Manual discrimination of force

Optimal design of human-machine interfaces for teleoperators and virtual-environment systems which involve the tactual and kinesthetic modalities requires knowledge of the human's resolving power in these modalities. The resolution of the interface should be appropriately matched to that of the human operator. We report some preliminary results on the ability of the human hand to distinguish small differences in force under a variety of conditions. Experiments were conducted on force discrimination with the thumb pushing an interface that exerts a constant force over the pushing distance and the index finger pressing against a fixed support. The dependence of the sensitivity index d' on force increment can be fit by a straight line through the origin and the just-noticeable difference (JND) in force can thus be described by the inverse of the slope of this line. The receiver operating characteristic (ROC) was measured by varying the a priori probabilities of the two alternatives, reference force and reference force plus an increment, in one-interval, two-alternative, forced-choice experiments. When plotted on normal deviate coordinates, the ROC's were roughly straight lines of unit slope, thus supporting the assumption of equal-variance normal distributions and the use of the conventional d' measure. The JND was roughly 6-8 percent for reference force ranging from 2.5 to 10 newtons, pushing distance from 5 to 30 mm, and initial finger-span from 45 to 125 mm. Also, the JND remained the same when the subjects were instructed to change the average speed of pushing from 23 to 153 mm/sec. The pushing was terminated by reaching either a wall or a well, and the JND's were essentially the same in both cases

Nonlinear Spring-Mass-Damper Modeling and Parameter Estimation of Train Frontal Crash Using CLGAN Model

Due to the complexity of a train crash, it is a challenging process to describe and estimate mathematically. Although different mathematical models have been developed, it is still difficult to balance the complexity of models and the accuracy of estimation. ,is paper proposes a nonlinear spring-mass-damper model of train frontal crash, which achieves high accuracy and maintains low complexity. ,e Convolutional Long-short-term-memory Generation Adversarial Network (CLGAN) model is applied to study the nonlinear parameters dynamic variation of the key components of a rail vehicle (e.g., the head car, anticlimbing energy absorber, and the coupler buffer devices). Firstly, the nonlinear lumped model of train frontal crash is built, and then the physical parameters are deduced in twenty different cases using D’Alembert’s principle. Secondly, the input/output relationship of the CLGAN model is determined, where the inputs are the nonlinear physical parameters in twenty initial conditions, and the output is the nonlinear relationship between the train crash nonlinear parameters under other initial cases. Finally, the train crash dynamic characteristics are accurately estimated during the train crash processes through the training of the CLGAN model, and then the crash processes under different given conditions can be described effectively. ,e estimation results exhibit good agreement with finite element (FE) simulations and experimental results. Furthermore, the CLGAN model shows great potential in nonlinear estimation, and CLGAN can better describe the variation of nonlinear spring damping compared with the traditional model. ,e nonlinear spring-mass-damper modeling is involved in improving the speed and accuracy of the train crash estimation, as well as being able to offer guidance for structure optimization in the early design stage

Seasonal changing effect on airflow and pollutant dispersion characteristics in urban street canyons

In this study, the effect of seasonal variation on air flow and pollutant dispersion characteristics was numerically investigated. A three-dimensional urban canopy model with unit aspect ratio (H/D = 1) was used to calculate surface temperature distribution in the street canyon. Four representative time events (1000 LST, 1300 LST, 1600 LST and 2000 LST) during typical clear summer and winter days were selected to examine the air flow diurnal variation. The results revealed the seasonal variation significantly altered the street canyon microclimate. Compared with the street canyon surface temperature distribution in summer, the winter case showed a more evenly distributed surface temperature. In addition, the summer case showed greater daily temperature fluctuation than that of the winter case. Consequently, distinct pollutant dispersion patterns were observed between summer and winter scenarios, especially for the afternoon (1600 LST) and night (2000 LST) events. Among all studied time events, the pollutant removal performance of the morning (1000 LST) and the night (2000 LST) events were more sensitive to the seasonal variation. Lastly, limited natural ventilation performance was found during the summer morning and the winter night, which induced relatively high pollutant concentration along the pedestrian height level

Thorium-doping induced superconductivity up to 56 K in Gd1-xThxFeAsO

Following the discovery of superconductivity in an iron-based arsenide LaO1-xFxFeAs with a superconducting transition temperature (Tc) of 26 K[1], Tc was pushed up surprisingly to above 40 K by either applying pressure[2] or replacing La with Sm[3], Ce[4], Nd[5] and Pr[6]. The maximum Tc has climbed to 55 K, observed in SmO1-xFxFeAs[7, 8] and SmFeAsO1-x[9]. The value of Tc was found to increase with decreasing lattice parameters in LnFeAsO1-xFx (Ln stands for the lanthanide elements) at an apparently optimal doping level. However, the F- doping in GdFeAsO is particularly difficult[10,11] due to the lattice mismatch between the Gd2O2 layers and Fe2As2 layers. Here we report observation of superconductivity with Tc as high as 56 K by the Th4+ substitution for Gd3+ in GdFeAsO. The incorporation of relatively large Th4+ ions relaxes the lattice mismatch, hence induces the high temperature superconductivity.Comment: 4 pages, 3 figure

Improving the sensitivity of a near-infrared nanocomposite photodetector by enhancing trap induced hole injection

We report the enhancement of the photoconductive gain of nanocomposite near-infrared photodetectors by a zinc oxide nanoparticles (ZnO NPs) rich surface at the nanocomposite/cathode interface. An argon plasma etching process was used to remove polymer at the surface of nanocomposite films, which resulted in a ZnO NPs rich surface. The other way is to spin-coat a thin layer of ZnO NPs onto the nanocomposite layer. The ZnO NPs rich surface, which acts as electron traps to induce secondary hole injection under reverse bias, increased hole injection, and thus the external quantum efficiency by 2–3 times. The darkcurrent declined one order of magnitude simultaneously as a result of etching the top nanocomposite layer. The specific detectivity at 800 nm was increased by 7.4 times to 1.11 × 1010 Jones due to the simultaneously suppressed noise and enhanced gain

Comparative study on the thermoelectric effect of parent oxypnictides LaTTAsO (TT = Fe, Ni)

The thermopower and Nernst effect were investigated for undoped parent compounds LaFeAsO and LaNiAsO. Both thermopower and Nernst signal in iron-based LaFeAsO are significantly larger than those in nickel-based LaNiAsO. Furthermore, abrupt changes in both thermopower and Nernst effect are observed below the structural phase transition temperature and spin-density wave (SDW) type antiferromagnetic (AFM) order temperature in Fe-based LaFeAsO. On the other hand, Nernst effect is very small in the Ni-based LaNiAsO and it is weakly temperature-dependent, reminiscent of the case in normal metals. We suggest that the effect of SDW order on the spin scattering rate should play an important role in the anomalous temperature dependence of Hall effect and Nernst effect in LaFeAsO. The contrast behavior between the LaFeAsO and LaNiAsO systems implies that the LaFeAsO system is fundamentally different from the LaNiAsO system and this may provide clues to the mechanism of high $T_c$ superconductivity in the Fe-based systems.Comment: 6 pages, 6 figure

Forebrain CRF₁ modulates early-life stress-programmed cognitive deficits

Childhood traumatic events hamper the development of the hippocampus and impair declarative memory in susceptible individuals. Persistent elevations of hippocampal corticotropin-releasing factor (CRF), acting through CRF receptor 1 (CRF1), in experimental models of early-life stress have suggested a role for this endogenous stress hormone in the resulting structural modifications and cognitive dysfunction. However, direct testing of this possibility has been difficult. In the current study, we subjected conditional forebrain CRF1 knock-out (CRF1-CKO) mice to an impoverished postnatal environment and examined the role of forebrain CRF1 in the long-lasting effects of early-life stress on learning and memory. Early-life stress impaired spatial learning and memory in wild-type mice, and postnatal forebrain CRF overexpression reproduced these deleterious effects. Cognitive deficits in stressed wild-type mice were associated with disrupted long-term potentiation (LTP) and a reduced number of dendritic spines in area CA3 but not in CA1. Forebrain CRF1 deficiency restored cognitive function, LTP and spine density in area CA3, and augmented CA1 LTP and spine density in stressed mice. In addition, early-life stress differentially regulated the amount of hippocampal excitatory and inhibitory synapses in wild-type and CRF1-CKO mice, accompanied by alterations in the neurexin-neuroligin complex. These data suggest that the functional, structural and molecular changes evoked by early-life stress are at least partly dependent on persistent forebrain CRF1 signaling, providing a molecular target for the prevention of cognitive deficits in adults with a history of early-life adversity