Cognitive styles as a function of locus of control

This research began as an examination of the problem solving strategies of individuals who believe they can control reinforcements they recelve (internals) and those who believe that outside forces control reinforcements (externals) under different conditions of skill and chance. This developed into a study of the cognitive functioning of internals and externals in concept formation tasks. Internal and external persons were identified using the internal-external locus of control scale developed by J.B. Rotter and his colleagues. Three studies were conducted uSlng different tasks and groups of Subjects. The subjects of the first study were required to find a principle relating one of two response words to a list of five stimulus words. There were fifty trials using different sets of words. Three groups of subjects were used, each made up of internals and externals. The group under the skill condition was instructed that their performance depended primarily on their own skill; the group under the chance 1 condition (quasi chance) was instructed that their performance on the task would probably be no better than chance due to the extreme difficulty of the task; and the group under chance 2 (pure chance) were told that their performance on the task was totally controlled by chance as the arrangement of the words was purely arbitrary. It was expected that internals would perform better than externals under the skill condition while externals would perform better than internals under chance 2. Subjects' perception of, and reactions to, the task were measured by a post-task questionnaire. The results did not uphold the predictions. Externals, relative to internals, utilised, produced and changed significantly more solution hypotheses while working on the task. The two groups did not differ in the number of correct answers and both of them were unsuccessful in deciphering the principle. In terms of subjects' reactions to the task, it was found that the internals reacted differently to the skill and chance 2 conditions, while externals were stable across these conditions. Moreover, subjects construed the chance 1 condition as resembling a skill condition. The different ways ln which internals and externals handled their solution hypotheses was further investigated in the second study. Two groups, one of internals and one of externals, were asked to scan a list of characteristics describing an object, and then to scan another list containing objects, one of which was best described by the characteristics. The two lists were presented separately to the subjects in order to discover whether subjects needed to switchback between the two lists while attempting to identify the correct object. The subjects' reaction times in studying the characteristics (preparation time) and in naming the appropriate objects (solution time) were recorded. The subjects' perception of and reactions to the task were measured by a post-task questionnaire. The results strongly supported the predictions: the internals preparation and solution times were significantly faster than those of the externals who also used more switchbacks than internals. Moreover, both groups performed equally well on the task (in terms of naming the appropriate objects). Analysis of the subjects' perception of the task indicated that internals perceived the task to be more skill controlled than externals. The third study was conducted to clarify some methodological problems associated with the first study and to further investigate the problem solving behaviour of internals and externals. Subjects were presented with a series of sets one per trial for twenty four trials, each of which consisted of two letters and two numbers. Certain sets were constructed using a common principle and subjects were required to identify the principle. Subjects perception of, and reactions to the experiment were measured by a post task questionnaire. The results showed that more externals were successful at finding the principle than internals. Externals used less trials per solution hypothesis and guessed on more trials than internals. Both groups had similar numbers of correct answers. More internals than externals, however, employed complex solution hypotheses. It was also found that the internals confidence in finding the principle before commencing the task was higher than that of the externals. Taken in conjunction the three studies indicate that finding the solution per se to the tasks did not differentiate internals from externals as readily as their different approaches to the tasks. The internals were more cautious and systematic in handling their solution hypotheses and processed information more efficiently and thoroughly. The externals, on the other hand, adopted a "butterfly" approach to testing their solution hypotheses, readily switching between them and returning to previously rejected hypotheses. They were less able than internals to process simultaneously two aspects of the task. It was concluded that the different problem solving behaviours of internals and externals resemble distinctive cognitive styles. Whether these cognitive styles are effective in terms of identifying the solution to a problem seems to depend largely on three main factors: the skill element of the task, the type of task, and the level of task difficulty

On The Karush – Kuhn – Tucker Reformulation of Bi – Level Geometric Programming Problem with an Interval Coefficients as Multiple Parameters

This paper presents a new approach to solve a special class of bi – level nonlinear programming (NLP) problems with an interval coefficients as multiple parameters. Geometric programming (GP) is a powerful technique developed for solving nonlinear programming (NLP) problems and it is useful in the study of a variety of optimization problems. Many applications of GP in various fields of science and engineering are used to solve certain complex decision making problems. In this paper a new mathematical formulations for a new class of nonlinear optimization models called bi – level geometric programming (BLGP) problem is presented. This problems are not necessarily convex and thus not solvable by standard nonlinear programming techniques. This paper proposed a method to solve BLGP problem where coefficient of objective function as well as coeffiaent of constraints are multiple parameters. Especially the multiple parameters are considered in an interval which are the Arithmetic mean (A.M), Geometric mean (G.M) and Harmonic mean (H. M) of the end points of the interval. In this paper, the values of objective function in interval range of parameters for A. M., G. M. and H. M. are preserved the same relationship. Also, BLGP problem can be converted to a single objective by using the classical karush – kuhn – Tucker (KKT) reformulation and the ability of calculating the bounds of objective value in KKT is basically presented in this paper that may help researchers in constructing more realistic model in optimization field.  Finally, numerical example is given to illustrate the efficiency of the method

Microbial β-Glucosidase: sources, production and applications

Cellulose is the most abundant biopolymer in biosphere and the major constituent of plant biomass. Cellulose polymer is made up of β-glucose units linked by β-glucosidic bonds. Cellulase is an enzymatic system that catalyzes the hydrolysis of cellulose polymer to glucose monomers. This enzymatic system consists of three individual enzymes namely endoglucanase, exoglucanase and β-glucosidase which act synergistically to degrade cellulose molecules into glucose. Cellulases are produced by bacteria, fungi, plants, and animals and used in many industrial applications such as textile industries, laundry and detergent industries, paper and pulp industry, animal feeds, and biofuels production. β-Glucosidase is a diverse group of enzymes with wide distribution in bacteria, fungi, plants and animals and has the potential to be utilized in various biotechnological processes such as biofuel production, isoflavone hydrolysis, flavor enhancement and alkyl/aryl β-D-glucoside and oligosaccharides synthesis. Thus, there is increased demand of β-glucosidase production from microbial sources under profitable industrial conditions. In this review, β-glucosidase classification, localization, and mechanism of action will be described. Subsequently, the various sources of β-glucosidase for industrial sector will be discussed. Moreover, Fermentation methods and various parameters affecting β-glucosidase production will be highlighted on the light of recent findings of different researchers. Finally, β-glucosidase applications in biofuel production, flavors enhancement, isoflavones hydrolysis, cassava detoxification and oligosaccharide synthesis will be described

Characterization of thermophilic β-Glucosidase of rhizospheric bacterial strain (LSKB15) isolated from Cholistan Desert, Pakistan

Fifty thermophilic bacterial strains isolated from rhizospheric soil of Cholistan desert, Pakistan, and designated as LSKB01-LSKB50 were screened for β-glucosidase gene (bgl) belonging to glycoside hydrolase family 1 (GH 1) using PCR technique. Subsequently, the same strains were screened for extracellular β-glucosidase production using esculin as substrate. All fifty strains were shown to be amplified for conserved region of bgl gene and to secrete extracellular β-glucosidase. One strain (LSKB15) secreted relative high amount of this enzyme as indicating by size of ferric-esculetin precipitate. This strain was further cultivated on cellulose containing media and β-glucosidase was purified by ammonium sulfate, dialysis and gel filtration chromatography. The purified enzyme showed an optimal temperature of 60°C and an optimal pH of 7. It also showed excellent temperature and pH stability retaining > 90% activity after incubation for 2 h at pH 5-8 and 40-60°C. Finally, the purified enzyme was run on Native-PAGE and subsequently incubated in phosphate buffer containing 5 mM of 4-methylumbelliferyl-β-D-glucoside (4-MUG) for 15 min at 50°C and visualized by UV light as white band. We concluded that thermophilic LSKB15 β- glucosidase may work with other cellulase to degrade available cellulose synthesized by plant and the properties exhibited by it such as high temperature and pH stability pointed out its potential industrial importance

AN ADAPTIVE ROLE-BASED ACCESS CONTROL APPROACH FOR CLOUD E-HEALTH SYSTEMS

Securing and protecting electronic medical records (EMR) stored in a cloud is one of the most critical issues in e-health systems. Many approaches with different security objectives have been developed to adapt this important issue.This paper proposes a new approach for securing and protecting electronic health records against unauthenticated access with allowing different hospitals, health centres and pharmacies access the system, by implementing role-based access control approach that could be applied smoothly in cloud e-health systems

Atypical Presentation of Mollaret’s Meningitis

Mollaret’s meningitis is mostly described in the setting of recurrent attacks of fever along with signs and symptoms of meningitis. It resolves spontaneously without any treatment and in most of the cases no causative organism is identified. Here we present an atypical case of mollaret’s meningitis in which the patient presented with headache and meningismus in the absence of fever

Boolean logic algebra driven similarity measure for text based applications

In Information Retrieval (IR), Data Mining (DM), and Machine Learning (ML), similarity measures have been widely used for text clustering and classification. The similarity measure is the cornerstone upon which the performance of most DM and ML algorithms is completely dependent. Thus, till now, the endeavor in literature for an effective and efficient similarity measure is still immature. Some recently-proposed similarity measures were effective, but have a complex design and suffer from inefficiencies. This work, therefore, develops an effective and efficient similarity measure of a simplistic design for text-based applications. The measure developed in this work is driven by Boolean logic algebra basics (BLAB-SM), which aims at effectively reaching the desired accuracy at the fastest run time as compared to the recently developed state-of-the-art measures. Using the term frequency–inverse document frequency (TF-IDF) schema, the K-nearest neighbor (KNN), and the K-means clustering algorithm, a comprehensive evaluation is presented. The evaluation has been experimentally performed for BLAB-SM against seven similarity measures on two most-popular datasets, Reuters-21 and Web-KB. The experimental results illustrate that BLAB-SM is not only more efficient but also significantly more effective than state-of-the-art similarity measures on both classification and clustering tasks

A set theory based similarity measure for text clustering and classification

© 2020, The Author(s). Similarity measures have long been utilized in information retrieval and machine learning domains for multi-purposes including text retrieval, text clustering, text summarization, plagiarism detection, and several other text-processing applications. However, the problem with these measures is that, until recently, there has never been one single measure recorded to be highly effective and efficient at the same time. Thus, the quest for an efficient and effective similarity measure is still an open-ended challenge. This study, in consequence, introduces a new highly-effective and time-efficient similarity measure for text clustering and classification. Furthermore, the study aims to provide a comprehensive scrutinization for seven of the most widely used similarity measures, mainly concerning their effectiveness and efficiency. Using the K-nearest neighbor algorithm (KNN) for classification, the K-means algorithm for clustering, and the bag of word (BoW) model for feature selection, all similarity measures are carefully examined in detail. The experimental evaluation has been made on two of the most popular datasets, namely, Reuters-21 and Web-KB. The obtained results confirm that the proposed set theory-based similarity measure (STB-SM), as a pre-eminent measure, outweighs all state-of-art measures significantly with regards to both effectiveness and efficiency

Towards Highly-Efficient k-Nearest Neighbor Algorithm for Big Data Classification

the k-nearest neighbors (kNN) algorithm is naturally used to search for the nearest neighbors of a test point in a feature space. A large number of works have been developed in the literature to accelerate the speed of data classification using kNN. In parallel with these works, we present a novel K-nearest neighbor variation with neighboring calculation property, called NCP-kNN. NCP-kNN comes to solve the search complexity of kNN as well as the issue of high-dimensional classification. In fact, these two problems cause an exponentially increasing level of complexity, particularly with big datasets and multiple k values. In NCP-kNN, every test point’s distance is checked with only a limited number of training points instead of the entire dataset. Experimental results on six small datasets, show that the performance of NCP-kNN is equivalent to that of standard kNN on small and big datasets, with NCP-kNN being highly efficient. Furthermore, surprisingly, results on big datasets demonstrate that NCP-kNN is not just faster than standard kNN but also significantly superior. The findings, on the whole, show that NCP-kNN is a promising technique as a highly-efficient kNN variation for big data classification