Distance Bias Research Articles

Orthogonal Matrix-Autoencoder-Based Encoding Method for Unordered Multi-Categorical Variables with Application to Neural Network Target Prediction Problems

Neural network models, such as BP, LSTM, etc., support only numerical inputs, so data preprocessing needs to be carried out on the categorical variables to convert them into numerical data. For unordered multi-categorical variables, existing encoding methods may produce dimensional catastrophes and may also introduce additional order misrepresentation and distance bias in neural network computation. To solve the above problems, this paper proposes an unordered multi-categorical variable encoding method O-AE using orthogonal matrix for encoding and encoding representation learning and dimensionality reduction via an autoencoder. Bayesian optimization is used for hyperparameter optimization of the autoencoder. Finally, seven experiments were designed with the basic O-AE, Bayesian optimization of the hyperparameters of the autoencoder for O-AE, and other encoding methods to encode unordered multi-categorical variables in five datasets, and they were input into a BP neural network to carry out target prediction experiments. The results show that the experiments using O-AE and O-AE-b have better prediction results, proving that the method proposed in this paper is highly feasible and applicable and can be an optional method for the data processing of unordered multi-categorical variables.

Grape Maturity Estimation Using Time-of-Flight and LiDAR Depth Cameras.

This article investigates the potential for using low-cost depth cameras to estimate the maturity of green table grapes after they have been harvested. Time-of-flight (Kinect Azure) and LiDAR (Intel L515) depth cameras were used to capture depth scans of green table grape berries over time. The depth scans of the grapes are distorted due to the diffused scattering of the light emitted from the cameras within the berries. This causes a distance bias where a grape berry appears to be further from the camera than it is. As the grape aged, the shape of the peak corresponding to the grape became increasingly flattened in shape, resulting in an increased distance bias over time. The distance bias variation with time was able to be fitted with an R2 value of 0.969 for the Kinect Azure and an average of 0.904 for the Intel L515. This work shows that there is potential to use time-of-flight and LIDAR cameras for estimating grape maturity postharvest in a non-contact and nondestructive manner.

Gaussian Mixture Probability Hypothesis Density Filter for Heterogeneous Multi-Sensor Registration

Spatial registration is a prerequisite for data fusion. Existing methods primarily focus on similar sensor scenarios and rely on accurate data association assumptions. To address the heterogeneous sensor registration in complex data association scenarios, this paper proposes a Gaussian mixture probability hypothesis density (GM-PHD)-based algorithm for heterogeneous sensor bias registration, accompanied by an adaptive measurement iterative update algorithm. Firstly, by constructing augmented target state motion and measurement models, a closed-form expression for prediction is derived based on Gaussian mixture (GM). In the subsequent update, a two-level Kalman filter is used to achieve an approximate decoupled estimation of the target state and measurement bias, taking into account the coupling between them through pseudo-likelihood. Notably, for heterogeneous sensors that cannot directly use sequential update techniques, sequential updates are first performed on sensors that can obtain complete measurements, followed by filtering updates using extended Kalman filter (EKF) sequential update techniques for incomplete measurements. When there are differences in sensor quality, the GM-PHD fusion filter based on measurement iteration update is sequence-sensitive. Therefore, the optimal subpattern assignment (OSPA) metric is used to optimize the fusion order and enhance registration performance. The proposed algorithms extend the multi-target information-based spatial registration algorithm to heterogeneous sensor scenarios and address the impact of different sensor-filtering orders on registration performance. Our proposed algorithms significantly improve the accuracy of bias estimation compared to the registration algorithm based on significant targets. Under different detection probabilities and clutter intensities, the average root mean square error (RMSE) of distance and angular biases decreased by 11.8% and 8.6%, respectively.

Key ingredients in regional climate modelling for improving the representation of typhoon tracks and intensities

Abstract. There is evidence of an increased frequency of rapid intensification events of tropical cyclones (TCs) in global offshore regions. This will not only result in increased peak wind speeds but may lead to more intense heavy precipitation events, leading to flooding in coastal regions. Therefore, high impacts are expected for urban agglomerations in coastal regions such as the densely populated Pearl River Delta (PRD) in China. Regional climate models (RCMs) such as the Weather Research and Forecasting (WRF) model are state-of-the-art tools commonly applied to predict TCs. However, typhoon simulations are connected with high uncertainties due to the high number of parameterization schemes of relevant physical processes (including possible interactions between the parameterization schemes) such as cumulus (CU) and microphysics (MP), as well as other crucial model settings such as domain setup, initial times, and spectral nudging. Since previous studies mostly focus on either individual typhoon cases or individual parameterization schemes, in this study a more comprehensive analysis is provided by considering four different typhoons of different intensity categories with landfall near the PRD, i.e. Typhoon Neoguri (2008), Typhoon Hagupit (2008), Typhoon Hato (2017), and Typhoon Usagi (2013), as well as two different schemes for CU and MP, respectively. Moreover, the impact of the model initialization and the driving data is studied by using three different initial times and two spectral nudging settings. Compared with the best-track reference data, the results show that the four typhoons show some consistency. For track bias, nudging only horizontal wind has a positive effect on reducing the track distance bias; for intensity, compared with a model explicitly resolving cumulus convection, i.e. without cumulus parameterization (CuOFF; nudging potential temperature and horizontal wind; late initial time), using the Kain–Fritsch scheme (KF; nudging only horizontal wind; early initial time) configuration shows relatively lower minimum sea level pressures and higher maximum wind speeds, which means stronger typhoon intensity. Intensity shows less sensitivity to two MP schemes compared with the CuOFF, nudging, and initial time settings. Furthermore, we found that compared with the CuOFF, using the KF scheme shows a relatively larger latent heat flux and higher equivalent potential temperature, providing more energy to typhoon development and inducing stronger TCs. This study could be used as a reference to configure WRF with the model's different combinations of schemes for historical and future TC simulations and also contributes to a better understanding of the performance of principal TC structures.

Patterns of Wolf Dispersal Respond to Harvest Density across an Island Complex.

Wolves are highly mobile predators and can disperse across a variety of habitats and over long distances. However, less is known about dispersal capabilities across water and among islands. The biogeography of island systems fosters spatially structured local populations, and their degree of connectivity may influence the dynamics and long-term viability of the regional population. We sought to quantify wolf dispersal rate, distance, and dispersal sex bias throughout Prince of Wales Island, a 6670 km2 island in southeast Alaska, and the surrounding islands that constitute the wildlife management unit (9025 km2). We also investigated patterns of dispersal in relation to hunting and trapping intensity and wolf population density. We used DNA data collected during 2012-2021 long-term monitoring efforts and genotyped 811 wolves, 144 of which (18%) were dispersers. Annual dispersal rates were 9-23% and had a weakly positive relationship with wolf density. Wolves dispersed 41.9 km on average (SD = 23.7 km), and males and females did not disperse at different rates. Of the dispersing wolves, 107 died, and the majority (n = 81) died before they were able to settle. The leading manner of death was trapping (97% of mortalities), and wolves tended to disperse from areas with low harvest density to areas where harvest density was relatively higher. Dispersal occurred both to and from small islands and the larger Prince of Wales Island, indicating bidirectional as opposed to asymmetrical movement, and the genetic overlap of wolf groups demonstrates connectivity throughout this naturally patchy system. Island ecosystems have different predator-prey dynamics and recolonization processes than large, intact systems due to their isolation and restricted sizes; thus, a better understanding of the degree of population connectivity including dispersal patterns among islands in the Prince of Wales archipelago could help inform the management and research strategies of these wolves.

Graph-enhanced and collaborative attention networks for session-based recommendation

Session-based recommendation uses short interaction sequences of anonymous users to predict the next item most likely to be clicked, and many methods have been proposed. However, there are still problems with the existing methods. Existing approaches can be divided into two groups based on data organization: (1) graph-based methods using graph neural networks to capture complex item transformations; (2) sequence-based approaches using self-attention networks to capture chained user interest patterns. Both methods are only applicable to specific kinds of user interest patterns due to the characteristics of the neural networks they use and cannot be adaptively used in all scenarios. Moreover, the recent approaches capture collaborative information from other sessions by constructing global graphs, etc., in order to enrich the current session, which can compromise personalized modeling due to the introduction of items that are not relevant to the current user. This work proposes a graph-enhanced and collaborative attention network (GCAN) to solve the above problems. In GCAN, graph-enhanced attention is designed to model user interest over item-specific subsequences with the help of a graph mask and distance bias, which include item transformations mined in session graphs and chained user interest in session sequences. In addition, collaborative attention is proposed to model the item representation within the current session at the collaborative level by exploiting the collaborative information from all sessions. Extensive experiments on three real benchmark datasets show that GCAN significantly outperforms state-of-the-art methods.

Globalisation and agri-food trade

Abstract This study utilises structural gravity models to examine the impact of globalisation on distance in agricultural and processed food trade, the relative effects of international distance and home bias effects. The results show that larger internal distances lower domestic sales. The home bias effect is substantially larger in agricultural commodities compared to processed agricultural commodities. Omitted variable bias is observed in friction variables when the relative intra-national distance and home market bias are excluded. After controlling for relative intra-national trade costs and the home bias effect, globalisation does not lead to a smaller impact of distance on trade. Substantial heterogeneity exists when analysing individual commodities.

Use of videogrammetry to estimate length to provide population demographics of dwarf minke whales in the northern Great Barrier Reef

Commercial swim-with-whale programmes, based on the dwarf minke whale (Balaenoptera acutorostrata), have been conducted in Great Barrier Reef waters since 1996 and under permit since 2003. Evaluating the effectiveness of management requires information on the biology of the whales, including possible impacts on their critical life stages, such as mating or calving. In this study, length measurements have been used as the best available proxy for age and thus state of sexual maturity. Underwater videogrammetry was used to estimate the lengths of dwarf minke whales interacting with boats and swimmers during June/July 2003 and 2004. The calibrations used to correct systematic biases in distance and length estimates are presented and other sources of error associated with the methodology and the behaviour of the whales are discussed. Mean lengths (from replicate measurements of individually identified whales) ranged 4.82-6.61m in 2003 (n=23, from five encounters) and 4.48-7.18m in 2004 (n=56, from 29 encounters). The overall mean length (2003: 5.90m; 2004: 5.73m) did not differ significantly between years. In both years, the mean lengths of the majority of whales (2003: 57%; 2004: 59%) were less than 6m, which is regarded as sexually immature based on available life history data. The size ranges within a single encounter were broad; no encounter was dominated by one size class. Segregation by size was not observed. This paper presents the first field measurements of dwarf minke whales on their tropical wintering grounds. While most whales interacting with vessels or swimmers were immature, adult whales, including cow-calf pairs, also were involved. More information, especially on cumulative effects, is needed to assess the impact of these swim-with programmes.

Concerning colour: The effect of environment on type Ia supernova colour in the dark energy survey

ABSTRACT Recent analyses have found intriguing correlations between the colour (c) of type Ia supernovae (SNe Ia) and the size of their ‘mass-step’, the relationship between SN Ia host galaxy stellar mass (Mstellar) and SN Ia Hubble residual, and suggest that the cause of this relationship is dust. Using 675 photometrically classified SNe Ia from the Dark Energy Survey 5-yr sample, we study the differences in Hubble residual for a variety of global host galaxy and local environmental properties for SN Ia subsamples split by their colour. We find a 3σ difference in the mass-step when comparing blue (c < 0) and red (c > 0) SNe. We observe the lowest r.m.s. scatter (∼0.14 mag) in the Hubble residual for blue SNe in low mass/blue environments, suggesting that this is the most homogeneous sample for cosmological analyses. By fitting for c-dependent relationships between Hubble residuals and Mstellar, approximating existing dust models, we remove the mass-step from the data and find tentative ∼2σ residual steps in rest-frame galaxy U − R colour. This indicates that dust modelling based on Mstellar may not fully explain the remaining dispersion in SN Ia luminosity. Instead, accounting for a c-dependent relationship between Hubble residuals and global U − R, results in ≤1σ residual steps in Mstellar and local U − R, suggesting that U − R provides different information about the environment of SNe Ia compared to Mstellar, and motivating the inclusion of galaxy U − R colour in SN Ia distance bias correction.

Distance travelled for brain tumour surgery: An Low and Middle Income Country's perspective.

To examine the effect of distance travelled for brain tumour surgery on patient outcomes in an LMIC. Data were collected as part of the Pakistan Brain Tumour Epidemiology Study (PBTES) for brain tumour patients who underwent surgery in 2019. Mapping software was used to calculate the distance travelled by each patient from their primary address to the hospital. This was analysed in correlation with outcomes (change in KPS score, current status) and demographic variables. Of 2366 patients, the median distance travelled across the country was 104 km (IQR: 9.07 - 304). Only 970 (41%) patients had access to brain tumour surgical care within 50 km of their primary address. A total of 372 (15.7%) patients requiring brain tumour surgery had to travel more than 500 km to reach their primary care hospital. Patients travelling more than 50 km for brain tumour surgery had better pre- and post-surgery Karnofsky performance scores (p<0.001) than those travelling less than 50 km. The overall survival for these patients was also better (82.4% vs 75.7%, p= 0.002) compared to patients travelling less than 50 km. The distance to a hospital dictates a patient's access to continuity of care through adjuvant chemoradiotherapy and regular follow-ups. Less than half of brain tumour patients in Pakistan had access to brain tumour surgery care within 50 km of their homes. Overall outcomes were significantly better in patients travelling more than 50km for neurosurgical care - suggesting a distance bias effect.

Impact of Coulomb scattering on argon plasma based thermionic converter performance

Particle-in-cell (PIC) simulations were performed to study the impact of Coulomb scattering on the performance of argon plasma based thermionic converters. Using a simplified model, studies from the 1970’s concluded that plasma resistance resulting from Coulomb collisions shifts the IV-curves of thermionic converters that use plasmas to mitigate space charge, thereby strongly limiting their electricity generation capability. In this work the impact of Coulomb collisions in such devices were studied as a function of the relative electrical potential between the electrodes, with higher fidelity than earlier models, using a fully kinetic approach (PIC-MCC with grid based electron–ion scattering). This approach overcomes the incorrect assumption in earlier models that the electron temperature is equal to the cathode temperature in all operating conditions. The fully self-consistent results presented here showed an increase in electron temperature with an increase in gap distance or electrode bias, which decreases the plasma resistivity. It was consequently found that earlier reports overestimated the negative impact of Coulomb collisions at the maximum power point, suggesting the practical usefulness of such devices is higher than previously believed. Electronic conversion efficiency higher than 15% was shown to be feasible at 1200 °C cathode temperature with reasonable electrode work-functions.

SNIa Cosmology Analysis Results from Simulated LSST Images: From Difference Imaging to Constraints on Dark Energy

The Vera Rubin Observatory Legacy Survey of Space and Time (LSST) is expected to process ∼106 transient detections per night. For precision measurements of cosmological parameters and rates, it is critical to understand the detection efficiency, magnitude limits, artifact contamination levels, and biases in the selection and photometry. Here we rigorously test the LSST Difference Image Analysis (DIA) pipeline using simulated images from the Rubin Observatory LSST Dark Energy Science Collaboration Data Challenge (DC2) simulation for the Wide-Fast-Deep survey area. DC2 is the first large-scale (300 deg2) image simulation of a transient survey that includes realistic cadence, variable observing conditions, and CCD image artifacts. We analyze ∼15 deg2 of DC2 over a 5 yr time span in which artificial point sources from Type Ia supernova (SNIa) light curves have been overlaid onto the images. The magnitude limits per filter are u = 23.66 mag, g = 24.69 mag, r = 24.06 mag, i = 23.45 mag, z = 22.54 mag, and y = 21.62 mag. The artifact contamination levels are ∼90% of all detections, corresponding to ∼1000 artifacts deg–2 in g band, and falling to 300 deg–2 in y band. The photometry has biases <1% for magnitudes 19.5 < m < 23. Our DIA performance on simulated images is similar to that of the Dark Energy Survey difference-imaging pipeline on real images. We also characterize DC2 image properties to produce catalog-level simulations needed for distance bias corrections. We find good agreement between DC2 data and simulations for distributions of signal-to-noise ratio, redshift, and fitted light-curve properties. Applying a realistic SNIa cosmology analysis for redshifts z < 1, we recover the input cosmology parameters to within statistical uncertainties.

Barriers to Seeking Psychotherapy for Mental Health Problems in Adolescents: a mixed method study

Given the large discrepancy between prevalence rates of mental disorders in adolescents and actual treatment rates, there is a need to understand what prevents this age group from seeking psychotherapy. We assessed the barriers to seeking psychotherapy in this age group, including their attitudes toward people with a mental disorder, using a convergent parallel mixed-methods design. Participants were 288 adolescents aged 12–21 years (M = 16.8 years, SD = 2.3; 37% identified as male, 63% as female, and 0% as nonbinary). Qualitative data were obtained with open-ended questions on barriers to initiating psychotherapy and attitudes toward people with a mental disorder. Barriers to seeking psychotherapy and information about psychotherapy were assessed with a questionnaire using a quantitative design. The qualitative assessment revealed as the main barriers fear of a negative interaction with a psychotherapist, fear of being confronted with their own emotions, self-stigma, and fear of public stigma. Further, lack of accessibility, lack of trust in the therapist, a desire for social distance from, and a negative attitude toward people with a mental disorder were associated with a decreased intention to initiate psychotherapy. Previous positive experience with a psychotherapist was a facilitator of seeking psychotherapy. We found gender differences, with higher desire for social distance and higher optimism bias scores as well as poorer mental health knowledge for participants identifying as male. Integrating results from both approaches results in further information for the improvement of prevention programs and interventions to lower barriers to seeking psychotherapy. Gender differences indicate a need for gender-role-specific interventions.

Atomic modeling assessment of the interaction distance and effective bias for small defect clusters absorption at a void in BCC Fe

Cavity swelling plays a vital role in the microstructural evolution of ferritic-martensitic alloys under irradiation. Recent research reported that the classic thermal criterion of the critical size in cavity growth loses its explanatory power outside the intermediate temperature regimes. In contrast, the newly proposed bias-driven criterion performs nearly equivalently at the entire temperature range by introducing the concept of cavity bias. Molecular statics calculations were performed to determine the interaction energy landscape of single self-interstitial atom (SIA), single vacancy, di-SIA, di-vacancy, 7-SIA cluster, and 7-vacancy cluster interaction with a 59-vacancy void in BCC Fe. Additionally, molecular dynamics simulations were conducted to investigate the rotation and migration behavior of varying size SIA clusters in close proximity to the void. A homogenization method was established to describe the capture volume and mimic the one-dimensional diffusion behavior for large SIA clusters. The interaction distance between the void and defects was determined, and the effective bias was calculated as a function of the void size at 25 ℃, 300 ℃, and 500 ℃. The results provide important atomistic inputs for mesoscale modeling of defect evolution and microstructural changes, such as cluster dynamics simulation.

ALMA-IMF

Aims.Thanks to the high angular resolution, sensitivity, image fidelity, and frequency coverage of ALMA, we aim to improve our understanding of star formation. One of the breakthroughs expected from ALMA, which is the basis of our Cycle 5 ALMA-IMF Large Program, is the question of the origin of the initial mass function (IMF) of stars. Here we present the ALMA-IMF protocluster selection, first results, and scientific prospects.Methods.ALMA-IMF imaged a total noncontiguous area of ~53 pc2, covering extreme, nearby protoclusters of the Milky Way. We observed 15 massive (2.5 −33 × 103M⊙), nearby (2−5.5 kpc) protoclusters that were selected to span relevant early protocluster evolutionary stages. Our 1.3 and 3 mm observations provide continuum images that are homogeneously sensitive to point-like cores with masses of ~0.2M⊙and ~0.6M⊙, respectively, with a matched spatial resolution of ~2000 au across the sample at both wavelengths. Moreover, with the broad spectral coverage provided by ALMA, we detect lines that probe the ionized and molecular gas, as well as complex molecules. Taken together, these data probe the protocluster structure, kinematics, chemistry, and feedback over scales from clouds to filaments to cores.Results.We classify ALMA-IMF protoclusters as Young (six protoclusters), Intermediate (five protoclusters), or Evolved (four proto-clusters) based on the amount of dense gas in the cloud that has potentially been impacted by H IIregion(s). The ALMA-IMF catalog contains ~700 cores that span a mass range of ~0.15M⊙to ~250M⊙at a typical size of ~2100 au. We show that this core sample has no significant distance bias and can be used to build core mass functions (CMFs) at similar physical scales. Significant gas motions, which we highlight here in the G353.41 region, are traced down to core scales and can be used to look for inflowing gas streamers and to quantify the impact of the possible associated core mass growth on the shape of the CMF with time. Our first analysis does not reveal any significant evolution of the matter concentration from clouds to cores (i.e., from 1 pc to 0.01 pc scales) or from the youngest to more evolved protoclusters, indicating that cloud dynamical evolution and stellar feedback have for the moment only had a slight effect on the structure of high-density gas in our sample. Furthermore, the first-look analysis of the line richness toward bright cores indicates that the survey encompasses several tens of hot cores, of which we highlight the most massive in the G351.77 cloud. Their homogeneous characterization can be used to constrain the emerging molecular complexity in protostars of high to intermediate masses.Conclusions.The ALMA-IMF Large Program is uniquely designed to transform our understanding of the IMF origin, taking the effects of cloud characteristics and evolution into account. It will provide the community with an unprecedented database with a high legacy value for protocluster clouds, filaments, cores, hot cores, outflows, inflows, and stellar clusters studies.

Analysis of Depth Cameras for Proximal Sensing of Grapes.

This work investigates the performance of five depth cameras in relation to their potential for grape yield estimation. The technologies used by these cameras include structured light (Kinect V1), active infrared stereoscopy (RealSense D415), time of flight (Kinect V2 and Kinect Azure), and LiDAR (Intel L515). To evaluate their suitability for grape yield estimation, a range of factors were investigated including their performance in and out of direct sunlight, their ability to accurately measure the shape of the grapes, and their potential to facilitate counting and sizing of individual berries. The depth cameras’ performance was benchmarked using high-resolution photogrammetry scans. All the cameras except the Kinect V1 were able to operate in direct sunlight. Indoors, the RealSense D415 camera provided the most accurate depth scans of grape bunches, with a 2 mm average depth error relative to photogrammetric scans. However, its performance was reduced in direct sunlight. The time of flight and LiDAR cameras provided depth scans of grapes that had about an 8 mm depth bias. Furthermore, the individual berries manifested in the scans as pointed shape distortions. This led to an underestimation of berry sizes when applying the RANSAC sphere fitting but may help with the detection of individual berries with more advanced algorithms. Applying an opaque coating to the surface of the grapes reduced the observed distance bias and shape distortion. This indicated that these are likely caused by the cameras’ transmitted light experiencing diffused scattering within the grapes. More work is needed to investigate if this distortion can be used for enhanced measurement of grape properties such as ripeness and berry size.

Evaluating Distance Bias in Chronic Rhinosinusitis Outcomes

The distance traveled by patients for medical care is associated with patient outcomes (ie, distance bias) and is a limitation in outcomes research. However, to date, distance bias has not been examined in rhinologic studies. To evaluate the association of distance traveled by a cohort of patients with chronic rhinosinusitis with baseline disease severity and treatment outcomes. A total of 505 patients with chronic rhinosinusitis were prospectively enrolled in a multi-institutional, cross-sectional study in academic tertiary care centers between April 2011 and January 2020. Participants self-selected continued appropriate medical therapy or endoscopic sinus surgery. The 22-item Sinonasal Outcome Test (SNOT-22) and Medical Outcomes Study Short Form 6-D (SF-6D) health utility value scores were recorded at enrollment and follow-up. Data on the distances traveled by patients to the medical centers, based on residence zip codes, and medical comorbid conditions were collected. Distance traveled by patient to obtain rhinologic care. SNOT-22 and SF-6D scores. Scores for SNOT-22 range from 0 to 110; and for SF-6D, from 0.0 to 1.0. Higher SNOT-22 total scores indicate worse overall symptom severity. Higher SF-6D scores indicate better overall health utility; 1.0 represents perfect health and 0.0 represents death. The median age for the 505 participants was 56.0 years (IQR, 41.0-64.0 years), 261 were men (51.7%), 457 were White (90.5%), and 13 were Hispanic or Latino (2.6%). These categories were collected according to criteria described and required by the National Institutes of Health and therefore do not equal the entire cohort. Patients traveled a median distance of 31.6 miles (50.6 km) (IQR, 12.2-114.5 miles [19.5-183.2 km]). Baseline (r = 0.00; 95% CI, 0.00-0.18) and posttreatment (r = 0.01; 95% CI, -0.07 to 0.10) SNOT-22 scores, as well as baseline (r = -0.12; 95% CI, -0.21 to -0.04) and posttreatment (r = 0.07; 95% CI, -0.02 to 0.16) SF-6D scores, were not associated with distance. There was no clinically meaningful correlation between distance traveled and mean comorbidity burden. Nevertheless, patients with a history of endoscopic sinus surgery were more likely to travel longer distances to obtain care (Cliff delta = 0.28; 95% CI, 0.19-0.38). Although this cross-sectional study found that some patients appear more willing to travel longer distances for chronic rhinosinusitis care, results suggest that distance traveled to academic tertiary care centers was not associated with disease severity, outcomes, or comorbidity burden. These findings argue for greater generalizability of study results across various cohorts independent of distance traveled to obtain rhinologic care. ClinicalTrials.gov Identifier: NCT02720653.

The effects of MDR-TB Treatment Regimens through Socioeconomic and Spatial characteristics on Environmental-Health Outcomes: Evidence from Chinese Hospitals

This study examines socioeconomic and spatial factors and its influences on the outcomes of environmental-health through “multi-drug resistance tuberculosis” (MDR-TB) treatment regimes in China. For this purpose, a survival analysis is conducted by applying “multivariable Cox Proportional Hazard model” on secondary data starts from 2010 to 2019. The data set is consisting of six hundred and fifty five (655) TB patients from different hospitals of China. The findings of this study indicate us that there is no alteration in patient survival rates between the two treatment departments, hospital and ambulatory care. The spatial characteristics of time expenditure and medical expenditure are significantly correlated with “supporting distance bias methods” and “environmental health results”. On the other side, travel expenditures and male gender results show negatively associated with the “environmental-health outcome”. This study reveals with these remarks that Ministry of Health, China should take serious actions to control MDR-TB and launch a comprehensive policy with the help of WHO recommendation.

The SEDIGISM survey: The influence of spiral arms on the molecular gas distribution of the inner Milky Way

The morphology of the Milky Way is still a matter of debate. In order to shed light on uncertainties surrounding the structure of the Galaxy, in this paper, we study the imprint of spiral arms on the distribution and properties of its molecular gas. To do so, we take full advantage of the SEDIGISM (Structure, Excitation, and Dynamics of the Inner Galactic Interstellar Medium) survey that observed a large area of the inner Galaxy in the13CO (2–1) line at an angular resolution of 28′′. We analyse the influences of the spiral arms by considering the features of the molecular gas emission as a whole across the longitude–velocity map built from the full survey. Additionally, we examine the properties of the molecular clouds in the spiral arms compared to the properties of their counterparts in the inter-arm regions. Through flux and luminosity probability distribution functions, we find that the molecular gas emission associated with the spiral arms does not differ significantly from the emission between the arms. On average, spiral arms show masses per unit length of ~105–106M⊙kpc−1. This is similar to values inferred from data sets in which emission distributions were segmented into molecular clouds. By examining the cloud distribution across the Galactic plane, we infer that the molecular mass in the spiral arms is a factor of 1.5 higher than that of the inter-arm medium, similar to what is found for other spiral galaxies in the local Universe. We observe that only the distributions of cloud mass surface densities and aspect ratio in the spiral arms show significant differences compared to those of the inter-arm medium; other observed differences appear instead to be driven by a distance bias. By comparing our results with simulations and observations of nearby galaxies, we conclude that the measured quantities would classify the Milky Way as a flocculent spiral galaxy, rather than as a grand-design one.

Assessment and Improvement of Distance Measurement Accuracy for Time-of-Flight Cameras

Time-of-flight depth cameras are interesting sensors for contact-less 3-D metrology because they combine mechanical robustness with independence of ambient lighting conditions. Their actual performance depends on many factors and is hard to predict from data sheets. In this study, we investigate the deviations of the distance measurements of a high-end phase-based depth camera. We focus on the impact of: 1) self-warming and external temperature; 2) on range noise as a function of distance and acquisition time; and 3) on distance-dependent biases. We present the dedicated experimental setups comprising a climate chamber, a calibration bench with a reference interferometer, and a laser tracker that provides controlled conditions and ground-truth data. These setups allow investigating the absolute accuracy and mitigating repeatable distance biases by adapting the measurement model based on experimental data. For demonstration, we apply the investigation to two state-of-the-art industrial depth cameras of the same brand and type (Helios Lucid), showing significantly different response to external temperature but similar distance-dependent biases. We adapt the measurement model of one of the cameras for distance-dependent interpixel biases and demonstrate that the resulting parameters reduce also the distance biases of the other camera by about 80% to less than 1 mm at ranges of up to 1 m. This indicates the potential for batch error compensation. This article contributes to better understanding distance deviations of depth cameras and to improving the accuracy of such cameras.