Data Acquisition Time Research Articles

X-ray absorption measurements at a bending magnet beamline with an Everhart–Thornley detector: A monolayer of Ho3N@C80 on graphene

X-ray Absorption Spectroscopy (XAS) is used for measuring monolayer quantities of Ho3N@C80 endofullerene molecules on graphene at a low flux bending magnet beamline. The total electron yield is measured with an Everhart–Thornley detector. In comparison to sample current measurements with the same noise level, our approach reduces data acquisition time and radiation dose by a factor of 25. As the first application of this setup, we report temperature-dependent measurements of the Ho M45 edge with per mille accuracy. This documents the advantages and capabilities of an Everhart–Thornely detector for XAS measurements under low x-ray flux.

Dual-wavelength photoacoustic atlas method to estimate fractional methylene blue and hemoglobin contents.

.SignificanceMethylene blue (MB) is an exogenous contrast agent that has the potential to assist with visualization and penetration challenges in photoacoustic imaging. However, monitoring the local concentration between MB and endogenous chromophores is critical for avoiding unnecessary MB accumulations that could lead to adverse effects such as hemolysis when exposed to increased dose and photodamage when exposed to high laser energies.AimWe developed a modified version of a previously proposed acoustic-based atlas method to estimate concentration levels from a mixture of two photoacoustic-sensitive materials after two laser wavelength emissions.ApproachPhotoacoustic data were acquired from mixtures of and either human or porcine blood (Hb) injected in a plastisol phantom, using laser wavelengths of 710 and 870 nm. An algorithm to perform linear regression of the acoustic frequency response from an atlas composed of pure concentrations was designed to assess the concentration levels from photoacoustic samples obtained from 11 known MB/Hb volume mixtures. The mean absolute error (MAE), coefficient of determination (i.e., ), and Spearman’s correlation coefficient (i.e., ) between the estimated results and ground-truth labels were calculated to assess the algorithm performance, linearity, and monotonicity, respectively.ResultsThe overall MAE, , and were 12.68%, 0.80, and 0.89, respectively, for the human Hb dataset and 9.92%, 0.86, and 0.93, respectively, for the porcine Hb dataset. In addition, a similarly linear relationship was observed between the acoustic frequency response at 2.3 MHz and 870-nm laser wavelength and the ground-truth concentrations, with and values of 0.76 and 0.88, respectively.ConclusionsContrast agent concentration monitoring is feasible with the proposed approach. The potential for minimal data acquisition times with only two wavelength emissions is advantageous toward real-time implementation in the operating room.

A Multicast-based MAC Protocol for Improving Data Transmission Efficiency in Industrial WSNs.

In industrial WSNs, attaining the desired level of data transmission efficiency is highly challenging not only for a frequently changing network topology but also for a short data acquisition cycle time. A short data acquisition time often restrains a sensor MAC protocol to incorporate an effective data packet salvaging technique. Therefore, we propose a data transmission technique in which a sending node multicasts data packets to all candidate parents including its primary parent. As a result, the necessity of data retransmission attempts is totally avoided, which greatly improves the data transmission efficiency in industrial WSNs. By simulation experiments, we have shown that the proposed multicast-based MAC protocol surpasses all the competing MAC protocols regarding the successful data packet delivery and power saving with a significant margin

Surface Kinetic Temperatures and Nontronite Single Scattering Albedo Spectra From Mars Reconnaissance Orbiter CRISM Hyperspectral Imaging Data Over Glen Torridon, Gale Crater, Mars

AbstractThe Mars Reconnaissance Orbiter Compact Imaging Spectrometer for Mars (CRISM) covers the spectral range from 0.362 to 3.92 μm with a midafternoon local solar time data acquisition. For equatorial to midlatitudes, depending on the season and surface materials, wavelengths longer than ∼2.65 μm exhibit spectral radiances on sensor that include sunlight and thermal‐emission related terms. We developed a radiative transfer based neural network approach to model both solar and emitted terms in which surface kinetic temperatures are retrieved for each image pixel, together with single scattering albedo spectra, over the full CRISM wavelength range. We applied the method to along‐track oversampled scene FRT00021C92 over Glen Torridon within Gale Crater, where the Curiosity rover traversed and acquired remote sensing and in‐situ data. Synergistic analysis of orbital and rover‐based data, coupled with laboratory analyses of ferric‐rich smectites, provide a self‐consistent set of results for the presence of desiccated nontronite associated with Murray formation mudstones exposed as periodic bedrock ridges located just to the south of Vera Rubin ridge. The desiccated nature is consistent with Curiosity's CheMin data, which for Glen Torridon drill samples indicate an abundance of nontronite having a collapsed structure resulting from loss of interlayer H2O.

Curvelet Transform-Based Sparsity Promoting Algorithm for Fast Ultrasound Localization Microscopy.

Ultrasound localization microscopy (ULM) based on microbubble (MB) localization was recently introduced to overcome the resolution limit of conventional ultrasound. However, ULM is currently challenged by the requirement for long data acquisition times to accumulate adequate MB events to fully reconstruct vasculature. In this study, we present a curvelet transform-based sparsity promoting (CTSP) algorithm that improves ULM imaging speed by recovering missing MB localization signal from data with very short acquisition times. CTSP was first validated in a simulated microvessel model, followed by the chicken embryo chorioallantoic membrane (CAM), and finally, in the mouse brain. In the simulated microvessel study, CTSP robustly recovered the vessel model to achieve an 86.94% vessel filling percentage from a corrupted image with only 4.78% of the true vessel pixels. In the chicken embryo CAM study, CTSP effectively recovered the missing MB signal within the vasculature, leading to marked improvement in ULM imaging quality with a very short data acquisition. Taking the optical image as reference, the vessel filling percentage increased from 2.7% to 42.2% using 50ms of data acquisition after applying CTSP. CTSP used 80% less time to achieve the same 90% maximum saturation level as compared with conventional MB localization. We also applied CTSP on the microvessel flow speed maps and found that CTSP was able to use only 1.6s of microbubble data to recover flow speed images that have similar qualities as those constructed using 33.6s of data. In the mouse brain study, CTSP was able to reconstruct the majority of the cerebral vasculature using 1–2s of data acquisition. Additionally, CTSP only needed 3.2s of microbubble data to generate flow velocity maps that are comparable to those using 129.6s of data. These results suggest that CTSP can facilitate fast and robust ULM imaging especially under the circumstances of inadequate microbubble localizations.

Fast wavefront sensing for X-ray optics with an alternating speckle tracking technique.

Advances in accelerator technologies have enabled the continuous development of synchrotron radiation and X-ray free electron laser (XFEL) sources. At the same time, it has been critical to perform in-situ wavefront sensing to aid delivery of high-quality X-ray beams to the end users of these facilities. The speckle-based scanning technique has obtained popularity due to its high spatial resolution and superior sensitivity compared to other wavefront sensing methods. However, these advantages often come at the expense of longer data acquisition times since multiple images have to be collected to derive the necessary wavefront information. Whereas initial speckle tracking techniques could obtain wavefront information relatively quickly, the installation of additional hardware was routinely required to do so. Here, we propose a novel speckle-based approach, termed Alternating Speckle Tracking (AST), to perform fast wavefront sensing within a conventional beamline setup. The wavefront information derived from the new technique has proven to be valuable for many applications that require temporal resolution. Importantly, both horizontal and vertical wavefront information can be simultaneously derived by moving the speckle generator along the diagonal direction. We expect this method will be widely used by the synchrotron radiation and XFEL community in the future.

Exploring the potential of machine learning for more efficient development and production of biopharmaceuticals.

Principles of Industry 4.0 direct us to predict how pharmaceutical operations and regulations may exist with automation, digitization, artificial intelligence (AI), and real time data acquisition. Machine learning (ML), a sub-discipline of AI, involves the use of statistical tools to extract the desired information either through understanding the underlying patterns in the information or by development of mathematical relationships among the critical process parameters (CPPs) and critical quality attributes (CQAs) of biopharmaceuticals. ML is still in its infancy for directly supporting the quality-by-design based development and manufacturing of biopharmaceuticals. However, adoption of ML-based models in place of conventional multi-variate-data-analysis (MVDA) is increasing with the accumulation of large-scale data. This has been majorly contributed by the real-time monitoring of process variables and quality attributes of products through the implementation of process analytical technology in biopharmaceutical manufacturing. All aspects of healthcare, from drug design to product distribution, are complex and multidimensional. Thus, ML-based approaches are being applied to achieve sophistication, accuracy, flexibility and agility in all these areas. This review discusses the potential of ML for addressing the complex issues in diverse areas of biopharmaceutical development, such as biopharmaceuticals design and assessment of early stage development, upstream and downstream process development, analysis, characterization and prediction of post-translational modifications (PTMs), formulation, and stability studies. Moreover, the challenges in acquisition, cleaning and structuring the bioprocess data, which is one of the major hurdles in implementation of ML in biopharma industry, have also been discussed. Regulatory perspectives on implementation of AI/ML in the biopharma sector have also been briefly discussed. This article is a bird's eye view on the recent developments and applications of ML in overcoming the challenges for adopting "Industry - 4.0" in the biopharma industry.

Segment Scan Mass Spectral Acquisition for Increasing the Metabolite Detectability in Chemical Isotope Labeling Liquid Chromatography-Mass Spectrometry Metabolome Analysis.

We report a segmented spectrum scan method using Orbitrap MS in chemical isotope labeling (CIL) liquid chromatography-mass spectrometry (LC-MS) for improving the metabolite detection efficiency. In this method, the full m/z range is divided into multiple segments with the scanning of each segment to produce multiple narrow-range spectra during the LC data acquisition. These segmented spectra are separately processed to extract the peak pair information with each peak pair arising from a differentially labeled metabolite in the analysis of a mixture of 13C and 12C reagent-labeled samples. The sublists of peak pairs are merged to form the final peak pair list from the LC-MS run. Various experimental conditions, including automatic gain control (AGC) values, mass resolutions, segment m/z widths, number of segments, and total data acquisition time in the LC run, were examined to arrive at an optimal setting in the segment scan for increasing the number of detectable metabolites while maintaining the same analysis time as in the full scan. The optimal method used a segment width of 120 m/z with 60k resolution for a 16 min CIL LC-MS run. Using dansyl-labeled human urine samples as an example, we demonstrated that this method could detect 5867 peak pairs or metabolites (not features), compared to 3765 peak pairs detectable in a full scan, representing a 56% gain. Out of 5867 peak pairs, 5575 (95.0%) could be identified or mass-matched. The relative quantification accuracy was slightly reduced (81% peak pairs were within ±25% of the expected peak ratio of 1.0 in full, compared to 87% in the full scan) due to the inclusion of more low-abundance peak pairs in the segment scan. The peak ratio measurement precision was not significantly affected by the segment scan. We also showed the increase of the peak pair number detectable from 3843 in the full scan to 7273 (89% gain) using the Orbitrap operated at 120k resolution with a 60 m/z segment width when multiple repeat sample injections were used. Thus, segment scan Orbitrap MS is an enabling method for detecting coeluting metabolites in CIL LC-MS for increasing the metabolomic coverage.

An Adaptive Sampling Strategy for Surface Measurement of Complex Curved Workpieces

Large complex workpieces are the core components in aerospace, shipbuilding, transportation and other fields. Such components usually have large size, complex shape characteristics and poor stiffness of workpieces, such as large common bottom components of launch vehicles, rocket panels, aircraft radomes, etc. When machining such high-performance parts, it is usually necessary to copy the parts according to the task requirements. The integration technology of complex surface measurement and machining is the organic combination of digital measurement technology and machining technology and is an effective means to ensure the high-precision copying machining of large complex workpieces. Data acquisition technology is the most critical part. For the data acquisition of some large and complex workpieces, laser scanning measurement technology with high reliability and local accuracy is usually used. In the process of data acquisition, the measuring track and the measuring point interval are planned according to the structural characteristics of the workpiece. The quality of the measurement data will directly affect the accuracy and overall quality of the follow-up profiling processing. The two most important factors in curved surface measurement are the scale of sampling quantity and the distribution of sampling points [1]. In the point cloud model, the larger the number of sampling points, the more appearance features are included, and the more accurate the 3D reconstruction results are [2]. However, the sampling scale is directly proportional to the time of data acquisition. An excessive sampling scale will lead to an increase of the measurement time cost, overload of data processing and the increase of overall product manufacturing cost. Therefore, the research on intelligent sampling point planning of complex surfaces is of great significance.

Monitoring lava dome extrusion of Merapi Volcano during 2018-2019, using low-cost UAV application

Merapi volcano has a well-known eruption type, namely Merapi type, in which an extruded lava dome collapses and is accompanied by pyroclastic density current (PDC). This type of eruption makes morphological monitoring of the lava dome crucial in the hazard mitigation process. After the VEI 4 eruption in 2010, a new lava dome of Merapi appeared on top of the 2010 lava dome in August 2018 and continuously grew. In November 2019, the lava dome started to collapse outward the crater area. We reported the lava dome morphological monitoring using a UAV (Unmanned Aerial Vehicle) photogrammetry conducted from August 2018 to February 2019. This UAV monitoring provides processed aerial photo data in Digital Terrain Model (DTM) and orthophoto with low operating costs and short data acquisition time. The lava domes erupted from the same eruptive canter within this period and grew evenly in all directions. The 2018-2019 Merapi lava dome has basal ratio of 0.183 to 0.290 with height of 11 to 41 m, respectively. Volume changed from 33,623 m3 in August 2018 to 658,075 m3 in February 2019, suggesting growth rate at ~3,500 m3/day. The lava base filled the crater base area (0.21 km2) and started to collapse outward in November 2019.

Design of Intelligent Evaluation System for College Students' Mental Health Based on Big Data.

The mental health problems of college students have attracted the attention of all sectors of society. In order to keep college students in a good mental state and effectively analyze their mental health, an intelligent evaluation system of college students' mental health based on big data is designed. Based on big data technology, this article constructs an intelligent evaluation system for college students' mental health, which is divided into six layers, namely, application layer, decision layer, interface layer, analysis layer, data layer, and basic layer. Then, the mental health data of college students were collected based on C/S architecture. On the basis of extracting and integrating data characteristics, six evaluation indexes of personality, will, emotion, depression, fear, and psychosis were screened, and then, the intelligent evaluation was completed according to the weight of indexes. On the basis of the preliminary verification of the performance of the system in this article, according to the comparative experimental results, the mental health data acquisition time of the system is less, the accuracy of data feature extraction and the recall rate of evaluation results are higher.

Compact laboratory-based X-ray microscope enabling nondestructive 3D structure acquisition of mouse nephron with high speed and better user accessibility.

X-ray microscopes adopting computed tomography enable nondestructive 3D visualization of biological specimens at micron-level resolution without conventional 2D serial sectioning that is a destructive/laborious method and is routinely used for analyzing renal biopsy in clinical diagnosis of kidney diseases. Here we applied a compact commercial system of laboratory-based X-ray microscope to observe a resin-embedded osmium-stained 1-mm strip of a mouse kidney piece as a model of renal biopsy, toward a more efficient diagnosis of kidney diseases. A reconstructed computed tomography image from several hours of data collection using CCD detector allowed us to unambiguously segment a single nephron connected to a renal corpuscle, which was consistent with previous reports using serial sectioning. Histogram analysis on the segmented nephron confirmed that the proximal and distal tubules were distinguishable on the basis of their X-ray opacities. A 3D rendering model of the segmented nephron visualized a convoluted structure of renal tubules neighboring the renal corpuscle and a branched structure of efferent arterioles. Furthermore, another data collection using scientific complementary metal-oxide semiconductor detector with a much shorter data acquisition time of 15 min provided similar results from the same samples. These results suggest a potential application of the compact laboratory-based X-ray microscope to analyze mouse renal biopsy.

Joint Optimization on Trajectory, Transmission and Time for Effective Data Acquisition in UAV-Enabled IoT

Unmanned aerial vehicle (UAV) energy consumption and task completion time are two significant aspects in UAV-enabled Internet of Things (IoT) data acquisition. However, while energy consumption and task completion time are not conflicting constraints, optimizing the former often does not automatically lead to optimization of the latter, and vice-versa. In this paper, we aim to further decrease UAV energy consumption while minimizing UAV task completion time. We propose a novel UAV utility function to describe task completion time for data acquisition and energy consumption for propulsion and receiver's circuits. Then, we formulate a mixed-integer nonconvex utility maximization problem, involving joint optimization of UAV trajectory, velocity, acceleration, task completion time and scheduling of IoT devices for data uploading. To tackle this problem, we decompose the original problem into two sub-problems and propose a two-layer joint task time and trajectory optimization (JTTTO) iterative algorithm, which converges in polynomial time. Accordingly, we apply block coordinate descent (BCD) and successive convex approximation (SCA) algorithms to optimize UAV trajectory, and bisection search algorithm to minimize task completion time, respectively. Simulation results show that the proposed data acquisition scheme significantly decreases the UAV's energy consumption while minimizing task completion time, compared with the other time-minimization schemes.

A Generic Online Nonparametric Monitoring and Sampling Strategy for High-Dimensional Heterogeneous Processes

With the rapid advancement of in-process measurements and sensor technology driven by zero-defect manufacturing applications, high-dimensional heterogeneous processes that continuously collect distinct physical characteristics frequently appear in modern industries. Such large-volume high-dimensional data place a heavy demand on data collection, transmission, and analysis in practice. Thus, practitioners often need to decide which informative data streams to observe given the resource constraints at each data acquisition time, which poses significant challenges for multivariate statistical process control and quality improvement. In this article, we propose a generic online nonparametric monitoring and sampling scheme to quickly detect mean shifts occurring in heterogeneous processes when only partial observations are available at each acquisition time. Our innovative idea is to seamlessly integrate the Thompson sampling (TS) algorithm with a quantile-based nonparametric cumulative sum (CUSUM) procedure to construct local statistics of all data streams based on the partially observed data. Furthermore, we develop a global monitoring scheme of using the sum of top- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${r}$ </tex-math></inline-formula> local statistics, which can quickly detect a wide range of possible mean shifts. Tailored to monitoring the heterogeneous data streams, the proposed method balances between exploration that searches unobserved data streams for possible mean shifts and exploitation that focuses on highly suspicious data streams for quick shift detection. Both simulations and a case study are comprehensively conducted to evaluate the performance and demonstrate the superiority of the proposed method. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper is motivated by the critical challenge of online process monitoring by considering the cost-effectiveness and resource constraints in practice (e.g., limited number of sensors, limited transmission bandwidth or energy constraint, and limited processing time). Unlike the existing methodologies which rely on the restrictive assumptions (e.g., normally distributed, exchangeable data streams) or require historical full observations of all data streams to be available offline for training, this paper proposes a novel monitoring and sampling strategy that allows the practitioners to cost-effectively monitor high-dimensional heterogeneous data streams that contain distinct physical characteristics and follow different distributions. To implement the proposed methodology, it is necessary: (i) to identify sample quantiles for each data stream based on historical in-control data offline; (ii) to determine which data streams to observe at each acquisition time based on the resource constraints; and (iii) to automatically screen out the suspicious data streams to form the global monitoring statistic. Experimental results through simulations and a case study have shown that the proposed method has much better performance than the existing methods in reducing detection delay and effectively dealing with heterogeneous data streams.

Inline-Feasible Contrast Separation in Luminescence Imaging for Silicon Solar Cells

We present an article about separating contrasts in luminescence images of silicon solar cells for industrial application, i.e., in very short measurement times significantly below one second. The well-known method “Coupled Determination of Dark Saturation Current Density and Series Resistance” by Glatthaar <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> is optimized to drastically decrease the required exposure time. The correction using an image under short-circuit conditions is omitted. Simulations with Quokka3, which give insight into the mechanisms of contrast separation, are performed. The operating points at which the images are acquired are adjusted allowing short data acquisition times. The optimized method decreases the necessary exposure time by more than a factor of 6 to 160 ms for a multicrystalline silicon passivated emitter and rear cell (PERC) solar cell. The quality regarding contrast separation can be maintained.

Photoacoustic signal-to-noise ratio comparison for pulse and continuous waveforms of very low optical fluence.

.Significance: A majority in the photoacoustic (PA) community unconditionally accepts that pulse PA signals show much higher signal-to-noise ratios (SNRs) than continuously excited PA signals. However, we indicate this existing notion would not be valid for very low optical-fluence light-emiting diodes (LEDs)/laser diodes (LDs)-based PA systems.Aim: We demonstrate in theory and simulation that when the optical fluence of PA-excitation waveforms is much lower than the American National Standards Institute (ANSI) maximum permission exposure (MPE), matched filtered PA signals from chirp waveforms show higher SNRs than those of pulse train waveforms.Approach: We theoretically derive the PA SNR expression considering the pulse fluence reduction factor based on the ANSI MPE. We investigate and analyze SNR ratios of the pulse train and chirp-waveform matched filtered PA signals with conceptual understanding. We also perform brute-force simulations to extract PA SNRs for the verification of the result.Results: The brute-force simulations show that the matched filtering with chirp waveforms could achieve better SNRs than pulse train waveforms for very low-fluence PA systems. As the fluence is smaller, the SNR of the matched filtered PA signals is more dominant than that of pulse trains in a wider PA data acquisition time range. In addition, estimated SNR ratios adopting actual parameters of LED/LD-based pulse train PA systems in previous literature support the finding of this paper.Conclusions: The result can extend the possibility of applying various continuous waveform techniques already studied in the conventional radar technology to PA systems of limited optical power, which would diversify and expedite the research and development of LED/LD-based, compact, and cost-effective PA systems.

Prediction of prognosis in patients with panic disorder using pre-treatment brain white matter features

BackgroundThe early identification of patients with panic disorder (PD) with a poor prognosis is important for improving treatment outcomes; however, it is challenging due to a lack of objective biomarkers. We investigated the reliability of characterizing structural white matter (WM) connectivity and its ability to predict PD prognosis after pharmacotherapy. MethodsA total of 138 patients (59 men) with PD and 153 healthy controls (HCs; 73 men) participated in this study. PD symptom severity was measured using the Panic Disorder Severity Scale (PDSS) at baseline and follow-up periods of 8 weeks, 6 months, and 1 year. The least absolute shrinkage and selection operator (Lasso) was utilized to identify prognosis-related WM regions on diffusion imaging features. ResultsLasso identified seven prognosis-related WM regions: the bilateral posterior corona radiata, bilateral posterior limb of the internal capsule, the left retrolenticular part of the internal capsule, the left sagittal stratum, and the right fornix/stria terminalis. Some of these regions showed lower mean fractional anisotropy (FA) values in patients with PD than in HCs. The predicted PDSS scores using FA from these regions consistently correlated with the actual prognosis in all periods. LimitationsThis study had limited ability to evaluate individual longitudinal changes in detail owing to the data acquisition time and brain atlas resolution. ConclusionsOur findings suggest the possibility of using structural WM connectivity as a biomarker for the clinical characterization of PD. Our findings will expand our understanding of the neurobiology of PD and improve biomarker-based prognosis prediction in clinical practice.

Stacked U-Nets with self-assisted priors towards robust correction of rigid motion artifact in brain MRI

Magnetic Resonance Imaging (MRI) is sensitive to motion caused by patient movement due to the relatively long data acquisition time. This could cause severe degradation of image quality and therefore affect the overall diagnosis. In this paper, we develop an efficient retrospective 2D deep learning method called stacked U-Nets with self-assisted priors to address the problem of rigid motion artifacts in 3D brain MRI. The proposed work exploits the usage of additional knowledge priors from the corrupted images themselves without the need for additional contrast data. The proposed network learns the missed structural details through sharing auxiliary information from the contiguous slices of the same distorted subject. We further design a refinement stacked U-Nets that facilitates preserving the spatial image details and improves the pixel-to-pixel dependency. To perform network training, simulation of MRI motion artifacts is inevitable. The proposed network is optimized by minimizing the loss of structural similarity (SSIM) using the synthesized motion-corrupted images from 83 real motion-free subjects. We present an intensive analysis using various types of image priors: the proposed self-assisted priors and priors from other image contrast of the same subject. The experimental analysis proves the effectiveness and feasibility of our self-assisted priors since it does not require any further data scans. The overall image quality of the motion-corrected images via the proposed motion correction network significantly improves SSIM from 71.66% to 95.03% and declines the mean square error from 99.25 to 29.76. These results indicate the high similarity of the brain's anatomical structure in the corrected images compared to the motion-free data. The motion-corrected results of both the simulated and real motion data showed the potential of the proposed motion correction network to be feasible and applicable in clinical practices.

An EC-QCL based dual-species (CH4/N2O) detection method at 7.8 µm in mid-IR region for simultaneous applications of atmospheric monitoring and breath diagnostics

A high-sensitive optical detection method was designed and developed at 7.8 μm mid-IR region by coupling a room-temperature operated continuous wave (cw) external-cavity quantum cascade laser (EC-QCL) with an astigmatic multipass cell. The detection strategy operates in the principle of wavelength modulation spectroscopy with second harmonic detection strategy (2f-WMS). We have shown that the methodology has the ability for simultaneous and real-time quantitative measurements of nitrous oxide (N2O) and methane (CH4) in ambient air and human exhaled breath in parts per billion (ppb) levels via an extremely narrow single QCL scan of ∼0.06 cm−1. The high-resolution rotational-vibrational interference-free 2f-WMS spectra of CH4 and N2O centred at 1297.8192 cm−1 and 1297.8314 cm−1, respectively were simultaneously acquired with 0.20 s data acquisition time in the optimized experimental conditions. Minimum detection limits of 6 ppb for N2O and 30 ppb CH4 were achieved by the cw-EC-QCL based 2f-WMS detection method, thus opening its broad applications in environmental monitoring and non-invasive biomedical diagnostics.

A general overview and comparative interpretation on element‐specific X‐ray spectroscopy techniques:XPS,XAS,andXRS

AbstractX‐ray photoelectron spectroscopy (XPS), X‐ray absorption spectroscopy (XAS) and X‐ray Raman scattering (XRS) spectroscopy are the element‐specific tools providing local electronic and chemical structure insights. XPS is widely used for qualitative/quantitative surface analysis of solids and replaced by synchrotron‐based ambient pressure‐XPS and hard‐XPS to benefit from the advantages of near‐ambient pressures and hard X‐rays to study complex sample environments. XAS is a well‐established soft and hard X‐ray probe to examine the coordination, spin and oxidation states in solids, liquids and gases with high resolutions and short data acquisition time. XRS, using the hard X‐rays of ~10 keV, is preferred to conventional XAS and XPS in the study of soft X‐ray absorption edges such as C, O, Li K‐edges bulk‐sensitively in vacuum‐free medium for energy storage systems during working operations, inner Earth elements under realistic conditions, probing chemical and biological reactions in the liquid phase, C speciation in archeological and paleontological samples and direct tomography. In this study, first the basics of XPS, XAS, and XRS techniques with their advantages and limitations are introduced to provide a general overview. In the Results and Discussion, the particular emphasis is given to the comparison of XPS, XAS, and XRS with the interpretation of the spectroscopic signatures for organic carbonate‐based electrolytes. The computed C 1 s binding energy shifts for ethylene carbonate (EC), propylene carbonate (PC), dimethyl carbonate (DMC), and diethyl carbonate (DEC) are presented to discuss the XPS peak assignments and the performed C K‐edge XRS measurements of 1 M LiPF6in DMC and EC are given for a detailed discussion of the XRS spectra. C 1 s XPS, C K‐edge XAS, and XRS of PC from the literature are discussed to interpret the spectra of each technique comparatively.