Average Noise Research Articles

A statistical-based automatic detection of a low-contrast object in the ACR CT phantom for measuring contrast-to-noise ratio of CT images

Abstract Purpose:
This study aimed to develop a new method for automated contrast-to-noise ratio (CNR) measurement using the low-contrast object in the ACR CT phantom.
Methods:
The ACR CT 464 phantom was scanned by 25 CT scanners installed at 25 different hospitals. AROI was placed in a specific radial location and was then rotated around 3600 increments of 20. At each position, the average CT number within the ROI was calculated. After one complete rotation, a profile of the average CT number around the 3600 degree was obtained. The center coordinate of the low-contrast object was determined from the maximum value of the profile. The CNR was calculated based on the average CT number and noise within the ROI in the low-contrast object and the ROI in the background, i.e., at the center of the phantom. The results were compared to a previous method based on a segmentation approach.
Results:
From 325 image samples of the 25 CT scanners, the proposed method successfully (100%) located the ROI within the low-contrast objects of all images used. The success rate of the segmentation method was only 56%.
Conclusion:
A new method for measuring CNR in the ACR CT phantom has been proposed and implemented. It is more powerful than a previous method based on segmentation.

A Study on the Listening Environment in Schools for the Inclusive Education of Students with Hearing Impairments

Purpose: The purpose of this study was to investigate the listening environment for students with hearing impairments in inclusive education, providing fundamental data for establishing performance standards and improving classroom listening environments for these students.Methods: The teacher’s voice signal, classroom noise, and reverberation time were measured and analyzed in general classrooms, special education classrooms, and gymnasiums from 10 elementary schools where students with hearing impairments receive inclusive education.Results: The average equivalent noise level in the general classrooms was 56.8 dBA, and it was 53.4 dBA in the special education classrooms. The signal-to-noise ratio was +12.3 dB in the general classrooms, +15.7 dB in the special education classrooms, and +5.1 dB in the gymnasiums. The reverberation time was 0.4 seconds in the general classrooms, 0.5 seconds in the special education classrooms, and 0.8 seconds in the gymnasiums.Conclusion: Currently, it can be difficult for students with hearing impairments to effectively listen in inclusive educational environments. Therefore, improvements are needed to create better listening conditions.

Time Irreversibility of Complex Time Series with Detrended Cross-Correlation Analysis Based on Visibility Graph

In this paper, we introduce detrended cross-correlation analysis (DCCA) method into visibility graph (VG) algorithm and propose VGDCCA method to reflect the time series irreversibility from a new perspective. The validity and reliability of the proposed VGDCCA method is supported by numerical simulations on synthesized short-term correlated chaotic systems and long-term correlated fractal processes, and by the empirical analysis on stock indices and traffic parameter. The VGDCCA planes suggest that autoregressive fractionally integrated moving average (ARFIMA) and fractional Gaussian noise (FGN) series show time reversible behavior, whatever the long-term correlation of the series is or however strong the persistence or anti-persistence of the series is. Meanwhile, Logistic map with [Formula: see text] and Hénon map show more time irreversible behaviors than those of ARFIMA, FGN series and other Logistic maps. It can be found that the relationship between cross-correlation of ingoing degree sequence and outgoing degree sequence for the simulated series with different parameters is consistent with the complexity and autocorrelation behavior in the corresponding definition of the time series. For the empirical analysis, VGDCCA method declares the similarity and dissimilarity between stock indices on time series irreversibility and captures the time irreversibility of traffic time series recorded by the detectors in different locations.

Design and Optimization of a Fan-Out Wafer-Level Packaging- Based Integrated Passive Device Structure for FMCW Radar Applications

This paper presents an integrated passive device (IPD) structure based on fan-out wafer-level packaging (FOWLP) for the front end of frequency-modulated continuous wave (FMCW) radar systems, focusing on enhancing the integration efficiency and performance of large passive components like antennas. Additionally, a new metric is introduced to assess this structure’s effect on the average noise figure in FMCW systems. Using this metric as a loss function, we apply the support vector machine (SVM) for electromagnetic simulation and the genetic algorithm (GA) for optimization. The sample fitting variance is 2.42 dB, reducing computation time from 12 min to under 1 millisecond, with the entire optimization completed in less than 100 s. The optimized IPD structure is 0.7 × 0.9 × 0.014 λ03 in size and achieves over 35 dB isolation between the transmitter and receiver. Compared to the IPD model calculated by empirical formulas, the optimized device lowers the average noise figure by 15.2 dB and increases maximum gain by 4.19 dB.

Influence of urban noise exposure on early-onset myocardial infarction risk prediction

Abstract Results from the DECIBEL-MI (Determination of Environmental CIty-noise's Burden, Exposure, and Link to Myocardial Infarction) Study Background Urban noise has not been incorporated into existing risk scores for cardiovascular events. The present study endeavors to explore the impact of noise exposure on the individual risk of early-onset myocardial infarction (MI). Methods All consecutive patients aged ≤ 50 years with acute MI who were admitted to a heart center between 2015 and 2023 and were residing in our city were included. The LIFE-CVD model was used to predict the 10-year cardiovascular risk of the patients based on traditional risk factors at the time of MI. The Geoportal Bremen was used to calculate both the daytime (Lden) and nighttime (Lnight) residential noise exposure levels. Results Among the 430 patients aged 50 years or younger diagnosed with MI, an elevated incidence of noise exposure compared to the general population in the same region was observed. This result was observed in both Lden (65% vs. 53%, p < 0.01) and Lnight (55% vs. 41%, p < 0.01). Patients with MI and a low LIFE-CVD score (≤2.5%) exhibited significantly higher noise exposure in comparison to those with a high LIFE-CVD score (2.32 vs. 1.36, p < 0.01 for Lden, and 1.72 vs. 1.00, p < 0.01 for Lnight). The average noise exposure increased with decreasing traditional risk factors exposure (see Figure 1). This inverse relationship persisted significantly in a linear regression analysis, even after adjusting for potential confounding factors such as socioeconomic status, PM10 and renal function (p < 0.01). Consequently, the incorporation of urban noise exposure contributes to optimizing the stratification of risk groups within LIFE-CVD risk categories. Conclusion Young patients with myocardial infarction (MI) who had fewer traditional risk factors were exposed to higher noise levels. Through comprehensive multivariate analysis, we identified a consistent inverse association between residential noise and the estimated cardiovascular risk based on traditional risk factors. Incorporating noise exposure into clinical care considerations has the potential to enhance the accuracy of MI prediction. Figure 1: Comparison of Lden in patients with MI ≤ 50 years with different 10-year LIFE-CVD risk categories (based on traditional risk factors)

Depressive symptoms modify the association between noise and adiposity biomarkers: Evidence from a population study of Czech adults

ObjectiveEnvironmental noise exposure is associated with adiposity. However, less is known about the individual vulnerability to environmental noise in abnormal adiposity development, particularly in relation to mental health. This study investigated the association between environmental noise exposure and four adiposity biomarkers and tested the moderation effect of depressive symptoms. MethodsA cross-sectional population-based sample of 2031 participants aged 25–64 years (54.70% women) was drawn from the Kardiovize study in 2013. Global combined (road, railway, and airport) Lden (day-evening-night) noise exposures were obtained from the geographical prediction modelling for the 2nd report of Strategic noise mapping in the Czech Republic (2012). Four adiposity biomarkers (BMI, body fat percentage, waist circumference, and visceral fat area) were assessed. Depressive symptoms were measured by PHQ-9. Linear regression was used to estimate the separate effects of quartiles of noise exposure and depressive symptoms on adiposity biomarkers and to examine the interaction between noise exposure and depressive symptoms. ResultsThe average noise exposure was 53.79 dB, ranging from 42.50 dB to 66.97 dB. All biomarkers were significantly elevated in the highest noise exposure quartile (>56 dB), compared to the lowest quartile (<51 dB) (p < 0.05). The association between noise and adiposity biomarkers was modified by presence of depressive symptoms; the increase in all adiposity biomarkers in the highest quartile of noise was significantly larger among subjects with moderate to severe depressive symptoms (p < 0.005). ConclusionThe study confirmed the association between environmental noise exposure and several adiposity measures. The association was stronger in the presence of depressive symptoms.

Association between residential noise exposure and burnout among healthcare workers in Taiwan: a cross-sectional study

Few studies have explored the association between residential noise exposure and burnout. In this study, we investigated the association between residential noise exposure and burnout prevalence among 5416 health-care workers in Taiwan from 2012 to 2017. Burnout was evaluated using the Mandarin version of the Copenhagen Burnout Inventory by considering both continuous and binary measures. We applied ordinary Kriging models to calculate the annual average residential noise exposure at an individual level. Multivariable linear regression models and logistic regression models were employed. Restricted cubic splines were used to explore dose–response relationships. The median age of the health-care workers was 31.5 years. In the multivariable linear regression models, exposure to residential noise (per 1 dBA) was associated with increases in personal burnout and work-related burnout scores by 1.59 ± 0.25 and 1.38 ± 0.20, respectively. In the multivariable logistic regression models, the adjusted odds ratios were 1.24 (95% confidence interval [CI]: 1.16, 1.32) for personal burnout and 1.19 (95% CI: 1.13, 1.26) for work-related burnout per 1-dBA increase in residential noise exposure. Linear dose–response associations of burnout with residential noise level were detected. Our findings suggest that exposure to residential noise may increase the risk of burnout among health-care workers.

Development, validation, and application of a generic image-based noise addition method for simulating reduced dose computed tomography images.

Major efforts in computed tomography (CT) have been focused on reducing radiation dose to patients while maintaining adequate diagnostic quality. To that end, research tools have been developed to simulate reduced-dose images via either image-based or projection-based methods. The former is limited to fully capturing realistic texture, streak, and non-stationary characteristics of reduced dose, while the latter is impractical clinically. To develop and validate an image-based noise addition method that accounts for such attributes while being practical in clinical settings. A noise addition method was developed to add realistic noise in the image domain. The method first estimates the noise power spectrum (NPS) of CT images, which are also forward-projected to form synthetic projections. The projection data are supplemented with random white noise proportional to their attenuation values. The noise sinogram is then back-projected onto the image, filtered by the NPS, and scaled according to the desired dose reduction level. The tool was evaluated using both phantom images and patient data. The phantom images were acquired using a multi-sized image quality phantom (Mercury Phantom 3.0, Duke University), and a thorax anthropomorphic phantom (Lungman Phantom, Kyoto Kagaku) at different dose levels and reconstruction settings. The patient images consisted of two dose levels of various CT examinations and reconstruction settings. The simulated and real reduced-dose images were compared in terms of the noise magnitude and texture (i.e., NPS average frequency, NPS-fav). The utility of this methodology was also assessed for routine clinical use for CT protocol review. For the phantom images, the percent errors in the noise magnitude between the simulated images and the actual images of the Mercury Phantom and anthropomorphic phantom images were 3.34% and 3.50%, respectively. The difference in fav was 0.07mm-1 for the Mercury Phantom and 0.06mm-1 for the anthropomorphic phantom between the simulated and actual images. The average noise magnitude percent error between the simulated and actual patient images was 4.61% with noise texture judged to be visually comparable with some kernel dependencies. When implemented clinically, the tool proved practical to simplify the process of estimating radiation dose reduction for CT protocols, resulting in a 50% dose reduction of our multiple myeloma protocol. The method generated simulated CT images with realistic noise properties similar to images acquired at the same radiation exposure without needing access to raw projection data.

Characterizing nonlinear, nonstationary, and heterogeneous hydrologic behavior using ensemble rainfall–runoff analysis (ERRA): proof of concept

Abstract. A classical approach to understanding hydrological behavior is the unit hydrograph and its many variants, but these often assume linearity (runoff response is proportional to effective precipitation), stationarity (runoff response to a given unit of rainfall is identical, regardless of when it falls), and spatial homogeneity (runoff response depends only on spatially averaged precipitation). In the real world, by contrast, runoff response is typically nonlinear, nonstationary, and spatially heterogeneous. Quantifying this nonlinearity, nonstationarity, and spatial heterogeneity is essential to unraveling the mechanisms and subsurface properties controlling hydrological behavior. Here, I present proof-of-concept demonstrations illustrating how nonlinear, nonstationary, and spatially heterogeneous rainfall–runoff behavior can be quantified, directly from data, using ensemble rainfall–runoff analysis (ERRA), a data-driven, model-independent method for quantifying rainfall–runoff relationships across a spectrum of time lags. I show how ERRA uses nonlinear deconvolution to quantify how catchments' runoff responses vary with precipitation intensity and to estimate their precipitation-weighted runoff response distributions. I further illustrate how ERRA combines nonlinear deconvolution with de-mixing techniques to reveal how runoff response depends jointly on precipitation intensity and nonstationary ambient conditions, including antecedent wetness and vapor pressure deficit. I demonstrate how ERRA's de-mixing techniques can be used to quantify spatially heterogeneous runoff responses in different parts of a catchment, even if those subcatchments are not separately gauged. I also illustrate how ERRA's broken-stick deconvolution capabilities can be used to quantify multiscale runoff responses that combine hydrograph peaks lasting for hours and recessions lasting for weeks, well beyond the average spacing between storms. ERRA can unscramble these multiple effects on runoff response even if they are overprinted on each other through time and even if they are corrupted by autoregressive moving average (ARMA) noise. Results from this approach may be informative for catchment characterization, process understanding, and model–data comparisons; they may also lead to a better understanding of storage dynamics and landscape-scale connectivity. An R script is provided to perform the necessary calculations, including uncertainty analysis.

Noise emission characteristics of cosmetics manufacturing plants

Recently, a rapid increase in the establishment of cosmetics manufacturing facilities has been observed in Korea because of the growing export of K-beauty products. Many of these factories are certified under good manufacturing practice (GMP) standards and show significant interest in quality management, industrial injury prevention, and productivity enhancement. Among industrial injuries, noise-induced hearing loss holds the largest share, rendering it crucial for industrial injury prevention. Therefore, this study aimed to assess the noise exposure status in cosmetics manufacturing facility spaces. Analysis revealed that the 24-h average continuous noise level (L_eq) was approximately 69.0 dB(A). The measured areas were divided into three rooms based on their purpose, of which the Manufacturing Room showed the highest noise levels ranging from 74.0 to 77.0 dB(A). Additionally, the Blister Packaging and Sealing Room ranged from 64.1 to 66.1 dB(A), and the Box Packing Room ranged from 64.3 to 65.8 dB(A), all within acceptable limits and not exceeding day-night noise correction exposure standards. From the perspective of industrial injury prevention and monitoring, this study aims to implement these measurement results within the metaverse in the future for visualization.

Assessing indoor and outdoor noise quality and associated health impacts at selected schools in Surat, India

An optimal ambient environment is crucial for healthy development, yet the excess of vehicles and inadequately planned educational infrastructures compromise cognitive growth in children. This study evaluates the impact of ambient noise on student cognition in five schools situated along city arterial and sub-arterial roads. A total of 717 students from grades 7, 8, 9, and 11 participated. Noise levels were measured in prevailing noisy conditions (PC) and controlled silence conditions (SC) within classrooms using the Kimo dB300 sound level meter. The observed differences in noise levels between PC and SC varied across grades, showing reductions of 4.7 ± 3.5 dB, 6.1 ± 0.7 dB, 6.7 ± 1.3 dB, and 4.4 ± 3.46 dB for grades 7, 8, 9, and 11, respectively. Statistical analyses revealed significant differences in the average noise levels under both conditions. Furthermore, cognitive task performance in PC and SC was significantly different, as evidenced by paired t-tests in tasks such as multiplication, word formation, reasoning, and sentence construction. Dimension reduction techniques identified room acoustics, task difficulty, speech intelligibility, mental deficiency, and annoyance as critical factors affecting learning abilities. Teachers reported that major impacts of noise include annoyance, sleeplessness, headache, and hearing impairment.

Noise modeling of UAM around the urban vertiport

The present study aims to investigate the noise distribution around urban vertiport caused by UAM(Multicopter) and helicopter. Noise modelings were carried out using SoundPLAN software at the some vertiports in Seoul, Korea. Sound source model of UAM and helicopter were used as a form of hemisphere. Noise predictions were undertaken with three different operations including depart, level flight and landing. In order to evaluate the noise impact on the buildings around the vertiport, noise levels at the outer wall of nearby buildings were calculated as the three noise units including Leq, Lmax and Lden. Also, noise maps were drawn around the vertiport. As a result, it was found that average noise level of UAM is approximately 12 dB lower than the helicopter noise levels. Regarding the noise frequency distribution, most high noises are made at the mid-frequencies during landing hours while low frequency noise level around 63 Hz was dominant at take-off time.

The challenges of mapping quiet urban areas from mobile sound surveys

Detailed spatial data on noise levels are needed to identify quiet areas. Yet noise maps derived from sound propagation models based on traffic flows or sensor networks are often incomplete. Surveys using mobile sound recording coupled with machine learning modelling have offered a possible solution, but so far have focused on averaged noise levels. We argue that quiet areas should ideally be consistently quiet, rather than simply quiet on average, yet mobile survey data usually lack the full temporal profile needed to quantify consistency in noise levels. Using data from our Southampton study area, we show that modification of the loss function used in machine learning modelling of mobile survey data can yield information on the likely range of noise levels at any location across an entire city. We use this approach not only to identify locations that are consistently quiet, but also to help understand which urban features may be associated with quiet, average and noisy events at any given location.

Optomechanics Driven by Noisy and Narrowband Fields

AbstractWe report a study of a cavity optomechanical system driven by narrowband electromagnetic fields, which are applied either in the form of uncorrelated noise, or as a more structured spectrum. The bandwidth of the driving spectra is smaller than the mechanical resonant frequency, and thus we can describe the resulting physics using concepts familiar from regular cavity optomechanics in the resolved-sideband limit. With a blue-detuned noise driving, the noise-induced interaction leads to anti-damping of the mechanical oscillator, and a self-oscillation threshold at an average noise power that is comparable to that of a coherent driving tone. This process can be seen as noise-induced dynamical amplification of mechanical motion. However, when the noise bandwidth is reduced down to the order of the mechanical damping, we discover a large shift of the power threshold of self-oscillation. This is due to the oscillator adiabatically following the instantaneous noise profile. In addition to blue-detuned noise driving, we investigate narrowband driving consisting of two coherent drive tones nearby in frequency. Also in these cases, we observe deviations from a naive optomechanical description relying only on the tones’ frequencies and powers.

Two improved generalized extended stochastic gradient algorithms for CARARMA systems

The paper innovatively proposes two improved generalized extended stochastic gradient (GESG) algorithms for the controlled autoregressive autoregressive moving average (CARARMA) system with autoregressive moving average (ARMA) model noise. Firstly, we propose a latest estimation based weighted generalized extended stochastic gradient (LE-WGESG) algorithm, which introduces multiple momentary corrections in the traditional parameter estimation process. By carefully adjusting the weighting coefficients of the correction quantities at different moments, the algorithm has a rapid and greater efficient convergence property. More importantly, utilizing the theory of moving data window, this paper also proposes a multi-innovation based latest estimated weighted generalized extended stochastic gradient (MI-LE-WGESG) algorithm, which can better capture the interactions among multiple correction terms and further improve the predictive ability of the model.

Eigenbin compression for reducing photon-counting CT data size.

Photon-counting CT (PCCT) systems acquire multiple spectral measurements at high spatial resolution, providing numerous image quality benefits while also increasing the amount of data that must be transferred through the gantry slip ring. This study proposes a lossy method to compress photon-counting CT data using eigenvector analysis, with the goal of providing image quality sufficient for applications that require a rapid initial reconstruction, such as to confirm anatomical coverage, scan quality, and to support automated advanced applications. The eigenbin compression method was experimentally evaluated on a clinical silicon PCCT prototype system. The proposed eigenbin method performs principal component analysis (PCA) on a set of PCCT calibration measurements. PCA finds the orthogonal axes or eigenvectors, which capture the maximum variance in the N dimensional photon-count data space, where N is the number of acquired energy bins. To reduce the dimensionality of the PCCT data, the data are linearly transformed into a lower dimensional space spanned by the M<N eigenvectors with highest eigenvalues (i.e., the vectors that account for most of the information in the data). Only M coefficients are then transferred per measurement, which we term eigenbin values. After transmission, the original N energy-bin measurements are estimated as a linear combination of the M eigenvectors. Two versions of the eigenbin method were investigated: pixel-specific and pixel-general. The pixel-specific eigenbin method determines eigenvectors for each individual detector pixel, while the more practically realizable pixel-general eigenbin method finds one set of eigenvectors for the entire detector array. The eigenbin method was experimentally evaluated by scanning a 20cm diameter Gammex Multienergy phantom with different material inserts on a clinical silicon-based PCCT prototype. The method was evaluated with the number of eigenbins varied between two and four. In each case, the eigenbins were used to estimate the original 8-bin data, after which material decomposition was performed. The mean, standard deviation, and contrast-to-noise ratio (CNR) of values in the reconstructed basis and virtual monoenergetic images (VMI) were compared for the original 8-bin data and for the eigenbin data. The pixel-specific eigenbin method reduced photon-counting CT data size by a factor of four with<5% change in mean values and a small noise penalty (mean change in noise of<12%, maximum change in noise of 20% for basis images). The pixel-general eigenbin compression method reduced data size by a factor of 2.67 with<5% change in mean values and a less than 10% noise penalty in the basis images (average noise penalty≤5%). The noise penalty and errors were less for the VMIs than for the basis images, resulting in<5% change in CNR in the VMIs. The eigenbin compression method reduced photon-counting CT data size by a factor of two to four with less than 5% change in mean values, noise penalty of less than 10%-20%, and change in CNR ranging from 15% decrease to 24% increase. Eigenbin compression reduces the data transfer time and storage space of photon-counting CT data for applications that require rapid initial reconstructions.

Integrated Amorphous Carbon Film Temperature Sensor with Silicon Accelerometer into MEMS Sensor.

Amorphous carbon (a-C) has promising potential for temperature sensing due to its outstanding properties. In this work, an a-C thin film temperature sensor integrated with the MEMS silicon accelerometer was proposed, and a-C film was deposited on the fixed frame of the accelerometer chip. The a-C film was deposited by DC magnetron sputtering and linear ion beam, respectively. The nanostructures of two types of films were observed by SEM and TEM. The cluster size of sp2 was analyzed by Raman, and the content of sp2 and sp3 of the carbon film was analyzed by XPS. It showed that the DC-sputtered amorphous carbon film, which had a higher sp2 content, had better temperature-sensitive properties. Then, an integrated sensor chip was designed, and the structure of the accelerometer was simulated and optimized to determine the final sizes. The temperature sensor module had a sensitivity of 1.62 mV/°C at the input voltage of 5 V with a linearity of 0.9958 in the temperature range of 20~150 °C. The sensitivity of the sensor is slightly higher than that of traditional metal film temperature sensors. The accelerometer module had a sensitivity of 1.4 mV/g/5 V, a nonlinearity of 0.38%, a repeatability of 1.56%, a total thermomechanical noise of 509 μg over the range of 1 to 20 Hz, and an average thermomechanical noise density of 116 µg/√Hz, which is smaller than the input acceleration amplitude for testing sensitivity. Under different temperatures, the performance of the accelerometer was tested. This research provided significant insights into the convenient procedure to develop a high-performance, economical temperature-accelerometer-integrated MEMS sensor.

High power lateral coupled InAs/GaAs quantum dot distributed feedback lasers grown on Si(001) substrates

High-quality single-frequency semiconductor lasers play a key role in silicon optical integrated systems. Combining high density 8-stacked quantum dot (QD) material and low–loss laterally coupled gratings, we here demonstrate a high output power, low noise, and insensitivity to light feedback 1.3 µm InAs/GaAs QD distributed feedback (DFB) laser grown on Si(001) substrates. For a QD DFB laser of a 3 × 1500 µm2 cavity, it exhibits a high single-mode output light power of up to 25 mW at 20 °C and 1.8 mW at 70 °C, respectively and maintains a stable single–mode operation in the entirely measured temperature range with a maximum side mode suppression ratio (SMSR) of 56.5 dB. Furthermore, the laser has an average relative intensity noise value low to –155.9 dB/Hz and a Lorentzian linewidth narrow to 243 kHz. In addition, the laser shows an insensitivity to optical feedback with a feedback level of –24.9 dB. Lastly, a 7-channel QD DFB laser array emitting at wavelengths from 1274.5 nm to 1290.0 nm are also exhibited with all SMSRs of higher than 45 dB. The results achieved here enable a practical single-frequency Si-based light source for the development of high-performance silicon photonic chips.

A Census of the Deep Radio Sky with the VLA. I. 10 GHz Survey of the GOODS-N Field*

Abstract We present the first high-resolution, high-frequency radio continuum survey that fully maps an extragalactic deep field: the 10 GHz survey of the Great Observatories Origins Deep Survey-North (GOODS-N) field. This is a Large Program of the Karl G. Jansky Very Large Array (VLA) that allocated 380 hr of observations using the X-band (8–12 GHz) receivers, leading to a 10 GHz mosaic of the GOODS-N field with an average rms noise σ n = 671 nJy beam−1 and angular resolution θ 1/2 = 0.″22 across 297 arcmin2. To maximize the brightness sensitivity we also produce a low-resolution mosaic with θ 1/2 = 1.″0 and σ n = 968 nJy beam−1, from which we derive our master catalog containing 256 radio sources detected with peak signal-to-noise ratio ≥ 5. Radio source size and flux density estimates from the high-resolution mosaic are provided in the master catalog as well. The total fraction of spurious sources in the catalog is 0.75%. Monte Carlo simulations are performed to derive completeness corrections of the catalog. We find that the 10 GHz radio source counts in the GOODS-N field agree, in general, with predictions from numerical simulations/models and expectations from 1.4 and 3 GHz radio counts.

Cologne ergonomic measurement for robotic surgery (CEMRobSurg) using the Hugo™ RAS System.

The ergonomic advantages and potential challenges that robotic surgery poses to the well-being of surgeons are mainly unexplored. The most recent surgical robot introduced on the European market is the Hugo™ RAS System by Medtronic. This study aims to evaluate the ergonomic benefits of the Hugo™ RAS System, which is available in our training laboratory, CeMIT (Center for Medical Innovation and Technology Cologne). Using the previously established Cologne Ergonomic Measurement Setup for Robotic Surgery (CEMRobSurg), we measured three parameters related to ergonomic posture from subjects with different levels of surgical expertise (laypeople, medical students, surgical residents, and expert robotic surgeons). The heart rate was measured continuously using a polar band. The noise level was measured while using the Hugo™ RAS System, and automated photographs using our locally developed methodology were captured of the participant every 2s to assess body posture. The ergonomic measurements were conducted while the subject performed the same standardized robotic training exercises (Peg Board, Rope Walk, and Ring Walk). A total of 53 participants were enrolled in this study. The average noise level during all measurements was 54.87 dB. The highest stress level was measured in surgical residents with a sympathetic nervous system index (SNS index) of 1.15 (min - 1.43, max 3.56). The lowest stress level was measured in robotic experts with an SNS index of 0.23 (min - 0.18, max 0.91). We observed a risk-prone positioning of the neck and elbow in medical students (mean 39.6° and 129.48°, respectively). Robotic experts showed a risk positioning in the knee and hip region (mean 107.89° and 90.31°, respectively). This is the first study to analyze and objectify the ergonomic posture of medical students, surgical trainees, surgeons, and laypeople using the open console, modular Hugo™ RAS System. Our findings offer recommendations for operating surgeons and allow for a comparative analysis between the different robotic systems. Further evaluations in real-time operative scenarios will follow.