We present a comprehensive study of the low-energy band structure and Fermi surface (FS) topology of $A$Co$_2$As$_2$ ($A=$ Ca, Sr, Ba, Eu) using high-resolution angle-resolved photoemission spectroscopy. The experimental FS topology and band dispersi
on data are compared with theoretical full-potential linearized augmented-plane-wave (FP-LAPW) calculations, which yielded reasonably good agreement. We demonstrate that the FS maps of $A$Co$_2$As$_2$ are significantly different from those of the parent compounds of Fe-based high-temperature superconductors. Further, the FSs of CaCo$_2$As$_2$ do not show significant changes across its antiferromagnetic transition temperature. The band dispersions extracted in different momentum $(k_{it x}, k_{it y})$ directions show a small electron pocket at the center and a large electron pocket at the corner of the Brillouin zone (BZ). The absence of the hole FS in these compounds does not allow nesting between pockets at the Fermi energy ({it E}$_{rm F}$), which is in contrast to $A$Fe$_2$As$_2$-type parent compounds of the iron-based superconductors. Interestingly, we find that the hole bands are moved 300--400~meV below $E_{rm F}$ depending on the $A$ element. Moreover, the existence of nearly flat bands in the vicinity of $E_{rm F}$ are consistent with the large density of states at $E_{rm F}$. These results are important to understand the physical properties as well as the possibility of the emergence of superconductivity in related materials.
CaCo2As2 is a unique itinerant system having strong magnetic frustration. Here we report the effect of electron doping on the physical properties resulting from Ni substitutions for Co. The A-type antiferromagnetic transition temperature TN = 52 K fo
r x = 0 decreases to 22 K with only 3 percent Ni substitution and is completely suppressed for x > 0.11. For 0.11 < x < 0.52 strong ferromagnetic (FM) fluctuations develop as revealed by magnetic susceptibility chi(T) measurements. Heat-capacity Cp(T) measurements reveal the presence of FM quantum spin fluctuations for 0.11 < x < 0.52. Our density-functional theory (DFT) calculations confirm that FM fluctuations are enhanced by Ni substitutions for Co. The Sommerfeld electronic heat-capacity coefficient is enhanced for x = 0, 0.21, and 0.42 by about a factor of two compared to DFT calculations of the bare density of states at the Fermi energy. The crystals with x > 0.52 do not exhibit FM spin fluctuations or magnetic order, which was found from the DFT calculations to arise from a Stoner transition. Neutron-diffraction studies of crystals with x = 0.11 and 0.16 found no evidence of A-type ordering as observed in CaCo2As2 or of other common magnetic structures.
Using density functional theory (DFT) methods, we have calculated X-ray absorption spectroscopy (XAS) and X-ray circular dichroism (XMCD) spectra in bulk and thin films of Fe$_3$GeTe$_2$, CrI$_3$, and CrGeTe$_3$. DFT+$U$ methods are employed for bett
er handling of correlation effects of 3$d$ electrons of transition metals. We discuss relations between the density of states, radial matrix elements, and the corresponding spectra. By comparing the calculated spectra with previously measured spectra, we discuss the reliability of DFT+$U$ methods to describe the electronic structures of these materials and determine the corresponding optimal $U$ and $J$ parameters.
We present a study of magnetic fields effects on the position resolution and energy response of hyper-pure germanium detectors. Our results provide realistic estimates of the potential impact on the resolving power of tracking-arrays from (fringe) ma
gnetic fields present when operating together with large spectrometers. By solving the equations of motion for the electron and holes in the presence of both electric and magnetic fields, we analyzed the drift trajectories of the charge carriers to determine the deviations in the positions at the end point of the trajectories, as well as changes in drift lengths affecting the energy resolution and peak shift due to trapping. Our results show that the major effect is in the deviation of the transverse (to the electric field direction) position and suggest that, if no corrective action is taken in the pulse-shape and tracking data analysis procedures, a field strength $gtrsim$ 0.1 T will start to impact the intrinsic position resolution of 2 mm (RMS). At fields above $sim$1 T, the degradation of the energy response becomes observable.
We report branching fraction measurements of four decay modes of the $Lambda_{c}^{+}$ baryon, each of which includes an $eta$ meson and a $Lambda$ baryon in the final state, and all of which are measured relative to the $Lambda_{c}^{+} rightarrow p K
^{-} pi^{+}$ decay mode. The results are based on a $980~mathrm{fb^{-1}}$ data sample collected by the Belle detector at the KEKB asymmetric-energy $e^{+}e^{-}$ collider. Two decays, $eta Sigma^{0} pi^{+}$ and $Lambda(1670) pi^{+}$, are observed for the first time, while the measurements of the other decay modes, $Lambda_{c}^{+} rightarrow etaLambdapi^{+}$ and $etaSigma(1385)^{+}$, are more precise than those made previously. We obtain $mathcal{B}(Lambda_{c}^{+} rightarrow eta Lambda pi^{+})/mathcal{B}(Lambda_{c}^{+} rightarrow p K^{-} pi^{+})$ = $0.293 pm 0.003 pm 0.014$, $mathcal{B}(Lambda_{c}^{+} rightarrow eta Sigma^{0} pi^{+})/mathcal{B}(Lambda_{c}^{+} rightarrow p K^{-} pi^{+})$ = $0.120 pm 0.006 pm 0.006$, $mathcal{B}(Lambda_{c}^{+} rightarrow Lambda(1670) pi^{+}) times mathcal{B}(Lambda(1670) rightarrow eta Lambda)/mathcal{B}(Lambda_{c}^{+} rightarrow p K^{-} pi^{+})$ = $(5.54 pm 0.29 pm 0.73 ) times 10^{-2}$, and $mathcal{B}(Lambda_{c}^{+} rightarrow eta Sigma(1385)^{+})/mathcal{B}(Lambda_{c}^{+} rightarrow p K^{-} pi^{+})$ = $0.192 pm 0.006 pm 0.016$. The mass and width of the $Lambda(1670)$ are also precisely determined to be $1674.3 pm 0.8 pm 4.9~{rm MeV}/c^{2}$ and $36.1 pm 2.4 pm 4.8~{rm MeV}$, respectively, where the uncertainties are statistical and systematic, respectively.
The initial cluster of severe pneumonia cases that triggered the 2019-nCoV epidemic was identified in Wuhan, China in December 2019. While early cases of the disease were linked to a wet market, human-to-human transmission has driven the rapid spread
of the virus throughout China. The ongoing outbreak presents a challenge for modelers, as limited data are available on the early growth trajectory, and the epidemiological characteristics of the novel coronavirus are yet to be fully elucidated. We provide timely short-term forecasts of the cumulative number of confirmed reported cases in Hubei province, the epicenter of the epidemic, and for the overall trajectory in China, excluding the province of Hubei. We collect daily reported cumulative case data for the 2019-nCoV outbreak for each Chinese province from the National Health Commission of China. Here, we provide 5, 10, and 15 day forecasts for five consecutive days, February 5th through February 9th, with quantified uncertainty based on a generalized logistic growth model, the Richards growth model, and a sub-epidemic wave model. Our most recent forecasts reported here based on data up until February 9, 2020, largely agree across the three models presented and suggest an average range of 7,409-7,496 additional cases in Hubei and 1,128-1,929 additional cases in other provinces within the next five days. Models also predict an average total cumulative case count between 37,415 - 38,028 in Hubei and 11,588 - 13,499 in other provinces by February 24, 2020. Mean estimates and uncertainty bounds for both Hubei and other provinces have remained relatively stable in the last three reporting dates (February 7th - 9th). Our forecasts suggest that the containment strategies implemented in China are successfully reducing transmission and that the epidemic growth has slowed in recent days.
A brain-machine interface (BMI) based on electroencephalography (EEG) can overcome the movement deficits for patients and real-world applications for healthy people. Ideally, the BMI system detects user movement intentions transforms them into a cont
rol signal for a robotic arm movement. In this study, we made progress toward user intention decoding and successfully classified six different reaching movements of the right arm in the movement execution (ME). Notably, we designed an experimental environment using robotic arm movement and proposed a convolutional neural network architecture (CNN) with inception block for robust classify executed movements of the same limb. As a result, we confirmed the classification accuracies of six different directions show 0.45 for the executed session. The results proved that the proposed architecture has approximately 6~13% performance increase compared to its conventional classification models. Hence, we demonstrate the 3D inception CNN architecture to contribute to the continuous decoding of ME.
A brain-computer interface (BCI) is used not only to control external devices for healthy people but also to rehabilitate motor functions for motor-disabled patients. Decoding movement intention is one of the most significant aspects for performing a
rm movement tasks using brain signals. Decoding movement execution (ME) from electroencephalogram (EEG) signals have shown high performance in previous works, however movement imagination (MI) paradigm-based intention decoding has so far failed to achieve sufficient accuracy. In this study, we focused on a robust MI decoding method with transfer learning for the ME and MI paradigm. We acquired EEG data related to arm reaching for 3D directions. We proposed a BCI-transfer learning method based on a Relation network (BTRN) architecture. Decoding performances showed the highest performance compared to conventional works. We confirmed the possibility of the BTRN architecture to contribute to continuous decoding of MI using ME datasets.
This study examines stable carbon isotope (d13C) and concentration dynamics of DIC, DOC, and POC over an entire year, using a high resolution dataset. This research was performed in the catchment of the Schwabach River, a small, karstic headwater str
eam in Germany. The DIC data indicated the dominance of mineral weathering as a DIC source, with a dilution effect during high flow periods. A weakly negative relationship between discharge and d13CDIC indicates an increase in plant-derived organic matter during floods, transported to river waters via overland runoff and intermediate flow. DOC inputs were enhanced during periods of high discharge, indicating a greater importance of overland runoff as a DOC source. POC concentrations seem unaffected by discharge, but a slight negative correlation between d13CPOC and discharge may be derived from increased C4 plant material inputs. CO2 concentrations exceeded ambient atmospheric values throughout the year, confirming that the river surface waters are a net CO2 source. The total riverine carbon flux was dominated by DIC (70%), followed by CO2 efflux (21%), DOC (7%), and POC (2%). While a bi-monthly sampling scheme yielded a similar carbon flux estimate to that utilizing the entire dataset, the use of a monthly sampling interval differed by up to 19%. This discrepancy is due to the inability of a monthly sampling scheme to capture sudden and large variations in river discharge and associated dissolved/particulate carbon concentration changes, such as those observed during flooding. Bi-monthly sampling may be the minimum timeframe required for an acceptable degree of accuracy in carbon flux calculations. The application of high sampling frequencies and comprehensive DIC, DOC, and POC studies in future research would reduce uncertainties in riverine carbon budgets, and clarify the role of small streams in the global carbon cycle.
In this paper, we strengthen the splitting theorem proved in [14, 15] and provide a different approach using ideas from the weak KAM theory.
