Efek Variasi Penambahan Carbomer Terhadap Nilai pH, Viskositas dan Aktivitas Mikroba pada Sediaan Hand Sanitizer

Hand sanitizer is an antiseptic in gel form. The gel texture in hand sanitizers is caused by the carbomer which acts as a gelling agent. Therefore, this research was done to observe the effect of carbomer on the value of pH, viscosity, and microbial activity in hand sanitizers. As for testing pH using pH meters, testing viscosity was carried out using the viscometer method, while microbial activity was carried out using the Total Plate Number, Yeast Mold Number and microbial pathogen tests. Based on the data obtained, the carbomer greatly influences the increase in the viscosity of the hand sanitizer gel, the pH value was obtained at 6.0 - 7.06, while in the microbial activity test none of the microbes grew in each medium. Therefore, it can be concluded that the hand sanitizer with code P3 is the best sample

Some aspects of plate number estimation of plate heat exchangers (PHEs). A case study

For the proper estimation of the plate number (N) of a plate heat exchanger (PHE) – in addition to the flow rates and thermophysical properties of fluids –, an appropriate correlation is needed for convective heat transfer coefficient (α) calculation. When one does not have a criterial equation for the corresponding plate shape, we propose a selecting method for α. With the suggested relationships from literature, we calculate the plate number of a geometrically known, similar heat duty PHE and choose those relationships that give the same plate number with the known heat exchanger. In our case study, the plate number determined by any of the screened equations for whole milk preheating has almost the same value (n = 10 ± 1) regardless of the method used to solve the PHE model (plate efficiency and Nconverg or Kconverg convergence methods). For liquids’ thermophysical property estimation, we recommend averaging the values given by equations from literature, followed by equation fitting.

Capillary electrophoresis-UV analysis using silica-layer coated capillary for separation of seven phenolic acids and caffeine and its application to tea analysis

This work presents an innovative silica-layer coated capillary with comparison study of the silica-layer coated capillary and the fused-silica capillary for the separation of seven phenolic acids viz. p-hydroxyphenylacetic acid (PHPA), p-coumaric acid (PCA), p-hydroxybenzoic acid (PHBA), caffeic acid (CFA), (3,4-dihydroxyphenyl)acetic acid (DHPA), gallic acid (GLA), and 2,3,4-trihydroxybenzoic acid (THBA), together with caffeine (CF), by capillary electro-chromatography (CEC) and micellar electrokinetic chromatography (MEKC), respectively. The running buffer was 25.0 mM borate at pH 9.0, with addition of 50.0 mM sodium dodecyl sulfate for the MEKC mode. The non-coated capillary could not separate all seven phenolic acids by CEC or MEKC. This was achieved using the coated capillary for both CEC and MEKC. The innovative coated capillary with CEC had plate number N ≥ 2.0 × 104 m−1 and resolution Rs ≥ 1.6 for all adjacent pairs of peaks. The capillary was also able to separate GLA and THBA which are structural isomers. Although MEKC mode provided comparable efficiency and selectivity, the reduced EOF of the coated capillary led to longer separation time. The linear calibration range of the seven phenolic acids and caffeine were different but the coefficients of determinations (r2) were all > 0.9965. The precisions of the relative migration times and peak area ratios of analyte to internal standard were 0.1–1.8% and 1.8–6.8%, respectively. There were no statistical differences in the efficiency of separation of the phenolic acids and caffeine for three coated capillaries. It was applied to the analysis of caffeine and phenolic acids in brewed tea using tyramine as the internal standard. The tea samples were diluted prior to analysis by CEC. The separation was less than 15 min. Caffeine, gallic acid and p-coumaric acid were detected and quantified. Caffeine and gallic acid contents were 10.8–15.0 and 2.6–4.8 mg g−1 dry tea leaves, respectively. p-Coumaric acid was detected in only one of the samples with a content of 0.4 mg g−1. Percent recoveries of spiked diluted samples were 90 ± 9 to 106 ± 13%, respectively. Article highlights Silica-layer coated capillary is first reported for simultaneous separation of seven phenolic acids by non-MEKC analysis. Performance between coated, and non-coated capillaries with analysis by CEC and MEKC were compared. Plate number, resolution, capillary reproducibility, and electroosmotic flow mobility are investigated. Graphical abstract

An Intelligent License Plate Detection and Recognition Model Using Deep Neural Networks

One of the largest automotive sectors in the world is India. The number of vehicles traveling by road has increased in recent times. In malls or other crowded places, many vehicles enter and exit the parking area. Due to the increase in vehicles, it is difficult to manually note down the license plate number of all the vehicles passing in and out of the parking area. Hence, it is necessary to develop an Automatic License Plate Detection and Recognition (ALPDR) model that recognize the license plate number of vehicles automatically. To automate this process, we propose a three-step process that will detect the license plate, segment the characters and recognize the characters present in it. Detection is done by converting the input image to a bi-level image. Using region props the characters are segmented from the detected license plate. A two-layer CNN model is developed to recognize the segmented characters. The proposed model automatically updates the details of the car entering and exiting the parking area to the database. The proposed ALPDR model has been tested in several conditions such as blurred images, different distances from the cameras, day and night conditions on the stationary vehicles. Experimental result shows that the proposed system achieves 91.1%, 96.7%, and 98.8% accuracy on license plate detection, segmentation, and recognition respectively which is superior to state-of-the-art literature models.

POTENSI UBI JALAR PUTIH (Ipomoea batatas linneaus varietas) SEBAGAI MEDIA ALTERNATIF PERTUMBUHAN BAKTERI Staphylococcus aureus

Bacterial growth is highly dependent on nutrient sources, energy sources and environmental conditions. Nutrien agar is a medium that is often used for bacterial growth. The use of Nutrien agar requires a high cost, so it is considered to have no economic value. The use of alternative media is cheap, easy to obtain, and has a large source of nutrients for the growth of the required bacteria. White sweet potato (Ipomoea batatas linneaus variety) has a high carbohydrate content to be used as a carbon source for bacterial growth. The purpose of this study was to reveal the potential of white sweet potato (Ipomoea batatas linneaus variety) as an alternative medium for the growth of Staphylococcus aureus bacteria. This research uses experimental laboratory methods. Staphylococcus aureus growth was observed, and total plate number was calculated on nutrien agar and white sweet potato (Ipomoea batatas linneaus varieties) media. Based on the research results, Nutrien agar is small in size, from 0.1 cm to a yellow colour and round shape. In comparison, the white sweet potato (Ipomoea batatas linneaus variety) has a small size of 0.1 cm, milky white with a round shape. The results of the calculation of the otal plate number from the Nutrien agar medium was 1.6 X (104) CFU. In comparison, the total plate number from the white sweet potato (Ipomoea batatas linneaus variety) medium was 8.9 X (104) CFU. From the results of this study, it can be concluded that white sweet potato (Ipomoea batatas linneaus variety) can be used as an alternative medium for the growth of Staphylococcus aureus bacteria.

Indonesian Plate Number Identification Using YOLACT and Mobilenetv2 in the Parking Management System

A vehicle registration plate is used for vehicle identity. In recent years, technology to identify plate numbers automatically or known as Automatic License Plate Recognition (ALPR) has grown over time. Convolutional Neural Network and YOLACT are used to do plate number recognition from a video. The number plate recognition process consists of 3 stages. The first stage determines the coordinates of the number plate area on a video frame using YOLACT. The second stage is to separate each character inside the plat number using morphological operations, horizontal projection, and topological structural. The third stage is recognizing each character candidate using CNN MobileNetV2. To reduce computation time by only take several frames in the video, frame sampling is performed. This experiment study uses frame sampling, YOLACT epoch, MobileNet V2 epoch, and the ratio of validation data as parameters. The best results are with 250ms frame sampling succeed to reduce computational times up to 78%, whereas the accuracy is affected by the MobileNetV2 model with 100 epoch and ratio of split data validation 0,1 which results in 83,33% in average accuracy. Frame sampling can reduce computational time however higher frame sampling value causes the system fails to obtain plate region area.

An Illegal Dumping Detection System Based on Image Processing in OpenALPR

Tipping or depositing large waste onto land using unauthorized and unlicensed methods are considered as illegal dumping. The increasing rate of illegal dumping becomes a crucial nation issue because this activity causes negative impacts to social, economy and environment. Thus, study on detecting the dumping activities is conducted to control the illegal dumping activities in Malaysia. Raspberry Pi with Python language is used as the microprocessor and a Raspberry Pi camera module with a microwave radar sensor are interfaced to it to capture the image of any vehicles entering the illegal dumping site. The image is captured to recognize the license plate of the vehicle. The method in this study is by using Open Automatic License Plate Recognition (ALPR), Open Computer Vision (CV) libraries and Optical Character Recognition (OCR) to detect the character of the plate registration number. The outcome of the study consists of recognition of Malaysia vehicles’ plate number and the automatic real time email notification on the illegal dumping case. The detection system can be used for case monitoring since the plate number recognition is done in real time. The system can be upgraded to ensure its sustainability in the harsh and isolated environment.

The Study of Plate Number Recognition for Parking Security System

Car Plate Number Recognition System is an important platform that can be used to identify a car vehicle identity. The Recognition System is based on image processing techniques and computer vision. A webcam is used to capture an image of the car plate number from different distance, and the identification is conducted through four processes of stages: Image Acquisition Pre-processing, Extraction, Segmentation, and Character Recognition. The Acquisition Pre-processing stage is extracted the region of interest of the image. The image is captured by live video of the webcam, then converted to grayscale and binary image. The Extraction stage is extracted the plate number characters from binary image using a connected components method. In the Segmentation stage is done by implementing horizontal projection as well as moving average filter. Lastly, in the Character Recognition, is used to identify the segmented characters of the plate number using optical character recognition. The proposed method is worked well for Malaysian's private cars plate number, and can be implemented in car park system to increase level of security of the system by confirming the bar code of the parking ticket and the plate number of the car at the incoming and outgoing gates.