out with the Functional Link Artificial Neural Network- Least Mean Square (FLANN-LMS), FLANN-Particle Swarm Optimization (FLANN-PSO), FLANN-Teaching Learning Based Optimization (FLANN-TLBO) and FLANN-Black Widow Optimization (FLANN-BWO) algorithm. The proposed LSTM model is a feed forward neural network trained by a simple iterative method called the ADAM algorithm. The obtained results indicate that the proposed network has better performance than the other competitive networks in terms of the MSE, CPU time and convergence rate. To validate the dominance of the proposed network, a statistical tests, i.e. the Friedman test, is also applied.

Identification of Real-Time Maglev Plant using Long-Short Term Memory Network based Deep Learning Technique

1101-1105Deep neural network has emerged as one of the most effective networks for modeling of highly non-linear complex real-time systems. The long-short term memory network (LSTM) which is a one of the variants of recurrent neural network (RNN) has been proposed for the identification of a highly nonlinear Maglev plant. The comparative analysis of its performance is carried out with the functional link artificial neural network- least mean square (FLANN-LMS), FLANN-particle swarm optimization (FLANN-PSO), FLANN-teaching learning based optimization (FLANN-TLBO) and FLANN-black widow optimization (FLANN-BWO) algorithm. The proposed LSTM model is a feed forward neural network trained by a simple iterative method called the ADAM algorithm. The obtained results indicate that the proposed network has better performance than the other competitive networks in terms of the MSE, CPU time and convergence rate. To validate the dominance of the proposed network, a statistical tests, i.e. the Friedman test, is also applied

Business models for circular sanitation: lessons from India

Providing safe sanitation in the developing world is still a major hurdle to achieving Sustainable Development Goal number six, with 61% of the global population lacking safely managed sanitation services. Circular economy in the context of sanitation focuses on the whole sanitation chain which includes the provision of toilets, the collection of waste, treatment and transformation into sanitation-derived products including fertiliser, fuel and clean water. As well as potentially reducing the cost of toilet provision, a circular economy approach also has the potential to enable positive environmental and health impacts, unlike other systems where waste may be discharged untreated into the environment. The implementation of a system level transformation is not simple, considering operator capacity, lack of funding, slowly growing acceptance by local communities, and a policy landscape which can be inconsistent in its support for the circular economy. As India invests in long-term infrastructure to improve citizens’ quality of life (e.g., Swachh Bharat Mission), it could incorporate circular economy principles into the design of infrastructure, creating effective urban nutrient and material cycles, enhancing economic development and welfare. This represents a significant opportunity for government and businesses in India to develop circular sanitation infrastructure to recover and valorise biological nutrients. After collecting information from five case studies across India, covering different treatment technologies, waste-derived products, markets and contexts; this research identifies the main barriers and enablers for circular sanitation business models to succeed. Whilst there were many different institutional and technological arrangements, common issues of managing and enforcing incoming waste and competing with chemical fertilisers were found

Business models for circular sanitation: lessons from India

Providing safe sanitation in the developing world is still a major hurdle to achieving Sustainable Development Goal number six, with 61% of the global population lacking safely managed sanitation services. Circular economy in the context of sanitation focuses on the whole sanitation chain which includes the provision of toilets, the collection of waste, treatment and transformation into sanitation-derived products including fertiliser, fuel and clean water. As well as potentially reducing the cost of toilet provision, a circular economy approach also has the potential to enable positive environmental and health impacts, unlike other systems where waste may be discharged untreated into the environment. The implementation of a system level transformation is not simple, considering operator capacity, lack of funding, slowly growing acceptance by local communities, and a policy landscape which can be inconsistent in its support for the circular economy. As India invests in long-term infrastructure to improve citizens’ quality of life (e.g., Swachh Bharat Mission), it could incorporate circular economy principles into the design of infrastructure, creating effective urban nutrient and material cycles, enhancing economic development and welfare. This represents a significant opportunity for government and businesses in India to develop circular sanitation infrastructure to recover and valorise biological nutrients. After collecting information from five case studies across India, covering different treatment technologies, waste-derived products, markets and contexts; this research identifies the main barriers and enablers for circular sanitation business models to succeed. Whilst there were many different institutional and technological arrangements, common issues of managing and enforcing incoming waste and competing with chemical fertilisers were found

Evaluating the circular economy for sanitation: Findings from a multi-case approach

Addressing the lack of sanitation globally is a major global challenge with 700 million people still practicing open defecation. Circular Economy (CE) in the context of sanitation focuses on the whole sanitation chain which includes the provision of toilets, the collection of waste, treatment and transformation into sanitation-derived products including fertiliser, fuel and clean water. After a qualitative study from five case studies across India, covering different treatment technologies, waste-derived products, markets and contexts; this research identifies the main barriers and enablers for circular sanitation business models to succeed. A framework assessing the technical and social system changes required to enable circular sanitation models was derived from the case studies. Some of these changes can be achieved with increased enforcement, policies and subsidies for fertilisers, and integration of sanitation with other waste streams to increase its viability. Major changes such as the cultural norms around re-use, demographic shifts and soil depletion would be outside the scope of a single project, policy or planning initiative. The move to CE sanitation may still be desirable from a policy perspective but we argue that shifting to CE models should not be seen as a panacea that can solve the global sanitation crisis. Delivering the public good of safe sanitation services for all, whether circular or not, will continue to be a difficult task