Journal of the Austrian Society of Agricultural Economics (JASAE) (ISSN:18158129, E-ISSN:18151027)

Aim and Scope

Aim-

Journal of the Austrian Society of Agricultural Economics (JASAE) is an Open Access International Journal Which Aims to Publish High-quality Scientific Articles in the Field of Horticulture, Agriculture and Soil Science, Agronomy; Biology; Economics Academic Field: Mathematical and Statistical Methods in Economics; Agriculture and Animal Husbandry; Forestry and Many More. Our Aim is to Give an Open Space to Scientists Who Can Publish and Deliver Scientific Knowledge About the Relevant Field for the People in the Society. Lizi Jiaohuan Yu Xifu/Ion Exchange and Adsorption Fa yi xue za zhi

Scope-

Journal of the Austrian Society of Agricultural Economics (JASAE) is a peer-reviewed journal. The journal seeks to publish original research articles that are hypothetical and theoretical in its nature and that provide exploratory insights in the following fields but not limited to:

Horticulture Agriculture Soil Science Agronomy
Biology Economics Biotechnology Agricultural chemistry
Soil development in plants aromatic plants subtropical fruits
Green house construction Growth Horticultural therapy Entomology
Medicinal Weed management in horticultural crops plant Analysis Tropical

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Latest Journals
Journal of the Austrian Society of Agricultural Economics (JASAE)
Journal ID : JASAE-28-11-2022-192
Total View : 188

Abstract : Cultivation practices are considered as the main factors which affect the product quantity and quality of all plants. The aim of this paper is to present how the cluster thinning contribute on yield features and wine quality of the autochthonous Black Shesh red wine grapevine cultivar. The study was conducted during three consecutive years, 2019-2021. A RCBD with three replications and three variants with a plot size of 10 vines for variant was used. Three cluster thinning (CT) treatments were applied: control (no CT), 25% CT, and 50% CT. Cluster thinning was applied at berries pea-size (stage 75), consisting on removing the upper clusters on shoots. Leaf area per vine, number of clusters per vine, yield per plant, cluster weight, berry weight, TSS, TA, pH of must and wine, alcohol fraction for volume, wine color intensity, total phenolics, total and individual anthocyanin content, were recorded for a 3-year period. There was observed that two CT treatments significantly accelerated grape ripening by 5 and 9 days, reduced grape production per plant, raised cluster weight and berry weight, raised TSS content in must, while TA was reduced. Wine produced form CT treatments showed higher color intensity, higher alcohol content per volume, lower acidity, higher total and individual anthocyanin content. The highest quality wine, considering alcohol content per volume, total phenolics content, total and individual anthocyanin content, was achieved by 50% CT..
Full article
Journal of the Austrian Society of Agricultural Economics (JASAE)
Journal ID : JASAE-24-11-2022-191
Total View : 6

Title : Analysis of Potential Of Indonesian Craft Exports
by Sri Herliana, Donald Crestofel Lantu, Mia Rosmiati, Rendra Chaerudin, Nur Lawiyah,
Abstract : The craft industry is an industry that is one of the contributors to the creative economy. The craft industry comes from cultural wealth, natural resources, craftsman creativity, and awareness of market potential. However, in its development, the Indonesian craft industry is still not optimally developed for the export market. This study will examine the factors that cause the role of the craft industry to be not optimal in foreign markets. The scope of this research is craft SMEs spread across 16 cities/districts in West Java. Primary data was obtained from distributing questionnaires to MSMEs in the craft sector and supported by the acquisition of secondary data. For deeper analysis, fishbone analysis, porter analysis, and swot-tows analysis were used in this study..
Full article
Journal of the Austrian Society of Agricultural Economics (JASAE)
Journal ID : JASAE-01-11-2022-185
Total View : 8

Abstract : Kalamansi orange is the main and superior commodity from Bengkulu Province, Indonesia. This orange has been used by the community as a source of beverages managed by local industries. However, there is no data reporting the chemical compounds in kalamansi oranges from Bengkulu Province. The purpose of this study was to analyze the chemical compounds detected in the extract of kalamansi orange from Bengkulu Province using LC-QTOF-MS/MS. Measurements with LC-QTOF-MS/MS carried out in the Laboratory of PT. Saraswanti Indo Genetech (SIG), Bogor, Indonesia. The results of this study show that the extract of kalamansi orange contains alkaloid compounds in the form of nortopane and cadambine (2α, 3β, 6exo-trihydroxy nortropane, 2α, 3β, dihydroxy nortropane and 3α-dihydrocadambine). In addition, the kalamansi orange extract also has 19 bioactive compounds from the flavonoid group. The results of the analysis of vitamin C levels in the orange extract using High Performance Liquid Chromatography (HPLC) showed that the vitamin C content in the extract was 0.04 ± 0.0002%. Information about the bioactive compounds in orange extract is expected to be the first step for greater utilization for this typical citrus commodity of Bengkulu Province..
Full article
Journal of the Austrian Society of Agricultural Economics (JASAE)
Journal ID : JASAE-20-10-2022-181
Total View : 9

Title : Quantitative Analysis of the Breast Mass characteristics and Image Textures on Ultrasound Images
by Le Thanh Luc, Le Nhat Tan, Huynh Thi Khanh Nhi, Pham Tan Thi,
Abstract : Analysis of breast mass characteristics via ultrasound images plays an important role in cancer screening and treatment planning. Currently, Radiomics is utilized as a useful quantitative tool for medical image analysis. In this work, there are a total of 96 Radiomics features of biological characteristics and image textures from 3 main groups were extracted to analyze. The values and importance values of features and feature groups were quantified by statistical methods. Based on these results, the differences in tumor characteristics and image textures between benign and malignant breast masses were analyzed in depth, which can reveal the biological properties discrimination among groups. Moreover, several classification models, such as the Support Vector Machine and Ensemble learning model, and an oversampling method were utilized to confirm the discriminant performance via Radiomics-based features in the limited-amount dataset. The statistical feature analysis results extensively reveal the biological characteristics and texture differences between benign and malignant tumors. This result orient the further feature extraction process, in order to improve the classification and biological analysis of tumors. In addition, the classification results of the ensemble learning approaches with oversampling technique presented the highest performance, with the F1 score of 87% for benign tumors and 65% for malignant tumors, although training in a small number of images..
Full article
Journal of the Austrian Society of Agricultural Economics (JASAE)
Journal ID : JASAE-20-10-2022-180
Total View : 7

Abstract : Early detection and diagnosis of breast cancer is the key to controlling, curing this disease as well as reducing costs for the patient. AI-based computer-aided diagnosis (CAD) systems designed to help physicians make faster and more accurate decisions, convolutional neural network (CNN) has earned many achievements in the medical field. However, the performance of the CNN model is highly dependent on the quantity and quality of the input data sets, which is a big challenge for medical imaging because the image collection is very limited. To solve this problem, Deep Convolutional Generative Adversarial Network (DCGAN) is proposed to generate breast tumor images from the original breast ultrasound image BUSI dataset consisting of 437 benign masses and 210 malignant tumors. Then, in order to determine the performance of the proposed model, the newly created images combined with the images in the original data will be evaluated qualitatively, visually and quantitatively through the classification problem by feeding to 2 classification models: simple self-designed and Densenet to evaluate their quality and clinical value. The classification performance using only the original data yielded a sensitivity of 37% and an F1-score of 51% then increased to a sensitivity of 81% and an F1-score of 70%. The results show that breast tumor images generated from the DCGAN network can be used to significantly increase the efficiency thus has the potential to assist physicians in medical image reading task..
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