Biased Distribution Research Articles

Evolution of Manufacturing Defects of 3D-Printed Thermoplastic Composites with Processing Parameters: A Micro-CT Analysis.

Owing to the melting and healing properties of thermoplastic resin, additive manufacturing or 3D printing is considered one of the most promising technologies for fiber-reinforced thermoplastic composites. However, manufacturing defects are still the main concern, which significantly limits the application of 3D-printed composite structures. To gain an insight into the effects of different processing parameters on the typical manufacturing defects, a micro-scale analysis was carried out via Micro-CT technology on the 3D-printed continuous carbon fiber-reinforced polylactic acid (PLA) composite specimens. The bias distribution of the fiber in the deposited filament was found. Moreover, when the feed rate of the filament was reduced from 100% to 50%, the a/b value was closer to 3.33, but the porosity increased from 7.077% to 25.352%. When the layer thickness was 0.2 mm, the increased nozzle pressure reduced the porosity but also increased the risk of fiber bundle breakage. The research provides an effective approach for analyzing the micro-structure of 3D printed composite structures and thus offers guidance for the processing control.

A-127 Bias and Precision of a NMR-based GFR Estimating Equation in Kidney Transplant Recipients

Abstract Background An accurate and reliable measurement of glomerular filtration rate (GFR) is essential to monitor for rejection and disease progression post-kidney transplantation. Measured GFR (mGFR) is the current gold standard. However, it has scarce availability at many centers and is labor intensive. Newer estimated GFR (eGFR) methods containing cystatin-C and/or other biomarkers have not been clinically validated in a post-transplant cohort. In this study, we evaluated a new NMR-based GFR (GFRNMR) equation that contains serum creatinine, cystatin C, myoinositol, and valine in post-kidney transplant recipients in a clinical routine setting. Methods Venipuncture for serum collection was performed immediately prior to mGFR measurement with urinary iothalamate clearance in 67 post-kidney transplant recipients. Serum was stored at 4°C and measured by NMR no later than four days after collection. eGFR was assessed using the following equations: i) GFRNMR, ii) CKD-EPI2021Cr (creatinine) and iii) CKD-EPI2021Cr,Cys (creatinine and cystatin-C). Bias was calculated as “eGFR—mGFR” for all equations. The Bootstrap method was employed to assess pairwise significance levels between bias distributions interquartile ranges (IQR), and P-value correction was determined with the Benjamini & Hochberg method. Precision, which was defined as percentage of samples within ±30% and ±15% from the mGFR value (P30 and P15, respectively), was calculated for each equation. Pairwise comparisons were made using McNemar’s Chi-squared and Benjamini & Hochberg correction. Precision was defined as the proportion of eGFR values concordant with mGFR values by CKD clinical stage. Results P30 for GFRNMR was significantly higher than CKD-EPI2021 Cr,Cys (97% vs 84%, P < 0.01) and higher than CKD-EPI2021 Cr (88%, P = 0.08). Similarly, P15 for GFRNMR was 63%; higher than for CKD-EPI2021 Cr (54%, P = 0.35) and CKD-EPI2021 Cr,Cys (51%, P = 0.19). Bias IQR for GFRNMR was 9.5 mL/min/1.73 m2 (median bias 2.0 mL/min/1.73 m2); significantly smaller than for CKD-EPI2021 Cr (Bias IQR 15.55 mL/min/1.73 m2, P < 0.05; median bias 1.47 mL/min/1.73 m2), and smaller than CKD-EPI2021 Cr,Cys (Bias IQR 14.4 mL/min/1.73 m2, P = 0.09; median bias 2.3 mL/min/1.73 m2). A broad range of medications such as immune modulators, and comorbidities such as hypertension, thyroid dysfunction, and coronary artery disease, did not interfere with GFRNMR performance. Conclusion NMR-based eGFR was less biased, more precise, and more accurate compared to creatinine and cystatin-C CKD-EPI eGFR equations in a post-kidney transplant setting. These findings suggest GFRNMR may more closely resemble mGFR in post-transplant patients and warrants further study on its potential use to improve clinical management in this patient group.

B-138 Skewed Demographics of Patient Population Contributes to Selection Biases in Clinical Study Population

Abstract Background The racial, ethnic and sex compositions of a study population may impact the clinical performance of a biomarker. In a prospective clinical study to evaluate biomarkers for asthmatic and allergic conditions in a large medical center in the upper Midwest, a largely White-alone patient population was encountered. Using several outreach strategies, potential eligible patients were contacted to enroll in a biomarker clinical study and to provide blood specimens. The patient demographics between the eligible candidates and the enrolled participants were compared. The analysis was carried out to understand the likelihood of enrollment in different race, ethnicity, and sex groups; and to identify factors that contribute to selection biases in enrolled cohorts. Methods Patients with eligible asthma and allergic conditions were selected based on relevant diagnostic codes in their electronic medical records, verified by clinical research coordinator. In accordance with Institutional Review Board approval, eligible patients were contacted via their electronic healthcare portal or with a phone call. The study information was also posted in an internal classified ad as an additional outreach strategy. Interested participants were then contacted a second time to complete consent. Upon consent, participants were considered as “enrolled”. Demographics information including sex, race, ethnicity, and age were de-identified for comparison analysis. Results Among all eligible patients, a disproportionally low representation of Blacks, Asians, and Hispanics (“non-White” groups) was observed, relative to regional demographics (22.2% non-White groups, 2021 census) and higher asthma and allergy prevalence in the non-White groups. In eligible adult patients (>18 years old) with asthma, only 13.4% were from non-White racial and ethnic groups. In eligible pediatric asthma patients, a larger proportion at 24.1% were from American Indian, Asian, Black, or Hispanic groups. In patients with allergic conditions, 10.3% of the adult patients were from non-White only groups, compared to 21.9% in pediatric patients. Among all eligible patients, White-alone patients were more likely to enroll in the study compared to their counterparts from non-White groups, further skewing the racial and ethnic distribution in the enrolled cohorts. As a result, less than 10% of enrolled adult patients were from non-White background in both allergy and asthma cohorts. Among the enrolled pediatric asthma and allergy patients, 16.9% and 10.3% respectively were from non-White groups. We also identified sex and age dependent enrollment behavior across different study cohorts. In adult groups, there were more female eligible patients (allergy: 66.7% female; asthma: 66.7% female) than male patients. Female adult patients were significantly more likely to participate, thus constituting 79.6% and 75.8% of the enrolled allergy and asthma cohorts. In the pediatric groups, male patients represented a larger share of eligible patients (allergy: 58.6%; asthma 64.7%). Female pediatric patients were still more likely to participate in the study. Hence, male and female enrolled patients were closer to equal distribution (allergy: 50.0% male; asthma 59.7% male). Conclusion The non-even distribution of demographics in eligible patients contributed to biased distribution in enrolled patients. Existing outreach approaches were not effective in reversing enrollment rates in underrepresented racial and ethnic groups.

Fighting selection bias in statistical learning: application to visual recognition from biased image databases

In practice, and especially when training deep neural networks, visual recognition rules are often learned based on various sources of information. On the other hand, the recent deployment of facial recognition systems with uneven performances on different population segments has highlighted the representativeness issues induced by a naive aggregation of the datasets. In this paper, we show how biasing models can remedy these problems. Based on the (approximate) knowledge of the biasing mechanisms at work, our approach consists in reweighting the observations, so as to form a nearly debiased estimator of the target distribution. One key condition is that the supports of the biased distributions must partly overlap, and cover the support of the target distribution. In order to meet this requirement in practice, we propose to use a low dimensional image representation, shared across the image databases. Finally, we provide numerical experiments highlighting the relevance of our approach.

Network equilibrium of battery electric vehicles considering drivers’ resting behavior

Driving fatigue cost is a major component of vehicle drivers’ travel costs in an intercity or regional network. The charging behavior of electric vehicle (EV) drivers, which is generally synchronized with drivers’ resting behavior, can contribute to the mitigation of drivers’ fatigue, especially after a prolonged driving period. Overlooking the impact of driving fatigue on the travel cost may overestimate the side-effects of the charging behavior for EV drivers, and result in biased flow and charging demand distribution. In this study, by considering the fatigue as part of the travel cost, we make the first attempt to investigate the impact of EV drivers’ charging and resting behaviors on their fatigue cost, and thus their travel plans and resultant flow distribution across the network. To this end, a novel network equilibrium modeling framework is first developed to capture the interaction among EV drivers’ travel plans, which specify the routing, recharging, and resting plans on a general intercity or regional network where charging stations and rest stops are deployed. When traveling between their origins and destinations, EV drivers are assumed to determine their travel plans to minimize their individual travel cost composed of driving time, rest time, charging cost, and fatigue cost, while preventing their batteries from being exhausted. The equilibrium model is then formulated as a variational inequality and transformed into a nonlinear optimization problem. An efficient solution algorithm integrating column generation and Benders decomposition approach is proposed to solve the established optimization problem. Numerical examples are presented to demonstrate the performance of the proposed models and solution algorithm. Numerical results validate that considering the impact of driving fatigue on the travel cost emphasizes the need for en-route charging for EV drivers with long-distance trips, and has an appreciable impact on the network flow and charging demand distribution. In addition, large-sized batteries and fast chargers may not necessarily reduce drivers’ travel costs for long-distance travel since charging behavior can be synchronized with drivers’ resting behavior and thus contribute to the mitigation of drivers’ fatigue.

Understanding the impact of along-transect resolution on acoustic surveys

Resolution can be defined as the minimum distance between two consecutive sampling points taken by an instrument. In acoustic surveys, the main parameter determining the resolution of sampling along a transect is the distance between successive echosounder transmissions or “pings”. An increase in either the time interval between pings or the speed of the vessel increases the inter-ping distance, hence decreasing the effective sampling resolution. This study investigated whether a loss in along-transect resolution affects the mean backscattered acoustic energy, leading to uncertainty and/or bias in abundance estimates. To this end, a real acoustic survey was echo-integrated, followed by the application of a systematic resampling scheme to simulate a decrease in pinging resolution. For each transect, the mean backscattered acoustic energy calculated at each resolution was compared with that at the original resolution. Transects were characterised according to their heterogeneity and spatial autocorrelation to investigate their effect on the relationship between abundance error and sampling resolution. Uncertainty was seen to increase with decreasing resolution, with higher heterogeneity and lower spatial autocorrelation accelerating the rise in imprecision. Although the mean bias across replicates was zero, the asymmetry of the bias distributions increased with decreasing resolution, leading to an increasing probability and magnitude of underestimation (https://aztigps.shinyapps.io/PingRateStudio/).

Situation-Aware Cluster and Quantization Level Selection Algorithm for Fast Federated Learning

In federated learning (FL), which clients and quantization levels are selected for the deep model parameters has a significant impact on learning time as well as learning accuracy. This is not a trivial issue because it is also significantly affected by factors such as computational power, communication capacity, and data distribution. Considering these factors, we formulate a joint optimization problem for clustering and selecting clusters with quantization levels. Due to the high complexity of the formulated problem, we propose a situation-aware cluster and quantization level selection (SITUA-CQ) algorithm. In this algorithm, the FL server first assembles clients into clusters to mitigate the impact of biased data distributions and determines the most suitable clusters and quantization levels based on their computing power and channel quality. Extensive simulation results show that SITUA-CQ can reduce the round time by up to 80.3% compared to conventional algorithms.

Stress Chain Analysis for an ABA Triblock Copolymer Using Principal Component Scores.

Researchers characterize the mechanical properties of ABA triblock copolymers by structures such as chain conformation. During elongation, bridge chains are stretched and act as a stress chain. Some loop chains also act as a stress chain because of the transmission of stress through an entanglement of loop chains. The stress chain, including the entangled loop chains, in an ABA triblock copolymer that exhibits a body-centered cubic structure was analyzed by principal component analysis (PCA), using the physical data for the B block obtained by coarse-grained molecular dynamics simulations. Local deformation of the A domains caused by the stress chains was also analyzed by PCA of the A block. The dynamics of the stress chain strongly corresponded to the recombination of the A domains; shrinkage because of domain breakage, replacement of stress chains, and biased stress distribution as well as its time dependence were observed.

Diagnosis and correction methods for spectral interference in the framework of LIBS imaging

Laser-Induced Breakdown Spectroscopy (LIBS) has become a powerful imaging technique for elemental characterization in analytical chemistry due to its advantages over other techniques. Major, minor, and trace elements are detected with high measurement dynamic, a low limit of detection and a high acquisition rate, allowing for the quick analysis of large sample surfaces. Today, chemometric tools are commonly used to ensure the most comprehensive and unbiased exploration of such spectroscopic data. However, the integration of the signal from a wavelength assumed to be specific to the element of interest remains the basic tool for generating a chemical distribution map from a hyperspectral dataset. This classical approach is based on a strong assumption, the specificity of the chemical information on the spectral domain being considered. Any spectral interference inevitably result in the generation of a biased distribution image. In this publication, we demonstrate how Principal Component Analysis (PCA) can diagnose the potential presence of a spectral interference and how Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) can ultimately correct it if necessary using a LIBS imaging dataset obtained from the analysis of a complex rock sample. The proposed approach combines the simplicity and effectiveness of the integration method with the diagnostic and correction capabilities of chemometric tools, providing a comprehensive solution for spectral interference in LIBS imaging.

Network feature-based phenotyping of leaf venation robustly reconstructs the latent space.

Despite substantial variation in leaf vein architectures among angiosperms, a typical hierarchical network pattern is shared within clades. Functional demands (e.g., hydraulic conductivity, transpiration efficiency, and tolerance to damage and blockage) constrain the network structure of leaf venation, generating a biased distribution in the morphospace. Although network structures and their diversity are crucial for understanding angiosperm venation, previous studies have relied on simple morphological measurements (e.g., length, diameter, branching angles, and areole area) and their derived statistics to quantify phenotypes. To better understand the morphological diversities and constraints on leaf vein networks, we developed a simple, high-throughput phenotyping workflow for the quantification of vein networks and identified leaf venation-specific morphospace patterns. The proposed method involves four processes: leaf image acquisition using a feasible system, leaf vein segmentation based on a deep neural network model, network extraction as an undirected graph, and network feature calculation. To demonstrate the proposed method, we applied it to images of non-chemically treated leaves of five species for classification based on network features alone, with an accuracy of 90.6%. By dimensionality reduction, a one-dimensional morphospace, along which venation shows variation in loopiness, was identified for both untreated and cleared leaf images. Because the one-dimensional distribution patterns align with the Pareto front that optimizes transport efficiency, construction cost, and robustness to damage, as predicted by the earlier theoretical study, our findings suggested that venation patterns are determined by a functional trade-off. The proposed network feature-based method is a useful morphological descriptor, providing a quantitative representation of the topological aspects of venation and enabling inverse mapping to leaf vein structures. Accordingly, our approach is promising for analyses of the functional and structural properties of veins.

LiteDEKR: End‐to‐end lite 2D human pose estimation network

AbstractThe 2D human pose estimation plays an important role in human‐computer interaction and action recognition. Although the method based on high‐resolution network has superior performance, there is still room for improvement in terms of speed and lightweight. Here, a LiteDEKR, a 2D pose estimation method that combines lightweight and accuracy, is proposed by designing a lightweight network based on DEKR and constructing two scientifically valid loss functions. The method, constructs a multi‐instance bias regression loss that matches the true distribution of keypoint bias, improves the accuracy of bias regression, and constructs a keypoint similarity loss with the object keypoint similarity index of keypoints as the optimization objective to achieve end‐to‐end training of the network. In addition, this paper has designed a lightweight DEKR, using LitePose as the backbone network. With the optimization of the above two loss functions, LiteDEKR not only achieves lightweight but also has high accuracy. Comparative experiments on the COCO and CrowdPose datasets show that compared to the current state‐of‐the‐art Contextual Instance Decoupling, LiteDEKR achieves a similar accuracy with only 10% of its network complexity. It also shows better robustness to low‐resolution input images.

ROCK2-RNA interaction map reveals multiple biological mechanisms underlying tumor progression in renal cell carcinoma.

Renal cell carcinoma (RCC) is the most common form of kidney cancer in adults. Despite new therapeutic modalities, the outcomes for RCC patients remain unsatisfactory. Rho-associated coiled-coil forming protein kinase 2 (ROCK2) has previously been shown to be upregulated in RCC, and its expression was negatively correlated with patient survival. However, the precise molecular function of ROCK2 has remained unclear. Herein, using RNA-seq analysis of ROCK2 knockdown and control cells, we identified 464 differentially expressed genes, and 1287 alternative splicing events in 786-O RCC cells. Furthermore, mapping of iRIP-seq reads in 786-O cells showed a biased distribution at 5' UTR, intronic and intergenic regions. By comparing ROCK2-regulated alternative splicing and iRIP-seq data, we found 292 overlapping genes that are enriched in multiple tumorigenic pathways. Taken together, our work defined a complex ROCK2-RNA interaction map on a genomic scale in a human RCC cell line, which deepens our understanding of the molecular function of ROCK2 in cancer development.

Dynamic Complex Network, Exploring Differential Evolution Algorithms from Another Perspective

Complex systems provide an opportunity to analyze the essence of phenomena by studying their intricate connections. The networks formed by these connections, known as complex networks, embody the underlying principles governing the system’s behavior. While complex networks have been previously applied in the field of evolutionary computation, prior studies have been limited in their ability to reach conclusive conclusions. Based on our investigations, we are against the notion that there is a direct link between the complex network structure of an algorithm and its performance, and we demonstrate this experimentally. In this paper, we address these limitations by analyzing the dynamic complex network structures of five algorithms across three different problems. By incorporating mathematical distributions utilized in prior research, we not only generate novel insights but also refine and challenge previous conclusions. Specifically, we introduce the biased Poisson distribution to describe the algorithm’s exploration capability and the biased power-law distribution to represent its exploitation potential during the convergence process. Our aim is to redirect research on the interplay between complex networks and evolutionary computation towards dynamic network structures, elucidating the essence of exploitation and exploration in the black-box optimization process of evolutionary algorithms via dynamic complex networks.

Investigating an extreme meteo-oceanographic event in the southern Brazil from in situ observations and modeling results

In March 2016, the passage of a cold front on the southern coast of Brazil generated extreme meteo-oceanographic conditions along the southern Brazilian coast. This work investigates the wave field evolution during this period from in situ observations and numerical modeling. The observations were obtained from high-frequency measurements performed in four sites along the coastline (Santos, Tramandaí, Rio Grande, and Cassino). The wavefield simulations were performed by coupling the Global Forecast System (GFS) reanalysis and the WAVEWATCH-III (WW3) ocean wave model. In a synoptic analysis was observed that not only the cold front caused the high wave in the area, but the interaction between the front post-high-pressure center and a low-pressure center generated a substantial pressure gradient area along the Brazilian coast with winds between 17.1 and 20.7 m s−1 reaching up to 24.4 m s−1. Model estimates confirm the presence of high waves between 6.0 and 6.5 m on the Southern coast of Brazil. Also, in the adjacent deep waters of the South Atlantic Ocean, highly simulated waves reached significant wave heights of 9 m. An analysis of the model input wind indicates that the GFS winds represented very well the trends and evolution of the wind registered by ocean buoys, with a small phase error. The t-test shows no significant differences in any buoys, with p-value = 0.73 and p-value = 0.78, with a significance level of 0.05.The best WW3 domain identified to simulate the wavefield off the coast of Southern do Brazil was 70°W - 4°W longitude and 55°S - 13°S latitude. Two buoys and two ondographic records in the area were used to evaluate the simulations in shallow water. In contrast, a reanalysis with satellite data assimilation was used to compare the simulations. In shallow water, the results for significant height of Wind and Swell Waves (SWH) show how the WW3 model slightly advances the maximum SWH time in relation to the buoys and ondographs records. This maximum SWH simulated by WW3 is in correspondence with the maximum GFS and records wind. The WW3 model underestimated the maximum SWH compared to the maximum buoys recorded and overestimated the maximum SWH compared to the maximum ondographs recorded. The Peak Period (Tp) simulations also showed an underestimate by the model in the two buoys and two ondograph locations. The BIAS distribution showed low values of the SWH in deep water between -1.2 m and 1.2 m. In general, in each analysis carried out, the t-test showed that there are no significant differences between the SWH and Tp simulated and the SWH and Tp records.In the analysis of the directions, it was observed that the strongest winds were recorded from the E direction for both Rio Grande (above 18 m s−1) and Santos (12-15 m s−1) buoys with frequency values of 27.8% and 12% respectively. As a reflection of the wind, predominant directions of E-SE waves were observed with high-frequency values in the Rio Grande (43.1%), Santos (54.2%), Cassino (67.4%), and Tramandaí (100%) records. The presence of waves with Tp (less than 12s) was observed in the records of buoys and wave gauges related to wind-wave. Only in the Santos buoy were observed waves with Tp higher than 12s (swell). The model showed a difference of 22.5° between the recorded wind and wave roses, and 45° between the recorded wind roses and the wave rose.

Accuracy of Non-Destructive Estimation of Length of Soil Nails

The effective length of soil nails is one of the critical parameters ensuring the reinforcing effect, and its accurate estimation is of great significance for the safety of the slope and deep foundation pit supporting projects. Traditional quality insurance methods, such as nail pullout tests, suffer from a series of drawbacks including being destructive, high cost, and time-consuming. In contrast, non-destructive testing (NDT) has been increasingly applied in various engineering fields in the past decades given its advantages of not damaging the material and easy operation. However, the current application of NDT in soil nail length measurement is relatively limited, and its accuracy and reliability are yet to be quantitatively evaluated. This paper introduces three methods for estimating soil nail length based on guided wave theory and collects 116 sets of NDT data for nail length. The accuracy of the NDT in soil nail prediction is statistically analyzed using the model bias method. The results show that those methods can accurately predict the nail length with an average error of less than 3% and a low dispersion of 12%. The accuracy of the NDT methods is not affected by the hammer type or estimation method. Furthermore, this paper proposes a model calibration to the original NDT method, which improves the model’s average accuracy by 3% and reduces dispersion by 4% without increasing computational complexity. Finally, the probability distributions of the model biases are characterized. This study can provide practical tools for fast estimation of in situ nail length, which is of high significance to supporting slopes and deep foundation pits.

Performance evaluation of multiple regional climate models to simulate rainfall in the Central Rift Valley Lakes Basin of Ethiopia and their selection criteria for the best climate model.

The historical datasets of five regional climate models (RCMs) available in the Coordinated Regional Downscaling Experiment (CORDEX)-Africa database are evaluated against ground-based observed rainfall in the Central Rift Valley Lakes Basin of Ethiopia. The evaluation is aimed at determining how well the RCMs reproduce monthly, seasonal, and annual cycles of rainfall and quantify the uncertainty between the RCMs in downscaling the same global climate model outputs. Root mean square, bias, and correlation coefficient are used to evaluate the ability of the RCM output. The multicriteria decision method of compromise programming was used to choose the best climate models for the climate condition of the Central Rift Valley Lakes subbasin. The Rossby Center Regional Atmospheric Model (RCA4) has downscaled ten global climate models (GCMs) and reproduces the monthly rainfall with a complex spatial distribution of bias and root mean square errors. The monthly bias varies in the range of - 35.8 to 189%. The summer (wet), spring, winter (dry), and annual rainfall varied within the range of 1.44 to 23.66%, - 7.08 to 20.04%, - 7.35 to 57%, and - 3.11 to 16.5%, respectively. To find the source of uncertainty, the same GCMs but downscaled by different RCMs were analyzed. The test results showed that each RCM differently downscaled the same GCM, and there was no single RCM model that consistently simulated the climate conditions over the stations in the study regions. However, the evaluation finds reasonable model skill in representing the temporal cycles of rainfall and suggests the use of RCMs where climate data is scarce after bias correction.

Democratic Ethos The Bane of Communal Conflicts in Nigeria

Previous studies have established traditional causes of communal conflicts. Conversely, this study established a link between democratic processes and communal conflicts in Nigeria. Through a three-component model focusing on various democratic processes, several democratic flaws were identified as root causes of communal conflicts. Specifically, we found a misalignment of democratic equity principles among warring communities, which surfaced because of differences in community hierarchies, structures, and personalities. A biased distribution of democratic dividends, unequal access to competent electoral staff, and extreme bias towards opposing communities in electoral/political institutions were observed. Moreover, some aggressive- frustrated residents were easily provoked because of a lack of confidence in democratic institutions and processes. It is therefore essential to focus on fairness in democratic processes to reduce tension in areas of communal conflict.

The relationship between the incidence of X-ray selected AGN in nearby galaxies & star-formation rate

ABSTRACT We present the identification and analysis of an X-ray selected AGN sample that lie within the local (z < 0.35) galaxy population. From a parent sample of 22 079 MPA-JHU (based on SDSS DR8) galaxies, we identified 917 galaxies with central, excess X-ray emission (from 3XMM-DR7) likely originating from an AGN. We measured the host galaxies’ star formation rates and classified them as either star-forming or quiescent based on their position relative to main sequence of star formation. Only 72 per cent of the X-ray selected sample were identified as AGN using BPT selection; this technique is much less effective in quiescent hosts, only identifying 50 per cent of the X-ray AGN. We also calculated the growth rates of the black holes powering these AGN in terms of their specific accretion rate (∝ LX/M*) and found quiescent galaxies, on average, accrete at a lower rate than star-forming galaxies. Finally, we measured the sensitivity function of 3XMM so we could correct for observational bias and construct probability distributions as a function of accretion rate. AGN were found in galaxies across the full range of star formation rates ($\log _{10} \, \mathrm{SFR/M_\odot \ yr^{-1}} = -3\ \mathrm{to}\ 2$) in both star-forming and quiescent galaxies. The incidence of AGN was enhanced by a factor 2 (at a 3.5σ significance) in star-forming galaxies compared to quiescent galaxies of equivalent stellar mass and redshift, but we also found a significant population of AGN hosted by quiescent galaxies.

Validation of FY-4A Temperature Profiles by Radiosonde Observations in Taklimakan Desert in China

The atmospheric temperature profiles (ATPs) retrieved through the geostationary Interferometric Infrared Sounder (GIIRS) onboard the FY-4A satellite (GIIRS/FY-4A) can effectively fill the gap of the scarce conventional sounding data in the Taklimakan Desert (TD), the second largest desert in the world, with an area of 330,000 square kilometers. In this study, we take the experimental radiosonde observations (RAOB) from one RAOB station in the hinterland of TD and seven conventional radiosondes in the oasis region around the desert as the true values and analyze the bias distribution characteristics of GIIRS/FY-4A ATPs with quality control (QC) flags 0 or 1 for this region. In addition, a bias comparison is made with GIIRS/FY-4A ATPs, and the fifth generation ECMWF atmospheric reanalysis of the global climate (ERA5) ATPs. The results show that (1) Missing measurements in GIIRS/FY-4A ATPs are the most frequent in the near-surface layer, accounting for more than 80% of all the retrieved grid points. The averaged total proportion of GIIRS/FY-4A ATPs with QC marks 0 or 1 is about 33.06%. (2) The root mean square error (RMSE) of GIIRS/FY-4A ATPs is less than 3 K, smaller than that of ERA5 ATPs. The RMSE of ERA5 ATPs can exceed 10 K in the desert hinterland. The absolute mean biases of GIIRS/FY-4A ATPs and ERA5 ATPs are, respectively, smaller than 3 K and 2 K, the former being slightly larger. The correlation coefficients of GIIRS/FY-4A ATPs with ERA5 ATPs and RAOB ATPs are higher than 0.98 and 0.99, respectively, and the correlation between GIIRS/FY-4A ATPs and RAOB ATPs is inferior to the latter. (3) The overall atmospheric temperature retrieved by GIIRS/FY-4A is 0.08 K higher than the temperature of RAOB, on average, while the overall temperature from ERA5 is 0.13 K lower than that of RAOB, indicating that the temperature profile obtained by integrating GIIRS/FY-4A ATPs and ERA5 ATPs may be much closer to RAOB ATPs. (4) The probability density of the GIIRS/FY-4A ATP biases in the TD region generally follows the Gaussian distribution so that it can be effectively assimilated in the 3-D variational assimilation modules. The probability density distribution characteristics of the GIIRS/FY-4A ATP biases in the desert hinterland and oasis are not much different. However, due to the fusion analysis of the relatively rich multi-source conventional observation data from the oasis stations, the probability density of ERA5 ATPs biases at the oasis stations is nearer to Gaussian distribution than that of the GIIRS/FY-4A ATPs. In the desert hinterland, where conventional observation is not enough, the probability density distributions of the ATPs biases from ERA5 and GIIRS/FY-4A are alike. Therefore, the GIIRS FY4A can contribute to a more accurate estimation of ERA5 ATPs in the TD region.

The biased distribution of existing information on biodiversity hinders its use in conservation, and we need an integrative approach to act urgently

The data collected by the Global Biodiversity Information Facility (GBIF), some 2.2 billion records, is arguably the largest international initiative to digitize and share primary biodiversity data. In this study, we examine the global distribution of completeness values discriminating those 30-minute cells that are likely to have reliable inventories for the most important terrestrial classes of Animalia and Plantae. The aim of this exploration is not only to show the biases and deficiencies in the biodiversity information collected so far, but also to estimate the climatic variability represented by these data in order to known their representativeness for conservation purposes. The results obtained show that information on biodiversity distribution is taxonomically and geographically biased towards regions and groups with more taxonomic resources and a longer naturalistic tradition. The amount of distributional data is very uneven across the different biological groups, and unrelated to the diversity they possess. The global patterns of completeness seem to be conditioned by the historical taxonomic, faunistic and floristic interest received by the different classes of organisms. In addition, well-surveyed global areas account for barely 1 % of global climate variability, leaving uncovered a large set of climatic conditions. All these results prevent us from relying exclusively on the available primary information on the distribution of organisms to identify biodiversity patterns and/or design conservation proposals. Given that the biodiversity crisis demands urgent action, biases and gaps in primary biodiversity information cannot be an excuse and conservation decisions must be made considering a broad set of criteria based on existing scientifically proven knowledge and techniques capable of providing the necessary answers.