Multi-learner based recursive supervised training

In this paper, we propose the Multi-Learner Based Recursive Supervised Training (MLRT) algorithm which uses the existing framework of recursive task decomposition, by training the entire dataset, picking out the best learnt patterns, and then repeating the process with the remaining patterns. Instead of having a single learner to classify all datasets during each recursion, an appropriate learner is chosen from a set of three learners, based on the subset of data being trained, thereby avoiding the time overhead associated with the genetic algorithm learner utilized in previous approaches. In this way MLRT seeks to identify the inherent characteristics of the dataset, and utilize it to train the data accurately and efficiently. We observed that empirically, MLRT performs considerably well as compared to RPHP and other systems on benchmark data with 11% improvement in accuracy on the SPAM dataset and comparable performances on the VOWEL and the TWO-SPIRAL problems. In addition, for most datasets, the time taken by MLRT is considerably lower than the other systems with comparable accuracy. Two heuristic versions, MLRT-2 and MLRT-3 are also introduced to improve the efficiency in the system, and to make it more scalable for future updates. The performance in these versions is similar to the original MLRT system

Experimental evidence on promotion of electric and improved biomass cookstoves.

Improved cookstoves (ICS) can deliver "triple wins" by improving household health, local environments, and global climate. Yet their potential is in doubt because of low and slow diffusion, likely because of constraints imposed by differences in culture, geography, institutions, and missing markets. We offer insights about this challenge based on a multiyear, multiphase study with nearly 1,000 households in the Indian Himalayas. In phase I, we combined desk reviews, simulations, and focus groups to diagnose barriers to ICS adoption. In phase II, we implemented a set of pilots to simulate a mature market and designed an intervention that upgraded the supply chain (combining marketing and home delivery), provided rebates and financing to lower income and liquidity constraints, and allowed households a choice among ICS. In phase III, we used findings from these pilots to implement a field experiment to rigorously test whether this combination of upgraded supply and demand promotion stimulates adoption. The experiment showed that, compared with zero purchase in control villages, over half of intervention households bought an ICS, although demand was highly price-sensitive. Demand was at least twice as high for electric stoves relative to biomass ICS. Even among households that received a negligible price discount, the upgraded supply chain alone induced a 28 percentage-point increase in ICS ownership. Although the bundled intervention is resource-intensive, the full costs are lower than the social benefits of ICS promotion. Our findings suggest that market analysis, robust supply chains, and price discounts are critical for ICS diffusion

Influence of analysis and design models on minimum weight design

The results of numerical experiments designed to illustrate how the minimum weight design, accuracy, and cost can be influenced by: (1) refinement of the finite element analysis model and associated load path problems, and (2) refinement of the design variable linking model are examined. The numerical experiments range from simple structures where the modelling decisions are relatively obvious and less costly to the more complex structures where such decisions are less obvious and more costly. All numerical experiments used employ the dual formulation in ACCESS-3 computer program. Guidelines are suggested for creating analysis and design models that predict a minimum weight structure with greater accuracy and less cost. These guidelines can be useful in an interactive optimization environment and in the design of heuristic rules for the development of knowledge-based expert optimization systems

Multilevel regulation of growth rate in yeast revealed using systems biology

The effect of changing growth rates on the transcriptome, proteome and metabolome has been systematically studied. Measurements made under varying nutrient conditions, corresponding to biochemical pathways that correlate primarily with growth rate, reveal a central role for mitochondrial metabolism and the TOR (target of rapamycin) signaling pathway

Mycobacterial antigen in tissues in diagnosis of cutaneous tuberculosis

Background: Cutaneous tuberculosis presents a diagnostic challenge, as it is difficult to demonstrate the causative organism by histopathology and also culture of organisms from skin lesions is a less rewarding and time consuming process. Aim: Present study was undertaken to evaluate the utility of immuno-histochemical staining to demonstrate Mycobacterium tuberculosis antigen in tissue sections. This is based on the finding that the mycobacterial antigen is the last to disappear from the tissues and thus can be used as a marker of mycobacterial infections. Material & Methods: Fifty randomly selected skin biopsy specimens were subjected to routine histopathological examination to corroborate the clinical diagnosis. Immuno-histochemical study was undertaken to demonstrate mycobacterial antigen. Observations: All the tissue sections were negative for AFB, both by Z-N stain and by culture. Mycobacterial antigen was demonstrable in 68% of cases of cutaneous tuberculosis. The highest positivity was recorded in scrofuloderma (89%), followed by Lupus Vulgaris (69%) and Tuberculosis Verrucosa Cutis (47%). Conclusions: Mycobacterial antigen was demonstrable in majority of cases of cutaneous tuberculosis using polyclonal antiserum. However, since cross reactivity was reported in cases of leprosy and also in some fungal infections, this test by itself cannot be considered as diagnostic. The results should be considered along with other findings