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| 1 | The NUBASE2012 evaluation of nuclear properties显示文摘This paper presents the Nubase2012 evaluation that contains the recommended values for nuclear and decay properties of nuclides in their ground and excited isomeric(T1/2>100 ns) states.All nuclides for which some experimental information is known are considered.NUBASE2012 covers all up to date experimental data published in primary(journal articles) and secondary(mainly laboratory reports and conference proceedings) references,together with the corresponding bibliographical information.During the development of NUBASE2012,the data available in the 'Evaluated Nuclear Structure Data File'(Ensdf) database were consalted,and critically assessed of their validity and completeness.Furthermore,a large amount of new and somewhat older experimental results that were missing in Ensdf were compiled,evaluated and included in NUBASE2012.The atomic mass values were taken from the 'Atomic Mass Evaluation'(AME2012,second and third parts of the present issue).In cases where no experimental data were available for a particular nuclide,trends in the behavior of specific properties in neighboring nuclei(TNN) were examined.This approach allowed to estimate,whenever possible,values for a range of properties,and are labeled in NUBASE2012 as 'non-experimental'(lagged '#').Evaluation procedures and policies that were used during the development of this database are presented,together with a detailed table of recommended values and their uncertainties. | G.Audi F.G.Kondev M.Wang B.Pfeiffer X.Sun J.Blachot M.MacCormick | 2012 | Chinese Physics C2012，36，12： | 14 |
| 2 | Reversible solid-oxide cells for clean and sustainable energy显示文摘This review gives first a brief view of the potential availability of sustainable energy.It is clear that over 100 times more solar photovoltaic energy than necessary is readily accessible and that practically available wind alone may deliver sufficient energy supply to the world.Due to the intermittency of these sources,effective and inexpensive energy-conversion and storage technology is needed.Motivation for the possible electrolysis application of reversible solid-oxide cells(RSOCs),including a comparison of power-to-fuel/fuel-to-power to other energy-conversion and storage technologies is presented.RSOC electrochemistry and chemistry of H_(2)O,CO_(2),H_(2),CO,CnHm(hydrocarbons)and NH3,including thermodynamics and cell performance,are described.The mechanical strength of popular cell supports is outlined,and newly found stronger materials are mentioned.Common cell-degradation mechanisms,including the effect of common impurities in gases and materials(such as S and Si),plus the deleterious effects of carbon deposition in the fuel electrode are described followed by explanations of how to avoid or ease the consequences.Visions of how RSOCs powered by sustainable energy may be applied on a large scale for the transportation sector via power-to-fuel technology and for integration with the electrical grid together with seasonal storage are presented.Finally,a brief comparison of RSOCs to other electrolysis cells and an outlook with examples of actions necessary to commercialize RSOC applications are sketched. | M.B.Mogensen M.Chen H.L.Frandsen C.Graves J.B.Hansen K.V.Hansen A.Hauch T.Jacobsen S.H.Jensen T.L.Skafte X.Sun | 2019 | Clean Energy2019，3，3： | 5 |
| 3 | Methods for a blind analysis of isobar data collected by the STAR collaboration显示文摘In 2018,the STAR collaboration collected data from^(96)_(44)Ru+^(96)_(44)Ru and^(96)_(40)Zr+^(96)_(40)Zr at√^(S)NN=200 Ge V to search for the presence of the chiral magnetic effect in collisions of nuclei.The isobar collision species alternated frequently between 9644 Ru+^(96)_(44)Ru and^(96)_(40)Zr+^(96)_(40)Zr.In order to conduct blind analyses of studies related to the chiral magnetic effect in these isobar data,STAR developed a three-step blind analysis procedure.Analysts are initially provided a'reference sample'of data,comprised of a mix of events from the two species,the order of which respects time-dependent changes in run conditions.After tuning analysis codes and performing time-dependent quality assurance on the reference sample,analysts are provided a species-blind sample suitable for calculating efficiencies and corrections for individual≈30-min data-taking runs.For this sample,species-specific information is disguised,but individual output files contain data from a single isobar species.Only run-by-run corrections and code alteration subsequent to these corrections are allowed at this stage.Following these modifications,the'frozen'code is passed over the fully un-blind data,completing the blind analysis.As a check of the feasibility of the blind analysis procedure,analysts completed a'mock data challenge,'analyzing data from Au+Au collisions at√^(S)NN=27 Ge V,collected in 2018.The Au+Au data were prepared in the same manner intended for the isobar blind data.The details of the blind analysis procedure and results from the mock data challenge are presented. | J.Adam L.Adamczyk J.R.Adams J.K.Adkins G.Agakishiev M.M.Aggarwal Z.Ahammed I.Alekseev D.M.Anderson A.Aparin E.C.Aschenauer M.U.Ashraf F.G.Atetalla A.Attri G.S.Averichev V.Bairathi K.Barish A.Behera R.Bellwied A.Bhasin J.Bielcik J.Bielcikova L.C.Bland I.G.Bordyuzhin J.D.Brandenburg A.V.Brandin J.Butterworth H.Caines M.Calderon de la Barca Sanchez D.Cebra I.Chakaberia P.Chaloupka B.K.Chan F-H.Chang Z.Chang N.Chankova-Bunzarova A.Chatterjee D.Chen J.Chen J.H.Chen X.Chen Z.Chen J.Cheng M.Cherney M.Chevalier S.Choudhury W.Christie X.Chu H.J.Crawford M.Csanad M.Daugherity T.G.Dedovich I.M.Deppner A.A.Derevschikov L.Didenko X.Dong J.L.Drachenberg J.C.Dunlop T.Edmonds N.Elsey J.Engelage G.Eppley S.Esumi O.Evdokimov A.Ewigleben O.Eyser R.Fatemi S.Fazio P.Federic J.Fedorisin C.J.Feng Y.Feng P.Filip E.Finch Y.Fisyak A.Francisco L.Fulek C.A.Gagliardi T.Galatyuk F.Geurts A.Gibson K.Gopal X.Gou D.Grosnick W.Guryn A.I.Hamad A.Hamed S.Harabasz J.W.Harris S.He W.He X.H.He Y.He S.Heppelmann S.Heppelmann N.Herrmann E.Hoffman L.Holub Y.Hong S.Horvat Y.Hu H.Z.Huang S.L.Huang T.Huang X.Huang T.J.Humanic P.Huo G.Igo D.Isenhower W.W.Jacobs C.Jena A.Jentsch Y.Ji J.Jia K.Jiang S.Jowzaee X.Ju E.G.Judd S.Kabana M.L.Kabir S.Kagamaster D.Kalinkin K.Kang D.Kapukchyan K.Kauder H.W.Ke D.Keane A.Kechechyan M.Kelsey Y.V.Khyzhniak D.P.Kikoła C.Kim B.Kimelman D.Kincses T.A.Kinghorn I.Kisel A.Kiselev M.Kocan L.Kochenda L.K.Kosarzewski L.Kramarik P.Kravtsov K.Krueger N.Kulathunga Mudiyanselage L.Kumar S.Kumar R.Kunnawalkam Elayavalli J.H.Kwasizur R.Lacey S.Lan J.M.Landgraf J.Lauret A.Lebedev R.Lednicky J.H.Lee Y.H.Leung C.Li C.Li W.Li W.Li X.Li Y.Li Y.Liang R.Licenik T.Lin Y.Lin M.A.Lisa F.Liu H.Liu P.Liu P.Liu T.Liu X.Liu Y.Liu Z.Liu T.Ljubicic W.J.Llope R.S.Longacre N.S.Lukow S.Luo X.Luo G.L.Ma L.Ma R.Ma Y.G.Ma N.Magdy R.Majka D.Mallick S.Margetis C.Markert H.S.Matis J.A.Mazer N.G.Minaev S.Mioduszewski B.Mohanty I.Mooney Z.Moravcova D.A.Morozov M.Nagy J.D.Nam Md.Nasim K.Nayak D.Neff J.M.Nelson D.B.Nemes M.Nie G.Nigmatkulov T.Niida L.V.Nogach T.Nonaka A.S.Nunes G.Odyniec A.Ogawa S.Oh V.A.Okorokov B.S.Page R.Pak A.Pandav Y.Panebratsev B.Pawlik D.Pawlowska H.Pei C.Perkins L.Pinsky R.L.Pinter J.Pluta J.Porter M.Posik N.K.Pruthi M.Przybycien J.Putschke H.Qiu A.Quintero S.K.Radhakrishnan S.Ramachandran R.L.Ray R.Reed H.G.Ritter O.V.Rogachevskiy J.L.Romero L.Ruan J.Rusnak N.R.Sahoo H.Sako S.Salur J.Sandweiss S.Sato W.B.Schmidke N.Schmitz B.R.Schweid F.Seck J.Seger M.Sergeeva R.Seto P.Seyboth N.Shah E.Shahaliev P.V.Shanmuganathan M.Shao A.I.Sheikh W.Q.Shen S.S.Shi Y.Shi Q.Y.Shou E.P.Sichtermann R.Sikora M.Simko J.Singh S.Singha N.Smirnov W.Solyst P.Sorensen H.M.Spinka B.Srivastava T.D.S.Stanislaus M.Stefaniak D.J.Stewart M.Strikhanov B.Stringfellow A.A.P.Suaide M.Sumbera B.Summa X.M.Sun X.Sun Y.Sun Y.Sun B.Surrow D.N.Svirida P.Szymanski A.H.Tang Z.Tang A.Taranenko T.Tarnowsky J.H.Thomas A.R.Timmins D.Tlusty M.Tokarev C.A.Tomkiel S.Trentalange R.E.Tribble P.Tribedy S.K.Tripathy O.D.Tsai Z.Tu T.Ullrich D.G.Underwood I.Upsal G.Van Buren J.Vanek A.N.Vasiliev I.Vassiliev F.Videbæk S.Vokal S.A.Voloshin F.Wang G.Wang J.S.Wang P.Wang Y.Wang Y.Wang Z.Wang J.C.Webb P.C.Weidenkaff L.Wen G.D.Westfall H.Wieman S.W.Wissink R.Witt Y.Wu Z.G.Xiao G.Xie W.Xie H.Xu N.Xu Q.H.Xu Y.F.Xu Y.Xu Z.Xu Z.Xu C.Yang Q.Yang S.Yang Y.Yang Z.Yang Z.Ye Z.Ye L.Yi K.Yip Y.Yu H.Zbroszczyk W.Zha C.Zhang D.Zhang S.Zhang S.Zhang X.P.Zhang Y.Zhang Y.Zhang Z.J.Zhang Z.Zhang Z.Zhang J.Zhao C.Zhong C.Zhou X.Zhu Z.Zhu M.Zurek M.Zyzak STAR Collaboration Abilene | 2021 | Nuclear Science and Techniques2021，32，5： | 3 |
| 4 | Measurement of away-side broadening with self-subtraction of flow in Au+Au collisions at √sNN=200 GeV显示文摘High transverse momentum(pT)particle production is suppressed owing to the parton(jet)energy loss in the hot dense medium created in relativistic heavy-ion collisions.Redistribution of energy at low-to-modest pT has been difficult to measure,owing to large anisotropic backgrounds.We report a data-driven method for background evaluation and subtraction,exploiting the away-side pseudorapidity gaps,to measure the jetlike correlation shape in Au+Au collisions at √sNN=200 GeV in the STAR experiment.The correlation shapes,for trigger particles pT>3GeV/c and various associated particle pT ranges within 0.5 | L.Adamczyk J.R.Adams J.K.Adkins G.Agakishiev M.M.Aggarwal Z.Ahammed I.Alekseev D.M.Anderson A.Aparin E.C.Aschenauer M.U.Ashraf F.G.Atetalla A.Attri G.S.Averichev V.Bairathi K.Barish A.Behera R.Bellwied A.Bhasin J.Bielcik J.Bielcikova L.C.Bland I.G.Bordyuzhin J.D.Brandenburg A.V.Brandin J.Butterworth H.Caines M.Calderón de la Barca Sánchez D.Cebra I.Chakaberia P.Chaloupka B.K.Chan F-H.Chang Z.Chang N.Chankova-Bunzarova A.Chatterjee D.Chen J.H.Chen X.Chen Z.Chen J.Cheng M.Cherney M.Chevalier S.Choudhury W.Christie X.Chu H.J.Crawford M.Csanád M.Daugherity T.G.Dedovich I.M.Deppner A.A.Derevschikov L.Didenko X.Dong J.L.Drachenberg J.C.Dunlop T.Edmonds N.Elsey J.Engelage G.Eppley S.Esumi O.Evdokimov A.Ewigleben O.Eyser R.Fatemi S.Fazio P.Federic J.Fedorisin C.J.Feng Y.Feng P.Filip E.Finch Y.Fisyak A.Francisco L.Fulek C.A.Gagliardi T.Galatyuk F.Geurts A.Gibson K.Gopal D.Grosnick W.Guryn A.I.Hamad A.Hamed S.Harabasz J.W.Harris S.He W.He X.H.He S.Heppelmann S.Heppelmann N.Herrmann E.Hoffman L.Holub Y.Hong S.Horvat Y.Hu H.Z.Huang S.L.Huang T.Huang X.Huang T.J.Humanic P.Huo G.Igo D.Isenhower W.W.Jacobs C.Jena A.Jentsch Y.JI J.Jia K.Jiang S.Jowzaee X.Ju E.G.Judd S.Kabana M.L.Kabir S.Kagamaster D.Kalinkin K.Kang D.Kapukchyan K.Kauder H.W.Ke D.Keane A.Kechechyan M.Kelsey Y.V.Khyzhniak D.P.Kikoła C.Kim B.Kimelman D.Kincses T.A.Kinghorn I.Kisel A.Kiselev M.Kocan L.Kochenda L.K.Kosarzewski L.Kramarik P.Kravtsov K.Krueger N.Kulathunga Mudiyanselage L.Kumar S.Kumar R.Kunnawalkam Elayavalli J.H.Kwasizur R.Lacey S.Lan J.M.Landgraf J.Lauret A.Lebedev R.Lednicky J.H.Lee Y.H.Leung C.Li W.Li W.Li X.Li Y.Li Y.Liang R.Licenik T.Lin Y.Lin M.A.Lisa F.Liu H.Liu P.Liu P.Liu T.Liu X.Liu Y.Liu Z.Liu T.Ljubicic W.J.Llope R.S.Longacre N.S.Lukow S.Luo X.Luo G.L.Ma L.Ma R.Ma Y.G.Ma N.Magdy R.Majka D.Mallick S.Margetis C.Markert H.S.Matis J.A.Mazer N.G.Minaev S.Mioduszewski B.Mohanty I.Mooney Z.Moravcova D.A.Morozov M.Nagy J.D.Nam Nasim Md K.Nayak D.Neff J.M.Nelson D.B.Nemes M.Nie G.Nigmatkulov T.Niida L.V.Nogach T.Nonaka A.S.Nunes G.Odyniec A.Ogawa S.Oh V.A.Okorokov B.S.Page R.Pak A.Pandav Y.Panebratsev B.Pawlik D.Pawlowska H.Pei C.Perkins L.Pinsky R.L.Pintér J.Pluta J.Porter M.Posik N.K.Pruthi M.Przybycien J.Putschke H.Qiu A.Quintero S.K.Radhakrishnan S.Ramachandran R.L.Ray R.Reed H.G.Ritter O.V.Rogachevskiy J.L.Romero L.Ruan J.Rusnak N.R.Sahoo H.Sako S.Salur J.Sandweiss S.Sato W.B.Schmidke N.Schmitz B.R.Schweid F.Seck J.Seger M.Sergeeva R.Seto P.Seyboth N.Shah E.Shahaliev P.V.Shanmuganathan M.Shao A.I.Sheikh F.Shen W.Q.Shen S.S.Shi Q.Y.Shou E.P.Sichtermann R.Sikora M.Simko J.Singh S.Singha N.Smirnov W.Solyst P.Sorensen H.M.Spinka B.Srivastava T.D.S.Stanislaus M.Stefaniak D.J.Stewart M.Strikhanov B.Stringfellow A.A.P.Suaide M.Sumbera B.Summa X.M.Sun X.Sun Y.Sun Y.Sun B.Surrow D.N.Svirida P.Szymanski A.H.Tang Z.Tang A.Taranenko T.Tarnowsky J.H.Thomas A.R.Timmins D.Tlusty M.Tokarev C.A.Tomkiel S.Trentalange R.E.Tribble P.Tribedy S.K.Tripathy O.D.Tsai Z.Tu T.Ullrich D.G.Underwood I.Upsal G.Van Buren J.Vanek A.N.Vasiliev I.Vassiliev F.Videbæk S.Vokal S.A.Voloshin F.Wang G.Wang J.S.Wang P.Wang Y.Wang Y.Wang Z.Wang J.C.Webb P.C.Weidenkaff L.Wen G.D.Westfall H.Wieman S.W.Wissink R.Witt Y.Wu Z.G.Xiao G.Xie W.Xie H.Xu N.Xu Q.H.Xu Y.F.Xu Y.Xu Z.Xu Z.Xu C.Yang Q.Yang S.Yang Y.Yang Z.Yang Z.Ye Z.Ye L.Yi K.Yip H.Zbroszczyk W.Zha C.Zhang D.Zhang S.Zhang S.Zhang X.P.Zhang Y.Zhang Y.Zhang Z.J.Zhang Z.Zhang Z.Zhang J.Zhao C.Zhong C.Zhou X.Zhu Z.Zhu M.Zurek M.Zyzak | 2020 | Chinese Physics C2020，44，10： | 2 |
| 5 | 查看详情显示文摘 | Y.Lin X.Sun | | 0，，： | 1 |
| 6 | Political connections and the process of going public:Evidence from China显示文摘 | Francis B.B I.Hasan X.Sun | | 0，，04： | 1 |
| 7 | The preparation and characterization of ZnO ultrafine particles显示文摘 | L.Jing Z.Xu J.Shang X.Sun W.Cai and H.Guo | | 0，，： | 1 |
| 8 | Political Connections and the Process of Going Public:Evidence from China显示文摘 | Francis B.B I.Hasan X.Sun | | 0，，04： | 1 |
| 9 | An Approach to Quantify the Heat Wave Strength and Price a Heat Derivative for Risk Hedging显示文摘Mitigating the heat stress via a derivative policy is a vital financial option for agricultural producers and other business sectors to strategically adapt to the climate change scenario. This study has provided an approach to identifying heat stress events and pricing the heat stress weather derivative due to persistent days of high surface air temperature (SAT). Cooling degree days (CDD) are used as the weather index for trade. In this study, a call-option model was used as an example for calculating the price of the index. Two heat stress indices were developed to describe the severity and physical impact of heat waves. The daily Global Historical Climatology Network (GHCN-D) SAT data from 1901 to 2007 from the southern California, USA, were used. A major California heat wave that occurred 20-25 October 1965 was studied. The derivative price was calculated based on the call-option model for both long-term station data and the interpolated grid point data at a regular 0.1 ×0.1 latitude-longitude grid. The resulting comparison indicates that (a) the interpolated data can be used as reliable proxy to price the CDD and (b) a normal distribution model cannot always be used to reliably calculate the CDD price. In conclusion, the data, models, and procedures described in this study have potential application in hedging agricultural and other risks. | Samuel S. P.SHEN Benedikt KRAMPS Shirley X.SUN Barbara BAILEY | 2012 | Advances in Atmospheric Sciences2012，29，1： | 1 |
| 10 | Generalized multiple kernel framework for multi-class geospatial objects显示文摘 | X.J.Li X.Sun H.Sun | | 0，，： | 1 |
| 11 | The Distribution of Foreign Direct Investment in China显示文摘 | Broadman H.G X.Sun | | 0，，3： | 1 |
| 12 | Adaptive algorithm for active controlof impulsive noise显示文摘 | X.Sun S.M.Kuo G.Meng | | 0，，291： | 1 |
| 13 | Solid solution strengthening of high-entropy alloys from first-principles study显示文摘Solid solution strengthening(SSS)is one kind of strengthening mechanisms and plays an important role in alloy design,in particular for single-phase alloys including high-entropy alloys(HEAs).The classical Labusch–Nabarro model and its expansions are most widely applicable to treating SSS of solid solution alloys including both conventional alloys(CAs)and HEAs.In this study,the SSS effects in a series of Febased CAs and HEAs are investigated by using the classical Labusch–Nabarro model and its expansions.The size misfit and shear modulus misfit parameters are derived from first-principles calculations.Based on available experimental data in combination with empirical SSS model,we propose fitting constants(i.e.,the ratio between experimental hardness and predicted SSS effect)for these two families of alloys.The predicted host/alloy family-dependent fitting constants can be used to estimate the hardness of these SSS alloys.General agreement between predicted and measured hardness values is satisfactory for both CAs and HEAs,implying that the proposed approach is reliable and successful. | H.L.Zhang D.D.Cai X.Sun H.Huang S.Lu Y.Z.Wang Q.M.Hu L.Vitos X.D.Ding | 2022 | Journal of Materials Science & Technology2022，，26： | 1 |
| 14 | The security analyses of rosettanet in grid显示文摘 | J.Liu R.Sun W.Kou X.Sun | | 0，，： | 1 |
| 15 | Time-varying steptransform algorithm for high squint SAR imaging显示文摘 | X.Sun T.S.Yeo C.Zhang Y.Lu P.S.Kooi | | 0，，6： | 1 |
| 16 | Multiple targets detection method based on binary hough transform and adaptive time-frequency filtering显示文摘 | W.Xue X.Sun | | 0，，： | 1 |
| 17 | Twinning and martensite in a 304 austenitic stainless steel显示文摘 | Y.F.Shen X.X.Li X.Sun | | 0，，： | 1 |
| 18 | A CRITERION FOR TWO KINDS OF PEIERLS TRANSITIONS显示文摘Two different kinds of Peierls transitions can exist depending on the anisotropic ratio y of intrachain and interchain coupling.γhas a critical valueγo;if_(γ>γo),the Peierls transition will exhibit a CDW accompanied by a metal-semiconductor transition(kind I);if_(γcγo),only CDW and superlattice occur at Peierls transition(kind II).Some experimental evidences are discussed. | Z.Wang C.Wu X.Sun | 1985 | Chinese Physics Letters1985，2，3： | 0 |
| 19 | Preface显示文摘There is a growing awareness that nanotechnology can have a profound impact on various fields by exploiting the significant differences of energy states and transport in nanostructures and macrostructures.Nanotechnology, in its original sense, refers to the projected ability to | Andy X.Sun | 2011 | Particuology2011，9，5： | 0 |
| 20 | ENERGY-LOSS FUNCTIONS DERIVED FROM REELS SPECTRA FOR ALUMINUM显示文摘The effective energy loss functions for Al have been derived from differential i nverse inelastic mean free path based on the extended Landau approach. It has be en revealed that the effective energy loss function is very close in value to th e theoretical surface energy loss function in the lower energy loss region but g radually approaches the theoretical bulk energy loss function in the higher ener gy loss region. Moreover, the intensity corresponding to surface excitation in e ffective energy loss functions decreases with the increase of primary electron e nergy. These facts show that the present effective energy loss function describe s not only surface excitation but also bulk excitation. At last, REELS spectra s imulated by Monte Carlo method based on use of the effective energy loss functio ns has reproduced the experimental REELS spectra with considerable success. | Z.M.Zhang Z.J.Ding H.M.Li K.Salma X.Sun R.Shimizu T.Koshikawa K.Goto | 2005 | Acta Metallurgica Sinica(English Letters)2005，18，3： | 0 |
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