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Optimization of Cerbera manghas Biodiesel Production Using Artificial Neural Networks Integrated with Ant Colony Optimization
No full textOptimizing the process parameters of biodiesel production is the key to maximizing biodiesel yields. In this study, artificial neural network models integrated with ant colony optimization were developed to optimize the parameters of the two-step Cerbera manghas biodiesel production process: (1) esterification and (2) transesterification. The parameters of esterification and transesterification processes were optimized to minimize the acid value and maximize the C. manghas biodiesel yield, respectively. There was excellent agreement between the average experimental values and those predicted by the artificial neural network models, indicating their reliability. These models will be useful to predict the optimum process parameters, reducing the trial and error of conventional experimentation. The kinetic study was conducted to understand the mechanism of the transesterification process and, lastly, the model could measure the physicochemical properties of the C. manghas biodiesel. © 2019 by the authors
Transformational leadership and teacher’s performance: the mediating role of motivation and commitment [Liderazgo transformacional y desempeño del docente: El papel mediador de la motivación y el compromiso]
No full textThis multilevel study examines the psychological mechanisms (i.e., motivation and commitment) that might underlie the relationship between transformational leadership and job performance. Models were tested using the sample of teachers, n = 266 embedded in 55 teams from secondary schools in Malaysia. Using hierarchical linear modeling (HLM), we found that there was a cross-level effect of transformational leadership on motivation, commitment and performance. In conclusion, our finding proposes that transformational leadership is a vital organizational leadership style construct, and acts to enhance teachers’ performance via psychological boost. © 2019, Universidad del Zulia. All rights reserved
Shariah-compliant status and investors’ demand for IPOs: the effects of information asymmetry
No full textEffect of different imidazolium-based ionic liquids on gel polymer electrolytes for dye-sensitized solar cells
No full textHost polymer electrolytes are prepared using poly(ethylene oxide) (PEO) as a polymer, ethylene carbonate (EC) and propylene carbonate (PC) as solvents, sodium iodide (NaI) as salt, and iodine (I2) and with either one of the ionic liquids (ILs) which are 1-methyl-3-propylimidazolium iodide (MPII), 1-butyl-3-methylimidozium (BMII), and 1-hexyl-3-methylimidozium iodide (HMII) in each gel polymer electrolyte. The ionic conductivity of the gel polymer electrolytes (GPEs) was measured by electrochemical impedance spectrometer (EIS). The ionic conductivity of GPEs increased with the presence of ionic liquids (ILs). At room temperature, the dielectric behaviors of the GPEs were studied. The highest ionic conductivity of 9.41 mS cm−1 is achieved after addition of MPII ionic liquid. Meanwhile, Fourier-transform infrared and X-ray diffraction studies confirm complexation between PEO polymer, NaI salt, and ionic liquids (MPII, BMII, and HMII). Dye-sensitized solar cells (DSSCs) were fabricated using polymer electrolytes and studied under Sun simulator. The DSSC using MPII ionic liquid shows the highest efficiency of 9.35%. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature
Transparent self-cleaning coating of modified polydimethylsiloxane (PDMS) for real outdoor application
No full textHydrophobic coating usually possesses a water contact angle (CA) of more than 90°. The fabrication of hydrophobic coating on the glass substrate has high potential to induce self-cleaning properties, anti-fogging and mud-repellent properties. In this study, a transparent hydrophobic self-cleaning coating with modified Polydimethylsiloxane (PDMS) has been successfully developed using 3-aminopropyltriethoxyslane (APTES). APTES has a flexible amino group for silica structuration of Polydimethylsiloxane to cross-link and form a hybrid coating system. The hydrophobic coating developed is applied onto glass plates using dip-coating method. The hydrophobicity of the coating is tested using the water contact angle (WCA) method and achieved WCA as high as 103.9°. The UV-VIS spectrometer measurement reveals that the transparency of the modified APTES/ PDMS coating is above 90% in visible region. The prepared hybrid coating also exhibits excellent self-cleaning properties in indoor and outdoor environment. In addition to that, the hybrid coating also exhibits great anti-fogging behavior where the tiny water droplets on the coated surface completely disappear after 6 min at ambient temperature. © 2019 Elsevier B.V
Topological Clustering via Adaptive Resonance Theory With Information Theoretic Learning
No full textThis paper proposes a topological clustering algorithm by integrating topological structure and information theoretic learning, i.e., correntropy, into adaptive resonance theory (ART). Specifically, the proposed algorithm utilizes the correntropy induced metric (CIM) for defining a similarity measure, a node insertion criterion, and an edge creation criterion. Other types of the ART-based topological clustering algorithms have been developed, however, these algorithms have various drawbacks such as a large number of parameters, sensitivity to noisy data. Moreover, generated topological networks cannot represent the distribution of data. In contrast, the proposed algorithm realizes a stable computation and reduces the number of parameters compared to existing algorithms. Furthermore, improving the ability to express the data structure more appropriately by the topological network, a mechanism that adaptively controls the node insertion criterion is introduced to the proposed algorithm. The experimental results showed that the proposed algorithm has superior performance with respect to the self-organizing and the classification abilities compared with the state-of-the-art topological clustering algorithms. © 2013 IEEE
The economic Gordian Knot of Brexit: an East and Southeast Asian perspective
No full textThe objective of this paper is to establish conceptual foundations of analyzing the economic dimensions of Brexit. The International Financial-Trade Exchange Leaking Index (TIFTEL-Index) attempts to analyze and compare pre-Brexit versus post-Brexit international trade and international financial transactions between East and Southeast Asia and Europe. TIFTEL-index is based on three main variables, namely (1) international trade exchange marginal rate (∆Τ′), (2) international financial exchange marginal rate (∆σ′), and (3) GDP in real prices growth marginal rate (∆γ). Simulation findings indicate that Brexit will have only a limited negative effect on the world economy. In addition, Brexit will affect East Asia more than Southeast Asia region. © 2019, Springer Nature B.V
Drivers and outcomes of eco-design initiatives: a cross-country study of Malaysia and Australia
No full textIn this study, we have developed and empirically tested a model of the antecedents of eco-design initiatives and their outcome using data that was collected from 255 environmental management representatives of certified manufacturing firms in Malaysia and Australia. Our model incorporates regulations, customer pressure, social responsibility, and expected business benefits as the antecedents to the eco-design initiatives, and investigates their environmental, economic, and intangible outcomes. The results indicate that regulation and social responsibility are positively associated with eco-design initiatives, which have a positive effect on the environmental, economic, and intangible outcomes of firms in both countries. Only in Australia, customer pressure is the motivator of eco-design initiatives. As for Malaysia, expected business benefit is the driver of eco-design initiatives. This study extends the knowledge on the drivers and outcomes of eco-design initiatives in the upper middle and high-income countries. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature
A deterministic approach for finding the T onset parameter of flatten T wave in ECG
No full textIdentification of the exact nature of flatten T wave in ECG signal is Classification of normal and abnormal T wave episodes especially, regarding Flatten T wave in electrocardiography (ECG) signal is still a complex phenomenon for cardiologists. Identification of Flatten T wave depends on four parameters; Time duration (T dur ), T onset (T on ), T offset (T off ) and T peak (T pk ) values which play a vital role to identify the exact nature of Flatten T wave. The proposed approach is used to extract the T on value of Flatten T wave with the detection of R peaks and RR intervals. The proposed approach is applied to ten different subjects of Flatten. It is divided into three distinct phases. Firstly, Flatten signals are segmented by lead wise. Secondly, noise filtration is done to identify the peak values and removal of low-frequency components. A third phase computes the R peak values and RR intervals with the help proposed algorithm. By using the R peak value as a fiducial point and considering the last interval of RR interval instead of complete T wave alternans detection algorithm (TWA) for determination the T on parameter of Flatten T wave. The experimental evaluation manifests that efficiency factors are high in rate during the operational investigations (closest to the range of 100%). These efficiency factors have been discussed in the context of accuracy, sensitivity, prediction and error rate. These operation efficiency factors will play a benchmark role in future for calculation the others parameters of Flatten T wave. © 2019 Institute of Information Science. All rights reserved
Street Sign Recognition Using Histogram of Oriented Gradients and Artificial Neural Networks
No full textStreet sign identification is an important problem in applications such as autonomous vehicle navigation and aids for individuals with vision impairments. It can be especially useful in instances where navigation techniques such as global positioning system (GPS) are not available. In this paper, we present a method of detection and interpretation of Malaysian street signs using image processing and machine learning techniques. First, we eliminate the background from an image to segment the region of interest (i.e., the street sign). Then, we extract the text from the segmented image and classify it. Finally, we present the identified text to the user as a voice notification. We also show through experimental results that the system performs well in real-time with a high level of accuracy. To this end, we use a database of Malaysian street sign images captured through an on-board camera. © 2019 by the authors