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| 1 | Cummlative Results of chemosensitivity tests for antitumor agents in Japan显示文摘 | Tsuhei Kon Do Tetsuro Kubota Hiroshi Tanim Ura | 2000 | Cancer Research2000，20，： | 1 |
| 2 | Modeling a 5 kWe planar solid oxide fuel cell based system operating on JP-8 fuel and a comparison with tubular cell based system for auxiliary and mobile power applications显示文摘 | TANIM T BAYLESS D J TREMBLY J P | 2014 | Journal of Power Sources2014，245，： | 1 |
| 3 | Modeling of a 5 kWe tubular solid oxide fuel cell based system operating on desulfurized JP-8 fuel for auxiliary and mobile power applications显示文摘 | TANIM T BAYLESS D J TREMBLY J P | 2013 | Journal of Power Sources2013，221，： | 1 |
| 4 | A permeameter for unsaturated soil显示文摘 | TANIME | 1991 | Transport in Porous Media1991，6，： | 1 |
| 5 | Indication and procedure for treatment of hepatolithiasis显示文摘 | Onishi H TaniM | 2002 | Arch Surg2002，137，2： | 1 |
| 6 | Effecs of Losartan, a nonpeptides angiotensin 11 recep to r antagonist, on cardiac hypertrophy and the tissue angiotensin II content in SHR 显示文摘 | Mizuno K TaniM Hashimoto S | 1992 | Life Science1992，515，： | 1 |
| 7 | 显示文摘 | Sorensen TL TaniM JensenJ etal | 1999 | J Clin Invest1999，103，6： | 1 |
| 8 | A new method of measuring bilirubin in serum by Vanadic acid显示文摘 | Tokuda K Tanim otok | 1993 | Jpn J Clin Chem (in Japanes with English abstract)1993，22，： | 1 |
| 9 | A new method of measuring bilirubin in serum by Vanadic acid显示文摘 | Tanim otok | 1993 | Jpn J Clin Chem(in Japanes with English abstract)1993，22，： | 1 |
| 10 | Anew methodofendo-scopicmucosalresectionofneoplasticlesionsinthestomach:itstechnicalfeaturesandresults显示文摘 | TakeshitaK TaniM InoueH etal | 2007 | HepatogastroEnterol2007，44，： | 1 |
| 11 | Endospictreatmentofearlyoesophagealorgastriccancer显示文摘 | TakeshitaK TaniM ZnoueH etal | 2013 | Gut2013，40，： | 1 |
| 12 | Possibilities of urban flood reduction through distributed-scale rainwater harvesting显示文摘Urban flooding in Chittagong City usually occurs during the monsoon season and a rainwater harvesting(RWH)system can be used as a remedial measure.This study examines the feasibility of rain barrel RWH system at a distributed scale within an urbanized area located in the northwestern part of Chittagong City that experiences flash flooding on a regular basis.For flood modeling,the storm water management model(SWMM)was employed with rain barrel low-impact development(LID)as a flood reduction measure.The Hydrologic Engineering Center's River Analysis System(HEC-RAS)inundation model was coupled with SWMM to observe the detailed and spatial extent of flood reduction.Compared to SWMM simulated floods,the simulated inundation depth using remote sensing data and the HEC-RAS showed a reasonable match,i.e.,the correlation coefficients were found to be 0.70 and 0.98,respectively.Finally,using LID,i.e.,RWH,a reduction of 28.66%could be achieved for reducing flood extent.Moreover,the study showed that 10%e60%imperviousness of the subcatchment area can yield a monthly RWH potential of 0.04e0.45 m3 from a square meter of rooftop area.The model can be used for necessary decision making for flood reduction and to establish a distributed RWH system in the study area. | Aysha Akter Ahad Hasan Tanim MdKamrul Islam | 2020 | Water Science and Engineering2020，13，2： | 1 |
| 13 | Metabolicandfunctionalrele-vanceofHDLsubspecies显示文摘 | AsztalosBF TaniM SchaeferEJ | 2011 | CurrOpinLipidol2011，22，3： | 1 |
| 14 | Deep learning long short-term memory combined with discrete element method for porosity prediction in gravel-bed rivers显示文摘The porosity of gravel riverbed material often is an essential parameter to estimate the sediment transport rate,groundwater-river flow interaction,river ecosystem,and fluvial geomorphology.Current methods of porosity estimation are time-consuming in simulation.To evaluate the relation between porosity and grain size distribution(GSD),this study proposed a hybrid model of deep learning Long Short-Term Memory(LSTM)combined with the Discrete Element Method(DEM).The DEM is applied to model the packing pattern of gravel-bed structure and fine sediment infiltration processes in threedimensional(3D)space.The combined approaches for porosity calculation enable the porosity to be determined through real time images,fast labeling to be applied,and validation to be done.DEM outputs based on the porosity dataset were utilized to develop the deep learning LSTM model for predicting bed porosity based on the GSD.The simulation results validated with the experimental data then segregated into 800 cross sections along the vertical direction of gravel pack.Two DEM packing cases,i.e.,clogging and penetration are tested to predict the porosity.The LSTM model performance measures for porosity estimation along the z-direction are the coefficient of determination(R^(2)),root mean squared error(RMSE),and mean absolute error(MAE)with values of 0.99,0.01,and 0.01 respectively,which is better than the values obtained for the Clogging case which are 0.71,0.14,and 0.03,respectively.The use of the LSTM in combination with the DEM model yields satisfactory results in a less complex gravel pack DEM setup,suggesting that it could be a viable alternative to minimize the simulation time and provide a robust tool for gravel riverbed porosity prediction.The simulated results showed that the hybrid model of the LSTM combined with the DEM is reliable and accurate in porosity prediction in gravel-bed river test samples. | Duong Tran Anh Ahad Hasan Tanim Daniel Prakash Kushwaha Quoc Bao Pham Van Hieu Bui | 2023 | International Journal of Sediment Research2023，38，1： | 0 |
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