Regions Of High Sensitivity Research Articles

Interface stiffness identification of rough and weak bonded interface using developed ultrasonic reflection phase derivative spectrum

The thickness and interface roughness of coatings both affect the interface bonded quality. Existed ultrasonic testing methods based on traditional phase screen approximation or spring model assumption are difficult to simultaneously identify the interface roughness and stiffness of coating. This paper, a new method for integrated identifying coating thickness, interface roughness, and interface stiffness using developed ultrasonic reflection phase derivative spectrum (URPDS) is proposed. A phase-screen-approximated spring-model (PSASM) for ultrasound vertically propagating into rough and weak bonded interface is constructed. On basis of PSASM, a URPDS of coating/substrate structure is developed for identifying the interface stiffness and other parameters of coated parts. Cross-correlation analysis is used to eliminate the phase deviation of URPDS introduced by reference signal and initial phase of tested signal. Sensitivity analysis is used to determine the high-sensitivity regions of URPDS to interface roughness and interface stiffness. Genetic algorithm optimization is used to achieve integrated identification of coating thickness, interface roughness, and interface stiffness. The rationality of PSASM is verified through numerical simulation using a series of coating/substrate models with rough and weak bonded interface, and the relationship between the high-sensitivity regions and the high-precision measurement ranges of interface roughness Rq and interface stiffness Kn is clarified. Ultrasonic experiments are implemented on Nickel-coating samples and coated parts using plane wave probe. The coating thickness, interface roughness, and interface stiffness could be identified accurately, which shows that the proposed URPDS method can identify the interface stiffness of rough contacted dissimilar media or coated parts with rough interface.

Land-atmosphere coupling amplified the record-breaking heatwave at altitudes above 5000 meters on the Tibetan Plateau in July 2022

In July 2022, regions with elevations exceeding 5 000 m on the inner Tibetan Plateau (TP) witnessed a record-breaking heatwave. But how the atmospheric circulation and soil moisture play roles in the occurrence and maintenance of the heatwave in such high elevation climate sensitive region remains unknown. Here, by using the flow analogue method, we find that negative soil moisture anomalies explain more than half of the extreme high temperature during the heatwave, while atmospheric circulation explains less than half. The high soil moisture-temperature coupling metric and the increased correlation between latent heat flux and soil moisture during heatwave revealed strong land-atmosphere feedback in the Qiangtang Plateau which has amplified the heatwave. Analyses of numerical experiments confirm that the presence of interaction between soil moisture and the atmosphere has increased the intensity of hot extreme event under the same atmospheric circulation conditions. Under the warming background, land-atmosphere coupling leads to a faster increase in extreme high temperatures compared to the global mean warming rate, and it is twice as fast as the increase in extreme high temperatures without coupling. We highlight the increased probability of extreme high temperature over the TP in the future due to soil moisture feedback and the results are hoped to inform policymakers in making decisions for climate adaptation activities.

Bayesian inversion of HFC-134a emissions in southern China from a new AGAGE site: Results from an observing system simulation experiment

The abundance of hydrofluorocarbons (HFCs) in the atmosphere is increasing and is of significant importance to the Earth's system. It is thus crucial to investigate the spatial distribution of HFC emissions. However, inversion modeling, a commonly used approach, is susceptible to errors from a-priori emissions, inversion grids, observations, and other parameters. In this study, we conduct Observing System Simulation Experiment (OSSE) to evaluate the impact of inversion parameters on HFC emission estimates, focusing on HFC-134a at the newly established Xichong (XCG) AGAGE site in Shenzhen, China. We regard the EDGAR (v7) dataset as a reference for “true” emissions and simulate the “true” atmosphere using the GEOS-Chem model. Our OSSE indicates that conducting inversions in the medium-sensitivity region with source-receptor sensitivity larger than 6.0 (nmol mol−1)/(mol m−2 s−1) minimizes the bias between a-posteriori and “true” total emissions to 0.58 %–1.88 %. In the high-sensitivity region, enhancing the spatial resolution of inversion grids lowers this error from −27.07 % to +0.36 %. The accuracy of a-priori spatial distribution crucially influences that of the a-posteriori: the higher correlation coefficient between a-priori and “true” emissions, the better a-posteriori agree with the “true” emissions, as evidenced by a reduced root mean square error (RMSE) of the a-posteriori vs. “true” emissions from 1.44 × 10−9 g m−2 s−1 (GDP-based) to 8.78 × 10−10 g m−2 s−1 (VIIRS-based). Furthermore, selecting regularization parameters and balancing instrumental and a-priori errors are also important. Our OSSE setups allow for parameter testing during the inversion, offering a framework to assess the regional representativeness of future HFC measurement sites.

Jacobian-scaled K-means clustering for physics-informed segmentation of reacting flows

This work introduces Jacobian-scaled K-means (JSK-means) clustering, which is a physics-informed clustering strategy centered on the K-means framework. The method allows for the injection of underlying physical knowledge into the clustering procedure through a distance function modification: instead of leveraging conventional Euclidean distance vectors, the JSK-means procedure operates on distance vectors scaled by matrices obtained from dynamical system Jacobians evaluated at the cluster centroids. The goal of this work is to show how the JSK-means algorithm – without modifying the input dataset – produces clusters that capture regions of dynamical similarity, in that the clusters are redistributed towards high-sensitivity regions in phase space and are described by similarity in the source terms of samples instead of the samples themselves. The algorithm is demonstrated on a complex reacting flow simulation dataset (a channel detonation configuration), where the dynamics in the thermochemical composition space are known through the highly nonlinear and stiff Arrhenius-based chemical source terms. Interpretations of cluster partitions in both physical space and composition space reveal how JSK-means shifts clusters produced by standard K-means towards regions of high chemical sensitivity (e.g., towards regions of peak heat release rate near the detonation reaction zone). The findings presented here illustrate the benefits of utilizing Jacobian-scaled distances in clustering techniques, and the JSK-means method in particular displays promising potential for improving former partition-based modeling strategies in reacting flow (and other multi-physics) applications.

Optimisation strategy for coplanar array capacitance imaging in rotational scanning mode

ABSTRACT Coplanar array capacitive imaging represents a non-destructive testing technology. To address the challenge of reduced utilisation of the high-sensitivity region caused by the ‘soft field’ effect characterised by severe nonlinear and inhomogeneous distribution of coplanar array capacitance sensitivity, resulting in difficulties in solving the problem of medium distribution, the proposed approach involves coplanar array capacitance sensor rotational scanning technology. This paper establishes both a physical rotation model and a sensitive field rotation model for a coplanar array capacitance sensor, demonstrating the inability to resolve the low-sensitivity region for medium distribution and illustrating how rotary scanning detection can enhance the utilisation rate of the high-sensitivity region. In conjunction with improved regional energy contrast, the images acquired under various rotation angles are fused, and experimental verification is conducted. The experimental results underscore that rotational scanning detection techniques can increase the utilisation rate of the high-sensitivity region within the coplanar array capacitance sensor by 74.69%. This enhancement effectively captures essential information within the reconstructed image and successfully addresses the challenge of solving difficulties in dielectric distribution. The research outcomes provide valuable insights for advancing non-destructive testing technologies employing coplanar array capacitance sensors.

Size matters: individual variation in auditory sensitivity may influence sexual selection in Pacific treefrogs (Pseudacris regilla).

The matched filter hypothesis proposes a close match between senders and receivers and is supported by several studies on variation in signal properties and sensory-processing mechanisms among species and populations. Importantly, within populations, individual variation in sensory processing may affect how receivers perceive signals. Our main goals were to characterize hearing sensitivity of Pacific treefrogs (Pseudacris regilla), assess patterns of individual variation in hearing sensitivity, and evaluate how among-individual variation in hearing sensitivity and call frequency content affect auditory processing of communication signals. Overall, males and females are most sensitive to frequencies between 2.0 and 2.5kHz, which matches the dominant frequency of the call, and have a second region of high sensitivity between 400 and 800Hz that does not match the fundamental frequency of the call. We found high levels of among-individual variation in hearing sensitivity, primarily driven by subject size. Importantly, patterns of among-individual variation in hearing differ between males and females. Cross-correlation analyses reveal that among-individual variation in hearing sensitivity may lead to differences on how receivers, particularly females, perceive male calls. Our results suggest that individual variation in sensory processing may affect signal perception and influence the evolution of sexually selected traits.

A History Matching Study for the FluidFlower Benchmark Project

In this study, we conduct a comprehensive history matching study for the FluidFlower benchmark model. This benchmark was prepared and organized by the University of Bergen, the University of Stuttgart, and Massachusetts Institute of Technology, for promoting understanding of the complex physics of geological carbon storage (GCS) through in-house experiments and numerical simulations. This paper synthesizes the experiences of history matching the benchmark data encountered by the Delft-DARTS and CSIRO participants. History matching is first performed based on a low-dimensional-zonated structured model using a simple Poisson-like solver. The permeability of six facies and two faults is inferred in this stage to match the digitized concentration data. The history matching is then further enhanced to richer spatial and physical models to capture the spatial variation of permeability and buoyancy effects, using an unstructured grid. Efficient adjoint methods are used to evaluate the gradient used in the optimization of data misfits or equivalent Bayesian log-likelihoods. With efficient optimization methods available for both maximum a posteriori model inference and Randomized Maximum Likelihood methods for model uncertainty, we perform history matching using both binary and continuous concentration observations. The results show that the tracer plumes in the enriched model match the experimental plumes more accurately compared with the results from the parsimonious-zonated model. The history matching results based on the concentration observations provide more similar plume shapes compared with the case based on the binary observations. The permeability difference between the model before and after history matching reveals that the tracer plume zone and the high permeable zone are the regions of high sensitivity in terms of data misfit between the model response and observations. Surprisingly, CO2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$_2$$\\end{document} concentration plume forecasts based on these history-matched models were not especially sensitive to the improvements observed in the enhanced model.

Synaptic Origins of the Complex Receptive Field Structure in Primate Smooth Monostratified Retinal Ganglion Cells.

Considerable progress has been made in studying the receptive fields of the most common primate retinal ganglion cell (RGC) types, such as parasol RGCs. Much less is known about the rarer primate RGC types and the circuitry that gives rise to noncanonical receptive field structures. The goal of this study was to analyze synaptic inputs to smooth monostratified RGCs to determine the origins of their complex spatial receptive fields, which contain isolated regions of high sensitivity called "hotspots." Interestingly, smooth monostratified RGCs co-stratify with the well-studied parasol RGCs and are thus constrained to receiving input from bipolar and amacrine cells with processes sharing the same layer, raising the question of how their functional differences originate. Through 3D reconstructions of circuitry and synapses onto ON smooth monostratified and ON parasol RGCs from central macaque retina, we identified four distinct sampling strategies employed by smooth and parasol RGCs to extract diverse response properties from co-stratifying bipolar and amacrine cells. The two RGC types differed in the proportion of amacrine cell input, relative contributions of co-stratifying bipolar cell types, amount of synaptic input per bipolar cell, and spatial distribution of bipolar cell synapses. Our results indicate that the smooth RGC's complex receptive field structure arises through spatial asymmetries in excitatory bipolar cell input which formed several discrete clusters comparable with physiologically measured hotspots. Taken together, our results demonstrate how the striking differences between ON parasol and ON smooth monostratified RGCs arise from distinct strategies for sampling a common set of synaptic inputs.

Mechanistic modelling of separating dispersions in pipes using model-based design of experiments techniques

This work presents a parametric study on a mechanistic model for separating liquid–liquid dispersions in pipes. The model considers drop-settling, drop-interface coalescence and drop-drop coalescence, predicting the evolution of four characteristic layers during separation. Parameter estimation, parametric sensitivity analysis (PSA), and model-based design of experiments (MBDoE) techniques are employed to acquire precise parameter estimates and propose optimal experimental conditions, thereby enhancing the accuracy of existing models. Experimental data from literature using oil-in-water dispersions are used for parameter estimation. PSA reveals regions of high sensitivity of the model outputs to uncertain parameters, which are corresponding to favourable sampling locations. Manipulating the mixture velocity, the dispersed phase fraction, and the layer heights at the inlet influences these sensitive regions. Clustered measurements around highly sensitive regions in the pipe enhance the information content they provide. MBDoE demonstrates that either of the A-, D-, or E-optimal experimental design criteria improves the expected parameter precision.

Comprehensive evaluation of natural landscapes before and after earthquake based on GST-FAHP-GIS analytical method: A case study of Jiuzhaigou Nature Reserve, China

The Jiuzhaigou earthquake caused severe damage to its natural landscape. The seismic-induced changes in landscapes represent a complex and diverse process. In order to creatively assess the extent of earthquake-induced landscape destruction and study the focal points of landscape restoration, this study proposes a comprehensive framework system that integrates multiple indicators and methods to evaluate the impact of earthquakes on the Jiuzhaigou scenic area. In comparison to previous single-method studies, our framework system offers a more holistic explanation and predictive capacity regarding the trends and characteristics of post-earthquake landscape changes. Within this approach, we amalgamate Grey Statistical Technique (GST), Fuzzy Analytic Hierarchy Process (FAHP), Delphi Technique, and Geographic Information System (GIS). By selecting 24 landscape evaluation indicators, we establish five levels of landscape assessment, ranging from broad to fine-grained. Areas with higher visibility sensitivity are categorized as high visual sensitivity regions, indicating the need for focused restoration efforts. Based on the degree of landscape visual sensitivity, we introduce four visual sensitivity evaluation factors: slope gradient, slope aspect, relative distance, and vegetation coverage. The result demonstrates that the overall landscape grade of Jiuzhaigou decreased by 15% after the earthquake; the aesthetic indicator of the landscape within geological hazard experienced the greatest decline, decreasing by 73.3%, while the landscape's resilience indicator showed relatively minor changes. Notably, regions with high visual sensitivity exhibit a distinctive “Y”-shaped distribution pattern, with 56.8% of environmental hazard points possessing the highest visual sensitivity. The study also underscores the concentrated presence of geological hazard points and high visual sensitivity between the Nuorilang Waterfall and the scenic area entrance, necessitating prioritized restoration efforts in this zone. This research innovatively provides a method for assessing the damage caused by earthquakes to natural landscapes, and it holds significant guiding implications for relevant national decision-making bodies in policy formulation and implementation.

Super low-frequency electric field measurement based on Rydberg atoms.

We demonstrate the measurement of super low-frequency electric field using Rydberg atoms in an atomic vapor cell with inside parallel electrodes, thus overcoming the low-frequency electric-field-screening effect at frequencies below a few kHz. Rydberg electromagnetically induced transparency (EIT) spectra involving 52D5/2 state is employed to measure the signal electric field. An auxiliary DC field is applied to improve the sensitivity. A DC Stark map is demonstrated, where the utilized 52D5/2 exhibits mj = 1/2, 3/2, 5/2 Stark shifts and splittings. The mj = 1/2 state is employed to detect the signal field because of its larger polarizability than that of mj = 3/2, 5/2. Also, we show that the strength of the spectrum is dependent on the angle between the laser polarizations and the electric field. With optimization of the applied DC field to shift the mj = 1/2 Rydberg energy level to a high sensitivity region and the laser polarizations to obtain the maximum mj = 1/2 signal, we achieve the detection of the signal electric field with a frequency of 100 Hz down to 214.8 µV/cm with a sensitivity of 67.9 µV cm-1Hz-1/2, and the linear dynamic range is over 37 dB. Our work extends the measurement frequency of Rydberg sensors to super low frequency with high sensitivity, which has the advantages of high sensitivity and miniaturization for receiving super low frequency.

A deep learning architecture with an object-detection algorithm and a convolutional neural network for breast mass detection and visualization

This study presents an integrated deep learning architecture with an object-detection algorithm and a convolutional neural network (CNN) for breast mass detection and visualization. Mammograms are analyzed to identify and localize breast mass lesions to aid clinician review. Two complementary forms of deep learning are used to identify the regions of interest (ROIs). An object-detection algorithm, YOLO v5, analyzes the entire mammogram to identify discrete image regions likely to represent masses. Object detections exhibit high precision, but the object-detection stage alone has insufficient overall accuracy for a clinical application. A CNN independently analyzes the mammogram after it has been decomposed into subregion tiles and is trained to emphasize sensitivity (recall). The ROIs identified by each analysis are highlighted in different colors to facilitate an efficient staged review. The CNN stage nearly always detects tumor masses when present but typically occupies a larger area of the image. By inspecting the high-precision regions followed by the high-sensitivity regions, clinicians can quickly identify likely lesions before completing the review of the full mammogram. On average, the ROIs occupy less than 20% of the tissue in the mammograms, even without removing pectoral muscle from the analysis. As a result, the proposed system helps clinicians review mammograms with greater accuracy and efficiency.

Water vapour products from ERA5, MERSI‐II/FY‐3D, OLCI/Sentinel‐3A, OLCI/Sentinel‐3B, MODIS/Aqua and MODIS/Terra in Australia: A comparison against in situGPS water vapour data

AbstractAustralia is a region of high sensitivity to the El Niño–Southern Oscillation events in association with atmospheric water vapour, which is an essential atmospheric parameter in hydrological cycle, energy transport and climate monitoring. In this article, we thoroughly validated the quality of multisource precipitable water vapour (PWV) products sourced from ERA5 reanalysis, advanced Medium Resolution Spectral Imager (MERSI‐II) sensor on the FY‐3D satellite, Ocean and Land Colour Instrument (OLCI) sensor on the Sentinel‐3A and Sentinel‐3B satellites, and Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on the Aqua and Terra satellites. The PWV estimates measured by 453 in situ Global Positioning System (GPS) sites from 1 June 2019 to 31 May 2020 over Australia were employed as the common reference. The validation results show that all the reanalysis‐based and satellite‐based PWV products had a good agreement with reference GPS‐based PWV data. ERA5 reanalysis had the highest PWV accuracy with a root‐mean‐square error (RMSE) of 2.016 mm and a relative RMSE (RRMSE) of 12.038%, while the MODIS/Terra instrument had the lowest PWV accuracy (RMSE = 4.903 mm and RRMSE = 34.187%). In contrast to the MERSI‐II/FY‐3D instrument that underestimated PWV, the PWV data from ERA5, OLCI/Sentinel‐3A, OLCI/Sentinel‐3B, MODIS/Aqua and MODIS/Terra overestimated the water vapour values. The quality of reanalysis‐based and satellite‐based PWV measurements showed significant dependencies on several variables – location, PWV, solar zenith angle, season, elevation, land surface cover and latitude. It is expected that this research can help enhance the understanding of the quality of reanalysis and satellite water vapour products in Australia, that is, ERA5, OLCI/Sentinel‐3A, OLCI/Sentinel‐3B, MODIS/Aqua and MODIS/Terra. This work could provide an insight into improving the accuracy of reanalysis‐based and satellite‐based PWV data products after exploring the dependency factors that affect PWV performance as presented in our work.

Continuously tunable radio frequency electrometry with Rydberg atoms

We demonstrate a continuously tunable electric field measurement based on the far off-resonant AC stark effect in a Rydberg atomic vapor cell. In this configuration, a strong far off-resonant field, denoted as a local oscillator (LO) field, acts as a gain to shift the Rydberg level to a high sensitivity region. An incident weak signal field with a few hundreds of kHz difference from the LO field is mixed with the LO field in the Rydberg system to generate an intermediate frequency signal, which is read out by Rydberg electromagnetically induced transparency (Rydberg-EIT) spectroscopy. Not like resonant EIT-Autler–Townes spectra, we realize the electric field measurement of the signal frequency from 2 to 5 GHz using a single Rydberg state. The detectable field strength is down to 2.25 μV/cm with sensitivity of the electrometry 712 nV cm−1 Hz−1/2, and a linear dynamic range is over 65 dB. The detectable field strength is comparable with a resonant microwave-dressed Rydberg heterodyne receiver using the same system, which is 0.96 μV/cm with sensitivity of 304 nV cm–1 Hz−1/2. We also show the system has an inherent polarization selectivity feature. Our method can provide high sensitivity of electric field measurement and be extended to arbitrary frequency measurements.

3D-printed endoplasmic reticulum rGO microstructure based self-powered triboelectric pressure sensor

Developing new generation of self-powered triboelectric sensors is urgent in the application of Internet of things (IoT) with low-power consumption. However, the traditional triboelectric pressure sensors demonstrate narrow detection range and are used to be less sensitive in large pressure range. In this work, a 3D-printed endoplasmic reticulum rGO microstructure based self-powered triboelectric pressure sensor (rGO-TPS) has been proposed. By employing PDMS@rGO framework as dielectric layer and spacer, a force-electric coupling model was built to investigate the electromechanical sensing mechanism. Owing to the ultra-low Young’s modulus of the 3D-printed materials and designed device structure, the rGO-TPS can reach the sensitivity of 6.28 kPa−1 and broaden high-sensitivity region from 0.65 Pa to 10 kPa. Besides, it also improves the sensitivity to 0.61 kPa−1 during the large pressure range from 10 kPa to 140 kPa. It also illustrates a fast response time of 92 ms and great stability of 3,000 cycles without fatigue. In addition, the dynamic pressure response can be monitored by detecting the change of pulse-like short-circuit current signal. Benefiting from the structural advantages and ultra-high performance, several potential applications in gauging water droplets and air flows, and vibration recognition have also been successfully demonstrated. This work provides a promising strategy to promote the progress toward the practical application of self-powered triboelectric pressure sensor.

Global Stability and Sensitivity Investigation of Nonlinear Dynamics of Compressor Diffuser with Numerical Volute Approach

Although the increase of the width ratio of a radial diffuser generally leads to a more unstable condition, the effect of volute was seldom considered in previous experimental and numerical studies because of the mismatching between the changed diffuser and the original volute. In this work, vaneless diffusers with different width ratios were theoretically and numerically investigated. In particular, a turbulent stability analysis based on different flow planes was performed using a spectral collocation method. To consider the combined impact of width ratio and volute, the base flow of a diffuser with a numerical volute imposed at the outlet was computed and validated against experimental data. The sensitivity of the eigenvalue was further obtained to localize the wave-maker region of the diffuser by means of a continuous adjoint procedure. The results indicated that the asymmetric flow field could be well recovered by the numerical volute. With a small width ratio of 0.06, the stability margin of the compressor was not significantly widened with a stall first occurring in the impeller, although the diffuser becomes more stable. The asymmetric flow condition led to the regional enhanced global modes in the circumferential direction for the diffuser with middle and large width ratios. The high-sensitivity region of the diffuser with a middle width ratio lay at the 90°–160° position in a scattered form, indicating that the combined effect of the inflow wake and outlet reverse flow near the shroud wall were crucial for flow instability. However, for a diffuser with a large width ratio, the dominant factor in instability is the outlet reverse flow due to the delay of the separation point.

Porous Pure MXene Fibrous Network for Highly Sensitive Pressure Sensors.

Wearable and elastic pressure sensors have caused widespread concern due to the popularity of smart terminals and human health monitoring. To obtain a flexible pressure sensor with a wide detection region and outstanding sensitivity, exploring new materials and novel structures has become the first choice for the research. Here, a wearable and flexible MXene fibrous network pressure sensor (MFNS) with a high sensitivity and wide detection region is reported. The holistic fiber network is composed of pure MXene fibers; among them, MXene fibers were prepared by wet-spinning of MXene nanosheets. The MFNS exhibits a high sensitivity in a wide detection region (51 kPa-1 for 14.7 kPa and 427 kPa-1 within the 14.7-19.9 kPa range), a low detection limit (8 Pa), a robust durability (10,000 cycles), and a prompt response (95 ms). Due to the superior performance of MFNS, it also proves prospective applications for human motion signal detection (such as swallowing, pulse beat, and joint motion) and measuring pressure distribution. This work provides an effective way to fabricate a high-performance pressure sensor for human-machine interactions, personal healthcare monitoring, and multitouch devices.

Rydberg atom-based AM receiver with a weak continuous frequency carrier.

We demonstrate an atom-based amplitude-modulation (AM) receiver for digital communication with a weak continuous frequency carrier using a Rydberg AC Stark effect in a vapor cell and achieve the operating carrier frequency continuously from 0.1 GHz to 5 GHz at a single Rydberg state. A strong local oscillator (LO) field ELO acts as a gain to shift the Rydberg level to a high sensitivity region, and a weak carrier field ECarr keeps the same frequency with the LO field. The digital baseband signals are encoded onto the ECarr using the amplitude modulation technique with the different modulation frequency. The response of Rydberg atom to the baseband signal is probed via a Rydberg electromagnetically induced transparency (EIT). The measured instantaneous bandwidth of the system is about 230 kHz. To demonstrate the performance of our system for an actual communication, we consider a color image as an example, the received image displays that the bit error rate (BER) is less than 5% when the maximum data transfer rate is about 238 kbps. Meanwhile, our system shows the weak carrier field of ECarr ≥ 13.52 μV/cm can be used for the practical communication with BER less than 5%. Our works break the limitation that EIT-AT based atomic receivers only operate at the near resonant frequencies of the Rydberg transitions, making this emerging of quantum technology close to the practical application with high sensitivity and broad bandwidth.

Complete head cerebral sensitivity mapping for diffuse correlation spectroscopy using subject-specific magnetic resonance imaging models.

We characterize cerebral sensitivity across the entire adult human head for diffuse correlation spectroscopy, an optical technique increasingly used for bedside cerebral perfusion monitoring. Sixteen subject-specific magnetic resonance imaging-derived head models were used to identify high sensitivity regions by running Monte Carlo light propagation simulations at over eight hundred uniformly distributed locations on the head. Significant spatial variations in cerebral sensitivity, consistent across subjects, were found. We also identified correlates of such differences suitable for real-time assessment. These variations can be largely attributed to changes in extracerebral thickness and should be taken into account to optimize probe placement in experimental settings.

Evaluating the Performances of Biomarkers over a Restricted Domain of High Sensitivity

The burgeoning advances in high-throughput technologies have posed a great challenge to the identification of novel biomarkers for diagnosing, by contemporary models and methods, through bioinformatics-driven analysis. Diagnostic performance metrics such as the partial area under the ROC (pAUC) indexes exhibit limitations to analysing genomic data. Among other issues, the inability to differentiate between biomarkers whose ROC curves cross each other with the same pAUC value, the inappropriate expression of non-concave ROC curves, and the lack of a convenient interpretation, restrict their use in practice. Here, we have proposed the fitted partial area index (FpAUC), which is computable through an algorithm valid for any ROC curve shape, as an alternative performance summary for the evaluation of highly sensitive biomarkers. The proposed approach is based on fitter upper and lower bounds of the pAUC in a high-sensitivity region. Through variance estimates, simulations, and case studies for diagnosing leukaemia, and ovarian and colon cancers, we have proven the usefulness of the proposed metric in terms of restoring the interpretation and improving diagnostic accuracy. It is robust and feasible even when the ROC curve shows hooks, and solves performance ties between competitive biomarkers.