Power sector development in India with CO2 emission targets: Effects of regional grid integration and the role of clean technologies

The power sector in India at present comprises of five separate regional electricity grids having practically no integrated operation in between them. This study analyses the utility planning, environmental and economical effects of integrated power sector development at the national level in which the regional electric grids are developed and operated as one integrated system. It also examines the effects of selected CO₂ emission reduction targets in the power sector and the role of renewable power generation technologies in India. The study shows that the integrated development and operation of the power system at the national level would reduce the total cost including fuel cost by 4912 million $, total capacity addition by 2784 MW, while the emission of CO₂, SO₂ and NO_x would be reduced by 231.6 (1.9%), 0.8 (0.9%), 0.4 (1.2%) million tons, respectively, during the planning horizon. Furthermore, the study shows that the expected unserved energy, one of the indices of generation system reliability, would decrease to 26 GWh under integrated national power system from 5158 GWh. As different levels of CO₂ emission reduction targets were imposed, there is a switching of generation from conventional coal plants to gas fired plants, clean coal technologies and nuclear based plants. As a result the capacity expansion cost has increased. It was found that wind power plant is most attractive and economical in the Indian perspective among the renewable options considered (Solar, wind and biomass). Copyright © 2003 John Wiley & Sons, Ltd.     

Cyclic variations in the permeability of the cell wall of Saccharomyces cerevisiae.

To study cell-cycle-related variations in wall permeability of Saccharomyces cerevisiae, two approaches were used. First, an asynchronous culture was fractionated by centrifugal elutriation into subpopulations containing cells of increasing size. The subpopulations represented different stages of the cell cycle as judged by light microscopy. Cell wall porosity increased when these subpopulations became enriched with budded cells. Secondly, synchronous cultures were obtained by releasing MATa cells from alpha-factor induced G1-arrest. These cultures grew synchronously for at least two generations. The cell wall porosity increased sharply in these cultures, shortly before buds became visible and was maximal during the initial stages of bud growth. It decreased in cells which had completed nuclear migration and before abscission of the bud had occurred. The porosity reached its lowest value during abscission and in unbudded cells. We examined the incorporation of mannoproteins into the wall during the cell cycle. SDS-extractable mannoproteins were incorporated continuously. However, the incorporation of glucanase-extractable mannoproteins, which are known to affect cell wall porosity, showed cyclic oscillations and reached its maximum after nuclear migration. This coincided with a rapid decrease in cell wall porosity, indicating that glucanase-extractable mannoproteins might contribute to this decrease.     

Process-based cost modeling of photonics manufacture: the cost competitiveness of monolithic integration of a 1550-nm DFB laser and an electroabsorptive modulator on an InP platform

The monolithic integration of components holds promise to increase network functionality and reduce packaging expense. Integration also drives down yield due to manufacturing complexity and the compounding of failures across devices. Consensus is lacking on the economically preferred extent of integration. Previous studies on the cost feasibility of integration have used high-level estimation methods. This study instead focuses on accurate-to-industry detail, basing a process-based cost model of device manufacture on data collected from 20 firms across the optoelectronics supply chain. The model presented allows for the definition of process organization, including testing, as well as processing conditions, operational characteristics, and level of automation at each step. This study focuses on the cost implications of integration of a 1550-nm DFB laser with an electroabsorptive modulator on an InP platform. Results show the monolithically integrated design to be more cost competitive over discrete component options regardless of production scale. Dominant cost drivers are packaging, testing, and assembly. Leveraging the technical detail underlying model projections, component alignment, bonding, and metal-organic chemical vapor deposition (MOCVD) are identified as processes where technical improvements are most critical to lowering costs. Such results should encourage exploration of the cost advantages of further integration and focus cost-driven technology development.     

A three-parameter equation for molecular weight distribution of low-density polyethylene

A new three-parameter distribution function is proposed which fits best the experimental molecular weight distribution curves of branched lowdensity polyethylenes. The data were interpreted from GPC measurements, and a special computer program was utilized in order to derive the best values of the empirical constants a, b, and c.     

Liver disorder detection using variable- neighbor weighted fuzzy K nearest neighbor approach

The human liver disorder is a genetic problem due to the habituality of alcohol or effect by the virus. It can lead to liver failure or liver cancer, if not been detected in initial stage. The aim of the proposed method is to detect the liver disorder in initial stage using liver function test dataset. The problem with many real-world datasets including liver disease diagnosis data is class imbalanced. The word imbalance refers to the conditions that the number of observations belongs to one class having more or less than the other class(es). Traditional K- Nearest Neighbor (KNN) or Fuzzy KNN classifier does not work well on the imbalanced dataset because they treat the neighbor equally. The weighted variant of Fuzzy KNN assign a large weight for the neighbor belongs to the minority class data and relatively small weight for the neighbor belongs to the majority class to resolve the issues with data imbalance. In this paper, Variable- Neighbor Weighted Fuzzy K Nearest Neighbor Approach (Variable-NWFKNN) is proposed, which is an improved variant of Fuzzy-NWKNN. The proposed Variable-NWFKNN method is implemented on three real-world imbalance liver function test datasets BUPA, ILPD from UCI and MPRLPD. The Variable-NWFKNN is compared with existing NWKNN and Fuzzy-NWKKNN methods and found accuracy 73.91% (BUPA Dataset), 77.59% (ILPD Dataset) and 87.01% (MPRLPD Dataset). Further, TL_RUS method is used for preprocessing and it improved the accuracy as 78.46% (BUPA Dataset), 78.46% (ILPD Dataset) and 95.79% (MPRLPD Dataset).

     

A proposal for student modeling based on ontologies and diagnosis rules

The advances in the educational field and the high complexity of student modeling have provoked it to be one of the aspects more investigated in Intelligent Tutoring Systems (ITSs). The Student Models (SMs) should not only represent the student’s knowledge, but rather they should reflect, as faithfully as possible, the student’s reasoning process. To facilitate this goal, in this article a new approach to student modeling is proposed that benefits from the advantages of Ontological Engineering, advancing in the pursue of a more granular and complete knowledge representation. It’s focused, mainly, on the SM cognitive diagnosis process, and we present a method providing a rich diagnosis about the student’s knowledge state – especially, about the state of learning objectives reached or not. The main goal is to achieve SMs with a good adaptability to the student’s features and a high flexibility for its integration in varied ITSs.     

An Analysis of Edge Detection by Using the Jensen-Shannon Divergence

This work constitutes a theoretical study of the edge-detection method by means of the Jensen-Shannon divergence, as proposed by the authors. The overall aim is to establish formally the suitability of the procedure of edge detection in digital images, as a step prior to segmentation. In specific, an analysis is made not only of the properties of the divergence used, but also of the method's sensitivity to the spatial variation, as well as the detection-error risk associated with the operating conditions due to the randomness of the spatial configuration of the pixels. Although the paper deals with the procedure based on the Jensen-Shannon divergence, some problems are also related to other methods based on local detection with a sliding window, and part of the study is focused to noisy and textured images.     

Investigation of temperature-dependent polarization, dielectric, and magnetization behavior of multiferroic layered nanostructure

The PbZr_0.53Ti_0.47O₃(PZT)/CoFe₂O₄(CFO) layered nanostructures show lowering of dielectric constant and polarization, and an enhanced magnetization with a decrease in temperature from 400 K to 100 K. The temperature dependence of the real part of the dielectric constant illustrates a step-like behavior, whereas the imaginary part gives a relaxation peak near the step maxima temperature. A slow decrease in the polarization was observed from 300 K to 200 K, with an eventual collapse of polarization at ~ 100 K, and a complete polarization recovery with heating, these phenomena is reproducible over cycles of experiment. Remanent magnetization of the layered nanostructure was found to be three times higher at 100 K than that at room temperature. There is a slow enhancement in remanent (internal) magnetization with lowering of temperature, resulting in slow polarization switch and finally the collapse. The temperature-dependent dielectric, polarization and magnetization were different from the parent layer, indicating a kind of dynamic magneto-electric coupling in the layered nanostructures.