Frequency limits of sequential readout for sensing AC magnetic fields using nitrogen-vacancy centers in diamond

The nitrogen-vacancy (NV) centers in diamond have ability to sense alternating-current (AC) magnetic fields with high spatial resolution. However, the frequency range of AC sensing protocols based on dynamical decoupling (DD) sequences has not been thoroughly explored experimentally. In this work, we aimed to determine the sensitivity of ac magnetic field as a function of frequency using sequential readout method. The upper limit at high frequency is clearly determined by Rabi frequency, in line with the expected effect of finite DD-pulse width. In contrast, the lower frequency limit is primarily governed by the duration of optical repolarization rather than the decoherence time (T$_2$) of NV spins. This becomes particularly crucial when the repetition (dwell) time of the sequential readout is fixed to maintain the acquisition bandwidth. The equation we provide successfully describes the tendency in the frequency dependence. In addition, at the near-optimal frequency of 1 MHz, we reached a maximum sensitivity of 229 pT/$\sqrt{\mathrm{Hz}}$ by employing the XY4-(4) DD sequence.Comment: 7 pages, 5 figure

Flexible Modeling of Epidemics with an Empirical Bayes Framework

Seasonal influenza epidemics cause consistent, considerable, widespread loss annually in terms of economic burden, morbidity, and mortality. With access to accurate and reliable forecasts of a current or upcoming influenza epidemic's behavior, policy makers can design and implement more effective countermeasures. We developed a framework for in-season forecasts of epidemics using a semiparametric Empirical Bayes framework, and applied it to predict the weekly percentage of outpatient doctors visits for influenza-like illness, as well as the season onset, duration, peak time, and peak height, with and without additional data from Google Flu Trends, as part of the CDC's 2013--2014 "Predict the Influenza Season Challenge". Previous work on epidemic modeling has focused on developing mechanistic models of disease behavior and applying time series tools to explain historical data. However, these models may not accurately capture the range of possible behaviors that we may see in the future. Our approach instead produces possibilities for the epidemic curve of the season of interest using modified versions of data from previous seasons, allowing for reasonable variations in the timing, pace, and intensity of the seasonal epidemics, as well as noise in observations. Since the framework does not make strict domain-specific assumptions, it can easily be applied to other diseases as well. Another important advantage of this method is that it produces a complete posterior distribution for any desired forecasting target, rather than mere point predictions. We report prospective influenza-like-illness forecasts that were made for the 2013--2014 U.S. influenza season, and compare the framework's cross-validated prediction error on historical data to that of a variety of simpler baseline predictors.Comment: 52 page

Quantum diamond microscopy with sub-ms temporal resolution

Quantum diamond magnetometers using lock-in detection have successfully detected weak bio-magnetic fields from neurons, a live mammalian muscle, and a live mouse heart. This opens up the possibility of quantum diamond magnetometers visualizing microscopic distributions of the bio-magnetic fields. Here, we demonstrate a lock-in-based wide-field quantum diamond microscopy, achieving a mean volume-normalized per pixel sensitivity of 43.9 $\mathrm{nT\cdot\mu m^{1.5}/Hz^{0.5}}$. We obtain the sensitivity by implementing a double resonance with hyperfine driving and magnetic field alignment along the $$ orientation of the diamond. Additionally, we have demonstrated that sub-ms temporal resolution ($\sim$ 0.4 ms) can be achieved at a micrometer scale with tens of nanotesla per-pixel sensitivity using quantum diamond microscopy. This lock-in-based diamond quantum microscopy could be a step forward in mapping functional activity in neuronal networks in micrometer spatial resolution

A real-time location-based construction labor safety management system

The construction industry continues to record a high number of accidents compared to other industries. Furthermore, the ramifications of construction accidents are growing in terms of both economic loss and loss of life with trends toward larger-scale, more complex projects. For this reason, there is an increasing awareness of the importance of safety management in the construction industry, and the need for more effective safety management techniques. This paper introduces a real-time location-based construction labor safety management system that tracks and visualizes workers’ locations in real-time and sends early warnings to endangered workers. The system is developed by integrating: a real-time locating system (RTLS) for tracking of workers’ location; a location monitoring system for mapping the workers location on a computerized building model; and alarm technology for sending early warnings. The developed system has been applied to an apartment project and an RTLS technology test center in Korea, and proved to be effective in tracking and monitoring workers in real-time and preventing construction accidents. It is envisioned that the developed system will enable proactive construction safety management in South Korea and the methodologies developed in this study will be applicable to other contexts with minimal customization

CO₂ Fixation by Membrane Separated NaCl Electrolysis

Atmospheric concentrations of carbon dioxide (CO2), a major cause of global warming, have been rising due to industrial development. Carbon capture and storage (CCS), which is regarded as the most effective way to reduce such atmospheric CO2 concentrations, has several environmental and technical disadvantages. Carbon capture and utilization (CCU), which has been introduced to cover such disadvantages, makes it possible to capture CO2, recycling byproducts as resources. However, CCU also requires large amounts of energy in order to induce reactions. Among existing CCU technologies, the process for converting CO2 into CaCO3 requires high temperature and high pressure as reaction conditions. This study proposes a method to fixate CaCO3 stably by using relatively less energy than existing methods. After forming NaOH absorbent solution through electrolysis of NaCl in seawater, CaCO3 was precipitated at room temperature and pressure. Following the experiment, the resulting product CaCO3 was analyzed with Fourier transform infrared spectroscopy (FT-IR); field emission scanning electron microscopy (FE-SEM) image and X-ray diffraction (XRD) patterns were also analyzed. The results showed that the CaCO3 crystal product was high-purity calcite. The study shows a successful method for fixating CO2 by reducing carbon dioxide released into the atmosphere while forming high-purity CaCO3

Ocean mover’s distance: using optimal transport for analysing oceanographic data

Remote sensing observations from satellites and global biogeochemical models have combined to revolutionize the study of ocean biogeochemical cycling, but comparing the two data streams to each other and across time remains challenging due to the strong spatial-temporal structuring of the ocean. Here, we show that the Wasserstein distance provides a powerful metric for harnessing these structured datasets for better marine ecosystem and climate predictions. The Wasserstein distance complements commonly used point-wise difference methods such as the root-mean-squared error, by quantifying differences in terms of spatial displacement in addition to magnitude. As a test case, we consider chlorophyll (a key indicator of phytoplankton biomass) in the northeast Pacific Ocean, obtained from model simulations, in situ measurements, and satellite observations. We focus on two main applications: (i) comparing model predictions with satellite observations, and (ii) temporal evolution of chlorophyll both seasonally and over longer time frames. The Wasserstein distance successfully isolates temporal and depth variability and quantifies shifts in biogeochemical province boundaries. It also exposes relevant temporal trends in satellite chlorophyll consistent with climate change predictions. Our study shows that optimal transport vectors underlying the Wasserstein distance provide a novel visualization tool for testing models and better understanding temporal dynamics in the ocean