Influence of silicoating,etching and heated glaze treatment on the surface of Y-TZP and its impact on bonding with veneering ceramic

The study aimed to investigate the influence of surface treatments of Y-TZP on its topography and their impact on shear bond strength (SBS) of Y-TZP to veneering porcelain. Thirty-four zirconia cubes (Cercon) (10 × 10 × 10 mm³) were divided randomly into 3 groups (n = 11) according to the different surface treatments, Silica Coating (SC); Hot Solution Etching (HE); and Heat Treatment after porcelain glaze and powder application (GP). Porcelain application (IPs e.max) was performed after surface treatment using a standardized technique. Porcelain application (Ceramco-3) was also performed on 10 metal cubes (I BOND 2) as controls (MC). Specimens were subjected to thermocycling (5–55 °C, 20,000 cycles) followed by SBS testing in a universal testing machine. Surface topography was assessed by scanning electron microscopy and surface roughness (Image J). SC developed significantly higher SBS (42.10 ± 5.84 MPa) of Y-TZP to veneering ceramics compared to treatment by HE (24.0 ± 6.4 MPa), GP (23.30 ± 4.72), and the MC (29.3 ± 5.4 MPa) control (p < 0.05). SBS among HE, GP and MC specimens was comparable (p > 0.05). Y-TZP specimens treated with SC and HE showed high surface roughness compared to GP. Silicoating of Y-TZP prior to veneering can potentially reduce the high failure rates of zirconia-based restorations by enhancing the chemical bond between the core and the veneer materials.     

Applied whispering gallery modes on ZnO nanorods coated glass for humidity sensing application

In this letter, a humidity sensor is demonstrated by applying a whispering gallery mode (WGM) from a microsphere resonator onto the ZnO nanorods coated glass surface. The diameter of the microsphere was 234 µm and the glass surface was coated with ZnO nanorods using the hydrothermal method at growth duration of 12 h. A significant response to humidity level ranging from 35%RH to 85%RH has been observed with the sensitivity of 0.014 2 nm/%RH. The proposed humidity sensor has successfully employed to enhance interaction between the whispering gallery mode evanescent and surrounds analyte with the assistance of ZnO nanorods coated glass.     

Towards the use of remote-sensing data for monitoring of abandoned oil palm lands in Malaysia: a semi-automatic approach

Oil palm is a commercial crop that is important for its food value and as a biofuel, along with its other benefits towards the economy and human health. Currently, Malaysia cultivates approximately 5.64 million ha of oil palm. To date, a study identifying abandoned oil palm areas using satellite images is almost non-existent. Conventionally, the monitoring of abandoned oil palm lands is tedious and time consuming, especially over large areas. Hence, in this article, the capability of high resolution satellite image via Satellite Pour I’Observation de la Terre-6 (SPOT-6) products to extract abandoned oil palm areas was explored, as was the use of multi-temporal Landsat Operational Land Imager (OLI) imagery to develop the phenology of abandoned oil palm sites. Homogeneity measures derived through SPOT images played a more important role to identify abandoned oil palm than crop phenology characteristics extracted from high spectral resolution of Landsat images. With the advancement of object-oriented classification, monitoring of abandoned oil palm areas can be done semi-automatically with an accuracy of 92±1%.     

Development of a hybrid PSO–ANN model for estimating glucose and xylose yields for microwave-assisted pretreatment and the enzymatic hydrolysis of lignocellulosic biomass

In this paper, two artificial intelligent systems, the artificial neural network (ANN) and particle swarm optimization (PSO), were combined to form a hybrid PSO–ANN model that was used to improve estimates of glucose and xylose yields from the microwave–acid pretreatment and enzymatic hydrolysis of lignocellulosic biomass based on pretreatment parameters. ANN is a powerful tool capable of determining the relationship between the desired input and output data while PSO was used as a robust population-based search algorithm to optimize the performance of the ANN model. Specifically, it was used to determine the optimum number of neurons in the hidden layer and the best value of the learning rate of the ANN model. The optimization method includes minimizing the fitness function mean absolute error that was found to be 0.0176. The PSO algorithm suggested an optimum number of neurons in the hidden layer as 15 and a learning rate of 0.761 these consequently used to construct the ANN model. After constructing the hybrid PSO–ANN model, the performance of the intelligent system was examined by determining the regression coefficient (R ²) for estimating the experimental values of glucose and xylose and compared to the results from a response surface methodology (RSM) model. The results of R ² of the hybrid PSO–ANN model for glucose and xylose were 0.9939 and 0.9479, respectively, while the RSM model results for the same sugars were 0.8901 and 0.8439. This analysis reveals that the hybrid PSO–ANN model offers a higher degree of accuracy in comparison with the more commonly used RSM model.

     

Visual interface for searching and browsing children’s WebOPAC

This study focuses on the visual interface design of WebOPAC, which assists children in carrying out searching and browsing activities for bibliographical information in a library database. The existing interface of WebOPAC, called Ilmu, is investigated in this study; the interface is efficient in retrieving information from the database; however, its poor graphic design causes major usability problems. The graphic design of Ilmu is important in helping children to understand and visualise concepts of searching and browsing. Therefore, the interface must be constructed in such a way that can meet the needs of children aged 7–11 years. In this study, an analysis has been carried out on children’s requirements for the graphic design through participatory design activities using two approaches: cooperative inquiry and sketching of ideas. The cooperative Inquiry approach involved 40 children, while sketching children’s ideas involved 20 children as respondents. A total of 14 requirement specifications were generated and categorised into four areas—use of space, information organisation, function and use of colours; these would serve as a basis in designing a new WebOPAC interface. Synthesis of these requirements has shown that they are best supported through the implementation of visual interface display techniques. Chi-squared (χ ²) test was used to test the responses of children towards the usability of the new interface; the results showed a high level of acceptance by the children, recording a rate of 83.98 %.     

A PSO-based model to increase the accuracy of software development effort estimation

Development effort is one of the most important metrics that must be estimated in order to design the plan of a project. The uncertainty and complexity of software projects make the process of effort estimation difficult and ambiguous. Analogy-based estimation (ABE) is the most common method in this area because it is quite straightforward and practical, relying on comparison between new projects and completed projects to estimate the development effort. Despite many advantages, ABE is unable to produce accurate estimates when the importance level of project features is not the same or the relationship among features is difficult to determine. In such situations, efficient feature weighting can be a solution to improve the performance of ABE. This paper proposes a hybrid estimation model based on a combination of a particle swarm optimization (PSO) algorithm and ABE to increase the accuracy of software development effort estimation. This combination leads to accurate identification of projects that are similar, based on optimizing the performance of the similarity function in ABE. A framework is presented in which the appropriate weights are allocated to project features so that the most accurate estimates are achieved. The suggested model is flexible enough to be used in different datasets including categorical and non-categorical project features. Three real data sets are employed to evaluate the proposed model, and the results are compared with other estimation models. The promising results show that a combination of PSO and ABE could significantly improve the performance of existing estimation models.     

Selective Extraction of Lutein from Alcohol Treated Chlorella vulgaris by Supercritical CO2

A pretreatment process using alcohol for the removal of chlorophyll a, b and β‐carotene from Chlorella vulgaris was developed to improve the yield and selectivity of lutein in the extract obtained by supercritical carbon dioxide extraction. Supercritical carbon dioxide extraction was carried out after pretreatment in the pressure range of 20 to 40 MPa and the temperature range of 40 to 80 °C. Ethanol and methanol were selected as elution solvents, of which ethanol was found most suitable for the elution, or pretreatment, process. The amounts of lutein and other compounds were analyzed by HPLC with the mixture of methanol and THF as the mobile phase. The amount of lutein in the extract increased with pressure, but decreased with extraction temperature. The highest recovery percentage and the selectivity of lutein were around 52.9 ± 0.02 % and 43.1 ± 0.02 %, respectively, obtained from supercritical carbon dioxide extraction with pretreatment and ethanol entrainer at 40 MPa and 40 °C.     

Generating Type 2 Trapezoidal Fuzzy Membership Function Using Genetic Tuning

Fuzzy inference system (FIS) is a process of fuzzy logic reasoning to produce the output based on fuzzified inputs. The system starts with identifying input from data, applying the fuzziness to input using membership functions (MF), generating fuzzy rules for the fuzzy sets and obtaining the output. There are several types of input MFs which can be introduced in FIS, commonly chosen based on the type of real data, sensitivity of certain rule implied and computational limits. This paper focuses on the construction of interval type 2 (IT2) trapezoidal shape MF from fuzzy C Means (FCM) that is used for fuzzification process of mamdani FIS. In the process, upper MF (UMF) and lower MF (LMF) of the MF need to be identified to get the range of the footprint of uncertainty (FOU). This paper proposes Genetic tuning process, which is a part of genetic algorithm (GA), to adjust parameters in order to improve the behavior of existing system, especially to enhance the accuracy of the system model. This novel process is a hybrid approach which produces Genetic Fuzzy System (GFS) that helps to enhance fuzzy classification problems and performance. The approach provides a new method for the construction and tuning process of the IT2 MF, based on the FCM outcomes. The result is compared to Gaussian shape IT2 MF and trapezoid IT2 MF generated by the classic GA method. It is shown that the proposed approach is able to outperform the mentioned benchmarked approaches. The work implies a wider range of IT2 MF types, constructed based on FCM outcomes, and an optimum generation of the FOU so that it can be implemented in practical applications such as prediction, analytics and rule-based solutions.     

Oxygenation absorption and light scattering driven facial animation of natural virtual human

Multimedia Tools and Applications - The color of skin is one of the key indicators of bodily change that affect facial expressions. The skin colour tenacity is majorly determined by the light...