Ensemble based groundwater level prediction using neural network pattern fitting

44-50Prediction of groundwater level is implemented using Time-series prediction model and combined prediction model for learning the pattern and trend in groundwater level fluctuation, result show that the combined prediction model using, groundwater level time series and precipitation time series as input predictors is a better predictor. Study also shows that prediction is dependent on the pattern and trends at a particular location as every dataset depends on the dynamics of the location namely the geomorphology of the aquifer, the drainage inside the aquifer and pumping from the aquifer. Ensemble based forecasting is studied to fix the upper and lower limit of the prediction. Ensembles helped in fixing a range for the forecast instead of relying on a single unique value

Ensemble based groundwater level prediction using neural network pattern fitting

Prediction of groundwater level is implemented using Time-series prediction model and combined prediction model for learning the pattern and trend in groundwater level fluctuation, result show that the combined prediction model using, groundwater level time series and precipitation time series as input predictors is a better predictor. Study also shows that prediction is dependent on the pattern and trends at a particular location as every dataset depends on the dynamics of the location namely the geomorphology of the aquifer, the drainage inside the aquifer and pumping from the aquifer. Ensemble based forecasting is studied to fix the upper and lower limit of the prediction. Ensembles helped in fixing a range for the forecast instead of relying on a single unique value

Modernized Wildlife Surveillance and Behaviour Detection using a Novel Machine Learning Algorithm

In a natural ecosystem, understanding the difficulties of the wildlife surveillance is helpful to protect and manage animals also gain knowledge around animals count, behaviour and location. Moreover, camera trap images allow the picture of wildlife as unobtrusively, inexpensively and high volume it can identify animals, and behaviour but  it has the issues of high expensive, time consuming, error, and low accuracy. So, in this research work, designed a novel wildlife surveillance framework using DCNN for accurate prediction of animals and enhance the performance of detection accuracy. The executed research work is implemented in the python tool and 2700 sample input frame datasets are tested and trained to the system. Furthermore, analyze whether animals are present or not using background subtraction and features extracted is performed to extract the significant features. Finally, classification is executed to predict the animal using the fitness of seagull. Additionally, attained results of the developed framework are compared with other state-of-the-art techniques in terms of detection accuracy, sensitivity, F-measure and error

Thrombotic thrombocytopenic purpura in a patient with snake bite

Snakebite envenomation is a common scenario in southern India. Among the various causes of acquired thrombotic thrombocytopenic purpura (TTP), snake bite envenomation is very rarely reported. We present a rare case of a 51-year-old man with TTP following snakebite envenomation. He developed features of TTP including impaired neurological state, renal dysfunction, low platelets, fever, microangiopathic hemolytic anemia on day 5 following snakebite. He received 7 cycles of plasma exchange and showed remarkable improvement. We are presenting this case to create awareness regarding TTP following snakebite as it has a very significant implication on treatment and mortality

Contemplating the relevance of Prajnaparadha as a root cause of Mental Disorder

Prajnaparada (Intellectual Blasphemy) is willfully ignoring one’s inner knowing and going against norms, intuition and common sense. It is the root cause for all diseases. Involving in verbal, mental or physical activities which are unfavourable to self, harms both body and mind. Actions generated by Prajnaparada aggravate Tridosha (bodily humors) and stimulate Rajas and Tamo Gunas (psychological attributes) allowing disease to be established

Negative Emission Power Plants:Thermodynamic Modeling and Evaluation of a Biomass-Based Integrated Gasification Solid Oxide Fuel Cell/Gas Turbine System for Power, Heat, and Biochar Co-Production—Part 1

This article is the first of a two-part series presenting the thermodynamic evaluation and techno-economics of developing negative-emission power plants. The aim of this research is to evaluate the potential of biochar co-production in negative-emission power plants based on biomass-fed integrated gasification solid oxide fuel cell systems with carbon capture and storage (BIGFC/CCS) units. The influence of two gasification agents, namely, air and steam-oxygen, on the proposed system is investigated. In Part I, we present the thermodynamic models. A sensitivity analysis is carried out to investigate the system response to stepwise increase in biochar co-production (up to 10% by weight). Providing a secondary oxy-combustor in the steam-oxygen gasification case has been shown to be a solution to meet the heat requirements of the allothermal gasification process. A comprehensive exergy analysis indicated significant efficiency improvement for the steam-oxygen gasification case. The results show that the biomass steam-oxygen gasification yields the higher electrical exergy efficiency (48.3%) and combined heat and power (CHP) exergy efficiency (54.6%) for the similar rates of biochar co-production. The specific power output per unit of CO2 stored is 2.65 MW/(kg/s) and 3.58 MW/(kg/s) for the air and steam-oxygen gasification cases, respectively, when the biochar is co-produced at 10% by weight for the given biomass flow of 20 kg/s. Moreover, the total CO2 stored due to the proposed system is calculated as 133.9 t/h, and it is estimated to remove 1.17 Mt of CO2 from the atmosphere annually (when the biochar-based carbon storage is also considered). The models are used for the techno-economic analysis presented in Part II of the series

Study of Tribological Characteristics of Journal Bearing using Mixture of different Bio-Lubricants

Wear is the main reason for material losses & degradation of any machine component. If we reduce the magnitude of this wear can bring about enhanced performance. This can be possible by decreasing friction. Usage of Lubricant is a viable method for controlling the friction. This decreases wear and it has wide application in operation of machine component, for example, bearing. Nowadays different oils are used to diminish erosion and wear between mechanical contact surfaces however mineral oils are known for higher manufacturing expenses and low biodegradability. A few research works are going ahead keeping in mind the end goal to create bio-oil and to have tribological characteristic study between interacting mechanical surfaces

A burst search for gravitational waves from binary black holes

Compact binary coalescence (CBC) is one of the most promising sources of gravitational waves. These sources are usually searched for with matched filters which require accurate calculation of the GW waveforms and generation of large template banks. We present a complementary search technique based on algorithms used in un-modeled searches. Initially designed for detection of un-modeled bursts, which can span a very large set of waveform morphologies, the search algorithm presented here is constrained for targeted detection of the smaller subset of CBC signals. The constraint is based on the assumption of elliptical polarisation for signals received at the detector. We expect that the algorithm is sensitive to CBC signals in a wide range of masses, mass ratios, and spin parameters. In preparation for the analysis of data from the fifth LIGO-Virgo science run (S5), we performed preliminary studies of the algorithm on test data. We present the sensitivity of the search to different types of simulated CBC waveforms. Also, we discuss how to extend the results of the test run into a search over all of the current LIGO-Virgo data set.Comment: 12 pages, 4 figures, 2 tables, submitted for publication in CQG in the special issue for the conference proceedings of GWDAW13; corrected some typos, addressed some minor reviewer comments one section restructured and references updated and correcte

Contemporary Information and Knowledge Management: Impact on Farming in India

Farming is an important part of Indian economy and it involves a wide range of stakeholders, of whom the small holder farmers are the largest group. Information sharing on new production processes with farmers was prominent in the ‘sixties which was key to the success of the Green Revolution. Agricultural extension, the process of enabling farmers and experts to exchange information with each other, has since been institutionalized to a high degree and is assessed to be not as effective as it had been a generation back. The advent of digital, technology-mediated information and knowledge management was thought to offer significant new opportunities for knowledge exchange in Indian farming as a whole. These hopes led to the launching of a number of initiatives in different parts of India, which has emerged as the host of the largest number of rural development projects where contemporary information and communication technology (ICT) play a pivotal role. While analyzing the outputs of such initiatives, many studies have pointed out that farming is not a priority concern of most of them. On the other hand, we can notice a non-complimentary strand of ICT in agriculture projects operated by a number of institutions with ICT resources playing a key role in some of them. These efforts, generally speaking, do not promote user participation in information flows quite unlike the contemporary trends