Conventional Measurements Research Articles

Contribution of body adiposity index and conicity index in prediction of metabolic syndrome risk and components

Background and aimsBody adiposity index (BAI) and conicity index have been known as useful measures in predicting cardio-metabolic diseases. This study aimed to evaluate the predictive potential of BAI and conicity index for the risk of metabolic syndrome (MetS) in comparison with body mass index (BMI), waist circumference (WC), hip circumference (HC), and waist-hip ratio (WHR). MethodsIn this cross-sectional study, 174 adults (87 with MetS and 87 healthy individuals) were recruited from a medical weight loss center. Anthropometric parameters, systolic and diastolic blood pressures (SDP and DBP), lipid profile, and fasting blood sugar (FBS) were measured. ResultsAll anthropometric parameters were significantly higher in subjects with MetS than in healthy subjects. Both in MetS and healthy subjects, females had significantly higher BAI and BMI than males. In the fully adjusted model, the odds of MetS increased for each unit increase in BAI by 27 % (p = 0.001), in BMI by 33 % (p = 0.001), in WC by 13 % (p < 0.001), and in HC by 9 % (p = 0.005). ROC curve analysis showed that all the anthropometric parameters displayed clinical importance in predicting MetS, but WHR had the largest area under the curve (AUC) in total, male, and female patients. In participants with MetS, the conicity index was negatively correlated with FBS; BAI was positively associated with HDL level. ConclusionAll studied anthropometric parameters had acceptable accuracy for predicting MetS. Traditional parameters, particularly the WHR, exhibited a higher predictive power concerning MetS. The results underscore the reliability of conventional anthropometric measures in clinical and epidemiological settings.

Evolution of the “4-D Approach” to Dynamic Vision for Vehicles

Spatiotemporal models for the 3-D shape and motion of objects allowed large progress in the 1980s in visual perception of moving objects observed from a moving platform. Despite the successes demonstrated with several vehicles, the “4-D approach” has not been accepted generally. Its advantage is that only the last image of the sequence needs to be analyzed in detail to allow the full state vectors of moving objects, including their velocity components, to be reconstructed by the feedback of prediction errors. The vehicle carrying the cameras can, thus, together with conventional measurements, directly create a visualization of the situation encountered. In 1994, at the final demonstration of the project PROMETHEUS, two sedan vehicles using this approach were the only ones worldwide capable of driving autonomously in standard heavy traffic on three-lane Autoroutes near Paris at speeds up to 130 km/h (convoy driving, lane changes, passing). Up to ten vehicles nearby could be perceived. In this paper, the three-layer architecture of the perception system is reviewed. At the end of the 1990s, the system evolved from mere recognition of objects in motion, to understanding complex dynamic scenes by developing behavioral capabilities, like fast saccadic changes in the gaze direction for flexible concentration on objects of interest. By analyzing motion of objects over time, the situation for decision making was assessed. In the third-generation system “EMS-vision” behavioral capabilities of agents were represented on an abstract level for characterizing their potential behaviors. These maneuvers form an additional knowledge base. The system has proven capable of driving in networks of minor roads, including off-road sections, with avoidance of negative obstacles (ditches). Results are shown for road vehicle guidance. Potential transitions to a robot mind and to the now-favored CNN are touched on.

Imagining Speeds up the Effect of Motor Imagery on Central Motor Conduction Time.

Although motor imagery (MI) has been reported to increase motor cortical excitability, its effect on central motor conduction time (CMCT), a widely used neurophysiological diagnostic method, has not been investigated. In this study, we sought to determine the effect of MI on CMCT. In this cross-sectional study, 21 healthy volunteers (11 females, 10 males) aged 24 to 67 years (mean age: 38.8 years) were recruited between April 2022 and June 2023.CMCT was calculated during MI from the abductor digiti minimi (ADM) and tibialis anterior (TA) muscles. Measurements were also performed with conventional measurement methods, such as resting and voluntary contraction, to compare the effect of MI on CMCT. The ANOVAtest revealed that the CMCT session (rest, MI, and voluntary contraction) was a significant factor (p < 0.05). In both muscles, CMCT was shorter in the imagery state than in the resting state but longer than in the voluntary contraction state (p < 0.05). Similarly, motor-evoked potential (MEP) latencies obtained during imagery were shorter for both muscles than the resting state but longer for the voluntary contraction state. The study's findings suggest that MI is a mental activity that modulates CMCT measurement.MI shows a voluntary contraction-like effect on CMCT and MEP latency, although the effect is more uncertain.

Comparison of Effects of Partial Discharge Echo in Various High-Voltage Insulation Systems

In this article, an extension of a conventional partial discharge (PD) approach called partial discharge echo (PDE), which is applied to different classes of electrical insulation systems of power devices, is presented. Currently, high-voltage (HV) electrical insulation is attributed not only to transmission and distribution grids but also to the industrial environment and emerging segments such as transportation electrification, i.e., electric vehicles, more-electric aircraft, and propulsion in maritime vehicles. This novel PDE methodology extends the conventional and established PD-based assessment, which is perceived to be one of the crucial indicators of HV electrical insulation integrity. PD echo may provide additional insight into the surface conditions and charge transport phenomena in a non-invasive way. It offers new diagnostic attributes that expand the evaluation of insulation conditions that are not possible by conventional PD measurements. The effects of partial discharge echo in various segments of insulation systems (such as cross-linked polyethylene [XLPE] power cable sections that contain defects and a twisted-pair helical coil that represents motor-winding insulation) are shown in this paper. The aim is to demonstrate the echo response on representative electrical insulating materials; for example, polyethylene, insulating paper, and Nomex. Comparisons of the PD echo decay times among various insulation systems are depicted, reflecting dielectric surface phenomena. The presented approach offers extended quantitative assessments of the conditions of HV electrical insulation, including its detection, measurement methodology, and interpretation.

Neurophysiological Approach for Psychological Safety: Enhancing Mental Health in Human–Robot Collaboration in Smart Manufacturing Setups Using Neuroimaging

Human–robot collaboration (HRC) has become increasingly prevalent due to innovative advancements in the automation industry, especially in manufacturing setups. Although HRC increases productivity and efficacy, it exposes human workers to psychological stress while interfacing with collaborative robotic systems as robots may not provide visual or auditory cues. It is crucial to comprehend how HRC impacts mental stress in order to enhance occupational safety and well-being. Though academics and industrial interest in HRC is expanding, safety and mental stress problems are still not adequately studied. In particular, human coworkers’ cognitive strain during HRC has not been explored well, although being fundamental to sustaining a secure and constructive workplace environment. This study, therefore, aims to monitor the mental stress of factory workers during HRC using behavioural, physiological and subjective measures. Physiological measures, being objective and more authentic, have the potential to replace conventional measures i.e., behavioural and subjective measures, if they demonstrate a good correlation with traditional measures. Two neuroimaging modalities including electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) have been used as physiological measures to track neuronal and hemodynamic activity of the brain, respectively. Here, the correlation between physiological data and behavioural and subjective measurements has been ascertained through the implementation of seven different machine learning algorithms. The results imply that the EEG and fNIRS features combined produced the best results for most of the targets. For subjective measures being the target, linear regression has outperformed all other models, whereas tree and ensemble performed the best for predicting the behavioural measures. The outcomes indicate that physiological measures have the potential to be more informative and often substitute other skewed metrics.

Safety Assessment of Camelid-Derived Single-Domain Antibody as Feed Additive for Juvenile Whiteleg Shrimp (Litopenaeus vannamei) Against White Spot Syndrome Virus.

A six-week feeding trial was conducted to assess the safety of single-domain antibodies (sdAbs) derived from camelids against the white spot syndrome virus (WSSV) (WSSVvp28 was used as the antigen), focusing on the whole-organism responses and molecular-level changes in juvenile whiteleg shrimp (Litopenaeus vannamei). Five experimental diets with varying levels of sdAbs were formulated: CON (no sdAb supplementation); SDA8.2 (8.20% of sdAbs); SDA16.4 (16.40% of sdAbs); SDA24.6 (24.60% of sdAbs); and SDA32.8 (32.80% of sdAbs). In the CON diet, 450 mL of water per kg of diet (45%) was used to form a feed dough, while sdAbs were used to replace the water in the treatment diets. A total of 450 shrimp, with an initial body weight of 3.27 ± 0.02 g (mean ± SEM), were randomly distributed in 15 tanks (30 shrimp per tank; three tanks per treatment). Each tank was filled with 30 L of seawater (77 L capacity) in an indoor semi-recirculating system with a constant water flow rate of 1.2 L min-1. The photoperiod was maintained at 12 h of light and 12 h of dark. The water temperature, pH, salinity, and dissolved oxygen were 27.3 ± 0.1 °C, 7.61 ± 0.01, 34 ± 1 ppt, and 5.94 ± 0.04 mg L-1, respectively. During the feeding trial, the shrimp were fed the experimental diet (40% protein and 11% lipid) three times a day for six weeks. Following the feeding trial, an acute cold-water-temperature stress test was conducted by abruptly exposing the shrimp from each treatment to 15 °C for 4 h, down from 27 °C. The results showed no significant differences in the growth performance (weight gain, feed utilization efficiency, survival, etc.), plasma metabolites (aspartate aminotransferase activity, alanine aminotransferase activity, total protein, and glucose), or antioxidant enzymes (superoxide dismutase and glutathione peroxidase) among all the experimental diets (p > 0.05). In the acute cold-temperature stress test, there was no significant interaction between sdAb supplementation and temperature stress, nor any main effect from either factor, except for the main effect of temperature stress on the glucose levels, which was significantly higher in shrimp exposed to cold-temperature stress (p < 0.05). The next-generation sequencing of differentially expressed genes (DEGs) in the hepatopancreases of shrimp fed the CON, SDA16.4, and SDA32.8 diets, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses, indicated that DEGs were significantly enriched in signaling pathways associated with growth, cold stress, and antioxidant systems. Overall, the results from conventional measurements suggest that the use of sdAbs against the WSSV may be safe for juvenile whiteleg shrimp. However, findings from the sophisticated analysis indicate that further research is needed to understand the molecular mechanisms underlying the observed changes, and to evaluate the long-term effects of sdAb supplementation in shrimp diets.

A landscape-based approach to design flower blocks may reduce mammalian predator activity and protect ground-nesting farmland birds

AbstractHigh predation rates threaten many ground-nesting farmland birds and are difficult to address through conventional measures such as lethal predator control or fencing. Landscape-based approaches for conservation measures promise an alternative by reducing predator - bird encounters, but require detailed knowledge of landscape effects on predation risk. Different habitat elements attractive to predators could have opposing effects on neighbouring nesting habitats, with implications for conservation: Increased predation risk due to higher predator activity (A) or reduced predation risk by distracting predators (B). Here we focus on the placement of conservation measures using flower blocks targeted at Grey Partridges in a Central European Farmland. Based on a three-year camera trap dataset, we investigated effects of landscape structure and composition on mammalian predator activity within flower blocks at two scales (100 m and 500 m radius around the camera) with generalized linear mixed models. Length of linear edge structures, i.e., field block borders, was most important, with a greater availability of linear edge structures leading to a decrease in predator activity at both scales (hypothesis B). Conversely, predator captures at both scales increased with increasing extensive vegetation area (i.e., permanent grassland, flower blocks and fallows) and in proximity to roads, indicating that these may attract predators and increase predator densities (hypothesis A). Our results suggest that a landscape-based approach can mitigate predation risk for ground-nesting birds in flower blocks and analogous conservation measures. Highly structured, small-scale agricultural landscapes seem to be particularly important for reducing mammalian predator activity in flower blocks.

Integrating basic human values with forest ecosystem services: pathways to sustainable forest management.

The article explores the intricate relationship between basic human values and forest ecosystem services (FES). The study highlights the critical role that forests play in providing essential services for biodiversity, forest products, climate stabilization and human well-being, and emphasizes the need to understand and integrate human values into forest management and planning. Through a novel approach, this study explores how the concept of "forest" can elicit considerations of fundamental human values that diverge from conventional classifications and measurements of forest values. The study uses a comprehensive methodology, including surveys and content analysis, to uncover the hierarchical structure of human values associated with forests. This approach enables the identification of fundamental values that remain constant despite situational variations. The main results reveal a hierarchical structure of values, with Apollonian values being more prevalent than Dionysian ones. The study shows significant differences in the importance attributed to different FES, reflecting underlying value differences between residents. The study makes a novel contribution by systematically examining the links between human values and FES and proposing a profound and sustainable approach to forest management that takes into account the psychological dimensions of human-forest interactions. The study suggests that recognizing and incorporating the intrinsic human values into forest ecosystem service frameworks can improve sustainable forest management practices and ultimately foster a deeper connection between people and the forest environment.

Dynamic perspectives on biodiversity quantification: beyond conventional metrics.

Our research addresses the pressing need to assess biodiversity in the face of increasing habitat destruction and species extinctions. Several researchers have modelled conventional measures to assess biodiversity. Every measure evaluates biodiversity by considering different properties. Among them Simpson and Shannon indices are widely used, they primarily focus on species richness and abundance, overlooking the importance of rare or unique species. This limitation makes it challenging to identify which species drive changes in biodiversity and hampers conservation efforts. Moreover, these measures are sensitive to sample size and biased towards dominant species, leading to inaccurate estimations. To overcome these challenges, we propose a novel mathematical model that provides a comprehensive assessment of biodiversity. Our model accounts for species dominance, addresses sample size sensitivity, and highlights the significance of rare species within a community. By applying our measure to real-time scenarios and comparing it with traditional methods using the same dataset, proposed measure demonstrated its efficacy in capturing biodiversity dynamics over time.

Gaze transition entropy as a measure of attention allocation in a dynamic workspace involving automation

Real-world work environments require operators to perform multiple tasks with continual support from an automated system. Eye movement is often used as a surrogate measure of operator attention, yet conventional summary measures such as percent dwell time do not capture dynamic transitions of attention in complex visual workspace. This study analyzed eye movement data collected in a controlled a MATB-II task environment using gaze transition entropy analysis. In the study, human subjects performed a compensatory tracking task, a system monitoring task, and a communication task concurrently. The results indicate that both gaze transition entropy and stationary gaze entropy, measures of randomness in eye movements, decrease when the compensatory tracking task required more continuous monitoring. The findings imply that gaze transition entropy reflects attention allocation of operators performing dynamic operational tasks consistently.

Assessment Rainfall-Induced Landslides Using Arbitrary Dipole–Dipole Direct Resistivity Configuration

Landslides are one of the primary geological disasters posing significant threats to life and property. Strengthening the monitoring of rainfall-induced landslides is, therefore, crucial. The Direct Resistivity (DC) method can accurately map the subsurface electrical resistivity distribution, making it an essential tool for predicting the position of the slide face. However, when conducting landslide surface DC surveys, various undulating terrains such as ridges and steep slopes often pose accessibility challenges. In such topographies, conventional regular grid measurements become very difficult. Additionally, when the terrain is highly undulating and complex, interpreting apparent resistivity data can lead to erroneous results. In this study, we propose using the DC method to monitor rainfall-induced landslides. By moving away from traditional device setups and utilizing an arbitrary dipole–dipole observation system, we aim to improve efficiency, enhance data resolution, and reduce costs. The resistivity of the slope was found to change significantly during the incubation, formation, and development of a landslide in physical model experiments. Furthermore, the feasibility of our proposed method for assessment rainfall-induced landslides was illustrated by a real case study in South China.

A Narrative Review on the Pandemic Zoonotic RNA Virus Infections Occurred During the Last 25 Years.

Pandemic zoonotic RNA virus infections have continued to threaten humans and animals worldwide. The objective of this review was to highlight the epidemiology and socioeconomic impacts of pandemic zoonotic RNA virus infections that occurred between 1997 and 2021. Literature search was done from Web of Science, PubMed, Google Scholar and Scopus databases, cumulative case fatalities of individual viral infection calculated, and geographic coverage of the pandemics were shown by maps. Seven major pandemic zoonotic RNA virus infections occurred from 1997 to 2021 and were presented in three groups: The first group consists of highly pathogenic avian influenza (HPAI-H5N1) and swine-origin influenza (H1N1) viruses with cumulative fatality rates of 53.5% and 0.5% in humans, respectively. Moreover, HPAI-H5N1 infection caused 90-100% death in poultry and economic losses of >$10billion worldwide. Similarly, H1N1 caused a serious infection in swine and economic losses of 0.5-1.5% of the Gross Domestic Product (GDP) of the affected countries. The second group consists of severe acute respiratory syndrome-associated coronavirus infection (SARS-CoV), Middle East Respiratory Syndrome (MERS-CoV) and Coronavirus disease 2019 (COVID-19) with case fatalities of 9.6%, 34.3% and 2.0%, respectively in humans; but this group only caused mild infections in animals. The third group consists of Ebola and Zika virus infections with case fatalities of 39.5% and 0.02%, respectively in humans but causing only mild infections in animals. Similar infections are expected in the near future, and hence strict implementation of conventional biosecurity-based measures and development of efficacious vaccines would help minimize the impacts of the next pandemic infection.

Pilot study for the development of an automatically generated and wearable-based early warning system for the detection of deterioration of hospitalized patients of an acute care hospital.

Acute deteriorations of health status are common in hospitalized patients and are often preceded by changes in their vital signs. Events such as heart attacks, death or admission to the intensive care unit can be averted by early detection, therefore so-called Early Warning Scores (EWS) such as the National Early Warning Score 2 (NEWS2), including basic vital parameters such as heart rate, blood pressure, respiratory rate, temperature and level of consciousness, have been developed for a systematic approach. Although studies have shown that EWS have a positive impact on patient outcomes, they are often limited by issues such as calculation errors, time constraints, and a shortage of human resources. Therefore, development of tools for automatic calculation of EWS could help improve quality of EWS calculation and may improve patient outcomes. The aim of this study is to analyze the feasibility of wearable devices for the automatic calculation of NEWS2 compared to conventional calculation using vital signs measured by health care professionals. We conducted a prospective trial at a large tertiary hospital in Switzerland. Patients were given a wristband with a photoplethysmogram (PPG) sensor that continuously recorded their heart rate and respiratory rate for 3 consecutive days. Combined with data from the electronic health record (EHR), NEWS2-score was calculated and compared to NEWS2 score calculated from vital parameters in the EHR measured by medical staff. The main objective of our study was to assess the agreement between NEWS2 scores calculated using both methods. This analysis was conducted using Cohen's Kappa and Bland-Altman analysis. Secondary endpoints were compliance concerning the medical device, patient acceptance, data quality analysis and data availability and signal quality for all time stamps needed for accurate calculation. Of 210 patients enrolled in our study, NEWS2 was calculated in 904 cases, with 191 cases being directly compared to conventional measurements. Thirty-three of these measurements resulted in a NEWS2 ≥ 5, 158 in a NEWS2 < 5. Comparing all 191 measurements, accordance was substantial (K = 0.76) between conventional and automated NEWS2. No adverse effects due to the device were recorded. Patient acceptance was high. In conclusion, the study found strong agreement between automated and conventional NEWS2 calculations using wearable devices, with high patient acceptance despite some data quality challenges. To maximize the potential of continuous monitoring, further research into fully automated EWS calculations without relying on spot measurements is suggested, as this could provide a reliable alternative to traditional methods. January 26, 2023, NCT05699967.

Smartphone based TLC approach versus conventional densitometric measurement for the simultaneous determination of donepezil and memantine, content uniformity testing along with greenness and whiteness assessment

Thin layer chromatography (TLC) is a straightforward and economical technique that plays a major role in analytical field. Introducing the amenities of smartphones as accessible alternative detectors to TLC separation methods elaborates the cost-effectiveness of this technique and supports the appropriate assay of various aliphatic compounds after visualization by suitable reagents. Herein, two TLC coupled with smartphone's camera approaches are proposed for the assay of donepezil hydrochloride (DON) and memantine hydrochloride (MEM), a combined medication used in the management of Alzheimer's disease. Separation was achieved using a developing system of ethyl acetate, methanol, toluene, and ammonia (33%, w/v) in a ratio of 4:1:4:0.2, by volume. The dried plates were scanned at 190 nm for the densitometric assay, then visualized with modified Dragendorff's reagent and photographed by smartphone's camera. The data were integrated by displaying the photos in either ImageJ software or Color-Picker application. The findings produced from the three detection approaches were contrasted over concentration ranges of 1–18 & 4–28 μg/spot for the densitometric method, while the range was 1–20 & 1–30 μg/spot for the ImageJ method and 3–18 & 2–20 μg/spot for the Color-Picker method for DON and MEM, respectively. The methods were suitable for assaying both drugs in their pure form and combined pharmaceutical formulations. Densitometric and ImageJ-based methods were applied to evaluate the dosage forms content uniformity. The developed methods were assessed by three metric tools for greenness and sustainability, namely Green Analytical Procedure Index, Analytical GREEnness Metric Approach, and White Analytical Chemistry.

Assessment of Biventricular Systolic and Diastolic Function Using Conventional and Strain Echocardiography in Children with Sickle Cell Disease Surviving 1-year After Hematopoietic Stem Cell Transplant.

Hematopoietic stem cell transplant (HSCT) is a potentially curative therapy for children with sickle cell disease (SCD). The effects of HSCT on ventricular function are not well characterized in children with SCD. Echocardiograms from children with SCD who underwent HSCT between 2007 and 2017 were retrospectively analyzed before and 1-year after HSCT. Left ventricular (LV) volumes, mass, and ejection fraction were calculated by the 5/6 area*length method. LV end-diastolic and systolic dimensions, septal, and posterior wall thickness, and fractional shortening were measured by M-mode. Mitral and tricuspid inflow Dopplers (E and A waves) as well as mitral, tricuspid, and septal tissue Dopplers (E', A') were assessed. E/A, E'/A' and E/E' ratios were calculated. Biventricular strain imaging was performed using speckle-tracking echocardiography. Peak global systolic longitudinal and circumferential LV strain, and global longitudinal right ventricular strain, as well as early and late diastolic strain rate, were measured on LV apical 4-chamber, LV short-axis mid-papillary, and RV apical views, respectively. Forty-seven children (9.7 ± 5.5years, 60% male) met inclusion criteria. Pre-HSCT, subjects had mild LV dilation with normal LV systolic function by conventional measure of ejection fraction and fractional shortening. There was a significant reduction in LV volume, mass, and ejection fraction after HSCT, but measurements remained within normal range. LV longitudinal and circumferential strain were normal pre-HSCT and showed no significant change post-HSCT. RV strain decreased after HSCT, but the absolute change was small, and mean values were normal both pre- and post-HSCT. Conventional measures of diastolic function were all normal pre-HSCT. Post-HSCT there was a reduction in select parameters, but all parameters remained within normal range. Early and late diastolic strain rate parameters showed no significant change from pre- to post-HSCT. At one-year after HSCT in children with SCD conventional measures of systolic and diastolic function are within normal limits. Except for a small decrease in RV systolic strain with values remaining within normal limits, systolic strain and diastolic strain rate values did not significantly change 1-year after HSCT.

Clustering uncertain overlapping symptoms of multiple diseases in clinical diagnosis

In various fields, including medical science, datasets characterized by uncertainty are generated. Conventional clustering algorithms, designed for deterministic data, often prove inadequate when applied to uncertain data, posing significant challenges. Recent advancements have introduced clustering algorithms based on a possible world model, specifically designed to handle uncertainty, showing promising outcomes. However, these algorithms face two primary issues. First, they treat all possible worlds equally, neglecting the relative importance of each world. Second, they employ time-consuming and inefficient post-processing techniques for world selection. This research aims to create clusters of observed symptoms in patients, enabling the exploration of intricate relationships between symptoms. However, the symptoms dataset presents unique challenges, as it entails uncertainty and exhibits overlapping symptoms across multiple diseases, rendering the formation of mutually exclusive clusters impractical. Conventional similarity measures, assuming mutually exclusive clusters, fail to address these challenges effectively. Furthermore, the categorical nature of the symptoms dataset further complicates the analysis, as most similarity measures are optimized for numerical datasets. To overcome these scientific obstacles, this research proposes an innovative clustering algorithm that considers the precise weight of each symptom in every disease, facilitating the generation of overlapping clusters that accurately depict the associations between symptoms in the context of various diseases.

Broadband nonlinear refraction transients in C-doped GaN based on absorption spectroscopy

Optical nonlinear response and its dynamics of wide-bandgap materials are key to realizing integrated nonlinear photonics and photonic circuit applications. However, those applications are severely limited by the unavailability of both dispersion and dynamics of nonlinear refraction (NLR) via conventional measurements. In this work, the broadband NLR dynamics with extremely high sensitivity (λ/1000) can be obtained from absorption spectroscopy in GaN:C using the refraction-related interference model. Both the absorption and refraction kinetics are found to be significantly modulated by the C-related defects. Especially, we demonstrate that the refractive index change Δn of GaN:C is negative and can be used to realize all-optical switching applications owing to the large NLR and ultrafast switching time. The NLR under different non-equilibrium carrier distributions originates from the capture of electrons by CN+ defect state, while the absorption modulation originates from the excitation of tri-carbon defects. We believe that this work provides a better understanding of the GaN:C nonlinear properties and an effective solution to broadband NLR dynamics of transparent thin films or heterostructure materials.

Acceleration of ferromagnetic resonance measurements by Bayesian experimental design.

Ferromagnetic resonance (FMR) is a broadly used dynamical measurement used to characterize a wide range of magnetic materials. Applied research and development on magnetic thin film materials is growing rapidly alongside a growing commercial appetite for magnetic memory and computing technologies. The ability to execute high-quality, fast FMR surveys of magnetic thin films is needed to meet the demanding throughput associated with rapid materials exploration and quality control. Here, we implement optimal Bayesian experimental design software developed by [McMichael etal. J. Res. Natl. Inst. Stand. Technol. 126, 126002 (2021)] in a vector network analyzer-FMR setup to demonstrate an unexplored opportunity to accelerate FMR measurements. A systematic comparison is made between the optimal Bayesian measurement and the conventional measurement. Reduced uncertainties in the linewidth and resonance frequency ranging from 40% to 60% are achieved with the Bayesian implementation for the same measurement duration. In practical terms, this approach reaches a target uncertainty of ±5MHz for the linewidth and ±1MHz for the resonance frequency in 2.5× less time than the conventional approach. As the optimal Bayesian approach only decreases random errors, we evaluate how large systematic errors may limit the full advantage of the optimal Bayesian approach. This approach can be used to deliver gains in measurement speed by a factor of 3 or more and as a software add-on has the flexibility to be added on to any FMR measurement system to accelerate materials discovery and quality control measurements, alike.

Measuring 60 pc-scale Star Formation Rate of the Nearby Seyfert Galaxy NGC 1068 with ALMA, HST, VLT/MUSE, and VLA

The star formation rate (SFR) is a fundamental parameter for describing galaxies and inferring their evolutionary course. H ii regions yield the best measure of instantaneous SFR in galaxies, although the derived SFR can have large uncertainties depending on tracers and assumptions. We present an SFR calibration for the entire molecular gas disk of the nearby Seyfert galaxy NGC 1068, based on our new high-sensitivity Atacama Large Millimeter/submillimeter Array 100 GHz continuum data at 55 pc (= 0.″8) resolution in combination with the Hubble Space Telescope Paα line data. In this calibration, we account for the spatial variations of dust extinction, electron temperature of H ii regions, AGN contamination, and diffuse ionized gas (DIG) based on publicly available multiwavelength data. Especially, given the extended nature and the possible nonnegligible contribution to the total SFR, a careful consideration of DIG is essential. With a cross-calibration between two corrected ionized gas tracers (free–free continuum and Paα), the total SFR of the NGC 1068 disk is estimated to be 3.2 ± 0.5 M ⊙ yr−1, one-third of the SFR without accounting for DIG (9.1 ± 1.4 M ⊙ yr−1). We confirmed a high SFR around the southern bar end and the corotation radius, which is consistent with the previous SFR measurements. In addition, our total SFR exceeds the total SFR based on 8 μm dust emission by a factor of 1.5. We attribute this discrepancy to the differences in the young stars at different stages of evolution traced by each tracer and their respective timescales. This study provides an example to address the various uncertainties in conventional SFR measurements and their potential to lead to significant SFR miscalculations.

Real-time hybrid testing using iterative control for periodic oscillations

Real-time hybrid testing is a method in which a substructure of the system is realized experimentally and the rest numerically. The two parts interact in real time to emulate the dynamics of the full system. Such experiments, however, are often difficult to realize as the actuators and sensors, needed to ensure compatibility and force–equilibrium conditions at the interface, can seriously affect the predicted dynamics of the system and result in stability and fidelity issues. The traditional approach of using feedback control to overcome the additional unwanted dynamics is challenging due to the presence of an outer feedback loop, passing interface displacements or forces to the numerical substructure. We, therefore, advocate for an alternative approach, removing the problematic interface dynamics with an iterative scheme to minimize interface errors, thus, capturing the response of the true assembly. The technique is examined by hybrid testing of a bench-top four-storey building with different interface configurations, where using conventional hybrid measurement techniques is very challenging. A case where the physical part exhibits nonlinear restoring force characteristics is also considered. These tests show that the iterative approach is capable of handling even scenarios which are theoretically infeasible with feedback control.