262 research outputs found
Biofuel from Wastes an Economic and Environmentally Feasible Resource
No full textAbstractEnvironmental arguments centre on the need to reduce greenhouse gas (GHG) emissions for the sake of both the global and local environments. In this respect, the replacement of fossil fuels by biofuels in the transportation sector is necessary to help the reduction of GHG. Climate change impacts have spurred researchers and industry to look at alternate clean energy options. The concentration of CO2 in the earth's atmosphere was about 280 parts per million by volume (ppm) in 1750, before the Industrial Revolution began. By 1994 it was 358ppm and rising by about 1.5ppm per year. If emissions continue at the 1994 rate, the concentration will be around 500ppm, nearly double the preindustrial level, by the end of the 21st century. Carbohydrate biomass is considered as the future feedstock for bio ethanol production because of its low cost and its huge availability.The major carbohydrate materials found in great quantities to be considered, is wheat bran, sugarcane bagasse and rape straw, which can be easily converted in to ethanol by following pretreatment either by acid or enzyme, hydrolysis and distillation process under feasible conditions. The effects of different pH and temperature with enzymatic saccharification treatment on conversion of these biomasses were studied. The produced glucose was fermented to bioethanol, using Saccharomyces cerevisiae yeast in combination with pentose fermenting enzymes as Pitchia stipititis and the amount of produced bio ethanol was measured by gas chromatography. Enzyme treatment at 30°C and pH 5 is an effective treatment method for converting lignocelluloses to glucose. Up to 23.35% glucose v/v could be achieved after enzyme treatment from bagasse than others. Fermentation of treated lignocelluloses shown that glucose after 3 days fermentation the maximum bio ethanol of 19.25% (v/v) by Saccharomyces cerevisiae and 26.75% (v/v) was attained in case of sugarcane bagasse by using Pitchia stipititis in combination with S. cerevisiae. This process is expected to be useful for the bio ethanol production from wheat bran, sugarcane bagasse and rape straw as a source of carbohydrate renewable biomass from abundant agricultural by product
A study of time-dependent properties and other physical properties of rocks
No full text© 1970 Dr. Devendra Pratap SinghThe strength and deformation behaviour of rocks are time-dependent. Therefore the design of rock structures underground should be based on the long-term strengths rather than the strength determined by short-term laboratory tests. The direct method of determining the long-term strength of a rock is tedious and time consuming. Hence it is considered important that short-term methods of predicting long-term strengths of rocks be evolved.
Short-term methods based on measurement of volumetric strain, stress-strain, log stress-log strain and loading rate were investigated. The long-term strength of Sicilian marble was determined by the direct method and was found to be close to the predicted long-term strengths by short-term methods. With the equipment used in this project, the leading rate method proved to be quicker and more accurate than other short-term methods as the processing and plotting of data were not involved in this case. The volumetric strain method predicted higher time-dependent strengths than the direct method.
Dilatancy was observed in many intact rock specimens tested in uniaxial compression. It was also found that the values of Young's modulus and Poisson's ratio of a specimen are stress-dependent.
All the three stages of the idealized creep curve i.e. transient, steady state and accelerated stage, were observed in Sicilian marble and Wombayan marble specimens. The lateral creep rate in the above two marbles was found to be larger than the axial creep rate at sustained stresses greater than their yield stresses.
In the triaxial tests, in which only Sicilian marble specimens were tested, the ratios of predicted long-term strength to maximum strength showed a declining trend with increasing confining pressures.
In the study of friction along the fractured surfaces of Sicilian marble specimens in the triaxial tests, it was found that during sliding the sheer stress and normal stress on the sliding plane have a straight line relationship. The average coefficient of friction for Sicilian marble was found to be 0.84
Effect Of Gamma Irradiation On The Post Harvest Quality Characteristics Of Banana (Var. Elakkibale) Fruit During Ripening at Ambient Storage Conditions
No full textThis Dissertation / Report is the outcome of investigation carried out by the creator(s) / author(s) at the department/division of Central Food Technological Research Institute (CFTRI), Mysore mentioned below in this page
Reactive Oxygen Species and Photosynthetic Functioning: Past and Present
No full textThe present chapter begins by presenting the basic introduction to Reactive Oxygen Species (ROS), then detailng the current knowledge in ROS research. In particular, Electron Paramagnetic Resonance (EPR) technique applicability in photosynthesis research was considered. Kinetics of superoxide formation by illuminated thylakoids was shown. The progress of the knowledge on the different sites of photosynthetic membranes involved in ROS formation was reported and the main defense mechanisms occurring in the chloroplast to detoxify from ROS were decribed
Caste, religion and power: an Indian case study
No full textReviews the book "Caste, Religion and Power: An Indian Case Study," by Pratap C. Aggarwal.; Reviews the book "Caste, Religion and Power: An Indian Case Study," by Pratap C. Aggarwal
Towards Intercultural Documentary
No full text‘Towards Intercultural Documentary’ is a PhD by Published Work that is comprised of four documentary films, an exhibition catalogue essay and an academic book chapter to form a collective body of work in film and text focused on what Rughani proposes as ‘intercultural documentary practice’. This body of work configures ‘intercultural documentary practice’ as a space or arena in which people of radically different perspectives encounter the other.1 Intercultural documentary aspires to create pluralised spaces of exchange by engaging difference within and between communities. In this work, voices traditionally overlooked, excluded or edged to the cultural margins are re-framed to find a new centrality in a broader encounter, more accurately reflecting the diverse influences that comprise polyglot societies. In the United Kingdom (UK) context, three submitted films, broadcast to peak-time audiences on BBC 2 and Channel 4, stood in contradistinction to mainstream narratives that typically portrayed British experience as largely monocultural and homogeneous.
The contribution to knowledge of this thesis is in deepening and extending the dynamics of documentary practice to embrace intercultural communication and to weld this to the ethics of documentary making. In so doing, this body of work situates ethics as central to the documentary encounter and offers new practice-based insights into navigating tensions in the process of making such work and its methodologies.
‘Towards Intercultural Documentary’ presents a case for the coherence of the body of work that makes a contribution to knowledge at the inter-disciplinary confluence of: documentary studies and practice, ethics and intercultural communication. The submission comprises: Islam and the Temple of’ ‘Ilm’ (BBC 2, 1990); One of the Family (Channel 4, 2000); Playing Model Soldiers (Channel 4, 2000); Glass Houses (British Council, 2004); the exhibition catalogue essay British Homeland in Home (British Council, 2004) and the book chapter ‘Are You a Vulture? Reflecting on the ethics and aesthetics of coverage of atrocity and its aftermath, in Peace Journalism (Peter Lang, 2010)
Autonomous Vehicle Navigation with Deep Learning: A Comprehensive Review
No full textAVs are a revolutionary technology in the intelligent transportation industry integrating a sophisticated sensing, computing, and controlling system to facilitate safe and effective self-driving. At the heart of this development lies deep learning (DL) that has become the foundation of perception, decision-making, and navigation on complex and dynamic driving environments. As opposed to classical rule-based algorithms, DL models can learn hierarchical representations using large volumes of sensor and traffic data and achieve major gains in object-detection, lane-recognition, obstacle-avoidance, and route-planning tasks. In this paper, I have reviewed deep learning methods in autonomous vehicle navigation in detail. It covers a few of the more well-known architectures such as Convolutional Neural Networks (CNNs), visual perception; Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) to make sequential decisions; and deep models based on Reinforcement Learning (RL), adaptive navigation strategies. Furthermore, other methods that might be employed to improve environmental knowledge are discussed, including the application of multimodal fusion technologies, which integrate LiDAR, radar, and vision cameras. The article talks about real-world application, benchmark datasets, and simulation environments that facilitate DL-based research on AV. Even after an accelerated development, explainability, robustness in adverse weather, real-time computational efficiency and ethical considerations of safety-critical decisions continue to be challenges. Lightweight DL systems, federated learning of collaborative AVs, and explainable AI systems are the next steps to control regulatory compliance and user trust. This review combines progress, issues, and opportunities to emphasize the revolution in deep learning in the field of autonomous vehicle navigation and find ways to enable sustainable, reliable, and large-scale implementation
The importance of plant growth–promoting rhizobacteria for plant productivity
No full textPlant growth-promoting rhizobacteria (PGPR) naturally colonizes the plant roots and has positive effects on plant growth and physiology. In the soil ecosystem, PGPRs are responsible for a broad scope of biotic activities which improve soil nutrient turnover. They are diversely found in the soils worldwide and essential for sustainable agriculture and soil fertility. To achieve more sustainable agricultural solutions by integrated management of plant nutrients, rhizobacteria are commonly studied for agricultural benefits such as heavy metal detoxification, environmental cleanup strategies, salinity tolerance, degradation of pesticides, nitrogen fixation, secretion of plant growth hormone, defense against pathogens, and phosphate solubilization. For these reasons the importance of PGPRs in plant productivity has recently been attracting a great deal of attention. Biofertilizers with PGPR promote the growth of host plants by increasing nutrient uptake and can be an alternative to mineral fertilizers. This review describes PGPRs, including what is known regarding the role of PGPR in plant development and physiology, and considers which activities and characteristics of PGPRs are essential for the development
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Gated Skip Connections for High Fidelity, Identity-Preserving, Continuous Face Modification
No full textFace image modification is a variant of the image-to-image translation task where we modify features of a face image to evoke given target attributes, while preserving the identity of the pictured person. Generative Adversarial Networks (GANs) using an encoder-decoder architecture have been widely used to modify both discrete and continuous face attributes, with a few different architectures designed to address the challenge of preserving identity through the modification. We propose a novel GAN architecture that introduces gated skip connections in the generator's decoder for this task. Our model enables high fidelity (512x512) modification with minimal changes to irrelevant facial regions, while using fewer parameters than existing approaches. We demonstrate the model on discrete CelebA attributes, continuous facial Action Unit labels, and perceived social impression traits such as "attractive", "kind", and "trustworthy". Our model is also able to selectively visualize the modified face features, allowing us to extract plausible visual explanations for face attributes, including, for the first time, social impression traits. An experiment with human raters validates that our model can effectively alter a face's social impressions
Reinforcement Learning Approaches for Energy-Efficient IoT Resource Allocation
No full textThe IoT has become a paradigm shift and already has connected billions of devices in the healthcare, transportation, production, and smart cities sectors. Since this growth is exponential, a great challenge has been provision of resources (particularly its energy efficiency). IoT devices are described as having low power, computing power, and bandwidth. The non-uniform and extremely dynamic nature of the IoT environment cannot be practically addressed using the classical optimization models. It can be quite promising to use the reinforcement Learning (RL) to attain autonomous and adaptive decisions in the resources allocation based on the data reduction without energy consumption. The article shall include a literature review of reinforcement learning systems to effectively distribute the IoT resources in terms of energy consumption. It introduces the theoretical models of RL, Markov Decision Process, Q-learning and Deep Reinforcement Learning (DRL) and applies them to maximize the power consumption, bandwidth allocation and offloading of computations. The paper discusses such popular RL-based architecture as Q-learning to dynamical spectrum accessing, Deep Q-Network to task allocation, and actor-critic architecture to power harvesting. It further talks about hybrid solutions using RL that could be used to solve the privacy and scalability problem by generalizing to non-metric type of edge computing and federated learning. It is revealed that the RL-based approaches is way better than the time-honoured heuristics since it accommodates the dynamical requirements of the network and consumes lesser powers but does not improve the performance of the Quality of Service (QoS). Scalability, speed of conversion, interpretability and practical application, however, remain an issue. As mentioned in the paper, reinforcement learning has been suggested as a strong paradigm to establish sustainable IoT ecosystems and that future research should also consider lightweight, explainable, and privacy-preserving instantiations of RL models, which can be implemented in the resource-constrained IoT setting
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