Oscillatory Potentials in Achromatopsia as a Tool for Understanding Cone Retinal Functions

Achromatopsia (ACHM) is an inherited autosomal recessive disease lacking cone photoreceptors functions. In this study, we characterize the time-frequency representation of the full-field electroretinogram (ffERG) component oscillatory potentials (OPs), to investigate the connections between photoreceptors and the inner retinal network using ACHM as a model. Time-frequency characterization of OPs was extracted from 52 controls and 41 achromat individuals. The stimulation via ffERG was delivered under dark-adaptation (DA, 3.0 and 10.0 cd·s·m−2) to assess mixed rod-cone responses. The ffERG signal was subsequently analyzed using a continuous complex Morlet transform. Time-frequency maps of both DA conditions show the characterization of OPs, disclosing in both groups two distinct time-frequency windows (~70–100 Hz and >100 Hz) within 50 ms. Our main result indicates a significant cluster (p < 0.05) in both conditions of reduced relative power (dB) in ACHM people compared to controls, mainly at the time-frequency window >100 Hz. These results suggest that the strongly reduced but not absent activity of OPs above 100 Hz is mostly driven by cones and only in small part by rods. Thus, the lack of cone modulation of OPs gives important insights into interactions between photoreceptors and the inner retinal network and can be used as a biomarker for monitoring cone connection to the inner retina.

Mesoscale energy balance and air-sea interaction in the Kuroshio Extension: low-frequency versus high-frequency variability

Using eddy-resolving Community Earth System Model (CESM) simulations, this study investigates mesoscale energetics and air-sea interaction at two different time-scale windows in the Kuroshio Extension (KE) region. Based on an energy budget analysis, it is found that both baroclinic and barotropic pathways contribute to eddy energy generation within the low-frequency window (longer than 3 weeks) in this region, while both air-sea heat fluxes and wind stresses act as prominent eddy killers that remove energy from ocean. In contrast, within the high-frequency window oceanic variability is mainly fed by baroclinic instability and regulated by turbulent thermal wind (TTW) processes, while the positive wind work is derived primarily from ageostrophic flow, i.e., Ekman drift, and along with air-sea heat fluxes has little influence on geostrophic mesoscale eddies.

Simulated predictions for H i at z = 3.35 with the Ooty Wide Field Array (OWFA) – II. Foreground avoidance

ABSTRACT Considering the upcoming OWFA, we use simulations of the foregrounds and the z = 3.35 H i 21-cm intensity mapping signal to identify the (k⊥, k∥) modes where the expected 21-cm power spectrum P(k⊥, k∥) is substantially larger than the predicted foreground contribution. Only these uncontaminated k modes are used for measuring P(k⊥, k∥) in the “Foreground Avoidance” technique. Though the foregrounds are largely localized within a wedge. we find that the small leakage beyond the wedge surpasses the 21-cm signal across a significant part of the (k⊥, k∥) plane. The extent of foreground leakage is extremely sensitive to the frequency window function used to estimate P(k⊥, k∥). It is possible to reduce the leakage by making the window function narrower; however, this comes at the expense of losing a larger fraction of the 21-cm signal. It is necessary to balance these competing effects to identify an optimal window function. Considering a broad class of cosine window functions, we identify a six term window function as optimal for 21-cm power spectrum estimation with OWFA. Considering only the k modes where the expected 21-cm power spectrum exceeds the predicted foregrounds by a factor of 100 or larger, a $5\, \sigma$ detection of the binned power spectrum is possible in the k-ranges $0.18 \le k \le 0.3 \, {\rm Mpc}^{-1}$ and $0.18 \le k \le 0.8 \, {\rm Mpc}^{-1}$ with 1000–2000 and 104 h of observation, respectively.

Early science with the LMT: molecular torus in UGC 5101

ABSTRACT As part of the Early Science Large Millimeter Telescope projects, we report the detection of nine double-peaked molecular lines, produced by a rotating molecular torus, in the ultraluminous infrared galaxies (ULIRG) – Compton-thick active galactic nuclei (AGN) galaxy UGC 5101. The double-peaked lines we report correspond to molecular transitions of HCN, HCO+, HNC, N2H+, CS, C18O, 13CO, and two CN lines; plus the detection of C2H that is a blend of six lines. The redshift search receiver spectra covers the 73–113 GHz frequency window. Low- and high-density gas tracers of the torus have different implied rotational velocities, with a rotational velocity of 149 ± 3 km s−1 for the low-density ones (C18O, 13CO) and 174 ± 3 km s−1 for high-density tracers (HCN, HCO+, HNC, N2H+, CS, and CN). In UGC 5101, we find that the ratio of integrated intensities of HCN to 13CO to be unusually large, probably indicating that the gas in the torus is very dense. Both the column densities and abundances are consistent with values found in AGN, starburst, and ULIRG galaxies. The observed abundance ratios cannot discriminate between X-ray and UV-field-dominated regions.

The current status of contribution activities in Japan for LISA

LISA is a space gravitational-wave mission that will open the unexplored gravitational-wave frequency window at around millihertz, shedding light on the study of supermassive black holes and the nature of gravity. The LISA project has been propelled by international collaboration in order to maximize the scientific outcome. With the aim of making scientifically important contributions to LISA, instrument and science groups were newly formed in Japan. This article summarizes the current status of the contribution activities conducted by each group to date, highlighting a few selected topics including the development of photoreceivers and theoretical studies on compact binaries and extreme mass ratio inspirals.

Putting the Cycle Back into Business Cycle Analysis

Are business cycles mainly a response to persistent exogenous shocks, or do they instead reflect a strong endogenous mechanism which produces recurrent boom-bust phenomena? In this paper we present evidence in favor of the second interpretation and we highlight the set of key elements that influence our answer. The elements that tend to favor this type of interpretation of business cycles are (i) slightly extending the frequency window one associates with business cycle phenomena, (ii) allowing for strategic complementarities across agents that arise due to financial frictions, and (iii) allowing for a locally unstable steady state in estimation. (JEL E22, E24, E23, E44)

Load State Identification Method for Ball Mills Based on Improved EWT, Multiscale Fuzzy Entropy and AEPSO_PNN Classification

To overcome the difficulty of accurately determining the load state of a wet ball mill during the grinding process, a method of mill load identification based on improved empirical wavelet transform (EWT), multiscale fuzzy entropy (MFE), and adaptive evolution particle swarm optimization probabilistic neural network (AEPSO_PNN) classification is proposed. First, the concept of a sliding frequency window is introduced based on EWT, and the adaptive frequency window EWT algorithm, which is used to decompose the vibration signals recorded under different load states to obtain the intrinsic mode components, is proposed. Second, a correlation coefficient threshold is used to select the sensitive mode components that characterize the state of the original signal for signal reconstruction. Finally, the MFE of the reconstructed signal is used as the characteristic vector to characterize the load state of the mill, and the partial mean value of MFE is calculated. The results show that the mean value of MFE under different load states varies. To further identify the load state, a characteristic mill load vector is constructed from the MFE values of the reconstructed signal under different load conditions and is used as the input of the AEPSO_PNN model, which then outputs the predicted ball mill load state. Thus, a load state identification model is established. The feasibility of the method is verified based on grinding experiments. The results show that the overall recognition rate of the proposed method is as high as 97.3%. Compared with the back propagation (BP) neural network, Bayes discriminant method, and PNN classification, AEPSO_PNN classification increases the overall recognition rate by 8%, 5.3%, and 3.3%, respectively, which indicates that this method can be used to accurately identify the different load states of a ball mill.

Buckled MEMS Beams for Energy Harvesting from Low Frequency Vibrations

Vibration energy harvesters based on the resonance of the beam structure work effectively only when the operating frequency window of the beam resonance matches with the available vibration source. None of the resonating MEMS structures can operate with low frequency, low amplitude, and unpredictable ambient vibrations since the resonant frequency goes up very high as the structure gets smaller. Bistable buckled beam energy harvester is therefore developed for lowering the operating frequency window below 100Hz for the first time at the MEMS scale. This design does not rely on the resonance of the MEMS structure but operates with the large snapping motion of the beam at very low frequencies when input energy overcomes an energy threshold. A fully functional piezoelectric MEMS energy harvester is designed, monolithically fabricated, and tested. An electromechanical lumped parameter model is developed to analyze the nonlinear dynamics and to guide the design of the nonlinear oscillator based energy harvester. Multilayer beam structure with residual stress induced buckling is achieved through the progressive residual stress control of the deposition processes along the fabrication steps. Surface profile of the released device shows bistable buckling of 200μm which matches well with the amount of buckling designed. Dynamic testing demonstrates the energy harvester operates with 50% bandwidth under 70Hz at 0.5g input, operating conditions that have not been demonstrated by MEMS vibration energy harvesters before.