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1

Fannin, H. B., J. J. Hurly, and F. R. Meeks. "Quantum-Statistical Modeling of ICPs: He(I)." Applied Spectroscopy 42, no. 7 (September 1988): 1181–86. http://dx.doi.org/10.1366/0003702884430065.

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Relative populations of excited states of He(I) in reduced-pressure ICPs have been shown to obey Fermi-Dirac statistical counting. A single thermodynamic temperature—2000 K—defines the distribution. The experimental relative populations and the Fermi-Dirac distributions agree within fractions of one percent.
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2

Boé, Jean-Marie, and Fabrice Philippe. "Partitions and the Fermi–Dirac Distribution." Journal of Combinatorial Theory, Series A 92, no. 2 (November 2000): 173–85. http://dx.doi.org/10.1006/jcta.2000.3059.

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3

Wildberger, K., P. Lang, R. Zeller, and P. H. Dederichs. "Fermi-Dirac distribution inab initioGreen’s-function calculations." Physical Review B 52, no. 15 (October 15, 1995): 11502–8. http://dx.doi.org/10.1103/physrevb.52.11502.

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4

MOLINARI, V., D. MOSTACCI, and F. PIZZIO. "QUANTUM–RELATIVISTIC DISTRIBUTION FUNCTION FOR BOSONS AND FERMIONS." International Journal of Modern Physics B 26, no. 12 (May 8, 2012): 1241004. http://dx.doi.org/10.1142/s0217979212410044.

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In the present work, quantum–relativistic equilibrium distribution functions are derived for bosons above the critical temperature and for weakly degenerate fermions, extending to the relativistic case the Bose–Einstein and Fermi–Dirac distributions.
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5

Melrose, D. B., and A. Mushtaq. "Plasma dispersion function for a Fermi–Dirac distribution." Physics of Plasmas 17, no. 12 (December 2010): 122103. http://dx.doi.org/10.1063/1.3528272.

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6

Moussa, Jonathan E. "Minimax rational approximation of the Fermi-Dirac distribution." Journal of Chemical Physics 145, no. 16 (October 28, 2016): 164108. http://dx.doi.org/10.1063/1.4965886.

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7

Rosser, W. G. V. "The Fermi-Dirac and Bose-Einstein distribution functions." European Journal of Physics 7, no. 4 (October 1, 1986): 297–98. http://dx.doi.org/10.1088/0143-0807/7/4/116.

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8

Brunel, Vivien. "From the Fermi–Dirac distribution to PD curves." Journal of Risk Finance 20, no. 2 (March 18, 2019): 138–54. http://dx.doi.org/10.1108/jrf-01-2018-0009.

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Purpose In machine learning applications, and in credit risk modeling in particular, model performance is usually measured by using cumulative accuracy profile (CAP) and receiving operating characteristic curves. The purpose of this paper is to use the statistics of the CAP curve to provide a new method for credit PD curves calibration that are not based on arbitrary choices as the ones that are used in the industry. Design/methodology/approach The author maps CAP curves to a ball–box problem and uses statistical physics techniques to compute the statistics of the CAP curve from which the author derives the shape of PD curves. Findings This approach leads to a new type of shape for PD curves that have not been considered in the literature yet, namely, the Fermi–Dirac function which is a two-parameter function depending on the target default rate of the portfolio and the target accuracy ratio of the scoring model. The author shows that this type of PD curve shape is likely to outperform the logistic PD curve that practitioners often use. Practical implications This paper has some practical implications for practitioners in banks. The author shows that the logistic function which is widely used, in particular in the field of retail banking, should be replaced by the Fermi–Dirac function. This has an impact on pricing, the granting policy and risk management. Social implications Measuring credit risk accurately benefits the bank of course and the customers as well. Indeed, granting is based on a fair evaluation of risk, and pricing is done accordingly. Additionally, it provides better tools to supervisors to assess the risk of the bank and the financial system as a whole through the stress testing exercises. Originality/value The author suggests that practitioners should stop using logistic PD curves and should adopt the Fermi–Dirac function to improve the accuracy of their credit risk measurement.
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9

BROWN, S. R., and M. G. HAINES. "Transport in partially degenerate, magnetized plasmas. Part 1. Collision operators." Journal of Plasma Physics 58, no. 4 (December 1997): 577–600. http://dx.doi.org/10.1017/s0022377897006041.

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The quantum Boltzmann collision operator is expanded to yield a degenerate form of the Fokker–Planck collision operator. This is analysed using Rosenbluth potentials to give a degenerate analogue of the Shkarofsky operator. The distribution function is then expanded about an equilibrium Fermi–Dirac distribution function using a tensor perturbation formulation to give a zeroth-order and a first-order collision operator. These equations are shown to satisfy the relevant conservation equations. It is shown that the distribution function relaxes to a Fermi–Dirac form through electron–electron collisions.
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10

Yasuda, Makoto, Takeshi Furuhashi, and Shigeru Okuma. "Phase Transitions in Fuzzy Clustering Based on Fuzzy Entropy." Journal of Advanced Computational Intelligence and Intelligent Informatics 7, no. 3 (October 20, 2003): 370–76. http://dx.doi.org/10.20965/jaciii.2003.p0370.

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We studied the statistical mechanical characteristics of fuzzy clustering regularized with fuzzy entropy. We obtained Fermi-Dirac distribution as a membership function by regularizing the fuzzy c-means with fuzzy entropy. We then formulated it as direct annealing clustering, and determined the meanings of the Fermi-Dirac function and fuzzy entropy from the statistical mechanical point of view, and showed that this fuzzy clustering is a part of Fermi-Dirac statistics. We also derived the critical temperature at which phase transition occurs in this fuzzy clustering. Then, with a combination of cluster divisions by phase transitions and an adequate division termination condition, we derived fuzzy clustering that automatically determined the number of clusters, as verified by numerical experiments.
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11

Kash, J. A., M. Zachau, E. E. Mendez, J. M. Hong, and T. Fukuzawa. "Fermi-Dirac distribution of excitons in coupled quantum wells." Physical Review Letters 66, no. 17 (April 29, 1991): 2247–50. http://dx.doi.org/10.1103/physrevlett.66.2247.

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12

Bae, Gi-Chan, and Seok-Bae Yun. "Quantum BGK Model near a Global Fermi--Dirac Distribution." SIAM Journal on Mathematical Analysis 52, no. 3 (January 2020): 2313–52. http://dx.doi.org/10.1137/19m1270021.

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13

STEFANESCU, ELIADE, and AUREL SANDULESCU. "MICROSCOPIC COEFFICIENTS FOR THE QUANTUM MASTER EQUATION OF A FERMI SYSTEM." International Journal of Modern Physics E 11, no. 02 (April 2002): 119–30. http://dx.doi.org/10.1142/s0218301302000739.

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In a previous paper, we derived a master equation for fermions, of Lindblad's form, with coefficients depending on microscopic quantities. In this paper, we study the properties of the dissipative coefficients taking into account the explicit expressions of: (a) the matrix elements of the dissipative potential, evaluated from the condition that, essentially, this potential induces transitions among the system eigenstates without significantly modifying these states, (b) the densities of the environment states according to the Thomas–Fermi model, and (c) the occupation probabilities of these states taken as a Fermi–Dirac distribution. The matrix of these coefficients correctly describes the system dynamics: (a) for a normal, Fermi–Dirac distribution of the environment population, the decays dominate the excitation processes; (b) for an inverted (exotic) distribution of this population, specific to a clustering state, the excitation processes are dominant.
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14

Maslov, V. P. "The relationship between the Fermi–Dirac distribution and statistical distributions in languages." Mathematical Notes 101, no. 3-4 (March 2017): 645–59. http://dx.doi.org/10.1134/s0001434617030221.

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15

Walker, A. L., D. L. Curry, and H. B. Fannin. "Comparison of Methodologies for the Determination of Excitation Temperatures of Plasma Support Gases." Applied Spectroscopy 48, no. 3 (March 1994): 333–37. http://dx.doi.org/10.1366/0003702944028290.

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Methodologies for the determination of excitation temperatures for the argon support gas in six inductively coupled plasma systems are compared. These methods include a Boltzmann plot, partial Boltzmann plots, a polynomial fit, and a Fermi-Dirac model. The temperature(s) and fitting statistics are reported for each method. Additionally, the theoretical basis for each method is briefly reviewed. All these methods, with the exception of the first and last, yield multiple excitation temperatures; however, the Fermi-Dirac model more closely models the observed population distribution of excited electronic states in the argon atom.
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16

Prostko, Eric P., Hsin-I. Wu, James M. Chandler, and Scott A. Senseman. "Modeling weed emergence as influenced by burial depth using the Fermi-Dirac distribution function." Weed Science 45, no. 2 (April 1997): 242–48. http://dx.doi.org/10.1017/s004317450009278x.

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Research was conducted to determine the suitability of the Fermi-Dirac distribution function for modeling the seedling emergence of downy brome, johnsongrass, and round-leaved mallow, as influenced by burial depth. Six sets of previously published emergence data were used to formulate the model and test its adequacy. Two independent johnsongrass emergence data sets were used to validate the model. Constant temperature growth chamber studies were conducted to evaluate the effects of temperature and moisture on the model parameters. The Fermi-Dirac distribution function was found to adequately describe the seedling emergence of downy brome, johnsongrass, and round-leaved mallow as indicated by a good visual data fit, narrow confidence intervals for the model parameters, and regression analysis of observed vs. modeled data. Although this function is a model used in physical science, its parameters can be related to abiotic factors such as soil texture, temperature, and moisture.
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17

Kim, Sung-Cheol, Adith S. Arun, Mehmet Eren Ahsen, Robert Vogel, and Gustavo Stolovitzky. "The Fermi–Dirac distribution provides a calibrated probabilistic output for binary classifiers." Proceedings of the National Academy of Sciences 118, no. 34 (August 19, 2021): e2100761118. http://dx.doi.org/10.1073/pnas.2100761118.

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Binary classification is one of the central problems in machine-learning research and, as such, investigations of its general statistical properties are of interest. We studied the ranking statistics of items in binary classification problems and observed that there is a formal and surprising relationship between the probability of a sample belonging to one of the two classes and the Fermi–Dirac distribution determining the probability that a fermion occupies a given single-particle quantum state in a physical system of noninteracting fermions. Using this equivalence, it is possible to compute a calibrated probabilistic output for binary classifiers. We show that the area under the receiver operating characteristics curve (AUC) in a classification problem is related to the temperature of an equivalent physical system. In a similar manner, the optimal decision threshold between the two classes is associated with the chemical potential of an equivalent physical system. Using our framework, we also derive a closed-form expression to calculate the variance for the AUC of a classifier. Finally, we introduce FiDEL (Fermi–Dirac-based ensemble learning), an ensemble learning algorithm that uses the calibrated nature of the classifier’s output probability to combine possibly very different classifiers.
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18

PRZENIOSŁO, R., T. BARSZCZAK, R. KUTNER, W. GUZICKI, and W. RENZ. "Monte Carlo Simulations of Lattice Gases Exhibiting Quantum Statistical Distributions." International Journal of Modern Physics C 02, no. 01 (March 1991): 450–54. http://dx.doi.org/10.1142/s0129183191000676.

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A non-interacting lattice gas with order preservation in a constant external field is studied by numerical and analytical methods. The equilibrium distribution is of the Bose-Einstein type. If additional hard-core repulsion is imposed, it becomes a distribution of Fermi-Dirac type. When relaxing the order preservation condition the classical Boltzmann distribution is recovered.
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19

Gasparini, Mauro, and Peiming Ma. "The multivariate Fermi-Dirac distribution and its applications in quality control." Journal of the Italian Statistical Society 5, no. 3 (December 1996): 307–22. http://dx.doi.org/10.1007/bf02589093.

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20

Gribakin, G. F., A. A. Gribakina, and V. V. Flambaum. "Quantum Chaos in Multicharged Ions and Statistical Approach to the Calculation of Electron - Ion Resonant Radiative Recombination." Australian Journal of Physics 52, no. 3 (1999): 443. http://dx.doi.org/10.1071/ph98093.

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We show that the spectrum and eigenstates of open-shell multicharged atomic ions near the ionisation threshold are chaotic, as a result of extremely high level densities of multiply excited electron states (103 eV–1 in Au24+) and strong configuration mixing. This complexity enables one to use statistical methods to analyse the system. We examine the dependence of the orbital occupation numbers and single-particle energies on the excitation energy of the system, and show that the occupation numbers are described by the Fermi–Dirac distribution, and the temperature and chemical potential can be introduced. The Fermi–Dirac temperature is close to the temperature defined through the canonical distribution. Using a statistical approach we estimate the contribution of multielectron resonant states to the radiative capture of low-energy electrons by Au25+ and demonstrate that this mechanism fully accounts for the 102 times enhancement of the recombination over the direct radiative recombination, in agreement with recent experimental observations.
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21

Chakraborty, P. K., S. K. Biswas, and K. P. Ghatak. "On the modification of the Fermi–Dirac distribution function in degenerate semiconductors." Physica B: Condensed Matter 352, no. 1-4 (October 2004): 111–17. http://dx.doi.org/10.1016/j.physb.2004.06.062.

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22

Conroy, J. M., H. G. Miller, and A. R. Plastino. "Thermodynamic consistency of the q-deformed Fermi–Dirac distribution in nonextensive thermostatics." Physics Letters A 374, no. 45 (October 2010): 4581–84. http://dx.doi.org/10.1016/j.physleta.2010.09.038.

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23

Mamedov, B. A. "Analytical evaluation of the plasma dispersion function for a Fermi Dirac distribution." Chinese Physics B 21, no. 5 (May 2012): 055204. http://dx.doi.org/10.1088/1674-1056/21/5/055204.

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24

Ray, Amritansu, and S. K. Majumder. "Derivation of some new distributions in statistical mechanics using maximum entropy approach." Yugoslav Journal of Operations Research 24, no. 1 (2014): 145–55. http://dx.doi.org/10.2298/yjor120912031r.

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The maximum entropy principle has been earlier used to derive the Bose Einstein(B.E.), Fermi Dirac(F.D.) & Intermediate Statistics(I.S.) distribution of statistical mechanics. The central idea of these distributions is to predict the distribution of the microstates, which are the particle of the system, on the basis of the knowledge of some macroscopic data. The latter information is specified in the form of some simple moment constraints. One distribution differs from the other in the way in which the constraints are specified. In the present paper, we have derived some new distributions similar to B.E., F.D. distributions of statistical mechanics by using maximum entropy principle. Some proofs of B.E. & F.D. distributions are shown, and at the end some new results are discussed.
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25

Bernegger, Stefan. "The Swiss Re Exposure Curves and the MBBEFD Distribution Class." ASTIN Bulletin 27, no. 1 (May 1997): 99–111. http://dx.doi.org/10.2143/ast.27.1.563208.

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AbstractA new two-parameter family of analytical functions will be introduced for the modelling of loss distributions and exposure curves. The curve family contains the Maxwell-Boltzmann, the Bose-Einstein and the Fermi-Dirac distributions, which are well known in statistical mechanics. The functions can be used for the modelling of loss distributions on the finite interval [0, 1] as well as on the interval [0, ∞]. The functions defined on the interval [0, 1] are discussed in detail and related to several Swiss Re exposure curves used in practice. The curves can be fitted to the first two moments μ and σ of a loss distribution or to the first moment μ and the total loss probability p.
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26

Cowan, Brian. "On the Chemical Potential of Ideal Fermi and Bose Gases." Journal of Low Temperature Physics 197, no. 5-6 (September 12, 2019): 412–44. http://dx.doi.org/10.1007/s10909-019-02228-0.

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Abstract Knowledge of the chemical potential is essential in application of the Fermi–Dirac and the Bose–Einstein distribution functions for the calculation of properties of quantum gases. We give expressions for the chemical potential of ideal Fermi and Bose gases in 1, 2 and 3 dimensions in terms of inverse polylogarithm functions. We provide Mathematica functions for these chemical potentials together with low- and high-temperature series expansions. In the 3d Bose case we give also expansions about $$T_{{{{\mathrm {B}}}}}$$ T B . The Mathematica routines for the series allow calculation to arbitrary order.
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27

CAI, SHUKUAN, GUOZHEN SU, and JINCAN CHEN. "GENERAL THERMOSTATISTICAL PROPERTIES OF A Q-DEFORMED FERMI GAS TRAPPED IN A POWER-LAW POTENTIAL." International Journal of Modern Physics B 24, no. 17 (July 10, 2010): 3323–30. http://dx.doi.org/10.1142/s0217979210055962.

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The q-deformed Fermi–Dirac distribution derived from the q-fermion algebra is used to study the thermostatistical properties of a q-deformed Fermi gas trapped in a generic power-law potential, and the effects of q-deformation on the properties of the system are discussed. It is shown that q-deformation leads to some novel characteristics compared with those of an original Fermi system. It is found that the effects of q-deformation display different characteristics in the cases of different kinematic characteristics of particles and different shapes of external potentials. The results obtained here present a unified description for the thermostatistical properties of a class of q-deformed as well as original Fermi systems, so that many important conclusions in the literature are included in this paper.
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28

Justice, Paul, Emily Marshman, and Chandralekha Singh. "Student understanding of Fermi energy, the Fermi–Dirac distribution and total electronic energy of a free electron gas." European Journal of Physics 41, no. 1 (December 6, 2019): 015704. http://dx.doi.org/10.1088/1361-6404/ab537c.

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29

Liu, Fu-Hu, Ya-Qin Gao, and Hua-Rong Wei. "On Descriptions of Particle Transverse Momentum Spectra in High Energy Collisions." Advances in High Energy Physics 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/293873.

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The transverse momentum spectra obtained in the frame of an isotropic emission source are compared in terms of Tsallis, Boltzmann, Fermi-Dirac, and Bose-Einstein distributions and the Tsallis forms of the latter three standard distributions. It is obtained that, at a given set of parameters, the standard distributions show a narrower shape than their Tsallis forms which result in wide and/or multicomponent spectra with the Tsallis distribution in between. A comparison among the temperatures obtained from the distributions is made with a possible relation to the Boltzmann temperature. An example of the angular distributions of projectile fragments in nuclear collisions is given.
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30

Tai, Yen-Ling, Shin-Jhe Huang, Chien-Chang Chen, and Henry Horng-Shing Lu. "Computational Complexity Reduction of Neural Networks of Brain Tumor Image Segmentation by Introducing Fermi–Dirac Correction Functions." Entropy 23, no. 2 (February 11, 2021): 223. http://dx.doi.org/10.3390/e23020223.

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Nowadays, deep learning methods with high structural complexity and flexibility inevitably lean on the computational capability of the hardware. A platform with high-performance GPUs and large amounts of memory could support neural networks having large numbers of layers and kernels. However, naively pursuing high-cost hardware would probably drag the technical development of deep learning methods. In the article, we thus establish a new preprocessing method to reduce the computational complexity of the neural networks. Inspired by the band theory of solids in physics, we map the image space into a noninteraction physical system isomorphically and then treat image voxels as particle-like clusters. Then, we reconstruct the Fermi–Dirac distribution to be a correction function for the normalization of the voxel intensity and as a filter of insignificant cluster components. The filtered clusters at the circumstance can delineate the morphological heterogeneity of the image voxels. We used the BraTS 2019 datasets and the dimensional fusion U-net for the algorithmic validation, and the proposed Fermi–Dirac correction function exhibited comparable performance to other employed preprocessing methods. By comparing to the conventional z-score normalization function and the Gamma correction function, the proposed algorithm can save at least 38% of computational time cost under a low-cost hardware architecture. Even though the correction function of global histogram equalization has the lowest computational time among the employed correction functions, the proposed Fermi–Dirac correction function exhibits better capabilities of image augmentation and segmentation.
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31

Cernohorsky, J., and S. A. Bludman. "Maximum entropy distribution and closure for Bose-Einstein and Fermi-Dirac radiation transport." Astrophysical Journal 433 (September 1994): 250. http://dx.doi.org/10.1086/174640.

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32

Changshi, Liu, and Li Feng. "Calibration of the limiting current of oxygen sensors by the Fermi-Dirac distribution." International Journal of Numerical Modelling: Electronic Networks, Devices and Fields 29, no. 1 (March 3, 2015): 109–14. http://dx.doi.org/10.1002/jnm.2050.

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33

Fukushima, Toshio. "Computation of a general integral of Fermi–Dirac distribution by McDougall–Stoner method." Applied Mathematics and Computation 238 (July 2014): 485–510. http://dx.doi.org/10.1016/j.amc.2014.04.028.

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34

Callelero, Marcielow J., and Danilo M. Yanga. "Mobility of spin polarons with vertex corrections." International Journal of Modern Physics B 33, no. 18 (July 20, 2019): 1950195. http://dx.doi.org/10.1142/s0217979219501959.

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The mobility of holes in the spin polaron theory is discussed in this paper using a representation where holes are described as spinless fermions and spins as normal bosons. The hard-core bosonic operator is introduced through the Holstein–Primakoff transformation. Mathematically, the theory is implemented in the finite temperature (Matsubara) Green’s function method. The expressions for the zeroth-order term of the hole mobility is determined explicitly for hole occupation factor taking the form of Fermi–Dirac distribution and the classical Maxwell–Boltzmann distribution function. These are proportional to the relaxation time and the square of the renormalization factor. In the Ising limit, we showed that the mobility is zero and the holes are localized. The calculation of the hole mobility is generalized by considering the vertex corrections, which included the ladder diagrams. One of the vertex functions in the hole mobility can be evaluated using the Ward identity for hole-spin wave weak interaction. We also derived an expression for the hole mobility with vertex corrections in the low-temperature limit and vanishing self-energy effects. Our calculation is made up to second-order correction in the case where the hole occupation factor follows the Fermi–Dirac distribution.
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35

SRIRAMKUMAR, L. "ODD STATISTICS IN ODD DIMENSIONS FOR ODD COUPLINGS." Modern Physics Letters A 17, no. 15n17 (June 7, 2002): 1059–66. http://dx.doi.org/10.1142/s0217732302007545.

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We consider the response of a uniformly accelerated monopole detector that is coupled non-linearly to the nth power of a quantum scalar field in (D + 1)-dimensional flat spacetime. We show that, when (D + 1) is even, the response of the detector in the Minkowski vacuum is characterized by a Bose-Einstein factor for all n. Whereas, when (D + 1) is odd, we find that a Fermi-Dirac factor appears in the detector response when n is odd, but a Bose-Einstein factor arises when n is even. We emphasize the point that, since, along the accelerated trajectory, the Wightman function and, as a result, the (2n)-point function satisfy the Kubo-Martin-Schwinger condition (as required for a scalar field) in all dimensions, the appearance of a Fermi-Dirac factor (instead of the expected Bose-Einstein distribution) for odd(D + 1)andn reflects a peculiar feature of the detector rather than imply a fundamental change in field theory.
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36

Mendl, Christian B. "Matrix-valued quantum lattice Boltzmann method." International Journal of Modern Physics C 26, no. 10 (June 24, 2015): 1550113. http://dx.doi.org/10.1142/s0129183115501132.

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We devise a lattice Boltzmann method (LBM) for a matrix-valued quantum Boltzmann equation, with the classical Maxwell distribution replaced by Fermi–Dirac functions. To accommodate the spin density matrix, the distribution functions become 2 × 2 matrix-valued. From an analytic perspective, the efficient, commonly used BGK approximation of the collision operator is valid in the present setting. The numerical scheme could leverage the principles of LBM for simulating complex spin systems, with applications to spintronics.
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37

Ahsanullah, Mohammad, and Mohammad Shakil. "Characterizations of continuous probability distributions occurring in physics and allied sciences by truncated moment." International Journal of Advanced Statistics and Probability 3, no. 1 (May 24, 2015): 100. http://dx.doi.org/10.14419/ijasp.v3i1.4612.

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<p>A probability distribution can be characterized through various methods. Before a particular probability distribution model is applied to fit the real-world data, it is necessary to confirm whether the given continuous probability distribution satisfies the underlying requirements by its characterization. In this paper, characterizations of some continuous probability distributions occurring in physics and allied sciences have been established. We have considered the normal, Laplace, Lorentz, logistic, Boltzmann, Rayleigh, log-normal, Maxwell, Fermi-Dirac, and Bose-Einstein distributions, and characterized them by applying a truncated moment method; that is, by taking a product of reverse hazard rate and another function of the truncated point. It is hoped that the proposed characterizations will be useful for researchers in various fields of physics and allied sciences.</p>
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38

Kim, Sang Pyo, Sung Ku Kim, Kwang-Sup Soh, and Jae Hyung Yee. "Remarks on Renormalization of Black Hole Entropy." International Journal of Modern Physics A 12, no. 29 (November 20, 1997): 5223–34. http://dx.doi.org/10.1142/s0217751x97002802.

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We elaborate the renormalization process of entropy of a nonextremal and an extremal Reissner–Nordström black hole by using the Pauli–Villars regularization method, in which the regulator fields obey either the Bose–Einstein or Fermi–Dirac distribution depending on their spin-statistics. The black hole entropy involves only two renormalization constants. We also discuss the entropy and temperature of the extremal black hole.
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39

Ydrefors, E., and J. Suhonen. "Charged-Current Neutrino-Nucleus Scattering off the Even Molybdenum Isotopes." Advances in High Energy Physics 2012 (2012): 1–15. http://dx.doi.org/10.1155/2012/373946.

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Neutrinos from supernovae constitute important probes of both the currently unknown supernova mechanisms and of neutrino properties. Reliable information about the nuclear responses to supernova neutrinos is therefore crucial. In this work, we compute the cross sections for the charged-current neutrino-nucleus scattering off the even-even molybdenum isotopes. The nuclear responses to supernova neutrinos are subsequently calculated by folding the cross sections with a Fermi-Dirac distribution.
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40

Changshi, Liu. "More precise determination of work function based on Fermi–Dirac distribution and Fowler formula." Physica B: Condensed Matter 444 (July 2014): 44–48. http://dx.doi.org/10.1016/j.physb.2014.03.037.

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41

Summers, Huw D., and Paul Rees. "Derivation of a modified Fermi-Dirac distribution for quantum dot ensembles under nonthermal conditions." Journal of Applied Physics 101, no. 7 (April 2007): 073106. http://dx.doi.org/10.1063/1.2709614.

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42

Wang, Z. B., M. G. Helander, M. T. Greiner, and Z. H. Lu. "The impact of the Fermi–Dirac distribution on charge injection at metal/organic interfaces." Journal of Chemical Physics 132, no. 17 (May 7, 2010): 174708. http://dx.doi.org/10.1063/1.3424762.

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Kim, Heung Soo, Anindya Ghoshal, Jaehwan Kim, and Seung-Bok Choi. "Transient analysis of delaminated smart composite structures by incorporating the Fermi–Dirac distribution function." Smart Materials and Structures 15, no. 2 (January 30, 2006): 221–31. http://dx.doi.org/10.1088/0964-1726/15/2/001.

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Furutani, Y., H. Totsuji, K. Mima, and H. Takabe. "Internal structure of a partially ionized heavy ion. Isolated ion model." Laser and Particle Beams 7, no. 3 (August 1989): 581–88. http://dx.doi.org/10.1017/s0263034600007552.

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An effective potential and an associated electron density in a partially ionized high-Z ion are evaluated within the framework of the Thomas–Fermi–Dirac–Weizsäcker statistical model of atoms. The results are then injected as an initial input into the one-electron Schrödinger equation, a procedure based on the density functional theory. The self-consistency between the two approaches is examined. For a partially ionized ion at zero and finite temperatures, a number of bound electrons is counted by a sum over the principal quantum number, which diverges due to the contribution from shallow bound (Rydberg) levels. A truncation of this sum is devised by application of the Planck–Larkin scheme to the Fermi distribution
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TREVISAN, LUIS AUGUSTO, and CARLOS MIREZ. "A NONEXTENSIVE STATISTICAL MODEL FOR THE NUCLEON STRUCTURE FUNCTION." International Journal of Modern Physics E 22, no. 07 (July 2013): 1350044. http://dx.doi.org/10.1142/s0218301313500444.

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We studied an application of nonextensive thermodynamics to describe the structure function of nucleon, in a model where the usual Fermi–Dirac and Bose–Einstein energy distribution were replaced by the equivalent functions of the q-statistical. The parameters of the model are given by an effective temperature T, the q parameter (from Tsallis statistics), and two chemical potentials given by the corresponding up (u) and down (d) quark normalizations in the nucleon.
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Rossani, A. "Semiconductor spintronics: The full matrix approach." Modern Physics Letters B 29, no. 35n36 (December 30, 2015): 1550243. http://dx.doi.org/10.1142/s0217984915502437.

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A new model, based on an asymptotic procedure for solving the spinor kinetic equations of electrons and phonons is proposed, which gives naturally the displaced Fermi–Dirac distribution function at the leading order. The balance equations for the electron number, energy density and momentum, plus the Poisson’s equation, constitute now a system of six equations. Moreover, two equations for the evolution of the spin densities are added, which account for a general dispersion relation.
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Rudakovskyi, A. V., and D. O. Savchenko. "New Model of Density Distribution for Fermionic Dark Matter Halos." Ukrainian Journal of Physics 63, no. 9 (September 24, 2018): 769. http://dx.doi.org/10.15407/ujpe63.9.769.

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We formulate a new model of density distribution for halos made of warm dark matter (WDM) particles. The model is described by a single microphysical parameter – the mass (or, equivalently, the maximal value of the initial phase-space density distribution) of dark matter particles. Given the WDM particle mass and the parameters of a dark matter density profile at the halo periphery, this model predicts the inner density profile. In the case of initial Fermi–Dirac distribution, we successfully reproduce cored dark matter profiles from N-body simulations. We calculate also the core radii of warm dark matter halos of dwarf spheroidal galaxies for particle masses mFD = 100, 200, 300, and 400 eV.
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Balasi, K. G., and T. S. Kosmas. "Neutrino scattering off the 95,97Mo isotopes." HNPS Proceedings 19 (January 1, 2020): 60. http://dx.doi.org/10.12681/hnps.2517.

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In this work we perform calculations of the cross sections for neutral-current neutrino scattering off the 95,97Mo isotopes. Both the incoherent and coherent con- tributions to the cross sections are considered. The wave functions of the initial and final nuclear states are constructed in the context of the quasiparticle phonon model (MQPM). The response of the aformentioned nuclei to supernova neutrinos are computed by folding the obtained cross sections with a two-parameter Fermi-Dirac distribution.
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Balasi, K. G., and T. S. Kosmas. "Neutrino scattering off the stable even-even Mo isotopes." HNPS Proceedings 18 (November 23, 2019): 25. http://dx.doi.org/10.12681/hnps.2534.

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A systematic study of neutrino-nucleus reaction rates at low and intermediate energies are presented and discussed, focusing on the even-even Mo isotopes. Contributions coming from both the vector and axial-vector components of the corresponding hadronic currents have been included. The response of these detectors to supernova neutrino is also studied, by exploiting the above results and utilizing the folding procedure assuming a two parameter Fermi-Dirac distribution for the supernova neutrino energy-spectra.
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Van Mieghem, P., and S. Tang. "WEIGHT OF THE SHORTEST PATH TO THE FIRST ENCOUNTERED PEER IN A PEER GROUP OF SIZE m." Probability in the Engineering and Informational Sciences 22, no. 1 (December 18, 2007): 37–52. http://dx.doi.org/10.1017/s026996480800003x.

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We model the weight (e.g., delay, distance, or cost) from an arbitrary node to the nearest (in weight) peer in a peer-to-peer (P2P) network. The exact probability generating function and an asymptotic analysis is presented for a random graph with independent and identically distributed exponential link weights. The asymptotic distribution function is a Fermi–Dirac distribution that frequently appears in statistical physics. The good agreement with simulation results for relatively small P2P networks makes the asymptotic formula for the probability density function useful for estimating the minimal number of peers to offer an acceptable quality (delay or latency).
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