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Safa, S.,Soleimani, A.,Heravi Moussavi, A. Asian Australasian Association of Animal Productio 2013 Animal Bioscience Vol.26 No.5
To determine the effects of dry period (DP) length on milk yield, milk composition, some blood metabolites, complete blood count (CBC), body weight and score and follicular status, twenty five primiparous and multiparous Holstein cows were assigned to a completely randomized design with DP-60 (n = 13) and DP-20 (n = 12) dry period lengths. Cows in the DP-60 produced more milk, protein, SNF, serum non-esterified fatty acids (NEFA) and beta hydroxyl butyrate acid (BHBA) compared with cows in DP-20 ($p{\leq}0.05$). Serum glucose, blood urea nitrogen (BUN), urea, and glutamic oxaloacetic transaminase (GOT) were all similar among the treatments. Body Condition Score (BCS), body weight (BW), complete blood count (CBC) and health problems were similar between the treatments. Diameter of the first dominant follicle and diameter of the dominant follicle on d 14 were different among the treatments. Thus, results of this study showed that reducing the dry period length to DP-20 had a negative effect on milk production, milk composition and reproductive performance in Holstein dairy cows.
Safa M.,Pandian A.,Mohammad Gouse Baig,Reddy Sadda Bharath,Kumar K. Satish,Banu A. S. Gousia,Srihari K.,Chandragandhi S. 대한전기학회 2024 Journal of Electrical Engineering & Technology Vol.19 No.4
Cardiac disease analysis in big data is an emerging factor for human health protection against heart attacks. Most cardiovascular diseases lead to heart failure due to an imbalance of immunity and attention in health conditions. Hence, immunity-based feature analysis of patients’ records is essential to predict accurate results. The machine learning methods make predictions depending on the extended-lasting features to analyze the health data. But the marginal features expose non-relational feature observation to reduce the classifi cation prediction accuracy. We propose a Deep Spectral Time-Variant Feature Analytic Model (DSTV-FAM) using SoftMax Recurrent Neural Network (SMRNN) in a wireless sensor network to improve cardiac disease prediction accuracy. Initially, the IoT sensor devices collect the data from patient observation to validate the data transmission in route propagation. The collected data is organized as features in the collective dataset. The parts are initially preprocessed into the redundant dataset and estimate the Cardiac Immunity Infl uence Rate (CIIR) depending on the time-variant feature selection model. The estimated weights are marginalized as spectral features trained into the classifi ers. Further, Soft-Max Activation Function (SMAF) creates a logical function depending on the Cardiac Aff ection Rate (CAR). Then the trained, rational neurons are constructed into a Recurrent Neural Network (RNN) Feed-forward feature values using a classifi er and Rate of Disease Aff ection (RDA) by Class Type. The proposed structure yields high prescient exactness concerning order, accuracy, and review to help early treatment for early cardiovascular gamble expectation.
S. SAFA,M. MOJTAHEDZADEH LARIJANI,V. FATHOLLAHI,O. R. KAKUEE 성균관대학교(자연과학캠퍼스) 성균나노과학기술원 2010 NANO Vol.5 No.6
Hydrogen storage capacity of a carbon nanotube (CNT) sample is investigated using Elastic Recoil Detection Analysis (ERDA) at constant hydrogen uptake pressure of 5 bar and different adsorption temperatures within 30°C–500°C. The results of hydrogen concentration versus temperature revealed three distinct temperature intervals in which a certain adsorption or desorption mechanism is dominant. Moreover, the results showed that hydrogen storage capacity of CNTs at the applied conditions of pressure and temperature is about 0.1 wt.% which is well below the DOE requirements for a viable hydrogen storage system. The physidesorption activation energy is calculated using the Arrhenius plot to be 6 kJmol-1.
Strength prediction of rotary brace damper using MLR and MARS
I. Mansouri,M. Safa,Z. Ibrahim,O. Kisi,M.M. Tahir,S. Baharom,M. Azimi 국제구조공학회 2016 Structural Engineering and Mechanics, An Int'l Jou Vol.60 No.3
This study predicts the strength of rotary brace damper by analyzing a new set of probabilistic models using the usual method of multiple linear regressions (MLR) and advanced machine-learning methods of multivariate adaptive regression splines (MARS), Rotary brace damper can be easily assembled with high energy-dissipation capability. To investigate the behavior of this damper in structures, a steel frame is modeled with this device subjected to monotonic and cyclic loading. Several response parameters are considered, and the performance of damper in reducing each response is evaluated. MLR and MARS methods were used to predict the strength of this damper. Displacement was determined to be the most effective parameter of damper strength, whereas the thickness did not exhibit any effect. Adding thickness parameter as inputs to MARS and MLR models did not increase the accuracies of the models in predicting the strength of this damper. The MARS model with a root mean square error (RMSE) of 0.127 and mean absolute error (MAE) of 0.090 performed better than the MLR model with an RMSE of 0.221 and MAE of 0.181.
Vibration analysis of different material distributions of functionally graded microbeam
Youcef Tlidji,Mohamed Zidour,Kadda Draiche,Abdelkader Safa,Mohamed Bourada,Abdelouahed Tounsi,Abdelmoumen Anis Bousahla,S. R. Mahmoud 국제구조공학회 2019 Structural Engineering and Mechanics, An Int'l Jou Vol.69 No.6
In the current research paper, a quasi-3D beam theory is developed for free vibration analysis of functionally graded microbeams. The volume fractions of metal and ceramic are assumed to be distributed through a beam thickness by three functions, power function, symmetric power function and sigmoid law distribution. The modified coupled stress theory is used to incorporate size dependency of micobeam. The equation of motion is derived by using Hamilton’s principle, however, Navier type solution method is used to obtain frequencies. Numerical results show the effects of the function distribution, power index and material scale parameter on fundamental frequencies of microbeams. This model provides designers with guidance to select the proper distributions and functions.
Advance Rate Simulation for Hard Rock TBMs
Jamal Rostami,Ebrahim Farrokh,Chris Laughton,S. Safa Eslambolchi 대한토목학회 2014 KSCE JOURNAL OF CIVIL ENGINEERING Vol.18 No.3
The existing methods for estimating the advance rate of hard rock Tunnel Boring Machine (TBM) often involves estimation of themachine utilization in a direct or an indirect manner. These methods are based on empirical systems and are limited in their capacityfor incorporating new machine capabilities or including many of the geological features along the tunnel. As such, these models arenot used as often due to their shortcomings. The objective of this study is to offer some improvements by using a new approach forsimulating all activities. The basic idea in simulation techniques is to predict the duration of different activities based on theirrecorded time distributions from past case histories or from the early stages of a project. A simulation model contains a series ofoperations, which should be repeated for a certain number of cycles. In each cycle, the model selects an anticipated duration timefrom time distribution curves for each activity and by using a logical relationship between activities, being parallel or in series and thepossibility of activity overlaps, it assigns a duration for the whole cycle. This is then continued to finish the task at hand with requirednumber of cycles. In this case, the cycles are the TBM going through each stroke or penetration cycle. This study includes discussionof each activity or failure of each subsystem and assigned and related duration accounts for parallel activities, and offers a distributionof cycle time, and finally estimation of tunnel completion time. A comprehensive study was conducted to evaluate the requirementsfor California switch final location and also the work arrangements for locomotives and trains for different scenarios. Differentarrangements were programmed with Arena© simulation software using field data of a double shield TBM, which recently completeda water conveyance tunnel. The simulation results show a very good agreement with the actual values of TBM advance rate valuesand also show the possible variation of the time required for completion of different sections of this tunnel. The outcome of this studywas to establish a framework for arranging and modeling the main time components of TBM operation. This will provide a usefultool for developing reliable estimates of machine advance rate for specific site, ground condition, and machine type.
Erbium on MgO(100)/Ag(100) as candidate for single-atom-qubit
Stefano Reale,Jiyoon Hwang,Jeongmin Oh,Aparajita Singha,Safa L. Ahmed,Denis Krylov,Luciano Colazzo,Christoph Wolf,Carlo S. Casari,Alessandro Barla,Edgard Fernandes,Francois Patthey,Marina Pivetta,Stef 한국자기학회 2023 한국자기학회 학술연구발표회 논문개요집 Vol.33 No.1