Institution
Islamic Azad University North Tehran Branch
Education•Tehran, Iran•
About: Islamic Azad University North Tehran Branch is a education organization based out in Tehran, Iran. It is known for research contribution in the topics: Adsorption & Catalysis. The organization has 868 authors who have published 968 publications receiving 9987 citations.
Topics: Adsorption, Catalysis, Hydrogen bond, Aqueous solution, Stock exchange
Papers published on a yearly basis
Papers
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23 Jul 2011
TL;DR: The striata of Scrophularia striata Boiss is characterized by its ability to “persistently reprogram” its constituent cells to secrete non-volatile substance such as polypeptide A and polymethine.
Abstract: گونه Scrophularia striata Boiss. متعلق به تیره Scrophulariaceae بوده که در استان لرستان با نام محلی تشنه دارو معروف است. این گیاه از دامنه کوههای شمال غرب شهرستان کوهدشت در منطقه تنگسیاب واقع در استان لرستان جمعآوری گردید و پس از خشک کردن گیاه در سایه، اسانسگیری از بخشهای هوایی آن شامل ساقه، برگ و میوه با روش تقطیر با آب انجام شد. شناسایی ترکیبهای موجود در اسانس به وسیله کروماتوگراف گازی (GC) و کروماتوگراف گازی متصل به طیفسنج جرمی (GC/MS) انجام گردید. مطالعات آناتومیکی نیز توسط روش رنگآمیزی مضاعف با استفاده از دو رنگ سبز متیل و قهوهای بیسمارک انجام شد. آنالیز اسانس S. striata منجر به شناسایی 34 ترکیب شد که 3/90% از کل اسانس را شامل میشود. نتایج این بررسی نشان داد که لینالول (3/18%)، 6، 10، 14- تریمتیل پنتا دکان- 2- اون (4/8%)، دیبوتیل فتالات (9/6%) و بتا-داماسکون (9/5%) مهمترین ترکیبهای تشکیلدهنده اسانس را شامل میشوند. از طرف دیگر، بررسیهای آناتومیکی نیز نشان داد که در زیر اپیدرم و در منطقه پوست ایدیوبلاستهای ترشحکننده اسانس و ترکیبهای ترپنوئیدی مشاهده میشوند.
10 citations
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TL;DR: Dispersive liquid-liquid microextraction method (DLLME) combined with high-performance liquid chromatography-ultraviolet detection (HPLC-UV) was used to determine thiamine, nicotinamide and pyridoxine in sour cherry juice, which was rapid, simple and sensitive.
Abstract: Dispersive liquid-liquid microextraction method (DLLME) combined with highperformance liquid chromatography-ultraviolet detection (HPLC-UV) was used to determine thiamine (B 1 ), nicotinamide (B 3 ) and pyridoxine (B 6 ) in sour cherry juice. This method was rapid, simple and sensitive. Separation was accomplished using a C 18 column. The optimum chromatographic conditions were found to be: mobile phase consisted of 8% methanol and 92% aqueous phase (1% (V/V) acetic acid water solution); flow rate, 0.7 mL/min; detection wavelength, 260 nm and pH, 3.3. The extraction efficiency of thiamine, nicotinamide and pyridoxine was influenced by factors such as: additional salt effect, the kind and volume of disperser and extraction solvents. In this research, the limit of detection (LOD) and quantification (LOQ) were 0.9 and 3 ng/mL for thiamine, 1.5 and 5 ng/mL for nicotinamide, 0.9 and 3 ng/mL for pyridoxine. The relative standard deviations (RSDs) were less than 2.87% (n=3). An appropriate linear behavior over the observed concentration range was obtained with the value of R²>0.996 for the target vitamins. This method was successfully applied to the sour cherry juice samples. Sour cherry var. Gise (Prunus cerasus var. Gise), which was used in this research, was a local variety of the sour cherry with large stone, double flowers, double fruits, dark red skin and dark red juice. This variety was identified in high altitude areas of Isfahan province after five years of study, since 2005, by Agricultural and Natural Resources Research Center of Isfahan.
10 citations
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TL;DR: The authors proposed two methods to address this task and introduced a novel dataset named pn-summary for Persian abstractive text summarization, i.e., mT5 and an encoder-decoder version of the ParsBERT model.
Abstract: Text summarization is one of the most critical Natural Language Processing (NLP) tasks. More and more researches are conducted in this field every day. Pre-trained transformer-based encoder-decoder models have begun to gain popularity for these tasks. This paper proposes two methods to address this task and introduces a novel dataset named pn-summary for Persian abstractive text summarization. The models employed in this paper are mT5 and an encoder-decoder version of the ParsBERT model (i.e., a monolingual BERT model for Persian). These models are fine-tuned on the pn-summary dataset. The current work is the first of its kind and, by achieving promising results, can serve as a baseline for any future work.
10 citations
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01 Dec 2007TL;DR: In this article, the authors suggest a conceptual model about knowledge flow in supply chain and show how knowledge creation activities create value in the supply chain, instead of material, money and information flows.
Abstract: In this paper we consider the knowledge flow in supply chain instead of material, money and information flows. The purpose of the paper is to suggest a conceptual model about knowledge flow in supply chain. We shows in this paper how KM activities create value in supply chain. In our perspective supply chains are competing to capture knowledge from environment and use it to gain competitiveness. Different partners in supply chain collaborate in knowledge creation; share and use and compete with others in the rate of externalization of the knowledge in their organization.
10 citations
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TL;DR: In this paper, the authors presented the solution of Dirac equation with an attractive potential in the presence of a Yukawa-like tensor interaction and obtained the bound-state energy spectra and the radial wave functions in the case of spin and pseudospin symmetries.
10 citations
Authors
Showing all 877 results
Name | H-index | Papers | Citations |
---|---|---|---|
Mohammad A. Behnajady | 38 | 107 | 5014 |
Mohammad Ramezanzadeh | 36 | 93 | 3595 |
Bahram Kazemi | 34 | 395 | 4333 |
Hossein Aghabozorg | 25 | 191 | 1977 |
Morteza Asghari | 23 | 91 | 2149 |
Akbar Esmaeili | 23 | 123 | 1979 |
Ahmad Majd | 21 | 165 | 1395 |
Mohammad Yari | 21 | 40 | 957 |
Amirhossein Amiri | 21 | 157 | 2451 |
Ali-Akbar Salari | 19 | 53 | 1101 |
Mahmoud Reza Sohrabi | 19 | 105 | 1348 |
Ebrahim Abouzari-Lotf | 19 | 93 | 1209 |
Nahid Ghasemi | 18 | 67 | 1270 |
Mohammad Rabbani | 18 | 54 | 2878 |
Hossein Golnabi | 18 | 98 | 1076 |