Search results

The purpose of this paper is to explore: the actual usage of generalized audit software (GAS) features among Egyptian external auditors, through the technology acceptance model (TAM); how the conceptual complexity of GAS features impact its actual usage; and what factors influencing the GAS use by Egyptian external auditors.

External audit professionals at 12 international audit firms, including the Big 4 and eight medium-sized firms, in Egypt were surveyed.

The results show that the basic features including database queries, ratio analysis, and audit sampling were higher in GAS use, perceived usefulness, and perceived ease of use among Egyptian external auditors than the advanced features: digital analysis, regression/ANOVA, and data mining classification. The structural equation modelling analysis by GAS features suggests that perceived ease of use has a stronger effect on GAS use when the conceptual complexity of GAS features is high. The analysis also support that the use of GAS by Egyptian external auditors is more affected by co-worker, supervisor, or organization through perceived usefulness, but not by job relevance, output quality, and result demonstration.

Although Egyptian external auditors participated in this study may limit the extent to which the findings may be generalized, the responses provide an insight into the actual usage of GAS features by external auditors and the impact of conceptual complexity of GAS features, which is consistent with the literature concerning the relatively low level of utilizing the advanced features of GAS by internal auditors, suggesting that the issues revealed should be of concern.

The results reported in this paper are useful to audit software developers and audit firms in their understanding of factors influencing GAS usage in a different audit context.

The study adds value to prior research by providing context-contingent insight into the application of TAM in an unexplored audit context.

The purpose of this paper is to design and validate an electronic nose (E-nose) prototype using commercially available metal oxide gas sensors (MOX). This prototype has a sensor array board that integrates eight different MOX gas sensors to handle multi-purpose applications. The number of sensors can be adapted to match different requirements and classification cases. The paper presents the validation of this E-nose prototype when used to identify three gas samples, namely, alcohol, butane and cigarette smoke. At the same time, it discusses the discriminative abilities of the prototype for the identification of alcohol, acetone and a mixture of them. In this respect, the selection of the appropriate type and number of gas sensors, as well as obtaining excellent discriminative abilities with a miniaturized design and minimal computation time, are all drivers for such implementation.

The suggested prototype contains two main parts: hardware (low-cost components) and software (Machine Learning). An interconnection printed circuit board, a Raspberry Pi and a sensor chamber with the sensor array board make up the first part. Eight sensors were put to the test to see how effective and feasible they were for the classification task at hand, and then the bare minimum of sensors was chosen. The second part consists of machine learning algorithms designed to ensure data acquisition and processing. These algorithms include feature extraction, dimensionality reduction and classification. To perform the classification task, two features taken from the sensors’ transient response were used.

Results reveal that the system presents high discriminative ability. The K-nearest neighbor (KNN) and support vector machine radial basis function based (SVM-RBF) classifiers both achieved 97.81% and 98.44% mean accuracy, respectively. These results were obtained after data dimensionality reduction using linear discriminant analysis, which is more effective in terms of discrimination power than principal component analysis. A repeated stratified K-cross validation was used to train and test five different machine learning classifiers. The classifiers were each tested on sets of data to determine their accuracy. The SVM-RBF model had high, stable and consistent accuracy over many repeats and different data splits. The total execution time for detection and identification is about 10 s.

Using information extracted from transient response of the sensors, the system proved to be able to accurately classify the gas types only in three out of the eight MQ-X gas sensors. The training and validation results of the SVM-RBF classifier show a good bias-variance trade-off. This proves that the two transient features are sufficiently efficient for this classification purpose. Moreover, all data processing tasks are performed by the Raspberry Pi, which shows real-time data processing with miniaturized architecture and low prices.

Palmprint recognition is a very interesting and promising area of research. Much work has already been done in this area, but much more needs to be done to make the systems more efficient. In this paper, a multimodal biometrics system based on fusion of left and right palmprints of a person is proposed to overcome limitations of unimodal systems.

Features are extracted using some proposed multi-block local descriptors in addition to MBLBP. Fusion of extracted features is done at feature level by a simple concatenation of feature vectors. Then, feature selection is performed on the resulting global feature vector using evolutionary algorithms such as genetic algorithms and backtracking search algorithm for a comparison purpose. The benefits of such step selecting the relevant features are known in the literature, such as increasing the recognition accuracy and reducing the feature set size, which results in runtime saving. In matching step, Chi-square similarity measure is used.

The resulting feature vector length representing a person is compact and the runtime is reduced.

Intensive experiments were done on the publicly available IITD database. Experimental results show a recognition accuracy of 99.17 which prove the effectiveness and robustness of the proposed multimodal biometrics system than other unimodal and multimodal biometrics systems.

The purpose of this paper is to deal with the classification improvement of pollutant using WO3 gases sensors. To evaluate the discrimination capacity, some experiments were achieved using three gases: ozone, ethanol, acetone and a mixture of ozone and ethanol via four WO3 sensors.

To improve the classification accuracy and enhance selectivity, some combined features that were configured through the principal component analysis were used. First, evaluate the discrimination capacity; some experiments were performed using three gases: ozone, ethanol, acetone and a mixture of ozone and ethanol, via four WO3 sensors. To this end, three features that are derivate, integral and the time corresponding to the peak derivate have been extracted from each transient sensor response according to four WO3 gas sensors used. Then these extracted parameters were used in a combined array.

The results show that the proposed feature extraction method could extract robust information. The Extreme Learning Machine (ELM) was used to identify the studied gases. In addition, ELM was compared with the Support Vector Machine (SVM). The experimental results prove the superiority of the combined features method in our E-nose application, as this method achieves the highest classification rate of 90% using the ELM and 93.03% using the SVM based on Radial Basis Kernel Function SVM-RBF.

Combined features have been configured from transient response to improve the classification accuracy. The achieved results show that the proposed feature extraction method could extract robust information. The ELM and SVM were used to identify the studied gases.

The purpose of this paper is to improve the performance of E-nose in the detection of wound infection. Feature extraction and selection methods have a strong impact on the performance of pattern classification of electronic nose (E-nose). A new hybrid feature matrix construction method and multi-objective binary quantum-behaved particle swarm optimization (BQPSO) have been proposed for feature extraction and selection of sensor array.

A hybrid feature matrix constructed by maximum value and wavelet coefficients is proposed to realize feature extraction. Multi-objective BQPSO whose fitness function contains classification accuracy and a number of selected sensors is used for feature selection. Quantum-behaved particle swarm optimization (QPSO) is used for synchronization optimization of selected features and parameter of classifier. Radical basis function (RBF) network is used for classification.

E-nose obtains the highest classification accuracy when the maximum value and db 5 wavelet coefficients are extracted as the hybrid features and only six sensors are selected for classification. All results make it clear that the proposed method is an ideal feature extraction and selection method of E-nose in the detection of wound infection.

The innovative concept improves the performance of E-nose in wound monitoring, and is beneficial for realizing the clinical application of E-nose.

This paper aims to focus on the basic principle of WO₃ gas sensors to achieve high gas-sensing performance with good stability and repeatability. Metal oxide-based gas sensors are widely used for monitoring toxic gas leakages in the environment, industries and households. For better livelihood and a healthy environment, it is extremely helpful to have sensors with higher accuracy and improved sensing features.

In the present review, the authors focus on recent synthesis methods of WO₃-based gas sensors to enhance sensing features towards toxic gases.

This work has proved that the synthesis method led to provide different morphologies of nanostructured WO₃-based material in turn to improve gas sensing performance along with its sensing mechanism.

In this work, the authors reviewed challenges and possibilities associated with the nanostructured WO₃-based gas sensors to trace toxic gases such as ammonia, H₂S and NO₂ for future research.

The purpose of this paper is to provide a linguistic perspective for corporate brochures. Corporate brochures are published to introduce an organization and to provide information about it but the brochures have been claimed to include promotional elements. By conducting a genre analysis the paper aims to confirm that genre-based writing is not formulaic but instead demonstrates versatility and creativity.

Corporate brochures from a specific industry, oil and gas were sourced from the websites and a total of 16 were available. The method of analysis was genre analysis to establish the generic structure of the brochures by examining the rhetorical moves and strategies, and to identify the textual features of the texts to explain why they are written the way they are.

The oil and gas brochures display a five-move generic structure with a number of strategies. The moves show high occurrence proving the industry as a specialized one. Although the moves are the same, the contents are varied. Versatility is also seen in the presentation style in terms of form, content and language.

Findings on generic structure and textual features of brochures can be used as a guide for corporate writers, as well as trainees and students of corporate communication.

The data for this research represent one industry which has not been explored before thus contributing to the body of knowledge in the field of genre analysis. Although generic in form, genre-based writing has proven to be versatile. The textual features show how companies project their corporate image via brochures.

Compliant foil thrust bearings are promising bearings for high-speed oil-free turbomachinery. However, most previous experimental and numerical approaches to investigate the performance of these bearings have ignored the effect of bearing runner misalignment. Therefore, this paper aims to evaluate the effects of static and dynamic angular misalignments of the bearing runner on the performance of a gas-lubricated foil thrust bearing.

The bearing runner is allowed a maximum angular misalignment that produces a minimum gas film thickness as low as 20 per cent of the nominal clearance. Then, the variations of bearing load carrying capacity, viscous power loss and stiffness and damping coefficients of the gas film with runner misalignment are thoroughly analyzed. The flow in the gas film is modeled with compressible Reynolds equation along with the Couette approximation technique, and the deformation of the compliant bearing is calculated with a robust analytical model. Small perturbations method is used to calculate the force and moment dynamic coefficients of the gas film.

The results show that misaligned foil thrust bearings are capable of developing a restoring moment sufficient enough to withstand the imposed misalignments. Furthermore, the enhanced hydrodynamic effect ensures a stable operation of the misaligned bearing, and the results highlighted the role of the compliant bearing structure to maintain foil bearing prominent features even at misaligned conditions.

The value of this study is the evaluation of the effects of runner angular misalignments on the static and dynamic characteristics of Generation II bump-type foil thrust bearing.

This paper seeks to evaluate the potential of heat exchanged aeroengines for future Unmanned Aerial Vehicle (UAV), helicopter, and aircraft propulsion, with emphasis placed on reduced emissions, lower fuel burn, and less noise.

Aeroengine performance analyses were carried out covering a wide range of parameters for more complex thermodynamic cycles. This led to the identification of major component features and the establishing of preconceptual aeroengine layout concepts for various types of recuperated and ICR variants.

Novel aeroengine architectures were identified for heat exchanged turboshaft, turboprop, and turbofan variants covering a wide range of applications. While conceptual in nature, the results of the analyses and design studies generally concluded that heat exchanged engines represent a viable solution to meet demanding defence and commercial aeropropulsion needs in the 2015‐2020 timeframe, but they would require extensive development.

As highlighted in Parts I and II, early development work was focused on the use of recuperation, but this is only practical with compressor pressure ratios up to about 10. For today's aeroengines with pressure ratios up to about 50, improvement in SFC can only be realised by incorporating intercooling and recuperation. The new aeroengine concepts presented are clearly in an embryonic stage, but these should enable gas turbine and heat exchanger specialists to advance the technology by conducting more in‐depth analytical and design studies to establish higher efficiency and “greener” gas turbine aviation propulsion systems.

It is recognised that meeting future environmental and economic requirements will have a profound effect on aeroengine design and operation, and near‐term efforts will be focused on improving conventional simple‐cycle engines. This paper has addressed the longer‐term potential of heat exchanged aeroengines and has discussed novel design concepts. A deployment strategy, aimed at gaining confidence with emphasis placed on assuring engine reliability, has been suggested, with the initial development and flight worthiness test of a small recuperated turboprop engine for UAVs, followed by a larger recuperated turboshaft engine for a military helicopter, and then advancement to a larger and far more complex ICR turbofan engine.

Carbon dioxide (CO₂) has attracted special scientific interest over the last years mainly because of its relation to climate change and indoor air quality. Except for this, CO₂ can be used as an indicator of food freshness, patients’ clinical state and fire detection. Therefore, the accurate monitoring and controlling of CO₂ levels are imperative. The development of highly sensitive, selective and reliable sensors that can efficiently distinguish CO₂ in various conditions of temperature, humidity and other gases’ interference is the subject of intensive research with chemi-resistive zinc oxide (ZnO)-based sensors holding a privileged position. Several ZnO nanostructures have been used in sensing applications because of their versatile features. However, the deficient selectivity and long-term stability remain major concerns, especially when operating at room temperature. This study aims to encompass an extensive study of CO₂ chemi-resistive sensors based on ZnO, introducing the most significant advances of recent years and the best strategies for enhancing ZnO sensing properties.

An overview of the different ZnO nanostructures used for CO₂ sensing and their synthesis methods is presented, focusing on the parameters that highly affect the sensing mechanism and, thus, the performance of CO₂ sensors.

The selectivity and sensitivity of ZnO sensors can be enhanced by adjusting various parameters during their synthesis and by doping or treating ZnO with suitable materials.

This paper summarises the advances in the rapidly evolving field of CO₂ sensing by ZnO sensors and provides research directions for optimised sensors in the future.