Participation through communicative action: A case study of GIS for addressing land/water development in India

Attempts to alleviate land degradation and water scarcity in arid/semi-arid regions of India have historically been carried out within the ambit of government schemes implemented disparately by concerned departments. These sectoral methods are being increasingly replaced by a watershed-based approach in which local communities are encouraged to assume ownership of development programs, albeit within the government's overarching control. This decentralized model of governance has also in some cases had a positive impact on the more effective use of ICTs like Geographic Information System (GIS) in locally relevant applications. In this paper, the need for integrating disparate knowledge systems around GIS-based applications to mitigate land degradation, and the facilitating role of participation in achieving such integration, are discussed. It is argued that such participatory processes can be effectively enabled through communicative action whilst taking into consideration the historically existing power asymmetries. The Habermasian Ideal Speech Situation (IDS) provides a conceptual framework to argue how such communicative action can be enabled. This framework is applied to an empirical analysis of a GIS project for land management in India. The paper contributes to unpacking knowledge systems implicated in the use of GIS for addressing land degradation, foregrounding the importance of indigenous knowledge, and in espousing the crucial need to draw upon critical social perspectives in IS research.     

Harnessing defocus blur to recover high-resolution information in shape-from-focus technique

Traditional shape-from-focus (SFF) uses focus as the singular cue to derive the shape profile of a 3D object from a sequence of images. However, the stack of low-resolution (LR) observations is space-variantly blurred because of the finite depth of field of the camera. The authors propose to exploit the defocus information in the stack of LR images to obtain a super-resolved image as well as a high-resolution (HR) depth map of the underlying 3D object. Appropriate observation models are used to describe the image formation process in SFF. Local spatial dependencies of the intensities of pixels and their depth values are accounted for by modelling the HR image and the HR structure as independent Markov random fields. Taking as input the LR images from the stack and the LR depth map, the authors first obtain the super-resolved image of the 3D specimen and use it subsequently to reconstruct a HR depth profile of the object.     

Effect of Utilization of Discrete Wavelet Components on Flood Forecasting Performance of Wavelet Based ANFIS Models

Wavelet based flood forecasting models are known to perform better than conventional models, yet the effect of the way wavelet components are combined to develop a model on the forecasting performance, is inadequately investigated. To demonstrate this, two types of wavelet- adaptive neuro-fuzzy inference system (WANFIS), i.e. WANFIS-split data model (WANFIS-SD) and WANFIS-modified time series model (WANFIS-MS) are developed to forecast river water levels with 1-day lead time. To develop these models, first the original level time series (OLTS) is decomposed into discrete wavelet components (DWCs) by discrete wavelet transform (DWT) upto three resolution levels. In WANFIS-SD, all wavelet components are used as inputs while WANFIS-MS ignores the noise wavelet components and utilizes only the effective wavelet components. The effectiveness of the developed models are evaluated through application to two Indian rivers, Kamla and Kosi, which vary significantly in their catchment area and flow patterns. The proposed models are found to forecast river water levels accurately. On comparison, the WANFIS-SD is found to perform better than WANFIS-MS for high flood levels.     

Predicting Monsoon Floods in Rivers Embedding Wavelet Transform,Genetic Algorithm and Neural Network

Monsoon floods are recurring hazards in most countries of South-East Asia. In this paper, a wavelet transform-genetic algorithm-neural network model (WAGANN) is proposed for forecasting 1-day-ahead monsoon river flows which are difficult to model as they are characterized by irregularly spaced spiky large events and sustained flows of varying duration. Discrete wavelet transform (DWT) is employed for preprocessing the time series and genetic algorithm (GA) for optimizing the initial parameters of an artificial neural network (ANN) prior to the network training. Depending on different inputs, four WAGANN models are developed and evaluated for predicting flows in two Indian Rivers, the Kosi and the Gandak. These rivers are infamous for carrying large flows during monsoon (June to Sept), making the entire North Bihar of India unsafe for habitation or cultivation. When compared, WAGANN models are found to be better than autoregression models (ARs) and GA-optimized ANN models (GANNs) which use original flow time series (OFTS) for inputs, in simulating river flows during monsoon. In addition, WAGANN models predicted relatively reasonable estimates for the extreme flows, showing little bias for underprediction or overprediction.     

Tailoring the Microstructural,Optical, and Electrical Properties of Nanocrystalline WO3 Thin Films Using Al Doping

We have studied the influence of Al doping on the microstructural, optical, and electrical properties of spray-deposited WO₃ thin films. XRD analyses confirm that all the films are of polycrystalline WO₃ in nature, possessing monoclinic structure. EDX profiles of the Al-doped films show aluminum peaks implying incorporation of Al ions into WO₃ lattice. On Al doping, the average crystallite size decreases due to increase in the density of nucleation centers at the time of film growth. The observed variation in the lattice parameter values on Al doping is attributed to the incorporation of Al ions into WO₃ lattice. Enhancement in the direct optical band gap compared to the undoped film has been observed on Al doping due to decrease in the width of allowed energy states near the conduction band edge. The refractive indices of the films follow the Cauchy relation of normal dispersion. Electrical resistivity compared to the undoped film has been found to increase on Al doping.     

An institutional analysis on the dynamics of the interaction between standardizing and scaling processes: a case study from Ethiopia

This paper presents an institutional theory-inspired analysis of the dynamics of interaction between processes of standardizing and scaling, in the context of Health Information Systems implementation in the Ethiopian public health care system. Standardizing and scaling have, in existing research, been treated primarily as independent technical processes that are isolated from the institutional context in which they take place. This paper tries to redress this balance in this research in two ways. Firstly, it argues for these processes to be taken as inter-related which can both support and undermine each other. Secondly, this mutual interaction is argued to be mediated by the institutional context. Specifically, we draw upon concepts from institutional theory inspired by Douglas North, focusing on the degree of overlap between formal institutions (attempts to establish formal policy on activities such as the definition of indicators and uniform reporting formats) and informal constraints in practice reflected in inadequate capacity – both technological and human, and existing work practices.     

Sprayed ZnO thin films for ethanol sensors

The electrical characteristics of ZnO thin films prepared by spray pyrolysis, have been studied for ethanol sensors. The sensitivities of the films are measured at various temperatures and concentrations of ethanol. It is observed that the sensitivity increases with increasing working temperature. At higher ethanol concentrations, the sensitivity increases more rapidly with increasing temperature. Further, the films show fast response and recovery times at higher working temperatures. The sensing mechanism of the films towards ethanol vapour has been explained.     

Sn-Doped In2O3 Nanocrystalline Thin Films Deposited by Spray Pyrolysis: Microstructural, Optical, Electrical, and Formaldehyde-Sensing Characteristics

Undoped and Sn-doped (1, 1.5 and 2 at.%) indium oxide (In₂O₃) thin films have been grown by the chemical spray pyrolysis technique on cleaned glass substrates using indium nitrate [In(NO₃)₃] and stannic tetrachloride hydrated (SnCl₄·5H₂O) as the host and dopant precursors, respectively, and deionized water as the solvent. Structural characterization using x-ray diffraction reveals that the films possess cubic structure, with the average crystallite size in the range 10-14 nm. The surface morphology and roughness of the films have been investigated by means of an atomic force microscope. UV-Vis measurements indicate an enhancement in the optical transmittance in the visible region on Sn doping. Further, the doping effect has been found to substantially reduce the electrical resistance to a few orders of magnitude of the undoped In₂O₃ film. We report a simultaneous improvement in both the optical and electrical properties of indium oxide thin film due to the doping of Sn ions. These results indicate that Sn-doped In₂O₃ thin film can be a potential candidate for use in various optoelectronic devices. Among all the films examined, the 1 at.% Sn-doped film shows the maximum response (~91%) at 300 °C for 80 ppm concentration of formaldehyde in air.     

An Efficient Model for Batch Annealing Using a Neural Network

A comprehensive process model, capturing heat transfer, phase transformation, and microstructural evolution, was recently developed for efficient process cycle design of a batch annealing operation. However, the high computation time for such an integrated model makes it difficult to design process cycles for a battery of furnaces or their model based online control. In the present work, an accurate prediction tool has been derived using neural network modeling from the simulations of the comprehensive process model. Using this approach, the computational efficiency of this prediction tool increases by over 100 times, making it amenable to cycle design of multiple furnaces or their online process control. Subsequently, an exhaustive search technique and genetic algorithm have been used to optimize the coil dimensions to maximize the plant productivity. It was found that batch annealing productivity can be maximized by optimizing the coil inner diameter, while constraining the coil width and outer diameter at the maximum permissible dimensions.