Characterization of bio-oil and biodiesel blends

Raising oil prices, depletion of oil reserves and imports burden of petroleum products on developing countries like India, makes the world look for alternatives. Extensive research has been going on alternative fuels like biodiesel, bio-alcohol and other biomass sources. Pyrolysis oil from wood is considered as an alternative fuel for diesel engine. However it cannot be used directly in a diesel engine due to high viscosity, low calorific value and corrosion of components. In order to mitigate these problems it has to blended with diesel or biodiesel. In the present study, bio-oil from waste package wood is extracted by fixed bed or vacuum pyrolysis process. It is blended with jatropha biodiesel with 2% mixed surfactant of Triton x100 and Span 80. The performance, emission and combustion characteristics of emulsions are analyzed and compared with diesel and biodiesel. It was observed from results that the brake thermal efficiency for JOE 15 is 2% more than diesel and for JOE5 it is 6% less than diesel. At full load specific energy consumption decreases as WPO concentration increases. The HC and CO emissions of JME and emulsions are lower than that of diesel. The NO emissions compared to diesel increases by 8.29%, 5.5% for JOE5, JOE10 and decreases by 1.3% for JOE15. NO emission decreases with increase in WPO concentration

Soundness and quality of semantic retrieval in DNA-based memories with abiotic data

Associative memories based on DNA-affinity have been proposed. Previously, we have quantified the quality of retrieval of genomic information in simulation. Here, the ability of two types of DNA-based memories to store abiotic data and retrieve semantic information is evaluated for soundness and compared to state-of-the-art symbolic methods available, such as LSA (Latent Semantic Analysis). Their ability is poor when performed without a proper compaction procedure. However, when the corpus is summarized through a selection protocol based on PCR or a training procedure to extract useful information, their performance is much closer to that of LSA, according to human expert ratings. These results are expected to improve and scale up when actual DNA molecules are employed in real test tubes, currently a feasible goal

Unexpected difficulty during transcatheter device closure of atrial septal defect associated with right aortic arch

Right aortic arch (RAA) associated with isolated atrial septal defect (ASD) is very rare. We report successful closure of ASD associated with RAA using a 26-mm atrial septal occluder in a 30-year-old male patient. The impingement of right descending aorta in RAA caused malposition of the device in the left atrium. Deployment of the device through the right upper pulmonary vein successfully closed the defect. Follow-up evaluation by computerized tomography scan and echocardiogram showed no pulmonary venous obstruction

A revised algorithm for latent semantic analysis

The intelligent tutoring system AutoTutor uses latent semantic analysis to evaluate student answers to the tutor\u27s questions. By comparing a student\u27s answer to a set of expected answers, the system determines how much information is covered and how to continue the tutorial. Despite the success of LSA in tutoring conversations, the system sometimes has difficulties determining at an early stage whether or not an expectation is covered. A new LSA algorithm significantly improves the precision of AutoTutor\u27s natural language understanding and can be applied to other natural language understanding applications

Postmyocardial infarction left ventricular dysfunction – Assessment and follow up of patients undergoing surgical ventricular restoration by the endoventricular patchplasty

Background: Surgical ventricular restoration with endoventricular patchplasty improves left ventricular function and restores left ventricular shape. Method: The study included patients who presented with transmural anterior myocardial infarctions between June 2007 and May 2008. Briefly the technique included – coronary revascularization, resection of the endocardial scar, left ventricular reconstruction using an endoventricular synthetic patch. Left ventricular geometric parameters were studied preoperatively, early postoperatively, at 3 and 6 months and statistically analyzed by SPSS 14 software package. Results: The ejection fraction increased from 33.5 ± 5.02 to 37.77 ± 7.17 immediate postoperatively. The preoperative left ventricular ejection fraction – a mean of 33.25% (±5.02%), increased by 10.3%–11% at the third and fourth follow up respectively after surgical ventricular restoration (p ≤ 0.001). The left ventricular end systolic volume index improved from a mean of 48.84 ± 11.37 preoperatively to 24.66 ± 5.92 postoperatively (p ≤ 0.001). Conclusions: Surgical ventricular restoration in our study has clearly demonstrated a positive effect on LV geometry

Using LSA in AutoTutor: Learning Through Mixed-Initiative Dialogue in Natural Language

AutoTutor is a computer tutor that holds conversations with students in natural language (Graesser, Hu, and McNamara, 2005; Graesser, Lu, et al., 2004; Graesser, Person, Harter, and the Tutoring Research Group, 2001; Graesser, VanLehn, Rose, Jordan, and Harter, 2001; Graesser, K. Wiemer-Hastings, P. Wiemer-Hastings, Kreuz, and Harter, 1999). AutoTutor simulates the discourse patterns of human tutors and a number of ideal tutoring strategies. It presents a series of challenging problems (or questions) from a curriculum script and engages in collaborative, mixed initiative dialog while constructing answers. AutoTutor speaks the content of its turns through an animated conversational agent with a speech engine; it was designed to be a good conversational partner that comprehends, speaks, points, and displays emotions, all in a coordinated fashion. For some topics, there are graphical displays, animations of causal mechanisms, or interactive simulation environments (Graesser, Chipman, Haynes, and Olney, 2005). So far, AutoTutor has been developed and tested for topics in Newtonian physics (VanLehn et al., in press) and computer literacy (Graesser, Lu, et al., 2004), showing impressive learning gains compared to pretest measures and suitable control conditions. One notable characteristic of AutoTutor, from the standpoint of the present edited volume, is that latent semantic analysis (LSA) was adopted as its primary representation of world knowledge