Spin-flip reflection at the normal metal-spin superconductor interface

We study spin transport through a normal metal-spin superconductor junction. A spin-flip reflection is demonstrated at the interface, where a spin-up electron incident from the normal metal can be reflected as a spin-down electron and the spin $2\times \hbar/2$ will be injected into the spin superconductor. When the (spin) voltage is smaller than the gap of the spin superconductor, the spin-flip reflection determines the transport properties of the junction. We consider both graphene-based (linear-dispersion-relation) and quadratic-dispersion-relation normal metal-spin superconductor junctions in detail. For the two-dimensional graphene-based junction, the spin-flip reflected electron can be along the specular direction (retro-direction) when the incident and reflected electron locates in the same band (different bands). A perfect spin-flip reflection can occur when the incident electron is normal to the interface, and the reflection coefficient is slightly suppressed for the oblique incident case. As a comparison, for the one-dimensional quadratic-dispersion-relation junction, the spin-flip reflection coefficient can reach 1 at certain incident energies. In addition, both the charge current and the spin current under a charge (spin) voltage are studied. The spin conductance is proportional to the spin-flip reflection coefficient when the spin voltage is less than the gap of the spin superconductor. These results will help us get a better understanding of spin transport through the normal metal-spin superconductor junction.Comment: 11 pages, 9 figure

Search for a light CP -odd Higgs boson in radiative decays of J /ψ

none406siWe search for a light Higgs boson A0 in the fully reconstructed decay chain of J/ψ→γA0, A0→μ+μ- using (225.0±2.8)×106 J/ψ events collected by the BESIII experiment. The A0 is a hypothetical CP-odd light Higgs boson predicted by many extensions of the Standard Model including two spin-0 doublets plus an extra singlet. We find no evidence for A0 production and set 90% confidence-level upper limits on the product branching fraction B(J/ψ→γA0)×B(A0→μ+μ-) in the range of (2.8-495.3)×10-8 for 0.212≤mA0≤3.0 GeV/c2. The new limits are five times below our previous results, and the nature of the A0 is constrained to be mostly singlet.noneAblikim, M.; Achasov, M.N.; Ai, X.C.; Albayrak, O.; Albrecht, M.; Ambrose, D.J.; Amoroso, A.; An, F.F.; An, Q.; Bai, J.Z.; Baldini Ferroli, R.; Ban, Y.; Bennett, D.W.; Bennett, J.V.; Bertani, M.; Bettoni, D.; Bian, J.M.; Bianchi, F.; Boger, E.; Boyko, I.; Briere, R.A.; Cai, H.; Cai, X.; Cakir, O.; Calcaterra, A.; Cao, G.F.; Cetin, S.A.; Chang, J.F.; Chelkov, G.; Chen, G.; Chen, H.S.; Chen, H.Y.; Chen, J.C.; Chen, M.L.; Chen, S.J.; Chen, X.; Chen, X.R.; Chen, Y.B.; Cheng, H.P.; Chu, X.K.; Cibinetto, G.; Dai, H.L.; Dai, J.P.; Dbeyssi, A.; Dedovich, D.; Deng, Z.Y.; Denig, A.; Denysenko, I.; Destefanis, M.; De Mori, F.; Ding, Y.; Dong, C.; Dong, J.; Dong, L.Y.; Dong, M.Y.; Dou, Z.L.; Du, S.X.; Duan, P.F.; Fan, J.Z.; Fang, J.; Fang, S.S.; Fang, X.; Fang, Y.; Fava, L.; Feldbauer, F.; Felici, G.; Feng, C.Q.; Fioravanti, E.; Fritsch, M.; Fu, C.D.; Gao, Q.; Gao, X.L.; Gao, X.Y.; Gao, Y.; Gao, Z.; Garzia, I.; Goetzen, K.; Gong, W.X.; Gradl, W.; Greco, M.; Gu, M.H.; Gu, Y.T.; Guan, Y.H.; Guo, A.Q.; Guo, L.B.; Guo, Y.; Guo, Y.P.; Haddadi, Z.; Hafner, A.; Han, S.; Harris, F.A.; He, K.L.; Held, T.; Heng, Y.K.; Hou, Z.L.; Hu, C.; Hu, H.M.; Hu, J.F.; Hu, T.; Hu, Y.; Huang, G.M.; Huang, G.S.; Huang, J.S.; Huang, X.T.; Huang, Y.; Hussain, T.; Ji, Q.; Ji, Q.P.; Ji, X.B.; Ji, X.L.; Jiang, L.W.; Jiang, X.S.; Jiang, X.Y.; Jiao, J.B.; Jiao, Z.; Jin, D.P.; Jin, S.; Johansson, T.; Julin, A.; Kalantar-Nayestanaki, N.; Kang, X.L.; Kang, X.S.; Kavatsyuk, M.; Ke, B.C.; Kiese, P.; Kliemt, R.; Kloss, B.; Kolcu, O.B.; Kopf, B.; Kornicer, M.; Kühn, W.; Kupsc, A.; Lange, J.S.; Lara, M.; Larin, P.; Leng, C.; Li, C.; Li, Cheng; Li, D.M.; Li, F.; Li, F.Y.; Li, G.; Li, H.B.; Li, J.C.; Li, Jin; Li, K.; Li, K.; Li, Lei; Li, P.R.; Li, T.; Li, W.D.; Li, W.G.; Li, X.L.; Li, X.M.; Li, X.N.; Li, X.Q.; Li, Z.B.; Liang, H.; Liang, Y.F.; Liang, Y.T.; Liao, G.R.; Lin, D.X.; Liu, B.J.; Liu, C.X.; Liu, D.; Liu, F.H.; Liu, Fang; Liu, Feng; Liu, H.B.; Liu, H.H.; Liu, H.H.; Liu, H.M.; Liu, J.; Liu, J.B.; Liu, J.P.; Liu, J.Y.; Liu, K.; Liu, K.Y.; Liu, L.D.; Liu, P.L.; Liu, Q.; Liu, S.B.; Liu, X.; Liu, Y.B.; Liu, Z.A.; Liu, Zhiqing; Loehner, H.; Lou, X.C.; Lu, H.J.; Lu, J.G.; Lu, Y.; Lu, Y.P.; Luo, C.L.; Luo, M.X.; Luo, T.; Luo, X.L.; Lyu, X.R.; Ma, F.C.; Ma, H.L.; Ma, L.L.; Ma, Q.M.; Ma, T.; Ma, X.N.; Ma, X.Y.; Maas, F.E.; Maggiora, M.; Mao, Y.J.; Mao, Z.P.; Marcello, S.; Messchendorp, J.G.; Min, J.; Mitchell, R.E.; Mo, X.H.; Mo, Y.J.; Morales Morales, C.; Muchnoi, N. Yu.; Muramatsu, H.; Nefedov, Y.; Nerling, F.; Nikolaev, I.B.; Ning, Z.; Nisar, S.; Niu, S.L.; Niu, X.Y.; Olsen, S.L.; Ouyang, Q.; Pacetti, S.; Pan, Y.; Patteri, P.; Pelizaeus, M.; Peng, H.P.; Peters, K.; Pettersson, J.; Ping, J.L.; Ping, R.G.; Poling, R.; Prasad, V.; Qi, M.; Qian, S.; Qiao, C.F.; Qin, L.Q.; Qin, N.; Qin, X.S.; Qin, Z.H.; Qiu, J.F.; Rashid, K.H.; Redmer, C.F.; Ripka, M.; Rong, G.; Rosner, Ch.; Ruan, X.D.; Santoro, V.; Sarantsev, A.; Savrié, M.; Schoenning, K.; Schumann, S.; Shan, W.; Shao, M.; Shen, C.P.; Shen, P.X.; Shen, X.Y.; Sheng, H.Y.; Song, W.M.; Song, X.Y.; Sosio, S.; Spataro, S.; Sun, G.X.; Sun, J.F.; Sun, S.S.; Sun, Y.J.; Sun, Y.Z.; Sun, Z.J.; Sun, Z.T.; Tang, C.J.; Tang, X.; Tapan, I.; Thorndike, E.H.; Tiemens, M.; Ullrich, M.; Uman, I.; Varner, G.S.; Wang, B.; Wang, B.L.; Wang, D.; Wang, D.Y.; Wang, K.; Wang, L.L.; Wang, L.S.; Wang, M.; Wang, P.; Wang, P.L.; Wang, S.G.; Wang, W.; Wang, W.P.; Wang, X.F.; Wang, Y.D.; Wang, Y.F.; Wang, Y.Q.; Wang, Z.; Wang, Z.G.; Wang, Z.H.; Wang, Z.Y.; Weber, T.; Wei, D.H.; Wei, J.B.; Weidenkaff, P.; Wen, S.P.; Wiedner, U.; Wolke, M.; Wu, L.H.; Wu, Z.; Xia, L.; Xia, L.G.; Xia, Y.; Xiao, D.; Xiao, H.; Xiao, Z.J.; Xie, Y.G.; Xiu, Q.L.; Xu, G.F.; Xu, L.; Xu, Q.J.; Xu, X.P.; Yan, L.; Yan, W.B.; Yan, W.C.; Yan, Y.H.; Yang, H.J.; Yang, H.X.; Yang, L.; Yang, Y.; Yang, Y.Y.; Ye, M.; Ye, M.H.; Yin, J.H.; Yu, B.X.; Yu, C.X.; Yu, J.S.; Yuan, C.Z.; Yuan, W.L.; Yuan, Y.; Yuncu, A.; Zafar, A.A.; Zallo, A.; Zeng, Y.; Zeng, Z.; Zhang, B.X.; Zhang, B.Y.; Zhang, C.; Zhang, C.C.; Zhang, D.H.; Zhang, H.H.; Zhang, H.Y.; Zhang, J.J.; Zhang, J.L.; Zhang, J.Q.; Zhang, J.W.; Zhang, J.Y.; Zhang, J.Z.; Zhang, K.; Zhang, L.; Zhang, X.Y.; Zhang, Y.; Zhang, Y.H.; Zhang, Y.N.; Zhang, Y.T.; Zhang, Yu; Zhang, Z.H.; Zhang, Z.P.; Zhang, Z.Y.; Zhao, G.; Zhao, J.W.; Zhao, J.Y.; Zhao, J.Z.; Zhao, Lei; Zhao, Ling; Zhao, M.G.; Zhao, Q.; Zhao, Q.W.; Zhao, S.J.; Zhao, T.C.; Zhao, Y.B.; Zhao, Z.G.; Zhemchugov, A.; Zheng, B.; Zheng, J.P.; Zheng, W.J.; Zheng, Y.H.; Zhong, B.; Zhou, L.; Zhou, X.; Zhou, X.K.; Zhou, X.R.; Zhou, X.Y.; Zhu, K.; Zhu, K.J.; Zhu, S.; Zhu, S.H.; Zhu, X.L.; Zhu, Y.C.; Zhu, Y.S.; Zhu, Z.A.; Zhuang, J.; Zotti, L.; Zou, B.S.; Zou, J.H.Ablikim, M.; Achasov, M. N.; Ai, X. C.; Albayrak, O.; Albrecht, M.; Ambrose, D. J.; Amoroso, A.; An, F. F.; An, Q.; Bai, J. Z.; Baldini Ferroli, R.; Ban, Y.; Bennett, D. W.; Bennett, J. V.; Bertani, M.; Bettoni, D.; Bian, J. M.; Bianchi, F.; Boger, E.; Boyko, I.; Briere, R. A.; Cai, H.; Cai, X.; Cakir, O.; Calcaterra, A.; Cao, G. F.; Cetin, S. A.; Chang, J. F.; Chelkov, G.; Chen, G.; Chen, H. S.; Chen, H. Y.; Chen, J. C.; Chen, M. L.; Chen, S. J.; Chen, X.; Chen, X. R.; Chen, Y. B.; Cheng, H. P.; Chu, X. K.; Cibinetto, G.; Dai, H. L.; Dai, J. P.; Dbeyssi, A.; Dedovich, D.; Deng, Z. Y.; Denig, A.; Denysenko, I.; Destefanis, M.; De Mori, F.; Ding, Y.; Dong, C.; Dong, J.; Dong, L. Y.; Dong, M. Y.; Dou, Z. L.; Du, S. X.; Duan, P. F.; Fan, J. Z.; Fang, J.; Fang, S. S.; Fang, X.; Fang, Y.; Fava, L.; Feldbauer, F.; Felici, G.; Feng, C. Q.; Fioravanti, E.; Fritsch, M.; Fu, C. D.; Gao, Q.; Gao, X. L.; Gao, X. Y.; Gao, Y.; Gao, Z.; Garzia, I.; Goetzen, K.; Gong, W. X.; Gradl, W.; Greco, M.; Gu, M. H.; Gu, Y. T.; Guan, Y. H.; Guo, A. Q.; Guo, L. B.; Guo, Y.; Guo, Y. P.; Haddadi, Z.; Hafner, A.; Han, S.; Harris, F. A.; He, K. L.; Held, T.; Heng, Y. K.; Hou, Z. L.; Hu, C.; Hu, H. M.; Hu, J. F.; Hu, T.; Hu, Y.; Huang, G. M.; Huang, G. S.; Huang, J. S.; Huang, X. T.; Huang, Y.; Hussain, T.; Ji, Q.; Ji, Q. P.; Ji, X. B.; Ji, X. L.; Jiang, L. W.; Jiang, X. S.; Jiang, X. Y.; Jiao, J. B.; Jiao, Z.; Jin, D. P.; Jin, S.; Johansson, T.; Julin, A.; Kalantar Nayestanaki, N.; Kang, X. L.; Kang, X. S.; Kavatsyuk, M.; Ke, B. C.; Kiese, P.; Kliemt, R.; Kloss, B.; Kolcu, O. B.; Kopf, B.; Kornicer, M.; Kühn, W.; Kupsc, A.; Lange, J. S.; Lara, M.; Larin, P.; Leng, C.; Li, C.; Li, Cheng; Li, D. M.; Li, F.; Li, F. Y.; Li, G.; Li, H. B.; Li, J. C.; Li, Jin; Li, K.; Li, K.; Li, Lei; Li, P. R.; Li, T.; Li, W. D.; Li, W. G.; Li, X. L.; Li, X. M.; Li, X. N.; Li, X. Q.; Li, Z. B.; Liang, H.; Liang, Y. F.; Liang, Y. T.; Liao, G. R.; Lin, D. X.; Liu, B. J.; Liu, C. X.; Liu, D.; Liu, F. H.; Liu, Fang; Liu, Feng; Liu, H. B.; Liu, H. H.; Liu, H. H.; Liu, H. M.; Liu, J.; Liu, J. B.; Liu, J. P.; Liu, J. Y.; Liu, K.; Liu, K. Y.; Liu, L. D.; Liu, P. L.; Liu, Q.; Liu, S. B.; Liu, X.; Liu, Y. B.; Liu, Z. A.; Liu, Zhiqing; Loehner, H.; Lou, X. C.; Lu, H. J.; Lu, J. G.; Lu, Y.; Lu, Y. P.; Luo, C. L.; Luo, M. X.; Luo, T.; Luo, X. L.; Lyu, X. R.; Ma, F. C.; Ma, H. L.; Ma, L. L.; Ma, Q. M.; Ma, T.; Ma, X. N.; Ma, X. Y.; Maas, F. E.; Maggiora, M.; Mao, Y. J.; Mao, Z. P.; Marcello, S.; Messchendorp, J. G.; Min, J.; Mitchell, R. E.; Mo, X. H.; Mo, Y. J.; Morales Morales, C.; Muchnoi, N. Y. u.; Muramatsu, H.; Nefedov, Y.; Nerling, F.; Nikolaev, I. B.; Ning, Z.; Nisar, S.; Niu, S. L.; Niu, X. Y.; Olsen, S. L.; Ouyang, Q.; Pacetti, S.; Pan, Y.; Patteri, P.; Pelizaeus, M.; Peng, H. P.; Peters, K.; Pettersson, J.; Ping, J. L.; Ping, R. G.; Poling, R.; Prasad, V.; Qi, M.; Qian, S.; Qiao, C. F.; Qin, L. Q.; Qin, N.; Qin, X. S.; Qin, Z. H.; Qiu, J. F.; Rashid, K. H.; Redmer, C. F.; Ripka, M.; Rong, G.; Rosner, C. h.; Ruan, X. D.; Santoro, V.; Sarantsev, A.; Savrie', Mauro; Schoenning, K.; Schumann, S.; Shan, W.; Shao, M.; Shen, C. P.; Shen, P. X.; Shen, X. Y.; Sheng, H. Y.; Song, W. M.; Song, X. Y.; Sosio, S.; Spataro, S.; Sun, G. X.; Sun, J. F.; Sun, S. S.; Sun, Y. J.; Sun, Y. Z.; Sun, Z. J.; Sun, Z. T.; Tang, C. J.; Tang, X.; Tapan, I.; Thorndike, E. H.; Tiemens, M.; Ullrich, M.; Uman, I.; Varner, G. S.; Wang, B.; Wang, B. L.; Wang, D.; Wang, D. Y.; Wang, K.; Wang, L. L.; Wang, L. S.; Wang, M.; Wang, P.; Wang, P. L.; Wang, S. G.; Wang, W.; Wang, W. P.; Wang, X. F.; Wang, Y. D.; Wang, Y. F.; Wang, Y. Q.; Wang, Z.; Wang, Z. G.; Wang, Z. H.; Wang, Z. Y.; Weber, T.; Wei, D. H.; Wei, J. B.; Weidenkaff, P.; Wen, S. P.; Wiedner, U.; Wolke, M.; Wu, L. H.; Wu, Z.; Xia, L.; Xia, L. G.; Xia, Y.; Xiao, D.; Xiao, H.; Xiao, Z. J.; Xie, Y. G.; Xiu, Q. L.; Xu, G. F.; Xu, L.; Xu, Q. J.; Xu, X. P.; Yan, L.; Yan, W. B.; Yan, W. C.; Yan, Y. H.; Yang, H. J.; Yang, H. X.; Yang, L.; Yang, Y.; Yang, Y. Y.; Ye, M.; Ye, M. H.; Yin, J. H.; Yu, B. X.; Yu, C. X.; Yu, J. S.; Yuan, C. Z.; Yuan, W. L.; Yuan, Y.; Yuncu, A.; Zafar, A. A.; Zallo, A.; Zeng, Y.; Zeng, Z.; Zhang, B. X.; Zhang, B. Y.; Zhang, C.; Zhang, C. C.; Zhang, D. H.; Zhang, H. H.; Zhang, H. Y.; Zhang, J. J.; Zhang, J. L.; Zhang, J. Q.; Zhang, J. W.; Zhang, J. Y.; Zhang, J. Z.; Zhang, K.; Zhang, L.; Zhang, X. Y.; Zhang, Y.; Zhang, Y. H.; Zhang, Y. N.; Zhang, Y. T.; Zhang, Yu; Zhang, Z. H.; Zhang, Z. P.; Zhang, Z. Y.; Zhao, G.; Zhao, J. W.; Zhao, J. Y.; Zhao, J. Z.; Zhao, Lei; Zhao, Ling; Zhao, M. G.; Zhao, Q.; Zhao, Q. W.; Zhao, S. J.; Zhao, T. C.; Zhao, Y. B.; Zhao, Z. G.; Zhemchugov, A.; Zheng, B.; Zheng, J. P.; Zheng, W. J.; Zheng, Y. H.; Zhong, B.; Zhou, L.; Zhou, X.; Zhou, X. K.; Zhou, X. R.; Zhou, X. Y.; Zhu, K.; Zhu, K. J.; Zhu, S.; Zhu, S. H.; Zhu, X. L.; Zhu, Y. C.; Zhu, Y. S.; Zhu, Z. A.; Zhuang, J.; Zotti, L.; Zou, B. S.; Zou, J. H

Effect of copper and magnesium on the precipitation characteristics of Al-Li-Mg, Al-Li-Cu and Al-Li-Cu-Mg alloys

The effects of copper and magnesium on the precipitation characteristics of Al-Li-Mg, Al-Li- Cu, and Al-Li-Cu-Mg alloys have been investigated during isochronal and isothermal ageing. In AI-Li-Mg alloys, increasing the magnesium concentration results in stimulation of δ'precipitation by a shift of the α/δ'solvus boundary to higher temperatures. It was shown that for each wt%Mg present in the alloy the α/δ'solvus boundary shifts by 7.0°C. In Al-Li-Cu alloys the concentration of copper has no effect on the position of the α/δ'solvus boundary. The significant stimulation of δ' observed in Al-Li-Cu alloys was shown to be due to the formation of GPCu zones that act as heterogeneous nucleation centres. TEM analysis showed that this heterogeneous nucleation produced composite precipitates consisting of an inner plate of GPI zone and an outer cylindrical shell of δ'. At high copper concentrations (Cu>2.0%) and long ageing times at 150°C, significant retardation of δ' precipitation takes place due to precipitation of the equilibrium T1 and T2 phases. The mechanisms by which copper and magnesium affect the precipitation characteristics of Al- Li-Cu-Mg alloys are different than those operating in the ternary AI-Li-Mg alloys and Al-Li-Cu alloys. In 1.7Lil. 2CuXMg alloys, increasing the magnesium concentration beyond 1.2% causes significant stimulation of δ'precipitation through the formation of Li-Cu-Mg clusters (mechanism referred to as CL δ') that are capable of rapidly developing into δ'. It is proposed that in 1.7Lil. 2CuXMg alloys the initial 1.2%Mg added is consumed in the formation of GPB zones that have very little effect on δ' precipitation. As the magnesium concentration increases to levels higher than 1.2%, the magnesium is free in the matrix to gather both copper and lithium thus forming Li-Cu-Mg clusters which are extremely effective at nucleating δ' In 1.7Li1.2MgXCu alloys the mechanisms by which stimulation of δ' precipitation takes place are again by formation of Li-Cu-Mg clusters (CL δ'), and by nucleation on GPB zones (mechanism referred to as GP δ'). During ageing at 70 and 100°C, and for copper concentrations in the range 0-1.2%, the dominant precipitation mechanism is GP δ'. For higher copper concentrations (1.2<Cu<3.0) the dominant process is CL δ'. Increasing the ageing conditions to 150°C causes precipitation of δ' through classical nucleation and growth for low copper concentrations. For high copper concentrations, the precipitation of δ'comes about through the GP δ'mechanism. Using Kissinger's method, it was found that the activation energy for a' formation in AI-Li-Cu-Mg is equal to 62 kJ/mol, suggesting that the kinetics of the δ'precipitation process are also controlled by the presence of excess vacancies quenched-in from solution heat treatment. It is likely that the Li-Cu-Mg clusters that develop in the alloy also gather excess vacancies thus making the clusters vacancy-rich. For all the alloy systems (Al-Li-Cu, Al-Li-Mg, and Al-Li-Cu-Mg alloys) and independently of the concentrations of copper and magnesium, the largest volume fraction of δ' precipitates form during ageing at 100°C where there is an optimum combination of thermodynamics and kinetics. Ageing the alloys at 150°C (standard heat treatment for lithium containing alloys) and subsequently exposing at 70°C (to simulate service conditions for an aerospace alloy) resulted in embrittlement due to precipitation of additional (fine) δ'. This embrittlement was shown to be closely related to the volume fraction of δ' that precipitates during exposure. In Al-Li-Mg and AI-Li-Cu ternary alloys, increasing the concentration of magnesium and copper respectively, resulted in increased volume fractions of δ' precipitated during exposure and hence increased degrees of embrittlement. For Al-Li-Cu-Mg alloys the maximum volume fraction of δ' precipitated during exposure occurred in the 1.7Li1.2Cu1.2Mg alloy. It was shown that this alloy composition also showed the maximum degree of embrittlement

Development of artificial intelligence model for supporting implant drilling protocol decision making

Purpose: This study aimed to develop an artificial intelligence (AI) model to support the determination of an appropriate implant drilling protocol using cone-beam computed tomography (CBCT) images. Methods: Anonymized CBCT images were obtained from 60 patients. For each case, after implant placement, images of the bone regions at the implant site were extracted from 20 slices of CBCT images. Based on the actual drilling protocol, the images were classified into three categories: protocols A, B, and C. A total of 1,200 images were divided into training and validation datasets (n = 960, 80%) and a test dataset (n = 240, 20%). Another 240 images (80 images for each type) were extracted from the 60 cases as test data. An AI model based on LeNet-5 was developed using these data sets. The accuracy, sensitivity, precision, F-value, area under the curve (AUC) value, and receiver operating curve were calculated. Results: The accuracy of the trained model is 93.8%. The sensitivity results for drilling protocols A, B, and C were 97.5%, 95.0%, and 85.0%, respectively, while those for protocols A, B, and C were 86.7%, 92.7%, and 100%, respectively, and the F values for protocols A, B, and C were 91.8%, 93.8%, and 91.9%, respectively. The AUC values for protocols A, B, and C are 98.6%, 98.6%, and 99.4%, respectively. Conclusions: The AI model established in this study was effective in predicting drilling protocols from CBCT images before surgery, suggesting the possibility of developing a decision-making support system to promote primary stability.Sakai T., Li H., Shimada T., et al. Development of artificial intelligence model for supporting implant drilling protocol decision making. Journal of Prosthodontic Research 67, 360 (2023); https://doi.org/10.2186/jpr.JPR_D_22_00053

Улучшенная формула универсальной оценки экспонента орграфа

Улучшена формула универсальной оценки экспонента n-вершинного примитивного орграфа, данная А. Далмэджем и Н. Мендельсоном (1964) с использованием множества контуров, длины которых взаимно простые. Предложенная формула использует в орграфе множество контуров C с множеством длин L(C) == {ll , ... ,lm}, где d = (ll , . . .,lm) A 1, и множество длин кратчайших путей { Г1^(С) : s = 0,..., d—1} из вершины i в вершину j, проходящих через множество контуров С и образующих полную систему вычетов по модулю d. Показано, что exp Г ^ 1 + F (L(C)) + R(C), где F(L) = d ■ F (li/d,..., lm/d); F (ai,..., am) — число Фробениуса; R(C) = max max { г ^ (С ) } . Указан класс орграфов с множество

Effect of ion species on the accumulation of ion-beam damage in GaN

Wurtzite GaN epilayers bombarded with a wide range of ion species (10 keV H-1, 40 keV C-12, 50 keV O-16, 600 keV Si-28, 130 keV Cu-63, 200 keV Ag-107, 300 keV Au-197, and 500 keV Bi-209) are studied by a combination of Rutherford backscattering/channeling (RBS/C) spectrometry and cross-sectional transmission electron microscopy. Results show that strong dynamic annealing processes lead to a complex dependence of the damage-buildup behavior in GaN on ion species. For room-temperature bombardment with different ion species, bulk disorder, as measured by RBS/C, saturates at some level that is below the random level, and amorphization proceeds layer-by-layer from the GaN surface with increasing ion dose. The saturation level of bulk disorder depends on implant conditions and is much higher for light-ion bombardment than for the heavy-ion irradiation regime. In the case of light ions, when ion doses needed to observe significant lattice disorder in GaN are large (greater than or similar to 10(16) cm(-2)), chemical effects of implanted species dominate. Such implanted atoms appear to stabilize an amorphous phase in GaN and/or to act as effective traps for ion-beam-generated mobile point defects and enhance damage buildup. In particular, the presence of a large conce ntration of carbon in GaN strongly enhances the accumulation of implantation-produced disorder. For heavier ions, where chemical effects of implanted species seem to be negligible, an increase in the density of collision cascades strongly increases the level of implantation-produced lattice disorder in the bulk as well as the rate of layer-by-layer amorphization proceeding from the surface. Such an increase in stable damage and the rate of planar amorphization is attributed to (i) an increase in the defect clustering efficiency with increasing density of ion-beam-generated defects and/or (ii) a superlinear dependence of ion-beam-generated defects, which survive cascade quenching, on the density of collision cascades. Physical mechanisms responsible for such a superlinear dependence of ion-beam-generated defects on collision cascade density are considered. Mechanisms of surface and bulk amorphization in GaN are also discussed

Quantum anti-Zeno effect without rotating wave approximation

In this paper, we systematically study the spontaneous decay phenomenon of a two-level system under the influences of both its environment and continuous measurements. In order to clarify some well-established conclusions about the quantum Zeno effect (QZE) and the quantum anti-Zeno effect (QAZE), we do not use the rotating wave approximation (RWA) in obtaining an effective Hamiltonian. We examine various spectral distributions by making use of our present approach in comparison with other approaches. It is found that with respect to a bare excited state even without the RWA, the QAZE can still happen for some cases, e.g., the interacting spectra of hydrogen. But for a physical excited state, which is a renormalized dressed state of the atomic state, the QAZE disappears and only the QZE remains. These discoveries inevitably show a transition from the QZE to the QAZE as the measurement interval changes.Comment: 14 pages, 8 figure

Creation of Entanglement between Two Electron Spins Induced by Many Spin Ensemble Excitations

We theoretically explore the possibility of creating spin entanglement by simultaneously coupling two electronic spins to a nuclear ensemble. By microscopically modeling the spin ensemble with a single mode boson field, we use the time-dependent Fr\"{o}hlich transformation (TDFT) method developed most recently [Yong Li, C. Bruder, and C. P. Sun, Phys. Rev. A \textbf{75}, 032302 (2007)] to calculate the effective coupling between the two spins. Our investigation shows that the total system realizes a solid state based architecture for cavity QED. Exchanging such kind effective boson in a virtual process can result in an effective interaction between two spins. It is discovered that a maximum entangled state can be obtained when the velocity of the electrons matches the initial distance between them in a suitable way. Moreover, we also study how the number of collective excitations influences the entanglement. It is shown that the larger the number of excitation is, the less the two spins entangle each other.Comment: 8 pages, 4 figure

Prognostic significance of proliferation rate and DNA ploidy in astrocytic gliomas treated with radiotherapy

AimThe proliferative potential, and DNA ploidy in 50 brain tumours (15 grade I & II, and 35 grade III & IV astrocytomas) were investigated using bromodeoxyuridine (BrdUrd) incorporation and flow cytometry.Materials/MethodsTumour samples taken from each patient during surgery were incubated in vitro for one hour at 37°C with bromodeoxyuridine (BrdUrd), using the high pressure oxygen method. The percentage of BrdUrd-labelled cells (BrdUrd Labelling index, BrdUrd LI), and the total DNA content were evaluated. After surgery, 21 patients received conventionally fractionated radiotherapy (RT), 11 patients received accelerated RT, and 18 patients underwent hypofractionated RT.ResultsThe tumours showed variability in BrdUrd LI values, which ranged from 0.3 to 15.8%. A significantly higher mean value for BrdUrd LI was shown in grades AIII & IV (3.5%), than in astrocytomas of grades AI & II (1.5%, p=0.005). A lower though not statistically significant percentage of DNA aneuploidy was observed in low-grade (40.2%) glioma than was seen in high-grade (65.7%) glioma. Univariate analysis showed that younger (≤50 years) patients (p=0.001), those with AI & II glioma (p=0.000), low tumour proliferation rate (BrdUrd LI ≤2.1%, p=0.006) and conventional or hypofractionated RT (p=0.000) had a significantly higher 5-year survival rate. Tumour ploidy had no influence on patients’ survival (p=0.261). However, a Cox multivariate analysis showed that only the patients’ age (>50 years), high grade tumours (AIII & IV) and accelerated RT were significantly unfavourable prognostic factors in terms of survival.ConclusionsTo improve RT results, younger patients (≤50 years) with fast proliferating tumours should receive more aggressive treatment