Effect of graft chain length of the stabilizer on dispersion polymerization of methyl methacrylate in petrol

To carry out dispersion polymerization of methyl methacrylate (MMA) in petrol, we have used a poly(MMA) grafted-poly(12-hydroxystearic acid) copolymer as the stabilizer. This special copolymer was prepared as a solution in a mixture of ethyl acetate and butyl acetate solvents. We investigated the effect of graft chain length of the stabilizer on the dispersion polymerization in petrol. We synthesized the stabilizer copolymer with n = 1–4 and determined the rate and the molecular weight of the PMMA formed. There is no dispersion polymerization for n = 1. However, for any other n, for a given stabilizer concentration, as the chain length of the graft is increased, the molecular weight as well as the rate of PMMA formation increases. As opposed to this for a given graft length as the concentration of the stabilizer increases, the molecular weight of PMMA first rises, but for a larger concentration, it begins to fall after undergoing a maximum. In this work, we modeled the heterogeneity of the reaction mass and proposed a mathematical model for dispersion polymerization of MMA in petrol. The computer results are found to conform to the experimentally observed molecular weight of the PMMA. © 1993 John Wiley & Sons, Inc.     

Distributed probabilistic inferencing in sensor networks using variational approximation

This paper considers the problem of distributed inferencing in a sensor network. It particularly explores the probabilistic inferencing problem in the context of a distributed Boltzmann machine-based framework for monitoring the network. The paper offers a variational mean-field approach to develop communication-efficient local algorithm for variational inferencing in distributed environments (VIDE). It compares the performance of the proposed approximate variational technique with respect to the exact and centralized techniques. It shows that the VIDE offers a much more communication-efficient solution at very little cost in terms of the accuracy. It also offers experimental results in order to substantiate the scalability of the proposed algorithm.     

A local asynchronous distributed privacy preserving feature selection algorithm for large peer-to-peer networks

In this paper we develop a local distributed privacy preserving algorithm for feature selection in a large peer-to-peer environment. Feature selection is often used in machine learning for data compaction and efficient learning by eliminating the curse of dimensionality. There exist many solutions for feature selection when the data are located at a central location. However, it becomes extremely challenging to perform the same when the data are distributed across a large number of peers or machines. Centralizing the entire dataset or portions of it can be very costly and impractical because of the large number of data sources, the asynchronous nature of the peer-to-peer networks, dynamic nature of the data/network, and privacy concerns. The solution proposed in this paper allows us to perform feature selection in an asynchronous fashion with a low communication overhead where each peer can specify its own privacy constraints. The algorithm works based on local interactions among participating nodes. We present results on real-world dataset in order to test the performance of the proposed algorithm.     

Random projection-based multiplicative data perturbation for privacy preserving distributed data mining

This paper explores the possibility of using multiplicative random projection matrices for privacy preserving distributed data mining. It specifically considers the problem of computing statistical aggregates like the inner product matrix, correlation coefficient matrix, and Euclidean distance matrix from distributed privacy sensitive data possibly owned by multiple parties. This class of problems is directly related to many other data-mining problems such as clustering, principal component analysis, and classification. This paper makes primary contributions on two different grounds. First, it explores independent component analysis as a possible tool for breaching privacy in deterministic multiplicative perturbation-based models such as random orthogonal transformation and random rotation. Then, it proposes an approximate random projection-based technique to improve the level of privacy protection while still preserving certain statistical characteristics of the data. The paper presents extensive theoretical analysis and experimental results. Experiments demonstrate that the proposed technique is effective and can be successfully used for different types of privacy-preserving data mining applications.     

Orthogonal decision trees

This paper introduces orthogonal decision trees that offer an effective way to construct a redundancy-free, accurate, and meaningful representation of large decision-tree-ensembles often created by popular techniques such as bagging, boosting, random forests, and many distributed and data stream mining algorithms. Orthogonal decision trees are functionally orthogonal to each other and they correspond to the principal components of the underlying function space. This paper offers a technique to construct such trees based on the Fourier transformation of decision trees and eigen-analysis of the ensemble in the Fourier representation. It offers experimental results to document the performance of orthogonal trees on the grounds of accuracy and model complexity.     

Salt‐leaching technique for the synthesis of porous poly(2,5‐benzimidazole) (ABPBI) membranes for fuel cell application

The first instance of synthesizing porous poly(2,5‐benzimidazole) (ABPBI) membranes for high‐temperature polymer electrolyte membrane fuel cells (HT‐PEMFCs), using solvent evaporation/salt‐leaching technique, is reported herein. Various ratios of sodium chloride/ABPBI were dissolved in methanesulfonic acid and cast into membranes by solvent evaporation, followed by porogen (salt) leaching by water washing. The membranes were characterized using SEM, FTIR, TGA, and DSC. The proton conductivity, water and acid uptake of the membranes were measured and the chemical stability was determined by Fenton's test. SEM images revealed strong dependence of sizes and shapes of pores on the salt/polymer ratios. Surface porosities of membranes were estimated with Nis Elements‐D software; bulk porosities were measured by the fluid resaturation method. Thermogravimetric analysis showed enhanced dopant uptake with introduction of porosity, without the thermal stability of the membrane compromised. Incorporating pores enhanced solvent uptake and retention because of capillarity effects, enhancing proton conductivities of PEMs. Upon acid doping, a maximum conductivity of 0.0181 S/cm was achieved at 130 °C for a porous membrane compared with 0.0022 S/cm for the dense ABPBI membrane at the same temperature. Results indicated that with judicious optimization of porogen/polymer ratios, porous ABPBI membranes formed by salt‐leaching could be suitably used in HT‐PEMFCs. © 2017 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2018 , 135, 45773.     

Employee dispositions to job and organization as antecedents and consequences of information systems use

Organizations implement information systems to improve employee productivity and engender favourable organizational outcomes. Although there is evidence of positive outcomes of system use, research has suggested that system use may lead to negative consequences for employees and organizations. There has been limited research that focuses on how employees' use of information systems in the workplace is associated with their positive and negative dispositions to job and organization. We develop and test a model that posits that dispositions to job (ie, job satisfaction, job security, job anxiety, and emotional exhaustion), and organization (ie, organizational commitment and organizational trust) will play a dual role of antecedents and consequences of system use. We conducted 2 longitudinal studies in the context of 2 different systems—a functional system and an enterprise system—and found support for our hypotheses (N = 257 and 181, respectively). We found that preimplementation job and organizational dispositions significantly predicted both lean and rich measures of system use. Further, we found that rich measures of system use (ie, cognitive absorption use and deep structure use) had differential impacts on postimplementation employee dispositions—functional system use had a positive impact and enterprise system use had a negative impact. Overall, our findings offer a comprehensive understanding of system use, and its antecedents and consequences for employees in organizations.