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Register a new userمساهمة في إيجاد بعض وسائل الوقاية من الإصابة بفيروس واي البطاطا على صنفي التبغ برلي وفرجينيا في سورية
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Tishreen University أطروحة دكتوراه
Publication date
2013
fields
Agricultural Engineering
and research's language is
العربية
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أثبتت نتائج اختبار بصمة النسيج النباتي المناعي (TBIA ) لعينات من اصناف التبغ المختلفة البالغ عددها 4145 عينة عشوائية و 1482 عينة انتقائية
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معطيات مديرية الارصاد الجوية في سوريا لعام 2010 و 2011
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فيروس البطاطا واي الفيروس الرئيس الأكثر وجوداً وتدميراً في معظم مناطق إنتاج محصول البطاطا .ويعد السبب الأهم في تدهور مرتبة بذار البطاطا, وانخفاض الإنتاجية .
This study was conducted in Tobacco fields in the Syrian coast to investigate Root-knot Nematode on two varieties of tobacco (Burley and Flue-cured). Two hundred and five samples of the roots of infected plants were collected from 32 fields in the pe
riod between early August and early October. The results showed that the degree of infestation was high (fifth degree) to Flue-cured tobacco (average was 179.20 knot for one sample), and fourth degree to the Burley tobacco (average 39.95 knot on one sample).
There were three species of Meloidogyne: M.javanica, it had the most frequency 46.25 % and 43.7 % of Burley and Flue-cured tobacco respectively, followed by M.incognita 26.25 % and 24.44% of samples respectively. These species were present together in less frequency on both studied varieties. Whereas the third specie M.arenaria only appeared in three sites of Flue-cured tobacco in less frequency (5.18 %) and was associated with M.incognita.
A survey of infection by Citrus tristeza virus (CTV) was conducted during a visit to 14 citrus orchards in different regions in Tartous governorate in the Syrian Coast during the spring of 2012. We collected a total of /691/ samples of different vari
eties. Most of the samples were collected from plants with symptoms similar to symptoms caused by viral diseases (dwarfing, yellowing, mosaic, quick- decline, vein clearing, boat or spoon-shaped leaves), while other samples did not carry such symptoms due to the possible presence of latent infections. The samples were tested by using Tissue Blot immunoassay (TBIA). The Results showed that the rate of infection by CTV was 34.15% in the tested samples and the distribution of CTV in citrus orchards was by various ratios. The highest percentage of infection was detected in AL- Hamidiah (62.50%), whereas the lowest infection percentage detected was in Talin nursery (10%). Common orange Balady was the most infected by Citrus tristeza virus (41.43%). No virus infection was recorded in Mandalina samples.
This research aimsto study the effect of single and mixed infection of Potato Y Virus (PVY) and Cucumber mosaic virus(CMV) on the number of leaves, number of branches, plant height and stem circumference of tomato plants (cv.Elegro and Local). The ex
periment was carried out in 2012 in a greenhouse in Tartous. Results show that the interactions in both varieties to virus infections are different. The mixed infections cause slight and weak effects compared to single infections. This is probably due to the antagonistic relationship between Potato Y virus and Cucumber mosaic virus and their effects on mean number of leaves, mean number of branches, mean height of plants, and mean stem circumference. Tomato plants' stems were (Elegro 106.42, 16.75, 103.58 cm, 4.84cm.; Local 94.42,15, 87.17 cm, 4.59 cm, respectively). Consequently, the timing of mixed infection playsarole in appearance, development and effect on other viruses.
Contemporary tobacco-related studies are mostly concerned with a single social media platform while missing out on a broader audience. Moreover, they are heavily reliant on labeled datasets, which are expensive to make. In this work, we explore senti
ment and product identification on tobacco-related text from two social media platforms. We release SentiSmoke-Twitter and SentiSmoke-Reddit datasets, along with a comprehensive annotation schema for identifying tobacco products' sentiment. We then perform benchmarking text classification experiments using state-of-the-art models, including BERT, RoBERTa, and DistilBERT. Our experiments show F1 scores as high as 0.72 for sentiment identification in the Twitter dataset, 0.46 for sentiment identification, and 0.57 for product identification using semi-supervised learning for Reddit.
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