Barium titanate compounds have great research attention due to their good electric and in some case interesting magnetic properties. The synthesis and characterization of iron doped barium strontium titanate (BSFTO) make an attempt to understand its structure and investigate electric/dielectric properties. The formation of a perovskite compound with tetragonal phase was confirmed through X-ray structural studies. Dielectric and electrical impedance properties of the sintered BSFTO ceramics were measured in the frequency range from 42Hz to 2MHz and at different temperatures (up to 600?C). It was shown that the properties of this material are highly dependent on temperature and frequency. The nature of frequency dependence of AC conductivity confirms the Jonscher?s power law. The temperature dependence of DC conductivity obeys the Arrhenius behaviour.

Classification of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) by humans remains challenging. We proposed a highly accessible method to develop a deep learning (DL) model and implement the model for mosquito image classification by using hardware that could regulate the development process. In particular, we constructed a dataset with 4120 images of Aedes mosquitoes that were older than 12 days old and had common morphological features that disappeared, and we illustrated how to set up supervised deep convolutional neural networks (DCNNs) with hyperparameter adjustment. The model application was first conducted by deploying the model externally in real time on three different generations of mosquitoes, and the accuracy was compared with human expert performance. Our results showed that both the learning rate and epochs significantly affected the accuracy, and the best-performing hyperparameters achieved an accuracy of more than 98% at classifying mosquitoes, which showed no significant difference from human-level performance. We demonstrated the feasibility of the method to construct a model with the DCNN when deployed externally on mosquitoes in real time.

This project is to investigate the counter electrode material from two different carbon sources to fabricate dye sensitized solar cell (DSSC). The carbon sources are extracted from battery and pencil lead. The method to prepare the DSSC is through the conventional Dr.Blading method. The same method is also used for the counter electrode which uses carbon from recycle batteries, the carbon from pencil lead are scribbled onto the ITO glass to get a uniform coating. Both thickness of the counter electrode vary accordingly. The solar cells are then placed under outdoor solar irradiation and the output is taken every 10 minutes. Based on observation, the solar cells which have the carbon from batteries shows higher cell efficiency which is 8.2 % with lower FF of 0.78, compared to by using the pencil lead, the cell efficiency is only 7.23% but with a higher FF of 0.93.

Measurement the outdoor efficiency of photovoltaic (PV) panels is essential, but it is not likely an exceptional circumstance at any given moment is always repeating itself. A solar simulator was designed and fabricated for the purpose of analyzing the performance of PV panel with and without an air cooling mechanism in indoor test. Twenty units of 500 W halogen lamps with build-in reflector support by the steel structure holder act as a natural sunlight. The uniformity of the solar radiation was measured in the test area. Two units of PV panel with same characteristics were experimental in three sets of uniformity of solar radiation, which are 620, 821 and 1016 W/m². The operating temperature of PV panel with an air cooling mechanism can be decreased 2-3 ˚C compared to PV panel reference. The PV panel with an air cooling mechanism can be increased in 3-7 % of maximum power output based on solar radiation. An overall method and procedure of the measurement by the solar simulator are discussed and proposed.

Renewable energy is rapidly gaining importance as an energy resource to help aid the national energy depletion crisis of fossil fuel and coal. One of the most potential renewable energy sources in Malaysia is hydropower followed by solar energy. This paper presents the fabrication of Dye Sensitized Solar Cell (DSSC) using organic dyes from dragon fruit and chlorophyll which is extracted from spinach. The fabrication of DSSC uses the Dr.blade method. Result shows that the efficiency by using dragon fruit as sensitizer at 40µm TiO2 Thickness is 6.45%, better than the usage of chlorophyll dye which is 4.23% at the same thickness. Result also shows that at 80µm by using the dyes from chlorophyll extract has higher solar cell efficiency compare to dragon fruit. This shows that both the chlorophyll extract and dragon fruit shows potential in the development of a feasible working organic dye.

<p>Hibiscus Sabdariffa L. well known as Roselle flower was used as sensitizers for Dye-Sensitized Solar Cell (DSSC). The dyes were extracted using distilled water (DI) and ethanol (E) extract solvent in an ultrasonic cleaner for 30 minutes with a frequency of 37 Hz by using ‘degas’ mode at the temperature of 30°. Doctor blade method was applied in the fabrication of titanium dioxide (TiO₂) on ITO glass. Absorption spectra of Roselle dye with different extract solvent were tested using Evolution 201 UV-Vis Spectrophotometer. Fourier-Transform Infrared (FTIR) was used to identify the functional active group in extract dye. Based on FTIR result, the broad absorption at peak 2889 cm-¹, 2976 cm-¹, and 3366 cm-¹ attributed to the O-H stretching which is the presence of hydroxyl group. The use Field Emission Scanning Electron Microscopy (FESEM) and Energy-Dispersive Spectroscopy (EDS) analysis are to characterize the surface morphology and element in the TiO₂ thin film.</p>

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BACKGROUND Public community engagement is crucial for mosquito surveillance programs. To support community participation, one of the approaches is assisting the public in recognizing the mosquitoes that carry pathogens. Therefore, this study aims to build an automatic recognition system to identify mosquitos at the public community level. We construct a customized image dataset consisting of three mosquito species in either damaged or un‐damaged body conditions. To distinguish the mosquito in harsh conditions, we explore two state‐of‐the‐art deep learning (DL) architectures: (i) a freezing convolutional base, with partial trainable weights, and (ii) training the entire model with most of the trainable weights. We project a weighted feature map on different layers of the model to visualize the morphological region used by the model in classification and compared it with the morphological key used by the expert. RESULT It was found that the model with architecture two and the Adam optimizer achieves at least 98% accuracy in mosquito and conditions identification and when implemented on an independent dataset, the Xception model generalizes the best result with an accuracy of 0.7775 and 0.795 precision. Moreover, most of the morphological regions used by the model are able to match those of the human expert. CONCLUSION We report a customized DL model for performing pest mosquito taxonomy identification, and through visualization, some regions using computers to discriminate mosquito species could be adopted later in systematic identification. © 2022 Society of Chemical Industry.