Search results for: A. Razzaghi

Authors: F. Halabchi, A. Esteghamati, A. Razzaghi, A. Noori

Abstract:

Nowadays, doping is an intricate dilemma. Wrestling is the nationally popular sport in Iran. Also the prevalence of doping may be high, due to its power demanding characteristics. So, we aimed to assess the knowledge and attitudes toward doping among the club wrestlers. In a cross sectional study, 426 wrestlers were studied. For this reason, a researcher made questionnaire was used. In this study, researchers selected the clubs by randomized clustered sampling and distributed the questionnaire among wrestlers. Knowledge of wrestlers in three categories of doping definitions, recognition of prohibited drugs and side effects was poor or moderate in 70.8%, 95.8% and 99.5%, respectively. Wrestlers have poor knowledge in doping. Furthermore, they believe some myths which are unfavorable. It seems necessary to design a comprehensive educational program for all of the athletes and coaches.

Keywords: Attitude, Doping, Knowledge, Wrestling

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Authors: E. H. N. Gashti, J. Razzaghi, K. Kujala

Abstract:

In general, codes and regulations consider seismic loads only for completed structures of the bridges while, evaluation of incomplete structure of bridges, especially those constructed by free cantilever method, under these loads is also of great importance. Hence, this research tried to study the behavior of incomplete structure of common bridge type (box girder bridge), in construction phase under vertical seismic loads. Subsequently, the paper provided suitable guidelines and solutions to resist this destructive phenomenon. Research results proved that use of preventive methods can significantly reduce the stresses resulted from vertical seismic loads in box cross sections to an acceptable range recommended by design codes.

Keywords: Box girder bridges, Prestress loads, Free cantilever method, Seismic loads, Construction phase.

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Authors: Leila Baghaarabani, Parvin Razzaghi, Mennatolla Magdy Mostafa, Ahmad Albaqsami, Al Warith Al Rushaidi, Masoud Al Rawahi

Abstract:

Predicting the interaction between drugs and their molecular targets is pivotal for advancing drug development processes. Given the time and cost constraints, computational approaches have emerged as an effective approach to drug-target interaction (DTI) prediction. While most existing computational methods use drug molecules and protein sequences as inputs, this study goes further by introducing a protein representation developed using a masked protein language model. In this representation, each amino acid residue in the protein sequence is assigned a probability distribution, reflecting the likelihood of that residue occupying a specific position. The similarity between amino acid pairs is then calculated to generate a similarity matrix. To leverage this matrix, the study employs Bi-Level Routing Attention (BiFormer), a model that integrates transformer-based architectures with protein sequence analysis, representing a significant advancement in DTI prediction. BiFormer identifies the most critical regions of the protein sequence responsible for interactions with drugs, thereby deepening our understanding of these interactions. This approach demonstrates its ability to capture the local structural relationships within proteins and enhance the accuracy of DTI predictions. The proposed method was evaluated on two widely recognized datasets, Davis and KIBA, through comprehensive experiments that showcased its effectiveness compared to state-of-the-art techniques.

Keywords: BiFormer, transformer, protein language processing, self-attention mechanism, binding affinity, drug target interaction, similarity matrix, protein masked representation, protein language model.

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