Bio-oil production is one of the feasible options for researchers due to its renewable nature and also the environmental benefits. This research work mainly emphasizes the suitability of a novel bio-oil for diesel engine. The mango seed methyl ester was produced with the help of transestrerification process. The various thermo-chemical properties were evaluated for the mango seed biodiesel blends. Preliminary tests were conducted with Diesel, MSME10, MSME20 and MSME30 blends on single-cylinder four-stroke diesel engines at varying load conditions. Among these three blends MSME20 showed better performance, emission and combustion properties. MSME20 blend is mixed with Aluminum oxide (Al2O3) nanoparticles at a concentration of 100ppm and 200ppm by the application of Ultrasonicator for homogeneous mixture. Then diverse characteristics of test fuels were analyzed with MSME20 100ppm Al2O3, MSME20 200ppm Al2O3 and compared with diesel. Test results revealed that 200 ppm addition of Al2O3 to the MSME20 was shown significant enhancement of 1.39% brake thermal efficiency and considerable reductions of HC and CO emissions by 35.48% and 13% respectively at full load. However, there was a slight increment in NOX emissions at full load.
W ith the rapid development of the World Wide Web, huge amount of data has been growing exponentially in our daily life. Users will spend much more time on searching the information they really need than before. Even when they make the exactly same searching input, different users would have various goals. Otherwise, users commonly annotate the information resources or make search query according to their own behaviors. As a matter of fact, this process will bring fuzzy results and be time-consuming. Based on the above problems, we propose our methodology that to combine user’s context, users’ profile with users’ Folksonomies together to optimize personal search. At the end of this paper, we make an experiment to evaluate our methodology and from which we can conclude that our work performs better than other sample s.
WETICE is an annual International conference on state-of-the-art research in enabling technologies for collaboration, consisting of a number of cognate conference tracks. What sets WETICE apart from larger conferences is that the conference tracks are kept small enough to promote fruitful discussions on the latest technology developments, directions, problems, and requirements
India being a predominantly farming country requires major attention for the fulfilment of energy demands for agriculture applications. Biodiesel is a clean burning renewable fuel. In this proposed work, mango seed biodiesel is extracted from the waste mango seeds through transesterification process. The thermophysical properties are evaluated as per ASTM standards. Base tests are conducted with diesel and different blends of mango seed biodiesel. From the base test results, it is found that MSME20 shows better performance parameters. In order to improve the engine performance further, decanol is added to MSME20 at three concentrations such as 5%, 10% and 15% on volume basis. The results revealed that 5% addition of decanol to the MSME20 shows a significant enhancement in brake thermal efficiency by 3.19% and considerable reductions in the exhaust emissions such as HC, CO and smoke. However, slight increment in NOX emissions is observed at full load.
The increased transportation and industrialisation sectors running on fossil fuels lead to depletion of petroleum resources and environmental contamination. In these days, biodiesel has turned into an emerging alternative for diesel fuel. The present investigation focuses on the exploration of various characteristics of a diesel engine fuelled with corn seed methyl ester (CSME) biodiesel blends such as B10 (10% biodiesel and 90% diesel), B15, B20 and B25. From the test results, it is found that the CSME B20 blend operated engine had shown improved thermal efficiency when compared to other corn biodiesel blends tested in this study. Also, combustion characteristics have shown competitive results with respect to diesel fuel at all load conditions. However, a slight increase in engine tailpipe emissions such as HC and CO is observed when compared to diesel fuel at full load. Overall, considering the positive impact shown by corn seed biodiesel blends on engine characteristics towards sustainable eco-environment, it is concluded that, CSME B20 can be considered as one of the promising fuels among the available alternative resources for diesel.
Traditional distributed collaboration frameworks based on assumption of static collaboration and usage patterns cannot fulfil many new requirements imposed by today's collaboration with heterogeneous and ever-changing environments and computing contexts. In this paper, we describe a collaboration framework, called as EkSarva, which enables adaptability by providing context-awareness through a set of ontologies with well-defined semantics and embedding workflow into collaboration processes
In response to the daunting threats of cyber attacks, a promising approach is computer and network forensics. Intrusion detection system is an indispensable part of computer and network forensics. It is deployed to monitor network and host activities including dataflows and information accesses etc. But current intrusion detection products presents many flaws including alert flooding, too many false alerts and isolated alerts etc. We describe an ongoing project to develop an intrusion alert management system $TRINETR. We present a collaborative architecture design for multiple intrusion detection systems to work together to detect real-time network intrusions. The architecture is composed of three parts: alert aggregation, knowledge-based alert evaluation and alert correlation. The architecture is aimed at reducing the alert overload by aggregating alerts from multiple sensors to generate condensed views, reducing false positives by integrating network and host system information into alert evaluation process and correlating events based on logical relations to generate global and synthesized alert report. The first two parts of the architecture have been implemented and the implementation results are presented.
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