Magnesium Hydroxide Dehydroxylation/Carbonation Reaction Processes: Implications for Carbon Dioxide Mineral Sequestration

Gas-phase magnesium hydroxide carbonation processes were investigated at high CO₂ pressures to better understand the reaction mechanisms involved. Carbon and hydrogen elemental analysis, secondary ion mass spectrometry, ion beam analysis, X-ray diffraction, and thermogravimetric analysis were used to follow dehydroxylation/rehydroxylation/carbonation reaction processes. Dehydroxylation is found to generally precede carbonation as a distinct but interrelated process. Above the minimum CO₂ pressure for brucite carbonation, both carbonation and dehydroxylation reactivity decrease with increasing CO₂ pressure. Low-temperature dehydroxylation before carbonation can form porous intermediate materials with enhanced carbonation reactivity at reduced (e.g., ambient) temperature and pressure. Control of dehydroxylation/rehydroxylation reactions before and/or during carbonation can substantially enhance carbonation reactivity.     

A simple method for optimal redundancy allocation for complex networks

In this paper a simple method for optimum redundancy allocation for complex networks is presented. A new heuristic criterion is introduced for solving the problem by decomposing the same in two phases. After finding all minimal path sets of the system in phase I, we choose a minimal path set having the highest value of the ‘sensitivity factor’. In phase II we add one component to the stage having the largest value of the selection factor amongst the stages of the selected minimal path set. This procedure is continued until no more redundant component can be added within the available resources. The proposed method is simple, easily computerizable, fast and capable of handling problems subject to any number of constraints. The method has been illustrated by examples and results are compared with the existing methods.     

PulsExpert: An expert system for the diagnosis and control of diseases in pulse crops

This paper presents design and development of an expert system for the diagnosis and control of diseases in pulse crops (PulsExpert). PulsExpert is an operational automatic diagnostic tool that helps farmers/extension workers to identify diseases of major pulse crops viz., Chickpea, Pigeonpea, Mungbean and Urdbean (highly consumed pulse crops) and suggests the appropriate control measures. Automatic knowledge acquisition system of PulsExpert provides user-friendly interface to the domain experts for entering, storing and structuring the domain specific knowledge. The knowledge base has been designed after examining the type and structure of the knowledge from different sources like literatures, books, databases, farmers, extension workers, etc. For a particular crop, knowledge can be entered by more than one expert using an automatic knowledge acquisition system and system automatically integrates the knowledge to build a consistent knowledge base. The knowledge base of PulsExpert contains up-to-date knowledge about 19 major diseases of pulses appearing right from seedling to maturity. The system provides user-friendly interface to farmers and asks the textual as well as pictorial questions. The order of questions to be asked is decided dynamically depending upon the answers of the farmer. On the basis of answers, PulsExpert diagnosis the pulse crop diseases along with its confidence factor and suggests most appropriate control measures which are composed of cultural practices as well as chemical controls. PulsExpert was evaluated by a team of field farmers and State Agriculture Officers and it was considered good with an average rank of 2.745 by farmers and 2.075 by State Agriculture Officers with a statistic mode ranking 3 in both the cases.     

Single disperser design for coded aperture snapshot spectral imaging

We present a single disperser spectral imager that exploits recent theoretical work in the area of compressed sensing to achieve snapshot spectral imaging. An experimental prototype is used to capture the spatiospectral information of a scene that consists of two balls illuminated by different light sources. An iterative algorithm is used to reconstruct the data cube. The average spectral resolution is 3.6 nm per spectral channel. The accuracy of the instrument is demonstrated by comparison of the spectra acquired with the proposed system with the spectra acquired by a nonimaging reference spectrometer.     

Impact of Particle Size on Room Temperature Ferrimagnetism of SrFe<Subscript>12</Subscript>O<Subscript>19</Subscript>

In this paper, we report the control of important hysteresis parameters, which are useful for memory devices, viz. M _s , H _c and M _r /M _s , by changing the particle size/calcination temperature. An investigation of SrFe₁₂O₁₉ nanopowder from the structural and magnetic aspect is performed using X-ray diffraction (XRD), High Resolution Transmission Electron Microscopy (HRTEM), Scanning Electron Microscopy (SEM) and Vibrating Sample Magnetometer (VSM). The average particle size (APS) of SrFe₁₂O₁₉(nanopowder) increases from 26 to 600 nm with calcination temperatures of 400 and 1100 °C in air, respectively. With the increase in calcination temperature, saturation magnetization (M _s ) increases with the decrease in coercivity for the respective sample. The change in saturation magnetization and coercive field are explained on the basis of transition from single domain structure to multi-domain geometry with an increase in the heating temperature. The sample heated at 1000 °C shows a minimum coercive field (2.71 kOe) and an appropriate squareness ratio (M _r /M _s ) compared to other calcined samples.     

An automatic human chromosome metaspread image selection technique

The human chromosome metaspread images are used to generate the karyogram that is used for the diagnosis of the genetic defects. The genetic defects occur due to variation in either the structure of the chromosomes or the number of chromosomes present in the cell. The human chromosome metaspread image selection process is very critical in the karyogram generation task. It is very tedious and time-consuming process and is generally done manually by an expert cytogeneticist. The manual selection results may be biased, and it is possible that the whole search space is not explored to find the best metaspread image. The mood of the cytogeneticist will also greatly affect the selection results. So there is a strong need to automate the process of human chromosome metaspread image selection process. The proposed approach ranks the metaspread images based upon the quality score that is calculated using the count of the chromosomes of various orientations present in the metaspread image. The ranking has been done based upon ordinal ranking process, wherein a unique rank is assigned to each image based upon a set of rules. The rule base aids in the tiebreaking process in case the same quality score is derived for more than one metaspread image. The decision-making process of the expert cytogeneticist has been emulated by using a set of if–then rules. The proposed technique helps to select the best metaspread image, by exploring the complete set of images that can be used for the karyogram generation.     

An emerging role for comprehensive proteome analysis in human pregnancy research

Elucidation of underlying cellular and molecular mechanisms is pivotal to the comprehension of biological systems. The successful progression of processes such as pregnancy and parturition depends on the complex interactions between numerous biological molecules especially within the uterine microenvironment. The tissue- and stage-specific expression of these bio-molecules is intricately linked to and modulated by several endogenous and exogenous factors. Malfunctions may manifest as pregnancy disorders such as preterm labour, pre-eclampsia and fetal growth restriction that are major contributors to maternal and perinatal morbidity and mortality. Despite the immense amount of information available, our understanding of several aspects of these physiological processes remains incomplete. This translates into significant difficulties in the timely diagnosis and effective treatment of pregnancy-related complications. However, the emergence of powerful mass spectrometry-based proteomic techniques capable of identifying and characterizing multiple proteins simultaneously has added a new dimension to the field of biomedical research. Application of these high throughput methodologies with more conventional techniques in pregnancy-related research has begun to provide a novel perspective on the biochemical blueprint of pregnancy and its related disorders. Further, by enabling the identification of proteins specific to a disease process, proteomics is likely to contribute, not only to the comprehension of the underlying pathophysiologies, but also to the clinical diagnosis of multifactorial pregnancy disorders. Although the application of this technology to pregnancy research is in its infancy, characterization of the cellular proteome, unearthing of functional networks and the identification of disease biomarkers can be expected to significantly improve maternal healthcare in the future.     

Active vibration control of a beam instrumented with MWCNT/epoxy nanocomposite sensor and PZT-5H actuator,robust to variations in temperature

Microsystem Technologies - Multi walled carbon nano tube (MWCNT)/epoxy nanocomposite strain sensor and piezoelectric actuator pair are used in active vibration control (AVC) of a smart cantilevered...     

Analysis and recognition of operations using SEMG from upper arm muscles

Accurate muscular force estimation (from upper arm muscles) based on surface electromyogram forms an important issue in upper limb prosthetic design applications. The whole system consists of surface electrodes, signal acquisition protocols, and signal conditioning at different levels. Labview soft scope was used to acquire the surface electromyogram signal from the designed hardware. The study is concerned with the estimation of characteristics of recorded signals, and for that, statistical techniques of PCA were exercised for verifying the effectiveness of the processed signal against different upper arm motions before its classification. Thereafter, artificial neural network classifier was implemented for the classification surface electromyogram signals with best classification rate of 89.30%. Finally, the processing technique was used to significantly (p < .05) improve classification rate, without much loss of information.