Optical spectroscopy was used to study the electrodynamics and hidden transport properties of a BaFe1.91Ni0.09As2thin superconducting film. We analyzed the normal state data using a Drude-Lorentz model with two Drude components one narrow (D1) and another broad one (D2). In the superconducting state, two gaps with 2Δ(2)0/kBTc= 1.9-2.0 and 2Δ(1)0/kBTc= 4.0-4.3 are formed from the narrow component D1while the broad component D2remains ungapped. The calculated total DC resistivity of the film and the low-temperature scattering rate for the narrow Drude component show a hidden Fermi-liquid behavior. The change of total electron-boson coupling (λtot) and representative energy (Ω0) in the normal state with respect to the superconducting state is typical of other iron-based materials as well as high-temperature superconducting (HTSC) cuprates. It is important to improve identification accuracy for possible early intervention of major depressive disorder (MDD). Recently, effective connectivity (EC), defined as the directed influence of spatially distant brain regions on each other, has been used to find the dysfunctional organization of brain networks in MDD. However, little is known about the ability of whole-brain resting-state EC features in identification of MDD. Here, we employed EC by whole-brain analysis to perform MDD diagnosis. In this study, we proposed a high-order EC network capturing high-level relationship among multiple brain regions to discriminate 57 patients with MDD from 60 normal controls (NC). In high-order EC networks and traditional low-order EC networks, we utilized the network properties and connection strength for classification. Meanwhile, the support vector machine (SVM) was employed for model training. Generalization of the results was supported by 10-fold cross-validation. The classification results showed that the high-order EC network performed better than the low-order EC network in diagnosing MDD, and the integration of these two networks yielded the best classification precision with 95% accuracy, 98.83% sensitivity, and 91% specificity. Furthermore, we found that the abnormal connections of high-order EC in MDD patients involved multiple widely concerned functional subnets, particularly the default mode network and the cerebellar network. The current study indicates whole-brain EC networks, measured by our high-order method, may be promising biomarkers for clinical diagnosis of MDD, and the complementary between high-order and low-order EC will better guide patients to get early interventions as well as treatments.The current study indicates whole-brain EC networks, measured by our high-order method, may be promising biomarkers for clinical diagnosis of MDD, and the complementary between high-order and low-order EC will better guide patients to get early interventions as well as treatments.We have measured the Zeeman splitting of quantum levels in few-electron quantum dots (QDs) formed in narrow bandgap InSb nanowires via the Schottky barriers at the contacts under application of different spatially orientated magnetic fields. The effective g-factor tensor extracted from the measurements is strongly anisotropic and level-dependent, which can be attributed to the presence of strong spin-orbit interaction (SOI) and asymmetric quantum confinement potentials in the QDs. We have demonstrated a successful determination of the principal values and the principal axis orientations of the g-factor tensors in an InSb nanowire QD by the measurements under rotations of a magnetic field in the three orthogonal planes. We also examine the magnetic field evolution of the excitation spectra in an InSb nanowire QD and extract a SOI strength of [Formula see text] ∼ 180 μeV from an avoided level crossing between a ground state and its neighboring first excited state in the QD.In this letter, the performance of Zn-Sn-O (ZTO) thin film transistors (TFTs) has been greatly improved by Mo doping as an oxygen vacancy to control the residual electrons. The results show that the TFT with 3 at% Mo doping exhibits the best electrical characteristics with a high saturation mobility of 26.53 cm2 V-1 s-1, a threshold voltage of 0.18 V, a subthreshold swing of 0.32 V dec-1 and a large switching ratio of 2 × 106. The saturation mobility and switching ratio of Mo-doped Zn-Sn-O (MZTO, 3 at%) TFTs improved almost five and two orders of magnitude compared with ZTO TFTs, respectively. Therefore, the MZTO TFT has much potential for future electrical applications with its excellent properties. A passive brain-computer interface (pBCI) is a system that continuously adapts human-computer interaction to the user's state. Key to the efficacy of such a system is the reliable estimation of the user's state via neural signals, acquired through non-invasive methods like electroencephalography (EEG) or near-infrared spectroscopy (fNIRS). Many studies to date have explored the detection of mental workload in particular, usually for the purpose of improving safety in high risk work environments. In these studies, mental workload is generally modulated through the manipulation of task difficulty, and no other aspect of the user's state is taken into account. In real-life scenarios, however, different aspects of the user's state are likely to be changing simultaneously-for example, their cognitive state (e.g. level of mental workload) and affective state (e.g. level of stress/anxiety). MK-4827 datasheet This inevitable confounding of different states needs to be accounted for in the development of state detection algorithms instressed condition, rather than both training and testing on the relaxed condition). The reduction in classification accuracy observed was as much as 15%. The results of this study indicate the importance of considering multiple aspects of a user's state when developing detection algorithms for pBCI technologies.The results of this study indicate the importance of considering multiple aspects of a user's state when developing detection algorithms for pBCI technologies. To examine the concurrent and construct validity of numerically blinded ratings of perceived exertion (RPEs). A total of 30 elite male youth soccer players (age 16.7 [0.5]y) were monitored during training and matches over a 17-wk in-season period. The players' external loads were determined via raw 10-Hz global positioning system. Heart rate (HR) was collected continuously and expressed as Bannister and Edwards training impulses, and minutes >80% of the players predetermined the maximum HR by the Yo-Yo Intermittent Recovery Test Level 1. RPE was collected confidentially 10 to 15min after training/matches using 2 methods (1)a traditional verbal response to the 0 to 100 category-ratio "centiMax" scale (RPE) and (2)numerically blinded RPE centiMax scale (RPEblind) with the response selected manually via a 5 × 7-in tablet "slider." The RPE and RPEblind were divided by 10 and multiplied by the duration to derive the sessional RPE. Linear mixed models compared ratings, and within-subject repeated-measures correlations assessed the sessional RPE versus HR and external load associations.