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| 1 | Study of the production of Λ_b^0 band ~0 hadrons in pp collisions and first measurement of the Λ_b^0→J/ψpK^- branching fraction显示文摘The product of the A_b^0(B^0) differential production cross-section and the branching fraction of the decay A_b^0→J/ψpK^-(B^0→J/ψK~*(892)~0) is measured as a function of the beauty hadron transverse momentum,p_T,and rapidity,y.The kinematic region of the measurements is p_T <20 GeV/c and 2.0 | O.Kochebina M.Kolpin I.Komarov R.F.Koopman P.Koppenburg M.Kozeiha L.Kravchuk K.Kreplin M.Kreps G.Krocker P.Krokovny F.Kruse W.Krzemien W.Kucewicz M.Kucharczyk V.Kudryavtsev A.K.Kuonen K.Kurek T.Kvaratskheliya D.Lacarrere G.Lafferty A.Lai D.Lambert G.Lanffanchi C.Langenbruch B.Langhans T.Latham C.Lazzeroni R.Le Gac J.van Leerdam J.-P.Lees R.Lefevre A.Leflat J.Lefrancois E.Lemos Cid O.Leroy T.Lesiak B.Leverington Y.Li T.Likhomanenko M.Liles R.Lindner C.Linn F.Lionetto B.Liu X.Liu D.Loh I.Longstaff J.H.Lopes D.Lucchesi M.Lucio Martinez H.Luo A.Lupato E.Luppi O.Lupton A.Lusiani F.Machefert F.Maciuc O.Maev K.Maguire S.Malde A.Malinin G.Manca G.Mancinelli P.Manning A.Mapelli J.Maratas J.F.Marchand U.Marconi C.Marin Benito P.Marino J.Marks G.Martellottil M.Martin M.Martinelli D.Martinez Santos F.Martinez Vidal D.Martins Tostes A.Massafferri R.Matev A.Mathad Z.Mathe C.Matteuzzi A.Mauri B.Maurin A.Mazurov M.McCann J.McCarthy A.McNab R.McNulty B.Meadows F.Meier M.Meissner D.Melnychuk M.Merk E Michielin D.A.Milanes M.-N.Minard D.S.Mitzel J.Molina Rodrigue I.A.Monroy S.Monteil M.Morandin P.Morawski A.Morda M.J.Morello J.Moron A.B.Morris R.Mountain F.Muheim D.Miiller J.Muller K.Muller V.Muller M.Mussini B.Muster P.Naik T.Nakada R.Nandakumar A.Nandi I.Nasteva M.Needham N.Neri S.Neubert N.Neufeld M.Neuner A.D.Nguyen T.D.Nguyen C.Nguyen-Mau V.Niess R.Niet N.Nikitin T.Nikodem D.Ninci A.Novoselov D.P.O'Hanlon A.Oblakowska-Mucha V.Obraztsov S.Ogilvy O.Okhrimenko R.Oldeman C.J.G.Onderwater B.Osorio Rodrigues J.M.Otalora Goicochea A.Otto P.Owen A.Oyanguren A.Palano F.Palombo M.Palutan J.Panman A.Papanestis M.Pappagallo L.L.Pappalardo C.Pappenheimer C.Parkes G.Passaleva G.D.Patel M.Patel C.Patrignani A.Pearce A.Pellegrino G.Penso M.Pepe Altarelli S.Perazzini P.Perret L.Pescatore K.Petridis A.Petrolini M.Petruzzo E.Picatoste Olloqui B.Pietrzyk T:.Pilar D.Pinci A.Pistone A.Piucci S.Playfer M.Plo Casasus T.Poikela F.Polci A.Poluektov I.Polyakov E.Polycarpo A.Popov D.Popov B.Popovici C.Potterat E.Price J.D.Price J.Prisciandaro A.Pritchard C.Prouve V.Pugatch A.Puig Navarro G.Punzi W.Qian R.Quagliani B.Rachwal J.H.Rademacker M.Rama M.S.Rangel I.Raniuk N.Rauschmayr G.Raven F.Redi S.Reichert M.M.Reid A.C.dos Reis S.Ricciardi S.Richards M.Rihl K.Rinnert V.Rives Molina P.Robbe A.B.Rodrigues E.Rodrigues J.A.Rodriguez Lopez P.Rodriguez Perez S.Roiser V.Romanovsky A.Romero Vidalt J.W.R onayne M.Rotondo J.Rouvinet T.Ruf P.Ruiz Valls J.J.Saborido Silva N.Sagidova P.Sail B.Saitta V.Salustino Guimaraes C.Sanchez Mayordomo B.Sanmartin Sedes R.Santacesaria C.Santamarina Rios M.Santimaria E.Santovetti A.Sarti C.Satriano A.Satta D.M.Saunders D.Savrina M.Schiller H.Schindler M.Schlupp M.Schmelling T.Schmelzer B.Schmidt O.Schneider A.Schopper M.Schubiger M.-H.Schune R.Schwemmer B.Sciascia A.Sciubba A.Semennikov N.Serra J.Serrano L.Sestini P.Seyfert M.Shapkin I.Shapoval Y.Shcheglov T.Shears L.Shekhtman V.Shevchenko A.Shires B.G.Siddi R.Silva Coutinho L.Silva de Oliveira G.Simi M.Sirendi N.Skidmore T.Skwarnicki E.Smith E.Smith I.T.Smith J.Smith M.Smith H.Snoek M.D.Sokoloff F.J.P.Soler F.Soomro D.Souza B.Souza De Paula B.Spaan P.Spradlin S.Sridharan F.Stagni M.Stahl S.Stahl S.Stefkova O.Steinkamp O.Stenyakin S.Stevenson S.Stoica S.Stone B.Storaci S.Stracka M.Straticiuc U.Straumann L.Sun W.Sutcliffe K.Swientek S.Swientek V.Syropoulos M.Szczekowski P.Szczypka T.Szumlak S.T'Jampens A.Tayduganov T.Tekampe M.T eklishyn G.Teilarini F.Teubert C.Thomas E.Thomas J.van Tilburg V.Tisserand M.Tobin J.Todd S.Tolk L.Tomassetti D.Tonelli S.Topp-Joergensen N.Torr E.Tournefier S.Tourneur K.Trabelsi M.T.Tran M.Tresch A.Trisovic A.Tsaregorodtsev P.Tsopelas N.Tuning A.Ukleja A.Ustyuzhanin U.Uwer C.Vacca V.Vagnonit G.Valentit A.Vallier R.Vazquez Gomez P.Vazquez Regueiro C.Vazquez Sierra S.Vecchi J.J.Velthuis M.Veltri G.Veneziano M.Vesterinen B.Viaud D.Vieira M.Vieites Diaz X.Vitasis-Cardona V.Volkov A.Vollhardt D.Volyanskyy D.Voong A.Vorobyev V.Vorobyev C.Voβ J.A.de Vries R.Waldi C.Wallace R.Wallace J.Walsh S.Wandernoth J.Wang D.R.Ward N.K.Watson D.Websdale A.Weiden M.Whitehead G.Wilkinson M.Wilkinson M.Williams M.P.Williams T.Williams F.F.Wilson J.Wimberley J.Wishahi W.Wislicki M.Witek G.Wormser S.A.Wotton S.Wright K.Wyllie Y.Xie Z.Xu Z.Yang J.Yu X.Yuan O.Yushchenko M.Zangoli M.Zavertyaev L.Zhang Y.Zhang A.Zhelezov A.Zhokhov L.Zhong S.Zucchelli | 2016 | Chinese Physics C2016，40，1： | 23 |
| 2 | Ancient subsurface structure beneath crater Clavius:constraint by recent high-precision gravity and topography data显示文摘With the increasing precision of the GRAIL gravity field models and topography from LOLA, it is possible to investigate the substructure beneath crater Clavius. An admittance between gravity and topography data is commonly used to estimate selenophysical parameters, including load ratio, crustal thickness and density, and elastic thickness. Not only a surface load, but also a subsurface load is considered in estimation. The algorithm of particle swarm optimization(PSO) with a swarm size of 400 is employed as well.Results indicate that the observed admittance is best-fitted by the modeled admittance based on a spherical shell model, which was proved to be unsatisfactory in the previous study. The best-fitted load ratio f is around-0.194. Such a small load ratio conforms to the direct proportion between the nearly uncompensated topography and its corresponding negative gravity anomaly. It also indicates that a surface load dominates all the loads. Constrained within 2σSTD, a small crustal thickness(~30 km) and a crustal density of ~2587 kg m-3are found, quite close to the results from previous GRAIL research. Considering the well constrained crustal thickness and density, the best-fitted elastic thickness(~7 km) is rational. This result is slightly smaller than the previous study(~12 km). Such difference can be attributed to the difference in crustal density used and the precision of gravity and topography data. Considering that the small difference between the modeled gravity anomaly and observations is quite small, a parameter inversed here could be an indicator of the subsurface structure beneath Clavius. | Zhen Zhong Jian-Guo Yan J.Alexis P.Rodriguez | 2019 | Research in Astronomy and Astrophysics2019，19，1： | 2 |
| 3 | A New High-Efficiency Single-Phase Transformerless PV Inverter Topology显示文摘 | T.Kerekes R.Teodorescu P.Rodriguez G.Vázquez E.Aldabas | | 0，，： | 1 |
| 4 | A new method to determine the gravity field of small bodies from line-of-sight acceleration data显示文摘We present a new method to derive line-of-sight acceleration observables from spacecraft radio tracking data. The observables can be used to estimate the mass and gravity of a natural satellite as a spacecraft flyby. The corresponding observation model adapts to one-way and two/three-way tracking modes. As a test case for method validation and application, we estimated the mass and degree two gravity field for the Martian moon Phobos using simulated tracking data when the spacecraft Mars Express flew by Phobos on 2013 December 29. We have a few real tracking data during flyby and they will be used to confirm raw data simulation. The main purpose of this paper is to demonstrate the method of line-of-sight acceleration reduction from raw tracking data and the feasibility to estimate mass and gravity of a natural satellite using this type of observable. This novel method is potentially applicable to planet and asteroid gravity field studies combined with Doppler tracking data. | Nian-Chuan Jian Jian-Guo Yan Jin-Song Ping Jean-Pierre Barriot J.Alexis P.Rodriguez | 2019 | Research in Astronomy and Astrophysics2019，19，3： | 1 |
| 5 | Influence of Structural Modification on Work-hardening Behaviour of Type 316 Stainless Steel显示文摘Work-hardening behaviour of type 316 austenitic stainless steel having difFerent initial dislocation structures introduced by swaging to various levels is analysed by a simplified Kock’s model which takes into account the structural changes through the dislocation accumulation and annihilation process during deformation. The dislocation accumulation and annihilation factors show a temperature and structure dependence. The dislocation annihilation factor shows a plateau or decreasing tendency in the dynamic strain ageing (DSA) temperature range. This is attributed as either due to dislocation accumulation being more pronounced than dislocation annihilation or as due to precipitates being formed at DSA temperatures acting as obstacles to dislocation motion in the DSA temperature range. | K.G.Samuel S.L.Mannan and P.Rodriguez (Metallurgy and Materials Group, Indira Gandhi Centre for Atomic Research, Kalpakkam 603 102, India) | 1998 | Journal of Materials Science & Technology1998，14，5： | 0 |
| 6 | Data Driven Modelling of Coronavirus Spread in Spain显示文摘During the late months of last year,a novel coronavirus was detected in Hubei,China.The virus,since then,has spread all across the globe forcing Word Health Organization(WHO)to declare COVID-19 outbreak a pandemic.In Spain,the virus started infecting the country slowly until rapid growth of infected people occurred in Madrid,Barcelona and other major cities.The government in an attempt to stop the rapssid spread of the virus and ensure that health system will not reach its capacity,implement strict measures by putting the entire country in quarantine.The duration of these measures,depends on the evolution of the virus in Spain.In this study,a Deep Neural Network approach using Monte Carlo is proposed for generating a database to train networks for estimating the optimal parameters of a SIR epidemiology model.The number of total infected people as of April 7 in Spain is considered as input to the Deep Neural Network.The adaptability of the model was evaluated using the latest data upon completion of this paper,i.e.,April 14.The date range for the peak of infected people(i.e.,active cases)based on the new information is estimated to be within 74 to 109 days after the first recorded case of COVID-19 in Spain.In addition,a curve fitting measure based on the squared Euclidean distance indicates that according to the current data the peak might occur before the 86th day.Collectively,Deep Neural Networks have proven accurate and useful tools in handling big epidemiological data and for peak prediction estimates. | G.N.Baltas F.A.Prieto M.Frantzi C.R.Garcia-Alonso P.Rodriguez | 2020 | Computers, Materials & Continua2020，，9： | 0 |
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