Development of Mathematical Models for the Assessment of Fire Risk of Some Indian Coals using Soft Computing Techniques

Coal is the dominant energy source in India and meets 56% of the country’s primary commercial energy supply. In the light of the realization of the supremacy of coal to meet the future energy demands, rapid mechanization of mines is taking place to augment the Indian coal production from 643.75 million tons (MT) per annum in 2014-15 to an expected level of 1086 MT per annum by 2024-25. Most of the coals in India are obtained from low-rank coal seams. Fires have been raging in several coal mines in Indian coalfields. Spontaneous heating of coal is a major problem in the global mining industry. Different researchers have reported that a majority (75%) of these fires owe their origin to spontaneous combustion of coal. Fires, whether surface or underground, pose serious and environmental problems are causing huge loss of coal due to burning and loss of lives, sterilization of coal reserves and environmental pollution on a massive scale. Over the years, the number of active mine fires in India has increased to an alarming 70 locations covering a cumulative area of 17 km2. In Indian coalfield, the fire has engulfed more than 50 million tons of prime coking coal, and about 200 million tons of coals are locked up due to fires. The seriousness of the problem has been realized by the Ministry of Coal, the Ministry of Labour, various statutory agencies and mining companies. The recommendations made in the 10th Conference on Safety in Mine held at New Delhi in 2007 as well as in the Indian Chamber of Commerce (ICC)-2006, New Delhi, it was stated that all the coal mining companies should rank their coal mines on a uniform scale according to their fire risk on scientific basis. This will help the mine planners/engineers to adopt precautionary measures/steps in advance against the occurrence and spread of coal mine fire. Most of the research work carried out in India focused on the assessment of spontaneous combustion liabilities of coals based on limited conventional experimental techniques. The investigators have proposed/established statistical models to establish correlation between various coal parameters, but limited work was done on the development of soft computing techniques to predict the propensity of coal to self-heating that is yet to get due attention. Also, the classifications that have been made earlier are based on limited works which were empirical in nature, without adequate and sound mathematical base. Keeping this in view, an attempt was made in this research work to study forty-nine coal samples of various ranks covering the majority of the Indian coalfields. The experimental/analytical methods that were used to assess the tendencies of coals to spontaneous heating were: proximate analysis, ultimate analysis, petrographic analysis, crossing point temperature, Olpinski index, flammability temperature, wet oxidation potential analysis and differential thermal analysis (DTA). The statistical regression analysis was carried out between the parameters of intrinsic properties and the susceptibility indices and the best-correlated parameters were used as inputs to the soft computing models. Further different ANN models such as Multilayer Perceptron Network (MLP), Functional Link Artificial Neural Network (FLANN) and Radial Basis Function (RBF) were applied for the assessment of fire risk potential of Indian coals. The proposed appropriate ANN fire risk prediction models were designed based on the best-correlated parameters (ultimate analysis) selected as inputs after rigorous statistical analysis. After the successful application of all the proposed ANN models, comparative studies were made based on Mean Magnitude of Relative Error (MMRE) as the performance parameter, model performance curves and Pearson residual boxplots. From the proposed ANN techniques, it was observed that Szb provided better fire risk prediction with RBF model vis-à-vis MLP and FLANN. The results of the proposed RBF network model was closely matching with the field records of the investigated Indian coals and can help the mine management to adopt appropriate strategies and effective action plans in advance to prevent occurrence and spread of fire

All Optical Packet Processing in Optical Network

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Cribado de la actividad hipoglucémica in vitro de Murraya koenigii y Catharanthus roseu

Objective: The study aimed to verify the hypoglycemic effect of Murraya koenigii (M. koenigii) and Catharanthus roseus (C. roseus) by using various in-vitro techniques. Method: The extracts were studied for their effects on glucose adsorption capacity, in-vitro glucose diffusion, in-vitro amylolysis kinetics and glucose transport across the yeast cells. Results: It was observed that the extracts of M. koenigii and C. roseus adsorbed glucose and the adsorption of glucose increased remarkably with an increase in glucose concentration. There were no significant (p≤0.05) differences between their adsorption capacities. In the amylolysis kinetic experimental model the rate of glucose diffusion was found to be increased with time from 30 to 180 min and both the plant extracts exhibited significant inhibitory effects on the movement of glucose into external solution across the dialysis membrane as compared to control. The extracts also promoted glucose uptake by the yeast cells and the enhancement of glucose uptake was dependent on both the sample and glucose concentration. The extract of M. koenigii exhibited significantly higher (p≤0.05) activity than the extract of C. roseus at all concentrations used in the study. Our report suggests the mechanism(s) for the hypoglycemic effect of M. koenigii and C. roseus. Conclusion: The said effect was observed to be mediated by inhibiting alpha amylase, inhibiting glucose diffusion by adsorbing glucose and by increasing glucose transport across the cell membranes as revealed by in-vitro model of yeast cells. However, these effects need to be affirmed by using different in vivo models and clinical trials.Objetivo: El estudio tuvo como objetivo verificar el efecto hipoglucémico de Murraya koenigii (M. koenigii) y Catharanthus roseus (C. roseus) mediante el uso de diversas técnicas in vitro. Método: Los extractos se estudiaron por sus efectos sobre la capacidad de adsorción de glucosa, la difusión de glucosa in vitro, la cinética de amilolisis in vitro y el transporte de glucosa a través de las células de levadura. Resultados: se observó que los extractos de M. koenigii y C. roseus adsorbieron glucosa y la adsorción de glucosa aumentó notablemente con un aumento en la concentración de glucosa. No hubo diferencias significativas (p≤0.05) entre sus capacidades de adsorción. En el modelo experimental cinético de amilolisis, se encontró que la velocidad de difusión de glucosa aumentaba con el tiempo de 30 a 180 min y ambos extractos de planta exhibían efectos inhibitorios significativos sobre el movimiento de la glucosa hacia la solución externa a través de la membrana de diálisis en comparación con el control. Los extractos también promovieron la absorción de glucosa por las células de levadura y la mejora de la captación de glucosa dependió tanto de la muestra como de la concentración de glucosa. El extracto de M. koenigii exhibió una actividad significativamente mayor (p≤0.05) que el extracto de C. roseus en todas las concentraciones utilizadas en el estudio. Nuestro informe sugiere el mecanismo (s) para el efecto hipoglucemiante de M. koenigii y C. roseus. Conclusión: Se observó que dicho efecto estaba mediado por la inhibición de la alfa amilasa, la inhibición de la difusión de glucosa por la adsorción de glucosa y el aumento del transporte de glucosa a través de las membranas celulares según lo revelado por el modelo in vitro de células de levadura. Sin embargo, estos efectos deben ser afirmados mediante el uso de diferentes modelos in vivo y ensayos clínicos

Anomaly Extraction Using Histogram-Based Detector

Now a day’s network traffic monitoring and performance of the network are more important aspect in the computer science. Anomaly Extraction is a method of detecting in large set of flow observed during an anomalous time interval, the flows associated with the one or more anomalous event. Anomaly extraction is important problem that essential for application ranging from root cause analysis and attack mitigation and anomaly extraction is also important problem for several application of testing anomaly detector. In this paper, use a meta-data provided by histogram detector for detect and identify the suspicious flow after successfully detection suspicious flow then applying the association rule mining for finding the anomalous flow. By using the rich traffic data from the meta-data of the histogram-based detector we can reduce the classification cost. In this paper, Anomaly extraction method reduce the working time which is required for analyzing alarm, its make system more practically. DOI: 10.17762/ijritcc2321-8169.15011

IoT-enabled water distribution systems - a comparative technological review

Water distribution systems are one of the critical infrastructures and major assets of the water utility in a nation. The infrastructure of the distribution systems consists of resources, treatment plants, reservoirs, distribution lines, and consumers. A sustainable water distribution network management has to take care of accessibility, quality, quantity, and reliability of water. As water is becoming a depleting resource for the coming decades, the regulation and accounting of the water in terms of the above four parameters is a critical task. There have been many efforts towards the establishment of a monitoring and controlling framework, capable of automating various stages of the water distribution processes. The current trending technologies such as Information and Communication Technologies (ICT), Internet of Things (IoT), and Artificial Intelligence (AI) have the potential to track this spatially varying network to collect, process, and analyze the water distribution network attributes and events. In this work, we investigate the role and scope of the IoT technologies in different stages of the water distribution systems. Our survey covers the state-of-the-art monitoring and control systems for the water distribution networks, and the status of IoT architectures for water distribution networks. We explore the existing water distribution systems, providing the necessary background information on the current status. This work also presents an IoT Architecture for Intelligent Water Networks - IoTA4IWNet, for real-time monitoring and control of water distribution networks. We believe that to build a robust water distribution network, these components need to be designed and implemented effectively

Initial Virologic Response and HIV Drug Resistance Among HIV-Infected Individuals Initiating First-line Antiretroviral Therapy at 2 Clinics in Chennai and Mumbai, India

Human immunodeficiency virus drug resistance (HIVDR) in cohorts of patients initiating antiretroviral therapy (ART) at clinics in Chennai and Mumbai, India, was assessed following World Health Organization (WHO) guidelines. Twelve months after ART initiation, 75% and 64.6% of participants at the Chennai and Mumbai clinics, respectively, achieved viral load suppression of <1000 copies/mL (HIVDR prevention). HIVDR at initiation of ART (P <.05) and 12-month CD4 cell counts <200 cells/μL (P <.05) were associated with HIVDR at 12 months. HIVDR prevention exceeded WHO guidelines (≥70%) at the Chennai clinic but was below the target in Mumbai due to high rates of loss to follow-up. Findings highlight the need for defaulter tracing and scale-up of routine viral load testing to identify patients failing first-line AR