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| 1 | Optimizing the aeration performance of a perforated pooled circular stepped cascade aerator using hybrid ANN-PSO technique显示文摘Artificial aeration system for aquaculture ponds becomes essential to meet the oxygen requirement posed by the aquatic species.The performance of an aerator is generally mea-sured in terms of standard aeration efficiency(SAE),which is significantly affected by the different geometric and dynamic parameters of the aerator.Therefore,to enhance the aer-ation performance of an aerator,these parameters need to be optimized.In the present study,a perforated pooled circular stepped cascade(PPCSC)aerator was developed,and the geometric and dynamic parameters of the developed aerator were optimized using the hybrid ANN-PSO technique for maximizing its aeration efficiency.The geometric parameters include consecutive step width ratio(W_(i-1)/W_(i))and the perforation diameter to the bottom-most radius ratio(d/R_(b)),whereas the dynamic parameter includes the water flow rate(Q).A 3–6-1 ANN model coupled with particle swarm optimization(PSO)approach was used to obtain the optimum values of geometric and dynamic parameters correspond-ing to the maximum SAE.The optimal values of the consecutive step width ratio(W_(i-1)/W_(i)),the perforation diameter to the bottom-most radius ratio(d/R_(b)),and the water flow rate(Q)for maximizing the SAE were found to be 1.15,0.0027 and 0.0167 m^(3)/s,respectively.The cross-validation results showed a deviation of 3.07%between the predicted and experimen-tal SAE values,thus confirming the adequacy of the proposed hybrid ANN-PSO technique. | Subha M.Roy C.M.Pareek Rajendra Machavaram C.K.Mukherjee | 2022 | Information Processing in Agriculture2022，9，4： | 2 |
| 2 | 着手制订卫生专业教育的国际标准显示文摘 | M.Roy Schwarz 梅人朗 | 2000 | 国外医学(医学教育分册)2000，21，3： | 2 |
| 3 | Separation of acid,basic and dispersed dyes by a single gradient elution reversed-phase high-performance liquid chromatography system显示文摘 | S.J.Speers B.H.Little M.Roy | | 0，，： | 1 |
| 4 | Long-term follow-up of primary glaucoma surgery with ahmed glaucoma valve implant versus trabeculectomy显示文摘 | M.Roy Wilson Upali Mendis Amit Paliwal Vera Haynatzka | 2003 | American Journal of Ophthalmology2003，，3： | 1 |
| 5 | Differential value of risk factors and clinical signs for diagnosing pulmonary embolism according to age显示文摘 | G.LE GAL M.RIGHINI P.‐M.ROY G.MEYER D.AUJESKY A.PERRIER H.BOUNAMEAUX | 2005 | Journal of Thrombosis and Haemostasis2005，，11： | 1 |
| 6 | Validation of a clinical prognostic model to identify low‐risk patients with pulmonary embolism显示文摘 | D.Aujesky A.Perrier P.‐M.Roy R. A.Stone J.Cornuz G.Meyer D. S.Obrosky M. J.Fine | 2007 | Journal of Internal Medicine2007，，6： | 1 |
| 7 | A physics informed bayesian optimization approach for material design: application to NiTi shape memory alloys显示文摘The design of materials and identification of optimal processing parameters constitute a complex and challenging task,necessitating efficient utilization of available data.Bayesian Optimization(BO)has gained popularity in materials design due to its ability to work with minimal data.However,many BO-based frameworks predominantly rely on statistical information,in the form of input-output data,and assume black-box objective functions.In practice,designers often possess knowledge of the underlying physical laws governing a material system,rendering the objective function not entirely black-box,as some information is partially observable.In this study,we propose a physics-informed BO approach that integrates physics-infused kernels to effectively leverage both statistical and physical information in the decision-making process.We demonstrate that this method significantly improves decision-making efficiency and enables more data-efficient BO.The applicability of this approach is showcased through the design of NiTi shape memory alloys,where the optimal processing parameters are identified to maximize the transformation temperature. | Danial Khatamsaz Raymond Neuberger Arunabha M.Roy Sina Hossein Zadeh Richard Otis Raymundo Arróyave | 2023 | npj Computational Materials2023，，1： | 0 |
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