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Afzal, M.,Zahid, Saleem Asian Australasian Association of Animal Productio 2004 Animal Bioscience Vol.17 No.7
Effects of addition of a mycotoxin detoxifier in poultry feed were studied in broilers. Aflatoxins were present in the poultry feed as 28 ppb (normal feed), 78 ppb (contaminated feed) and 170 ppb (highly contaminated feed). The mycotoxin detoxifier was used in 3 concentrations i.e. 1, 3 and 5 kg/ton of feed. Aflatoxins reduced the body weight in broiler chicken and treatment of contaminated feed with low level of detoxifier improved the body weight equivalent to that of normal feed. Higher level of detoxifier proved better than lower level addition in alleviating the effects of highly contaminated feed. Addition of detoxifier also resulted in improvement of FCR to the level of normal feed. Antibody levels against Newcastle disease virus on day 28 of age were significantly lower in chicken fed on contaminated feed. Addition of detoxifier in feed improved the antibody levels in chicken. Mortality was highest in groups given contaminated feed throughout the study period of 7 weeks. Significant mortality was also observed in groups given highly contaminated feed for 2 weeks. Mortality in chicken given detoxifier added contaminated feed was lowest and similar to the group given normal feed. The study shows that mycotoxin detoxifier containing oxyquinol, dichloro-thymol and micronized yeast can effectively neutralize the ill-effects of aflatoxins in poultry feed.
A deep learning approach for prediction of Parkinson’s disease progression
Afzal Hussain Shahid,Maheshwari Prasad Singh 대한의용생체공학회 2020 Biomedical Engineering Letters (BMEL) Vol.10 No.2
This paper proposes a deep neural network (DNN) model using the reduced input feature space of Parkinson’s telemonitoringdataset to predict Parkinson’s disease (PD) progression. PD is a chronic and progressive nervous system disorder that aff ectsbody movement. PD is assessed by using the unifi ed Parkinson’s disease rating scale (UPDRS). In this paper, fi rstly, principalcomponent analysis (PCA) is employed to the featured dataset to address the multicollinearity problems in the dataset andto reduce the dimension of input feature space. Then, the reduced input feature space is fed into the proposed DNN modelwith a tuned parameter norm penalty (L2) and analyses the prediction performance of it in PD progression by predictingMotor and Total-UPDRS score. The model’s performance is evaluated by conducting several experiments and the result iscompared with the result of previously developed methods on the same dataset. The model’s prediction accuracy is measuredby fi tness parameters, mean absolute error (MAE), root mean squared error (RMSE), and coeffi cient of determination (R 2 ). The MAE, RMSE, and R 2 values are 0.926, 1.422, and 0.970 respectively for motor-UPDRS. These values are 1.334, 2.221,and 0.956 respectively for Total-UPDRS. Both the Motor and Total-UPDRS score is better predicted by the proposed method. This paper shows the usefulness and effi cacy of the proposed method for predicting the UPDRS score in PD progression.
Energy Efficient Design of a Jet Pump by Ensemble of Surrogates and Evolutionary Approach
Afzal Husain,Arihant Sonawat,Sarath Mohan,Abdus Samad 한국유체기계학회 2016 International journal of fluid machinery and syste Vol.9 No.3
Energy systems working coherently in different conditions may not have a specific design which can provide optimal performance. A system working for a longer period at lower efficiency implies higher energy consumption. In this effort, a methodology demonstrated by a jet pump design and optimization via numerical modeling for fluid dynamics and implementation of an evolutionary algorithm for the optimization shows a reduction in computational costs. The jet pump inherently has a low efficiency because of improper mixing of primary and secondary fluids, and multiple momentum and energy transfer phenomena associated with it. The high fidelity solutions were obtained through a validated numerical model to construct an approximate function through surrogate analysis. Pareto-optimal solutions for two objective functions, i.e., secondary fluid pressure head and primary fluid pressure-drop, were generated through a multi-objective genetic algorithm. For the jet pump geometry, a design space of several design variables was discretized using the Latin hypercube sampling method for the optimization. The performance analysis of the surrogate models shows that the combined surrogates perform better than a single surrogate and the optimized jet pump shows a higher performance. The approach can be implemented in other energy systems to find a better design.
EFFECT OF VITAMIN E AND SELENIUM ON IMMUNITY IN NEWBORN JERSEY AND BUFFALO CALVES
Afzal, M.,Hussain, M.,Khan, K.N.M.,Munir, R. Asian Australasian Association of Animal Productio 1988 Animal Bioscience Vol.1 No.1
Effect of vitamin E and selenium supplementation on immunity was studied in newborn Jersey and buffalo calves. The supplement contained 500 mg vitamin E and $200\;{\mu}g$ selenium; and was fed daily from birth to day 30. Differences in weight gain, total leucocytic count, differential leucocytic count, antibody titre and susceptibility to disease were found to be nonsignificant between supplemented and control calves during the study period of 3 months. Vitamin E seemed to enhance the recovery from disease in buffalo calves. Buffalo calves were found to be more sensitive to selenium toxicity than Jersey calves.
Shape Optimization of Micro-channel Heat Sink for Micro-electronic Cooling
Afzal Husain(후세인아프잘),Kwang-Yong Kim(김광용) 한국유체기계학회 2006 유체기계 연구개발 발표회 논문집 Vol.- No.-
A numerical investigation of three dimensional fluid flow and heat transfer in a rectangular micro-channel has been carried out using water as a cooling fluid in a silicon substrate. Navier-Stokes and energy equations for laminar flow and conjugate heat transfer are solved using a finite volume solver. Solutions are first carefully validated with the experimental results available; the shape of the micro-channel is then optimized using surrogate methods. Ratios of width of the micro-channel to depth and width of the fin to depth are selected as design variables. Design points are selected through three level D-optimal designs. A single objective function thermal resistance, formulated using pumping power as constraint, is optimized. Response surface analysis, Kriging and Radial basis neural network methods are applied to construct surrogates and the optimum point is searched by sequential quadratic programming.
Afzal, Ramsha,Shim, Won-Sik The Korean Society of Applied Pharmacology 2017 Biomolecules & Therapeutics(구 응용약물학회지) Vol.25 No.5
Recent reports claimed that glucosylsphingosine (GS) is highly accumulated and specifically evoking itch-scratch responses in the skins of atopic dermatitis (AD) patients. However, it was unclear how GS can trigger itch-scratch responses, since there were no known molecular singling pathways revealed yet. In the present study, it was verified for the first time that GS can activate mouse serotonin receptor 2a (mHtr2a) and 2b (mHtr2b), but not 2c (mHtr2c) that are expressed in HEK293T cells. Specifically, effects of GS on all mouse serotonin receptor 2 subfamily were evaluated by calcium imaging techniques. The GS-induced intracellular calcium increase was dose-dependent, and antagonists such as ketanserin (Htr2a antagonist) and RS-127445 (Htr2b antagonist) significantly blocked the GS-induced responses. Moreover, the proposed GS-induced responses appear to be mediated by phospholipase C (PLC), since pretreatment of a PLC inhibitor U-73122 abolished the GS-induced responses. Additionally, the GS-induced calcium influx is probably mediated by endogenous TRPC ion channels in HEK293T cells, since pretreatment of SKF-96365, an inhibitor for TRPC, significantly suppressed GS-induced response. In conclusion, the present study revealed for the first time that GS can stimulate mHtr2a and mHtr2b to induce calcium influx, by utilizing PLC-dependent pathway afterwards. Considering that GS is regarded as a pruritogen in AD, the present study implicates a novel GS-induced itch signaling pathway.
Effects of Latin hypercube sampling on surrogate modeling and optimization
Afzal, Arshad,Kim, Kwang-Yong,Seo, Jae-won Korean Society for Fluid machinery 2017 International journal of fluid machinery and syste Vol.10 No.3
Latin hypercube sampling is widely used design-of-experiment technique to select design points for simulation which are then used to construct a surrogate model. The exploration/exploitation properties of surrogate models depend on the size and distribution of design points in the chosen design space. The present study aimed at evaluating the performance characteristics of various surrogate models depending on the Latin hypercube sampling (LHS) procedure (sample size and spatial distribution) for a diverse set of optimization problems. The analysis was carried out for two types of problems: (1) thermal-fluid design problems (optimizations of convergent-divergent micromixer coupled with pulsatile flow and boot-shaped ribs), and (2) analytical test functions (six-hump camel back, Branin-Hoo, Hartman 3, and Hartman 6 functions). The three surrogate models, namely, response surface approximation, Kriging, and radial basis neural networks were tested. The important findings are illustrated using Box-plots. The surrogate models were analyzed in terms of global exploration (accuracy over the domain space) and local exploitation (ease of finding the global optimum point). Radial basis neural networks showed the best overall performance in global exploration characteristics as well as tendency to find the approximate optimal solution for the majority of tested problems. To build a surrogate model, it is recommended to use an initial sample size equal to 15 times the number of design variables. The study will provide useful guidelines on the effect of initial sample size and distribution on surrogate construction and subsequent optimization using LHS sampling plan.