Comment on 'Anti-tumour activity of abiraterone and diethylstilboestrol when administered sequentially to men with castration-resistant prostate cancer'

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Dynamical Casimir effect for magnons in a spinor Bose-Einstein condensate

Magnon excitation in a spinor Bose-Einstein condensate by a driven magnetic field is shown to have a close analogy with the dynamical Casimir effect. A time-dependent external magnetic field amplifies quantum fluctuations in the magnetic ground state of the condensate, leading to magnetization of the system. The magnetization occurs in a direction perpendicular to the magnetic field breaking the rotation symmetry. This phenomenon is numerically demonstrated and the excited quantum field is shown to be squeezed.Comment: 8 pages, 3 figure

Rat floods and water floods: the ecological and sociological dynamics of rodent management in Bangladesh

Chakma, N., Belmain, S.R., Sarker, N.J., Sarker, S.U., Kamal, N.Q., Sarker, S.K

Schwinger-Boson Mean-Field Theory of Mixed-Spin Antiferromagnet L2BaNiO5L_2BaNiO_5

The Schwinger-boson mean-field theory is used to study the three-dimensional antiferromagnetic ordering and excitations in compounds $L_2BaNiO_5$, a large family of quasi-one-dimensional mixed-spin antiferromagnet. To investigate magnetic properties of these compounds, we introduce a three-dimensional mixed-spin antiferromagnetic Heisenberg model based on experimental results for the crystal structure of $L_2BaNiO_5$. This model can explain the experimental discovery of coexistence of Haldane gap and antiferromagnetic long-range order below N\'{e}el temperature. Properties such as the low-lying excitations, magnetizations of $Ni$ and rare-earth ions, N\'{e}el temperatures of different compounds, and the behavior of Haldane gap below the N\'{e}el temperature are investigated within this model, and the results are in good agreement with neutron scattering experiments.Comment: 12 pages, 6 figure

DC and small-signal physical models for the AlGaAs/GaAs high electron mobility transistor

Analytical and numerical models are developed for the microwave small-signal performance, such as transconductance, gate-to-source capacitance, current gain cut-off frequency and the optimum cut-off frequency of the AlGaAs/GaAs High Electron Mobility Transistor (HEMT), in both normal and compressed transconductance regions. The validated I-V characteristics and the small-signal performances of four HeMT's are presented

Smaller sample sizes for phase II trials based on exact tests with actual error rates by trading-off their nominal levels of significance and power

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A strong-coupling expansion for the Hubbard model

We reconsider the strong-coupling expansion for the Hubbard model recently introduced by Sarker and Pairault {\it et al.} By introducing slave particles that act as projection operators onto the empty, singly occupied and doubly occupied atomic states, the perturbation theory around the atomic limit distinguishes between processes that do conserve or do not conserve the total number of doubly occupied sites. This allows for a systematic $t/U$ expansion that does not break down at low temperature ($t$ being the intersite hopping amplitude and $U$ the local Coulomb repulsion). The fermionic field becomes a two-component field, which reflects the presence of the two Hubbard bands. The single-particle propagator is naturally expressed as a function of a $2 \times 2$ matrix self-energy. Furthermore, by introducing a time- and space-fluctuating spin-quantization axis in the functional integral, we can expand around a ``non-degenerate'' ground-state where each singly occupied site has a well defined spin direction (which may fluctuate in time). This formalism is used to derive the effective action of charge carriers in the lower Hubbard band to first order in $t/U$. We recover the action of the t-J model in the spin-hole coherent-state path integral. We also compare our results with those previously obtained by studying fluctuations around the large-$U$ Hartree-Fock saddle point.Comment: 20 pages RevTex, 3 figure

Offloading SLAM for Indoor Mobile Robots with Edge-Fog-Cloud Computing

Indoor mobile robots are widely used in industrial environments such as large logistic warehouses. They are often in charge of collecting or sorting products. For such robots, computation-intensive operations account for a significant per- centage of the total energy consumption and consequently affect battery life. Besides, in order to keep both the power con- sumption and hardware complexity low, simple micro-controllers or single-board computers are used as onboard local control units. This limits the computational capabilities of robots and consequently their performance. Offloading heavy computation to Cloud servers has been a widely used approach to solve this problem for cases where large amounts of sensor data such as real-time video feeds need to be analyzed. More recently, Fog and Edge computing are being leveraged for offloading tasks such as image processing and complex navigation algorithms involving non-linear mathematical operations. In this paper, we present a system architecture for offloading computationally expensive localization and mapping tasks to smart Edge gateways which use Fog services. We show how Edge computing brings computational capabilities of the Cloud to the robot environment without compromising operational reliability due to connection issues. Furthermore, we analyze the power consumption of a prototype robot vehicle in different modes and show how battery life can be significantly improved by moving the processing of data to the Edge layer

Offloading SLAM for Indoor Mobile Robots with Edge, Fog, Cloud Computing

Indoor mobile robots are widely used in industrial environments such as large logistic warehouses. They are often in charge of collecting or sorting products. For such robots, computation-intensive operations account for a significant per- centage of the total energy consumption and consequently affect battery life. Besides, in order to keep both the power con- sumption and hardware complexity low, simple micro-controllers or single-board computers are used as onboard local control units. This limits the computational capabilities of robots and consequently their performance. Offloading heavy computation to Cloud servers has been a widely used approach to solve this problem for cases where large amounts of sensor data such as real-time video feeds need to be analyzed. More recently, Fog and Edge computing are being leveraged for offloading tasks such as image processing and complex navigation algorithms involving non-linear mathematical operations. In this paper, we present a system architecture for offloading computationally expensive localization and mapping tasks to smart Edge gateways which use Fog services. We show how Edge computing brings computational capabilities of the Cloud to the robot environment without compromising operational reliability due to connection issues. Furthermore, we analyze the power consumption of a prototype robot vehicle in different modes and show how battery life can be significantly improved by moving the processing of data to the Edge layer

Complexes of stationary domain walls in the resonantly forced Ginsburg-Landau equation

The parametrically driven Ginsburg-Landau equation has well-known stationary solutions -- the so-called Bloch and Neel, or Ising, walls. In this paper, we construct an explicit stationary solution describing a bound state of two walls. We also demonstrate that stationary complexes of more than two walls do not exist.Comment: 10 pages, 2 figures, to appear in Physical Review