Post-processing Data Research Articles

Rapid Classification and Identification of Chemical Compounds and Semi-Quantitative Metabolism of Huangkui Capsules and the Protective Effects of Its Quercetin Derivatives against Tacrolimus-induced HK-cell Reduction

Abstract Objective: The study aimed to establish an effective strategy for systematically characterizing and verifying compounds in Huangkui capsules (HKCs). Materials and Methods: An ultrahigh-performance liquid chromatography quadrupole time-of-flight mass spectrometry (MS) method was effectively established and utilized for the chemical compound characterization in HKC, with the support of MS-DIAL, MS-FINDER, and Global Natural Product Social Molecular Network. Multiple rat samples were analyzed after the oral administration. Metabolites were identified based on specific cleavage behaviors, and metabolic pathways were predicted. Semi-quantitative analysis of the metabolome profiles was conducted using post-data processing. High concentrations in vivo were investigated for their role in tacrolimus-induced death of HK-2 cells. Results: In total, 129 compounds were identified in HKC, of which 74 were reported for the first time. In this study, we successfully identified and uncovered 19 prototypes and 123 metabolites from the biosamples. The concentrations of glucuronidation and methylation metabolites of quercetin were the highest in the kidney and intestinal tissues. In contrast, significant glucuronidation of quercetin metabolites was observed with high blood concentrations. Notably, quercetin glucuronidation and methylation metabolites protected HK-cell against tacrolimus-induced injury in a dose-dependent manner. Conclusion: This study successfully established a reliable and efficient strategy for comprehensive characterization of traditional Chinese medicine compounds. This strategy harnessed the power of various intelligent postprocessing technologies to provide a more thorough understanding of active components and their metabolic transformations in the body. These results suggest that quercetin metabolites should be evaluated for their protective capacity against kidney disease.

Accelerated semantic segmentation of additively manufactured metal matrix composites: Generating datasets, evaluating convolutional and transformer models, and developing the MicroSegQ+ Tool

Data-driven applications are penetrating every aspect of Additive Manufacturing (AM) to enable efficient resolution of key existing challenges. One such application is semantic segmentation which automatically quantifies post-process structure data from graphic or 3D computer representations. In industrial settings, these models can expedite the development of new materials or processes. However, Additively Manufactured (AMed) materials offer unique challenges, which require new models to be developed and trained. These challenges include out-of-balance classes (e.g., defects), massive labeling efforts, and significant data preparation costs. Recent applications of semantic segmentation in AM have shown the potential of ensemble-based approaches at the expense of increased parameters and computational burden while ignoring the minority classes. In this work, we propose a reproducible method and develop an associated tool to rapidly segment and subsequently quantify industrial AMed metallographic images. First, two new datasets representing AMed materials are generated from extensive experimentation. Subsequently, state-of-the-art models from convolutional and self-attention categories are evaluated in their ability to segment the datasets of interest. Finally, a modular software tool is developed for industrial applications. The developed Microstructure Segmentation, Quantification, and Fusion (+) tool or MicroSegQ+ enables a weighted pairing of predictions from different semantic segmentation models. The pairing strategy helps to exploit the complimentary performance of convolutional and transformer models on multi-class metallographic images from a Metal Matrix Composite (MMC) material system. In addition to developing models on this MMC dataset, the proposed strategy is evaluated on an open-source metal AM dataset. We also evaluate the generalizability of the best-performing model on a second MMC dataset from a different industrial setting that shows an overall accuracy of 93 % without fine-tuning. The proposed tool enables rapid quantification under 1 min, unlike the existing semi-automatic approach that takes 1–2 h of segmentation effort on single bead cross-sections. The tool is wrapped into a single executable file for industrial deployment. The work also contributes to two annotated datasets from a direct energy deposition (DED) AM process. The datasets can be used to develop and validate new semantic segmentation models of AMed microstructures, whereas the functionality of the tool can be expanded upstream to include data and modeling steps, enabling a no-code pipeline for the industry.

Development of GNSS measurement models with the use of base stations and radio modules

Global navigation satellite systems (GNSS) historically been known as one of the newest technologies since the 1970s. GNSS originally developed for military purposes in the USA (GPS – Global Position System). There are several satellite systems in the world. Satelites, International Research Base Stations, regional/national and local base stations form a permanent geodetic frame. Research on the size and shape of the Earth-planet, climate, sea, urban planning. In geodesy, a network of global positioning base stations makes it possible to asses the movements of continents, land plates at international level. GNSS is an important technology in navigation, logistics, economics, land surveying and other “geo” sectors. GNSS equipment/receivers and their manufacturers are applying new designs and electronics. Initially GNSS instruments used with single frequency signal reception, later expanding the number of GNSS signal channels to two frequencies. Such technological improvements nowadays improve the certainty, reliability and accuracy – the overall quality – of GNSS measurements. The GNSS base station enables the surveyors, other user’s GNSS receiver to determine coordinates with an accuracy of two centimeters in real time (RTK) and with an accuracy of five millimetres using the accumulated post-processing data. Various types of factors hamper GNSS measurements. The GNSS signal (radio wave) travels in airspace, in urban environments and is a physical parameter. Any obstacle – tree, building walls, and atmospheric effect – makes GNSS measurements less accurate. The GNSS signal must be strong and free from attenuation and suppression effects. This study develops GNSS models that show the comparison, certainty and reliability of GNSS measurements using different types of GNSS techniques. Evaluation of Latvian Global Positioning Reference Station Network – LatPos system measurements against a corresponding RTK solution method using Latvian Global Positioning Network geodetic point (G2 class).

Feasibility of diffusion tensor imaging in cervical spondylotic myelopathy using MUSE sequence

The most frequent type of spinal cord injury is cervical spondylotic myelopathy (CSM). Conventional structural magnetic resonance imaging (MRI) is the gold diagnosis standard for CSM. Diffusion tensor imaging (DTI) could reflect microstructural changes in the spinal cord by tracing water molecular diffusion in early stages of CSM. However, due to the complex local anatomical structure and small field of view of the spinal cord, the imaging effect of traditional DTI imaging on the spinal cord is limited. MUSE (MUltiplexed Sensitivity-Encoding) -DTI is a novel diffusion-weighted imaging (DWI) sequence that achieves higher signal intensity through multiple excitation acquisition. MUSE sequence may improve the quality of spinal cord DTI imaging. Prospective study. This study aimed to investigate the clinical diagnosis value of a novel protocol of MUSE-DTI in patients with cervical spondylotic myelopathy (CSM). From August 2021 to March 2022, a total of 60 subjects (22-71 years) were enrolled, including 51 CSM patients (22 males, 29 females) and 9 healthy subjects (4 males and 5 females). Each subject underwent a MUSE-DTI examination and a clinical Japanese Orthopedic Association (JOA) scale. We measured values of FA (Fractional Anisotropy), MD (Mean Diffusivity), AD (Axial Diffusivity), and RD (Radial Diffusivity), and collected the clinical JOA scores of each subject before the MR examination. A 3.0T MR scanner (Signa Architect, GE Healthcare) performed the MUSE-DTI sequence on each subject. The cervical canal stenosis of subjects was classified from grade 0 to grade Ⅲ according to the method of an MRI grading system. FA, MD, AD, and RD maps were generated by postprocessing MUSE-DTI data on the GE workstation. Regions of interest (ROIs) were manually drawn at the C2 vertebral body level and C2/3-C6/7 intervertebral disc levels by covering the whole spinal cord. The clinical severity of myelopathy of subjects was assessed by the clinical Japanese Orthopedic Association scale (JOA). MUSE-DTI can acquire a high-resolution diffusion image compared to traditional DTI. The FAMCL values showed a decreasing trend from grade 0 to grade Ⅲ, while the MDMCL, ADMCL, and RDMCL values showed an overall increasing trend. Significant differences in MDMCL, ADMCL, and RDMCL values were found between adjacent groups among grades Ⅰ-Ⅲ (p<.05). The ADC2 values in CSM patients (grade I-Ⅲ) were significantly lower than in healthy individuals (grade 0) (p=.019). The clinical JOA score has a significant correlation with FAMCL (p=.035), MDMCL (p<.001), ADMCL (p<.001), and RDMCL (p<.001) values. MUSE-DTI displayed a better image quality compared to traditional DTI. MUSE-DTI parameters displayed a grade-dependent trend. All the MUSE-DTI parameters at MCL were correlated with the clinical JOA scores. The ADC2 values can reflect the secondary damage of distal spinal cord. Therefore, MUSE-DTI could be a reliable biomarker for clinical auxiliary diagnosis of spinal cord injury severity in cervical spondylotic myelopathy.

DYNAMIC ASSESSMENT OF CEREBRAL PERFUSION BLOOD FLOW IN THE EARLY POST-STROKE PERIOD ACCORDING TO NON-CONTRAST MRI DATA

HighlightsThe non-contrast perfusion MR sequence pCASL (pseudocontinuous arterial spin labeling) is a technique that allows specialist to quantify and trace the changes in cerebral blood flow in the early post-stroke period. At the same time, there is a change in perfusion indices not only in the ischemic stroke area, but also in visually intact areas of the ipsilateral and contralateral hemispheres of the brain, which indicates the involvement of the blood supply area and the brain as a whole in the pathological process. The obtained data on the dynamics in perfusion, verified by the results of cognitive tests, open up the prospect of a comprehensive study of stroke as a complex brain disease with the possibility of predictive assessment of rehabilitation. Aim. To assess the dynamics of microcirculatory changes in the brain in the early post-stroke period by non-contrast perfusion MRI.Methods. The study included 42 patients with acute ischemic stroke. The patients were examined by 3T MRI using the following sequences: DWI-EPI, 3D FLAIR-SPIR, T2-TSE, T1W 3D-TFE, and pseudocontinuous arterial spin labeling (pCASL) on 1–3 days, 7–10 days and 3 months later after the manifestation of acute ischemic stroke. The cerebral blood flow (CBF) was obtained in 4 supratentorial regions of the brain: in the ischemic stroke and contralateral areas, as well as in a visually intact area of the ipsilateral and contralateral hemispheres (mL/100g/min).Results. In the ischemic stroke area, CBF increased from 19.86±5.69 (mean+Std) to 27.57±4.86 by first to the second examination, which was associated with the infusion therapy, local inflammatory response and compensatory reperfusion. By the 3rd examination CBF decreased to 14.48±3.66 (Student t-test, p&lt;0.05), which reflects cystic and atrophic transformation of the affected area. In visually intact white matter of the ipsilateral and contralateral hemispheres, CBF increased from hypoperfused to normoperfused from the first to the third examination. There was a significant decrease (p&lt;0.001) in tissue blood flow (by 54% for the 1st, 38% for the 2nd, 67% for the 3rd examination) in the ischemia area relative to intact parts of the brain.Conclusion. The pCASL allowed us to assess CBF in the post-stroke period in the ischemic area and in visually intact area, nevertheless, it requires additional pre- and postprocessing of data to obtain quantitative values. At the same time, we observed changes in perfusion in the ischemic stroke area, and in the visually intact areas of the ipsilateral and contralateral hemispheres of the brain, indicating the involvement of the cerebral blood flow and brain as a whole in the pathological process.

Optimizing Postprocessing of Range-Gated Viewing Data for Maritime Search and Rescue Operations at Night and in Bad Weather Conditions

At night and in bad weather conditions the detection of persons and objects floating in the sea represents a major challenge for search and rescue operations (SAR). If conventional searchlights are used, backscattering from rain, fog and snow decreases detection range. Therefore, a compact and inexpensive range-gated viewing system which significantly reduces atmospheric backscattering was developed. The instrument was designed for detection ranges of several hundred meters. In this study, different image processing techniques were analyzed in terms of improved object detectability for a human observer and for a machine learning-based object detector, based on a real-world image dataset. On the one hand, noise of the camera is reduced by performing a non-uniformity correction (NUC) and on the other, the dynamic range of the images is adjusted and dark objects are accentuated by equalizing (EQ). The aim of this field study with the subsequent post processing steps was to improve visibility for both human observers and machine learning-based object detectors with low computing power, based on real-world image datasets. The results show that processing requirements are different in both cases, mainly due to human eye perception, which an automated detector does not rely on and therefore the performance of the object detector before the equalizing step is slightly better. However, the NUC improves the image quality in any case.

Impact of High-Pressure-Turbine Purge Flow on the Evolution of Turbulence in a Turbine Vane Frame

Abstract This paper describes the measurement and postprocessing of turbulence data. The experiments were carried out in a two-stage, two-spool transonic turbine test rig at the Institute for Thermal Turbomachinery and Machine Dynamics at the Graz University of Technology, which includes relevant purge and turbine rotor tip leakage flows. The test setup consists of a high-pressure turbine (HPT) stage, a turbine vane frame (TVF) with turning struts and splitters, and a counter-rotating low-pressure turbine (LPT) to allow engine realistic measurements. Time-resolved area traverse measurements have been performed for three different operating conditions in three measurement planes downstream of the HPT rotor, which enable the measurement of the turbulence quantities at the TVF inlet and outlet as well as the LPT outlet. The turbulence quantities are evaluated using triaxial- and single hot-film probes by means of Constant-Temperature-Anemometry, and their results were validated with five-hole probe (FHP) measurements. Ensemble- and time-averaging, as well as Fourier transforms, were applied for data reduction. It is shown how the turbulence intensity and integral length scale vary over different purge flowrates (PFR). The acquired measurement data illustrate that the interaction of the ejected purge flow with mean flow enhances the turbulent mixing in the secondary flow structures at the TVF inlet and TVF outlet, respectively. Furthermore, the flow downstream of the LPT rotor is affected by TVF and LPT rotor secondary flow structures, which are identified as high turbulence regions. These regions strongly depend on the purge flowrate. These results acquired under engine realistic rig flow conditions enable a deeper understanding of the relationship between loss and turbulence quantities and will help improve the application of computational fluid dynamics turbulence models to turbomachinery modeling.

SPATIAL INVENTORY OF SOLAR PHOTOVOLTAIC (PV) INSTALLATIONS USING REMOTE SENSING AND MACHINE LEARNING: CASE OF PAMPANGA PROVINCE, PHILIPPINES

Abstract. In recent years, the cost of photovoltaic (PV) technologies significantly declined, boosting the expansion of solar PV installations in the country. This rapid development in solar PV utilization necessitates an effective detection method capable of delineating both utility-scale and distributed PV installations to generate a complete inventory of solar PV installations for status monitoring and implementation of appropriate programs of stakeholders and decision-makers. This study aims to detect and delineate solar PV installations in Pampanga, Philippines using different band combinations of Sentinel-2, Sentinel-1, and indices (NDVI, NDWI, and PVSI) through machine learning with the aid of open-source geographic information system and remote sensing software. Moreover, an alternative approach to identifying solar PVs using the combination of pixel-based classification (PBC) and object-based classification (OBC) was introduced. Training and validation data were acquired from the satellite images. The accuracy of each approach in PV detection was then compared using three classifiers: Support Vector Machine (SVM), Random Forest, and Naive Bayes. Results showed that SVM has the best performance for PBC while Random Forest demonstrated the highest accuracy for OBC. A post-processing procedure was also implemented using a set of spectral rules to further refine the results of image classification. The delineation accuracies of the post-processed data over the ground-truth data showed that the methodology is effective in delineating utility-scale installations for both Sentinel-2 and Planetscope. However, the detection of distributed PV systems showed limitations particularly when dealing with small solar PV installations (less than 45 pixels) due to Sentinel-2’s coarse spatial resolution.

The Influence of Kinetic Models and Attenuation Correction on Cadmium-Zinc-Telluride Single-Photon Emission Computed Tomography (CZT SPECT)-Derived Myocardial Blood Flow and Reserve: Correlation with Invasive Angiography Data.

(1) Background: The objective of this study was to determine the optimal post-processing model for dynamic cadmium-zinc-telluride single-photon emission computed tomography (CZT-SPECT). (2) Methods: A total of 235 patients who underwent diagnostic invasive coronary angiography within three months of the SPECT and those who had coronary computed tomography angiography (CCTA) before SPECT (within 3 months) were enrolled in this study. Each SPECT study was processed to obtain global and regional stress myocardial blood flow (sMBF), rest-MBF (rMBF), myocardial flow reserve (MFR) and flow difference (FD) estimates obtained with 1-tissue-compartment (1TCM) and net retention (NR) modes, both with and without attenuation correction. (3) Results: The use of AC led to significantly higher sMBF, rMBF and DF values obtained by 1TCM compared those values derived by 1TCM with NAC; the lowest values of stress MBF and rest MBF were obtained by 1TCM_NAC. The resting flow, MFR and DF were significantly (p < 0.005) higher in the AC model than in NAC. All quantitative variables were significantly (p < 0.05) higher in NR_NAC than in the 1TC_NAC model. Finally, sMBF, rMBF and FD showed significantly (p < 0.05) higher values by using 1TMC_AC compared to NR_AC. (4) Conclusions: We suggested that 1-compartment and net retention models correctly reflect coronary microcirculation and can be used for clinical practice for evaluating quantitative myocardial perfusion by dynamic SPECT. Attenuation correction is an important step in post-processing dynamic SPECT data, which increases the consistency and diagnostic accuracy of models.

Using the CLEAN Method in Three-Dimensional Polarimetric ISAR Imaging Based on Volumetric Scattering Points

We developed a new technique for generating three-dimensional monostatic inverse synthetic aperture radar (ISAR) images using various types of polarimetric information on targets. To remove clutter around a target, we adopted the CLEAN method, in which only the main signals were reconstructed using an iterative algorithm. We conducted a simulation as follows: electric field data that were subjected to polarimetric scattering were obtained for the target using a full-wave electromagnetic analysis simulator. The linear polarization data of a 2 × 2 matrix were compressed into the linear to circular polarization data of a 2 × 1 matrix using the Jones vector based on circular polarization. On the grounds of the post-processed data, inverse Fourier transform was carried out to map scattering points to the spatial domain, after which the CLEAN method was used to retain only the primary scattering points.

Ground- and ship-based microwave radiometer measurements during EUREC4A

Abstract. During the EUREC4A field study, microwave radiometric measurements were performed at Barbados Cloud Observatory (BCO) and aboard RV Meteor and RV Maria S Merian in the downstream winter trades of the North Atlantic. We present retrieved integrated water vapor (IWV), liquid water path (LWP), and temperature and humidity profiles as a unified, quality-controlled, multi-site data set on a 3 s temporal resolution for a core period between 19 January and 14 February 2020 in which all instruments were operational. Multi-channel radiometric measurements were performed at BCO and aboard RV Meteor between 22 and 31 GHz (K-band) and from 51 to 58 GHz (V-band). Combined radar–radiometer measurements of a W-band Doppler radar with a single-channel radiometer instrument were conducted at 89 GHz aboard RV Meteor and RV Maria S Merian. We present a novel retrieval method to retrieve LWP from single-channel 89 GHz measurements, evaluate retrieved quantities with independent measurements, and analyze retrieval uncertainties by site and instrument intercomparison. Mean IWV conditions of 31.8 kg m−2 match independent radiosoundings at BCO with a root-mean-square difference of 1.1 kg m−2. Mean LWP conditions in confidently liquid cloudy, non-precipitating conditions ranged between 63.1 g m−2 at BCO and 46.8 g m−2 aboard RV Maria S Merian. Aboard the ships, 90 % of LWP was below 120 g m−2 with a 30 % uncertainty for LWP of 50 g m−2. Up to 20 % of confidently liquid cloudy profiles ranged below the LWP detection limit due to optically thin clouds. The data set comprises of processed raw data (Level 1), full quality-controlled post-processed instrument data (Level 2), a unified temporal resolution (Level 3), and a ready-to-use multi-site time series of IWV and LWP (Level 4), available to the public via AERIS (https://doi.org/10.25326/454##v2.0; Schnitt et al., 2023a). The data set complements the airborne LWP measurements conducted during EUREC4A and provides a unique benchmark tool for satellite evaluation and model–observation studies.

On the prediction of tibiofemoral contact forces for healthy individuals and osteoarthritis patients during gait: a comparative study of regression methods.

Knee osteoarthritis (OA) is a public health problem affecting millions of people worldwide. The intensity of the tibiofemoral contact forces is related to cartilage degeneration, and so is the importance of quantifying joint loads during daily activities. Although simulation with musculoskeletal models has been used to calculate joint loads, it demands high-cost equipment and a very time-consuming process. This study aimed to evaluate consolidated machine learning algorithms to predict tibiofemoral forces during gait analysis of healthy individuals and knee OA patients. Also, we evaluated three different datasets to train each model, considering different combinations of primary kinematic and kinetic data, and post-processing data. We evaluated 14 patients with severe unilateral knee OA and 14 healthy individuals during 3-5 gait trials. Data were split into 70% and 30% of the samples as training and test data. Test data was independently evaluated considering a mixture of pathological and healthy individuals, and only OA and Control patients. The main results showed that accurate predictions of the tibiofemoral contact forces were achieved using machine learning methods and that the predictions were sensitive to changes in the input data as training. The present study provided insights into the most promising regressions methods to predict knee contact forces representing an important starting point for the broader application of biomechanical analysis in clinical environments.

3D integral imaging of acoustically trapped objects

3D imaging provides crucial details about the objects and scenes that may not be obtained via 2D imaging methods. However, there are several applications in which the object to be 3D-imaged requires to be immobilized. The integrated digital holographic microscopy (DHM) and optical trapping (OT) system is a useful solution for such a task, but both DHM and OT are mostly suitable for microscopic specimens. Here, for the first time to the best of our knowledge and as an analogy to the DHM-OT system, we introduce integral imaging (InIm) and acoustic trapping (AT) integrated system for 3D imaging of immobilized mesoscopic and macroscopic objects. Post-processing of InIm data enables reconstructing the scene at any arbitrary plane, therefore, it re-focuses any particular depth of the object, which is a curtail task, especially when the object is trapped by AT. We demonstrate the capability of our system by simultaneous trapping and 3D imaging of single and multiple irregularly shaped objects with mm sizes.

Multisensor Integrated Autonomous Navigation Based on Intelligent Information Fusion

A major inadequacy of the Kalman filter is the necessity of accurate measurement models, which may not be possible in the case of complicated disturbances. A new solution to multisensor integrated autonomous navigation based on intelligent information fusion technology for launch vehicles is proposed in this paper, which can improve navigation performance based on the existing measurement models. Artificial neural networks (ANNs) are designed to discover the hidden relationship between the available data and trajectory parameters. The tracking, telemetry, and control system can provide centimeter-level accuracy of the launch vehicle after every flight. While postprocessing data is irrelevant to real-time navigation using traditional methods, it is crucial for ANNs to learn the rules of information fusion and identify the inaccuracy of the input variables. In this way, artificial-intelligence-modified trajectory parameters are expected to be closer to the truth value. The experimental results indicate that the proposed method can significantly improve navigation performance compared with the unscented Kalman filter, and ANNs can achieve good performance in various situations.

Effect Of Abdominal Stretching Exercises Toward The Intensity Of Menstrual Pain (Dismenorea) Of Adolescent Women

Menstrual pain (dysmenorrhea) is one of the menstrual disorders that most women experience in adolescents. With one of the nonpharmacological practice stretching techniques can be done with the goal of knowing if there is an application effect on the intensity of menstrual pain (dysmenorrhea) in adolescents women. The type of research is an experimental design with the design approach used is the pre test- post test control group design, which is done in University Megarezky with 32 people research respondents, according to the two-split criteria for interagency and control groups as the pre-and post data processing technique on each group. Based on data normality tests, non-distribution of normal results, the tests used were wilcoxon tests. Furthermore, the pre-post normality test of unregulated data control interventions using the mann-whitney test. Research shows that test results on the pre post interventions using wilcoxon tests came p value (0,000 &lt; 0.05). control groups using mann-whitney tests got p value (0,000&lt;0.05). Thus it may be determined that there is an intermediate effect on the intensity of menstrual pain (dysmenorrhea). Based on the results of this study is expected for all elements that include educational agencies, subsequent researchers, teenagers and communities are able to provide information about abdominal tracks exercises as non-pharmacological therapy to reduce the intensity of hairsty pain (dysmenornea).

Influence of multiple detection events on compositional accuracy of TiN coatings in atom probe tomography

The accuracy of composition measurements by atom probe tomography is often dependent on the selected operation mode as well as the applied measurement parameters. The detected hit characteristics, distinguishing between single and multiple events, along with the electric field, are also affected by parameter selection. In this study, atom probe tomography experiments were performed on a stoichiometric TiN coating in voltage as well as in laser-assisted mode with systematically varied laser pulse energies. The observed elemental compositions were compared with complementary ion beam analysis measurements. The influence of multiple detection events was investigated by two approaches: I) A modified local electrode served as a hardware filter, reducing multiple detection events from 78.8 % to 41.9 % and from 40.9 % to 5.6 % using voltage mode and laser-assisted APT (0.6 nJ), respectively, and II) unfiltered datasets were analyzed by data post processing. The latter allowed the study of ion species, particularly of emerging complex molecular ions associated with dissociation processes. Additionally, average electric fields were estimated and spatial considerations were made to investigate the evolution of charge state ratios and hit characteristics during the measurement. Filtering the measurements significantly improved the elemental accuracy. In voltage mode, hardware and software filtering reduced the discrepancy between reference and observed composition from 3.8 at.% to 2.1 at.% and 0.1 at.% within uncertainty limits. In laser-assisted mode, higher laser pulse energy increased the difference between unfiltered data and the reference composition, from 1.4 at.% (0.1 nJ) to 8.1 at.% (2.0 nJ). Ion species analysis of the datasets shows an increasing presence of complex ions (Ti2N) with raising laser pulse energy. Electric field studies reveal a decline from 40 V/nm in voltage mode to 36 V/nm applying a high laser pulse energy of 2.0 nJ, indicating insufficient field strength for neutral nitrogen re-ionization.

Year-long buoy-based observations of the air–sea transition zone off the US west coast

Abstract. Two buoys equipped with Doppler lidars owned by the US Department of Energy (DOE) were deployed off the coast of California in autumn of 2020 by Pacific Northwest National Laboratory. The buoys collected data for an entire annual cycle at two offshore locations proposed for offshore wind development by the Bureau of Ocean Energy Management. One of the buoys was deployed approximately 50 km off the coast near Morro Bay in central California in 1100 m of water. The second buoy was deployed approximately 40 km off Humboldt County in northern California in 625 m of water. The buoys provided the first-ever continuous measurements of the air–sea transition zone off the coast of California. The atmospheric and oceanographic characteristics of the area and estimates of annual energy production at both the Morro Bay and Humboldt wind energy areas show that both locations have a high wind energy yield and are prime locations for future floating offshore wind turbines. This article provides a description and comprehensive analysis of the data collected by the buoys, and a final post-processed dataset is uploaded to a data archive maintained by the DOE. Additional analysis was conducted to show the value of the data collected by the DOE buoys. All post-processed data from this study are available on the Wind Data Hub website: https://a2e.energy.gov/data# (last access: 14 September 2023). Near-surface, wave, current, and cloud datasets for Humboldt and Morro Bay are provided at https://doi.org/10.21947/1783807 (Krishnamurthy and Sheridan, 2023b) and https://doi.org/10.21947/1959715 (Krishnamurthy and Sheridan, 2023a), respectively. Lidar datasets for Humboldt and Morro Bay are provided at https://doi.org/10.21947/1783809 (Krishnamurthy and Sheridan, 2023d) and https://doi.org/10.21947/1959721 (Krishnamurthy and Sheridan, 2023c), respectively.

Scalability of the differential-(quadrature)-phase-shift quantum digital signature

The quantum digital signature (QDS) is a quantum communication technology that distributes authentication keys to candidates of message recipients who will confirm if a message received afterward is the legitimate one, referring to the authentication keys. Although accommodating many recipients may be one of the significant QDS system performances, conventional QDS protocols seem indifferent to this issue. In this study, we investigate the number of accommodable recipients in recently proposed differential-phase-shift (DPS) QDS and differential-quadrature-phase-shift (DQPS) QDS systems. They are unique QDS protocols in which quantum signals are broadcasted to all recipients simultaneously, and there is no post data-processing between the sender and recipients, which is favorable to accommodate a large number of recipients. We analyze the system conditions required to satisfy the QDS criteria, and evaluate the maximum number of recipients. The results indicate that around 100 recipients can be accommodated in a 100-km transmission system.

NREL-5MW Wind Turbine Noise Prediction by FWH-LES

This paper deals with large onshore wind turbine aeroacoustics. Noise from the NREL 5 MW device is predicted by the permeable-surface Ffowcs Williams–Hawkings equation (FWH-P), starting from the postprocessing of LES data on different acoustic surfaces S. Their size and placement is aimed at embedding most of the aerodynamic sources of sound surrounding rotor and nacelle. Due to the presence of eddies that inevitably cross S, this paper compares results from open and closed acoustic surfaces, and the outflow disk averaging technique. The issues related to the interpolation process of LES data on S is discussed as well. In order to assess the LES/FWH-P aeroacoustic platform, LES and FWH-P pressures are compared in the very-near field. It is shown that, within the limits of the discretization settings imposed by the interpolation procedure and for the Reynolds number working condition investigated herein, the lack of quadrupole sources outside the permeable surface(s) deeply affect the quality of FWH-P acoustic pressures with respect to direct LES signals.

UVA Hyperspectral Light-Sheet Microscopy for Volumetric Metabolic Imaging: Application to Preimplantation Embryo Development.

Cellular metabolism is a key regulator of energetics, cell growth, regeneration, and homeostasis. Spatially mapping the heterogeneity of cellular metabolic activity is of great importance for unraveling the overall cell and tissue health. In this regard, imaging the endogenous metabolic cofactors, nicotinamide adenine dinucleotide (phosphate) (NAD(P)H) and flavin adenine dinucleotide (FAD), with subcellular resolution and in a noninvasive manner would be useful to determine tissue and cell viability in a clinical environment, but practical use is limited by current imaging techniques. In this paper, we demonstrate the use of phasor-based hyperspectral light-sheet (HS-LS) microscopy using a single UVA excitation wavelength as a route to mapping metabolism in three dimensions. We show that excitation solely at a UVA wavelength of 375 nm can simultaneously excite NAD(P)H and FAD autofluorescence, while their relative contributions can be readily quantified using a hardware-based spectral phasor analysis. We demonstrate the potential of our HS-LS system by capturing dynamic changes in metabolic activity during preimplantation embryo development. To validate our approach, we delineate metabolic changes during preimplantation embryo development from volumetric maps of metabolic activity. Importantly, our approach overcomes the need for multiple excitation wavelengths, two-photon imaging, or significant postprocessing of data, paving the way toward clinical translation, such as in situ, noninvasive assessment of embryo viability.