NEW ACID-BASIC INDICATORS: RESEARCH USING A SMARTPHONE

Modern cameras, desktop scanners, smartphones allow not only registering an image, but also determining its color characteristics. That allows us to quickly, objectively and automatically determine the color characteristics of colored samples in acid-base titration, because there is a significant error at visually determining the pH range of the color transition. In analytical chemistry the characteristics of acid-base indicators are very important, in particular their pH transition interval. But the disadvantage of most indicators is the wide range of color transition: from 1 to 3 pH units. The aim of this work is to find new acid-base indicators that change color in an alkaline environment and have a narrow pH range of the color transition. We have developed the apparatus and technique of convenient and highprecision simultaneous determination of the pH of the medium and the color of the acidbase indicators. In acid-base titration the PH measurements were performed with a combined glass electrode AD1131 by рН-meter AD1000. The color transition was determined with help of a smartphone with the subsequent processing of the results by computer software. The color characteristics were measured for each channel of the RGB model in the range from 0 to 255. Our apparatus is small and mobile, and allows us simultaneously to measure the pH of the medium and accurately to determine the color characteristics. As a result, we can construct graphical dependencies of color on pH for each channel of the RGB model. We found the N-arylsulfonyl-2-aroylamido-1,4-benzo(naphto)quinone monoimines and 2,5-dibenzoylamido-1,4-benzoquinone are good acid-base indicators. They “work” in the pH range from 8.82 to 11.35 and have a very narrow color transition interval from 0.10 to 0.61. Solutions of these compounds in an alkaline medium have bright intense colors due to formation of mesomeric ions. That allows using of these indicators in the titration of weak acids with strong bases and vice versa by the method of neutralization.

RGB Three-Channel SWE-Based Ultrasomics Model: Improving the Efficiency in Differentiating Focal Liver Lesions

ObjectiveTo explore a new method for color image analysis of ultrasomics and investigate the efficiency in differentiating focal liver lesions (FLLs) by Red, Green, and Blue (RGB) three-channel SWE-based ultrasomics model.MethodsOne hundred thirty FLLs were randomly divided into training set (n = 65) and validation set (n = 65). The RGB three-channel and direct conversion methods were applied to the same color SWE images. Ultrasomics features were extracted from the preprocessing images establishing two feature data sets. The least absolute shrinkage and selection operator (LASSO) logistic regression model was applied for feature selection and model construction. Two models, named RGB model (based on RGB three-channel conversion) and direct model (based on direct conversion), were used to differentiate FLLs. The diagnosis performance of the two models was evaluated by area under the curve (AUC), calibration curves, decision curves, and net reclassification index (NRI).ResultsIn the validation cohort, the AUC of the direct model and RGB model in characterization on FLLs were 0.813 and 0.926, respectively (p = 0.038). Calibration curves and decision curves indicated that the RGB model had better calibration efficiency and provided greater clinical benefits. NRI revealed that the RGB model correctly reclassified 7% of malignant cases and 25% of benign cases compared to the direct model (p = 0.01).ConclusionThe RGB model generated by RGB three-channel method yielded better diagnostic efficiency than the direct model established by direct conversion method. The RGB three-channel method may be promising on ultrasomics analysis of color images in clinical application.

Study on Combining Two Faster R-CNN Models for Landslide Detection with a Classification Decision Tree to Improve the Detection Performance

This study aims to improve the accuracy of landslide detection in satellite images by combining two object detection models based on a faster region-based convolutional neural network (Faster R-CNN) with a classification decision tree. The proposed method combines the predicted results from the two Faster R-CNN models and classifies their features with a classification decision tree to generate a bounding-box that surrounds the landslide area in the input image. The first Faster R-CNN model is trained by using a training set of color images (RGB images). The second model is trained by using grayscale images that represent digital elevation models (DEMs). The results from both models are used to construct features for training a classification decision tree. The resulting bounding-box is selected from the following four classes: the box obtained from the RGB model, the box obtained from the DEM model, the intersection of those two boxes, and the smallest box that contains the union of them. The evaluation results show that the proposed method is better than the RGB model in terms of accuracy, precision, recall, F-measure, and Intersection-over-Union (IoU) score. It is slightly better than the DEM model in almost all evaluation metrics, except the precision.

Response of Population Canopy Color Gradation Skewed Distribution Parameters of the RGB model to Micrometeorology Environment in Begonia Fimbristipula Hance

Background: The high quality and efficient production of greenhouse vegetation depend on the micrometeorology environmental adjusting such as the system warming, illumination supplement. In order to improve the quantity, quality and efficiency of greenhouse vegetation, it is necessary to figure out the relationship between the crop growth conditions and environmental meteorological factors, which could give constructive suggestions for precise control of greenhouse environment and reducing the running cost. The parameters from the color information of plant canopy reflect the internal physiological conditions, thus, RGB model has been widely used in the color analysis of digital pictures of leaves.Results: The color scale for single leaf, single plant, and the populate canopy of Begonia Fimbristipula Hance (BFH) photographs are all have a skewed cumulative distribution histograms. The color gradation skewness-distribution (CGSD) parameters of the RGB model were increased from 4 to 20 after the skewness analysis, which greatly expanded the canopy leaf color information and could simultaneously describe the depth and distribution characteristics of canopy color. The 20 CGSD parameters were sensitive to the micrometeorology factors, especially to the radiation and temperature accumulation. The multiple regression models of mean, median, mode and kurtosis parameters to microclimate factors were established, and the spatial models of skewness parameters were optimized.Conclusions: The models constructed based on the color gradation skewness-distribution (CGSD) parameters of the RGB model, can well explain the response of canopy color to microclimate factors and can be used to monitor the variation of plant canopy color under different micrometeorology.

GRADING WARNA DAUN TEMBAKAU BAWAH NAUNGAN MENGGUNAKAN JARINGAN SARAF TIRUAN. (Color Grading of Shaded Tobacco Leaves Using Artificial Neural Network)

Various cigars, which are present in the community among the elite and prestigious venues, the raw material is a Java Tabak cigars, tobacco from Java, especially Klaten and Jember. Recent years, the availability of labor more difficult with increasing costs skyrocketing, so it must start leading to mechanization. The purpose of this research was to Generate Design of Tobacco Leaf Analysis Applications, Getting Segmentation Model for pixel readout from tobacco leaves, Generate classification models that can be used for the separation of tobacco leaves which is expected to ease the process of evaluation and classification of color in the first sorting Tobacco leaves. Tobacco Leaf used is The Under Shade Tobacco leaf (TBN) consisted of five classes, namely the color Blue / Green (B), Yellow (K), Yellow Sprayed (KV), Red (M), Red Sprayed (MV). Before analyzed the leaves image photographed using a cabinet that unaffected the outside light. TBN leaf image is then analyzed using the RGB model and models HSV, RGB image of the model is analyzed using the characteristic leaf color values, The image of leaf TBN that meets the characteristics become an input of Bakcpropagation Neural Networks with the target are 5 color grade which converted into a binary form. The research resulted Segmentation Model for pixel readout TBN tobacco leaves using RGB models, classification model that can be used for the classification of TBN leaves use Neural Network Back Training RGB with an error value = 8.7%.”keywords : besuki tobacco, shaded tobacco, image processingABSTRAK Aneka cerutu, yang hadir di kalangan komunitas elit dan tempat-tempat yang prestisius, bahan bakunya adalah Java Tabak Cerutu, tembakau asal Jawa, khususnya Klaten dan Jember. Beberapa tahun belakangan ini, ketersediaan tenaga kerja semakin sulit den gan biaya yang semakin meroket, sehingga harus mulai mengarah ke mekanisasi. Tujuan Penelitian ini adalah menghasilkan Rancang Bangun Aplikasi Analisa Daun Tembakau, mendapatkan Model Segmentasi untuk pembacaan piksel daun tembakau, menghasilkan Model Klasifikasi yang dapat digunakan untuk Pemisahan daun tembakau,sehingga diharapkan dapat mempermudah proses evaluasi dan klasifikasi warna pada Sortasi I daun Tembakau. Daun Tembakau yang digunakan adalah Daun Tembakau Bawah Naungan (TBN) jenis besuki terdiri dari 5 kelas warna yaitu Biru / Hijau (B), Kuning (K), Kuning Tidak Merata (KV), Merah (M), Merah Tidak Merata (MV). Sebelum dianalisa citra daun difoto menggunakan cabinet yang tidak terpengaruh cahaya luar. Citra daun TBN tersebut kemudian dianalisa menggunakan model RGB, dari model RGB citra daun dianalisa menggunakan karakteristik nilai warna, citra daun TBN yang memenuhi karakteristik menjadi masukan Jaringan Saraf Tiruan Bakcpropagation dengan target 5 kelas warna yang sudah diubah menjadi bentuk biner. Penelitian menghasilkan Model Segmentasi untuk pembacaan piksel daun tembakau TBN menggunakan model RGB, Model Klasifikasi yang dapat digunakan untuk klasifikasi daun TBN menggunakan Neural Network Back PropagationTraining RGB dengan nilai error = 8.7%.Kata Kunci : tembakau besuki, tembakau bawah naungan, pengolahan citra