Search

The stellar kinematics of low-mass galaxies are usually observed to be very unsmooth with significant kinematical fluctuations in small scales, which cannot be consistent with the projected centrosymmetric stellar kinematics obtained from commonly used dynamical models. In this work, we aim to test whether the high degree of kinematical fluctuations affects the dynamical mass estimate of galaxies. We use the asymmetry parameter η obtained from the 180° rotation self-subtraction of stellar kinematics of galaxies to quantify the degree of kinematical small-scale fluctuations. We use TNG50 numerical simulation to construct a large sample of mock galaxies with known total masses, and then obtained the virial dynamical mass estimator of these mock galaxies. We find that the dynamical masses within three-dimensional R e to the mock galaxy centres are overall averagely accurate within around 0.1 dex under the symmetric assumption, while R e means the projected circularized half-stellar mass radius in this work. We study the local virial mass estimation bias for mock galaxies of different η. The maximum bias difference of two η bins is around 0.16 dex, which with other local biases may help apply the observational virial mass estimators obtained from massive galaxies to other types of galaxies. We find that the Spearman's ρ of η with the intrinsic mass estimation deviations is near zero if the local bias is eliminated properly. The results indicate that even for low-mass galaxies, the existence of high degree of kinematical small-scale fluctuations does not affect the measurement of the dynamical mass of galaxies. [ABSTRACT FROM AUTHOR]

Background: The "woody clade" in Saxifragales (WCS), encompassing four woody families (Altingiaceae, Cercidiphyllaceae, Daphniphyllaceae, and Hamamelidaceae), is a phylogenetically recalcitrant node in the angiosperm tree of life, as the interfamilial relationships of the WCS remain contentious. Based on a comprehensive sampling of WCS genera, this study aims to recover a robust maternal backbone phylogeny of the WCS by analyzing plastid genome (plastome) sequence data using Bayesian inference (BI), maximum likelihood (ML), and maximum parsimony (MP) methods, and to explore the possible causes of the phylogenetic recalcitrance with respect to deep relationships within the WCS, in combination with molecular and fossil evidence. Results: Although the four WCS families were identically resolved as monophyletic, the MP analysis recovered different tree topologies for the relationships among Altingiaceae, Cercidiphyllaceae, and Daphniphyllaceae from the ML and BI phylogenies. The fossil-calibrated plastome phylogeny showed that the WCS underwent a rapid divergence of crown groups in the early Cretaceous (between 104.79 and 100.23 Ma), leading to the origin of the stem lineage ancestors of Altingiaceae, Cercidiphyllaceae, Daphniphyllaceae, and Hamamelidaceae within a very short time span (∼4.56 Ma). Compared with the tree topology recovered in a previous study based on nuclear genome data, cytonuclear discordance regarding the interfamilial relationships of the WCS was detected. Conclusions: Molecular and fossil evidence imply that the early divergence of the WCS might have experienced radiative diversification of crown groups, extensive extinctions at the genus and species levels around the Cretaceous/Paleocene boundary, and ancient hybridization. Such evolutionarily complex events may introduce biases in topological estimations within the WCS due to incomplete lineage sorting, cytonuclear discordance, and long-branch attraction, potentially impacting the accurate reconstruction of deep relationships. [ABSTRACT FROM AUTHOR]

Summary: The binomial proportion is a classic parameter with many applications and has also been extensively studied in the literature. By contrast, the reciprocal of the binomial proportion, or the inverse proportion, is often overlooked, even though it also plays an important role in various fields. To estimate the inverse proportion, the maximum likelihood method fails to yield a valid estimate when there is no successful event in the Bernoulli trials. To overcome this zero‐event problem, several methods have been introduced in the previous literature. Yet to the best of our knowledge, there is little work on a theoretical comparison of the existing estimators. In this paper, we first review some commonly used estimators for the inverse proportion, study their asymptotic properties, and then develop a new estimator that aims to eliminate the estimation bias. We further conduct Monte Carlo simulations to compare the finite sample performance of the existing and new estimators, and also apply them to handle the zero‐event problem in a meta‐analysis of COVID‐19 data for assessing the relative risks of physical distancing on the infection of coronavirus. [ABSTRACT FROM AUTHOR]

Satellite precipitation products (SPPs) have emerged as an alternative to estimate rainfall erosivity. However, prior studies showed that SPPs tend to underestimate rainfall erosivity but without reported bias-correction methods. This study evaluated the efficacy of two SPPs, namely, GPM_3IMERGHH (30-min and 0.1°) and GPM_3IMERGDF (daily and 0.1°), in estimating two erosivity indices in mainland China: the average annual rainfall erosivity (R-factor) and the 10-year event rainfall erosivity (10-yr storm EI), by comparing with that derived from gauge-observed hourly precipitation (Gauge-H). Results indicate that GPM_3IMERGDF yields higher accuracy than GPM_3IMERGHH, though both products generally underestimate these indices. The Percent Bias (PBIAS) is −55.48% for the R-factor and −56.38% for the 10-yr storm EI using GPM_3IMERGHH, which reduces to −10.86% and −32.99% with GPM_3IMERGDF. A bias-correction method was developed based on the systematic difference between SSPs and Gauge-H. A five-fold cross validation shows that with bias-correction, the accuracy of the R-factor and 10-yr storm EI for both SPPs improve considerably, and the difference between two SSPs is reduced. The PBIAS using GPM_3IMERGHH decreases to −0.06% and 0.01%, and that using GPM_3IMERGDF decreases to −0.33% and 0.14%, respectively, for the R-factor and 10-yr storm EI. The rainfall erosivity estimated with SPPs with bias-correction shows comparable accuracy to that obtained through Kriging interpolation using Gauge-H and is better than that interpolated from gauge-observed daily precipitation. Given their high temporal and spatial resolution, and timely updates, GPM_3IMERGHH and GPM_3IMERGDF are viable data products for rainfall erosivity estimation with bias correction. [ABSTRACT FROM AUTHOR]

The aims of this study were to investigate the congruence and discrepancy between Chinese therapist trainees' estimated client working alliance (WA) and their clients' actual WA rating, and how the congruence and discrepancy predicted client symptom outcome. Participants were 211 beginning therapist trainees and 1216 clients. Data from their 6888 sessions were analyzed using Truth and Bias Model and Response Surface Model. (i) Chinese trainees' estimation of client WA was on average significantly lower than actual client WA. (ii) At the between-person level, whether the trainee generally over- or underestimated client WA was not related to the client's initial symptom level or symptom improvement rate. (iii) At the within-person between-session level, a session where a trainee accurately perceived high client WA, compared to a session where the trainee accurately perceived low client WA, was followed by greater client symptom relief before the next session. In the case of estimation bias, a session where the trainee underestimated client WA was followed by greater client symptom reduction in the next session, rather than the other way around when the trainee overestimated client WA. Implications on therapist training were discussed. [ABSTRACT FROM AUTHOR]

Humans have the ability to rapidly extract summary statistics from object groupings through a specific capability known as ensemble coding. Previous literature has reported that this ability can become biased by prior perceptual experiences at the individual level. However, it remains unknown whether interpersonal prior information could also bias ensemble perception through a co-representation process. Experiment 1 found that participants' summary estimations were biased toward their co-actor's stimuli. Experiment 2 confirmed a causal relationship between the bias effect and the co-representation process by showing a reduction in biased estimation after pairing participants with an out-group partner. These findings extend the sources of prior information exploited by humans during perceptual average from individual-level information (i.e., self-tasks) to interpersonal-level information (i.e., co-actor's tasks). More specifically, interpersonal prior information is shown to act in a top-down and implicit manner, biasing ensemble perception. [ABSTRACT FROM AUTHOR]

Approximation of the value functions in value-based deep reinforcement learning induces overestimation bias, resulting in suboptimal policies. We show that when the reinforcement signals received by the agents have a high variance, deep actor-critic approaches that overcome the overestimation bias lead to a substantial underestimation bias. We first address the detrimental issues in the existing approaches that aim to overcome such underestimation error. Then, through extensive statistical analysis, we introduce a novel, parameter-free Deep Q-learning variant to reduce this underestimation bias in deterministic policy gradients. By sampling the weights of a linear combination of two approximate critics from a highly shrunk estimation bias interval, our Q-value update rule is not affected by the variance of the rewards received by the agents throughout learning. We test the performance of the introduced improvement on a set of MuJoCo and Box2D continuous control tasks and demonstrate that it outperforms the existing approaches and improves the baseline actor-critic algorithm in most of the environments tested. [ABSTRACT FROM AUTHOR]

Summary: Identifying risk factors that have significant explanatory power for the cross‐sectional asset returns is fundamental in asset pricing. We adopt a novel automatic debiased machine learning (ADML) method proposed by Chernozhukov, Newey, and Singh (2022) to robustly estimate partial pricing effect of a certain factor controlling for a large number of confounding factors under a nonlinear stochastic discount factor (SDF) assumption. The ADML resolves biased estimation, non‐robustness, and overfitting issues that are common to traditional machine learning approaches. We find that the most significant factors selected by the ADML outperform the Fama–French sparse factors and factors identified via the double‐selection LASSO method under a linear factor model assumption. Out of a high‐dimensional zoo of US stock market factors commonly tested in the finance literature, we identify approximately 30 to 50 factors having significant but declining pricing power in explaining the cross‐section of stock returns. Our findings are robust to hyperparameter settings and choices of test assets and machine learning methods. [ABSTRACT FROM AUTHOR]

The exponentiated Teissier distribution (ETD) offers an alternative for modeling survival data, taking into account flexibility in modeling data with increasing and decreasing hazard rate functions. The most popular method for parameter estimation of the ETD distribution is the maximum likelihood estimation (MLE). The MLE, on the other hand, is notoriously biased for its small sample sizes. We are therefore driven to generate virtually unbiased estimators for ETD parameters. More specifically, we focus on two methods of bias correction, bootstrapping and analytical approaches, to reduce MLE biases to the second order of bias. The performances of these approaches are compared through Monte Carlo simulations and two real-data applications. [ABSTRACT FROM AUTHOR]

Deep Q-networks, as a representation of several classical techniques, have emerged as one of the primary branches in the field of value function-based reinforcement learning. The paper addresses two issues that come up in the realm of reinforcement learning for value function solving: estimating bias and maximizing projected action value function evaluation. By treating the estimation of the highest expected action value as a random selection estimation problem, the suggested approach addresses the estimation bias issue from the standpoint of random selection. A random choice estimate procedure forms the basis of the technique. Firstly, a proposed random choice estimator is presented and its theoretical fairness is established. Second, the estimator is applied to create a reinforcement learning method in a different application. Two techniques, namely stochastic two-depth Q-networks and double-Q learning, are suggested based on the random choice estimation technique. The main parameters of the suggested algorithms are then investigated, and parameter formulas for both predictable and unpredictable scenarios are created. Lastly, a random choice estimation perspective suggests a stochastic two-depth Q-network. The new approach may effectively remove bias in value function estimate, enhance learning performance, and stabilise the learning process, according to simulation findings on Grid World and Atari games.

To obtain an accurate cosmological inference from upcoming weak lensing surveys such as the one conducted by Euclid, the shear measurement requires calibration using galaxy image simulations. As it typically requires millions of simulated galaxy images and consequently a substantial computational effort, seeking methods to speed the calibration up is valuable. We study the efficiency of different noise cancellation methods that aim at reducing the simulation volume required to reach a given precision in the shear measurement. The more efficient a method is, the faster we can estimate the relevant biases up to a required precision level. Explicitly, we compared fit methods with different noise cancellations and a method based on responses. We used GalSim to simulate galaxies both on a grid and at random positions in larger scenes. Placing the galaxies at random positions requires their detection, which we performed with SExtractor. On the grid, we neglected the detection step and, therefore, the potential detection bias arising from it. The shear of the simulated images was measured with the fast moment-based method KSB, for which we note deviations from purely linear shear measurement biases. For the estimation of uncertainties, we used bootstrapping as an empirical method. We extended the response-based approach to work on a wider range of shears and provide accurate estimates of selection biases. We find that each method we studied on top of shape noise cancellation can further increase the efficiency of calibration simulations. The improvement depends on the considered shear amplitude range and the type of simulations (grid-based or random positions). The response method on a grid for small shears provides the biggest improvement. Here the runtime for the estimation of multiplicative biases can be lowered by a factor of 145 compared to the benchmark simulations without any cancellation. In the more realistic case of randomly positioned galaxies, we still find an improvement factor of 70 for small shears using the response method. Alternatively, the runtime can be lowered by a factor of 7 already using pixel noise cancellation on top of shape noise cancellation. Furthermore, we demonstrate that the efficiency of shape noise cancellation can be enhanced in the presence of blending if entire scenes are rotated instead of individual galaxies. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

We propose two model selection criteria relying on the bootstrap approach, denoted by QAICb1 and QAICb2, in the framework of linear mixed models. Similar to the justification of Akaike Information Criterion (AIC), the proposed QAICb1 and QAICb2 are proved as asymptotically unbiased estimators of the Kullback–Leibler discrepancy between a candidate model and the true model. However, they are defined on the quasi-likelihood function instead of the likelihood and are proven to be asymptotically equivalent. The proposed selection criteria are constructed by the quasi-likelihood of a candidate model and a bias estimation term in which the bootstrap method is adopted to improve the estimation for the bias caused by using the candidate model to estimate the true model. The simulations across a variety of mixed model settings are conducted to demonstrate that the proposed selection criteria outperform some other existing model selection criteria in selecting the true model. Generalized estimating equations (GEE) are utilized to calculate QAICb1 and QAICb2 in the simulations. The effectiveness of the proposed selection criteria is also demonstrated in an application of Parkinson's Progression Markers Initiative (PPMI) data. [ABSTRACT FROM AUTHOR]

With the rapid progress of Global Navigation Satellite Systems (GNSS), integer ambiguity resolution has gradually become one of the key factors that affecting Precise Point Positioning (PPP) to achieve high-precision positioning results and fast convergence. Fortunately, the proposal of observable-specific code/phase biases (OSB) has greatly alleviated this problem tremendously. Different from obtaining OSB by dual-frequency linear combinations, this research proposed BeiDou Navigation Satellite System (BDS) OSB estimation method from raw observations. This method allows for more flexible estimation in a global network, without the need to compose the complex linear combinations when involving multi-frequency measurements processing. The BDS-3 observation data collected from 112 stations over one month (October 2022), containing B1C/B1I/B2a/B2b/B3I frequencies, were used for five-frequency phase OSBs estimation. Subsequently, static and kinematic PPP were executed utilizing two different ambiguity resolution strategies to corroborate the efficacy of this method. The results revealed that the BDS-3 five-frequency phase OSBs possessed good monthly stability, with standard deviation less than 0.3 cycle. High position accuracy was achieved in the static mode by fixing the ambiguity directly, with root mean square (RMS) of 3.0, 4.8 and 9.9 mm. Meanwhile, the ambiguity fixed rate was maintained at a high level around 96 % in multi-frequency tests. Simultaneously, the kinematic mode also presented the centimeter-level dynamic positioning results and the positioning accuracy improved by 55, 31 and 17 % in the East, North and Up components, respectively. Compared to the float solutions, the adoption of multi-frequency OSBs significantly accelerated the convergence speed by 55 % on average. [ABSTRACT FROM AUTHOR]

Mendelian randomization (MR) is an epidemiological framework using genetic variants as instrumental variables (IVs) to examine the causal effect of exposures on outcomes. Statistical methods based on unidirectional MR (UMR) are widely used to estimate the causal effects of exposures on outcomes in observational studies. To estimate the bidirectional causal effects between two phenotypes, investigators have naively applied UMR methods separately in each direction. However, bidirectional causal effects between two phenotypes create a feedback loop that biases the estimation when UMR methods are naively applied. To overcome this limitation, we proposed two novel approaches to estimate bidirectional causal effects using MR: BiRatio and BiLIML, which are extensions of the standard ratio, and limited information maximum likelihood (LIML) methods, respectively. We compared the performance of the two proposed methods with the naive application of UMR methods through extensive simulations of several scenarios involving varying numbers of strong and weak IVs. Our simulation results showed that when multiple strong IVs are used, the proposed methods provided accurate bidirectional causal effect estimation in terms of median absolute bias and relative median absolute bias. Furthermore, compared to the BiRatio method, the BiLIML method provided a more accurate estimation of causal effects when weak IVs were used. Therefore, based on our simulations, we concluded that the BiLIML should be used for bidirectional causal effect estimation. We applied the proposed methods to investigate the potential bidirectional relationship between obesity and diabetes using the data from the Multi-Ethnic Study of Atherosclerosis cohort. We used body mass index (BMI) and fasting glucose (FG) as measures of obesity and type 2 diabetes, respectively. Our results from the BiLIML method revealed the bidirectional causal relationship between BMI and FG in across all racial populations. Specifically, in the White/Caucasian population, a 1 kg/m2 increase in BMI increased FG by 0.70 mg/dL (95% confidence interval [CI]: 0.3517–1.0489; p = 8.43×10−5), and 1 mg/dL increase in FG increased BMI by 0.10 kg/m2 (95% CI: 0.0441–0.1640; p = 6.79×10−4). Our study provides novel findings and quantifies the effect sizes of the bidirectional causal relationship between BMI and FG. However, further studies are needed to understand the biological and functional mechanisms underlying the bidirectional pathway. [ABSTRACT FROM AUTHOR]

Quantitative real-time PCR of phylogenetic and functional marker genes is among the most commonly used techniques to quantify the abundance of microbial taxa in environmental samples. However, in most environmental applications, the approach is a rough assessment of population abundance rather than an exact absolute quantification method because of PCR-based estimation biases caused by multiple factors. Previous studies on these technical issues have focused on primer or template sequence features or PCR reaction conditions. However, how target gene sequence characteristics (e.g., evenness and dominance) in environmental samples affect qPCR quantifications has not been well studied. Here, we compared three primer sets targeting the beta subunit of the dissimilatory sulfite reductase (dsrB) to investigate qPCR quantification performance under different target gene sequence evenness and dominance conditions using artificial gBlock template mixtures designed accordingly. Our results suggested that the qPCR quantification performance of all tested primer sets was determined by the comprehensive effect of the target gene sequence evenness and dominance in environmental samples. Generally, highly degenerate primer sets have equivalent or better qPCR quantification results than a more target-specific primer set. Low template concentration in this study (~105 copies/L) will exaggerate the qPCR quantification results difference among tested primer sets. Improvements to the accuracy and reproducibility of qPCR assays for gene copy number quantification in environmental microbiology and microbial ecology studies should be based on prior knowledge of target gene sequence information acquired by metagenomic analysis or other approaches, careful selection of primer sets, and proper reaction conditions optimization. [ABSTRACT FROM AUTHOR]

Bycatch of cetaceans by European Union (EU) fleets is mostly monitored by onboard observers under the EU Data Collection Framework (DCF) programme. There is concern that the resulting estimates of cetacean bycatch mortality are biased and imprecise, because bycatch events are rare, sampling coverage is limited, and DCF observers prioritize the sampling of the discard of commercial species. Here, we explore the utility of the recently established Spanish cetacean bycatch monitoring programme to estimate cetacean bycatch mortality and compare it to the EU DCF programme. Data from October 2020 to September 2021 from this new Spanish programme were analysed to explore the following issues: (i) representativeness of the sampling frame; (ii) estimation of bycatch per unit of effort (BPUE); and (iii) sampling coverage. The probabilistic sampling design of the programme, with a record of refusals, has allowed identification and correction of biases in BPUE estimation. BPUE estimates for the bottom pair trawl fleet were recalculated to account for the fact that although boats work in pairs, each pair is monitored by a single observer located in a single boat. In relation to the sampling coverage, our results point to a need for levels up to 31% to achieve statistically robust bycatch rates. The current Spanish EU DCF at-sea sampling programme (which includes the sampling of bycatch of non-target protected species) and the Spanish (dedicated) cetacean bycatch monitoring programme provided similar results on cetacean bycatch for some of the ICES Divisions and Metiers monitored but sampling coverage of both is insufficient to obtain robust estimates. [ABSTRACT FROM AUTHOR]

Chlorophyll a concentration (Chl) is a key variable for estimating primary production (PP) through ocean-color remote sensing (OCRS). Accurate Chl estimates are crucial for better understanding of the spatio-temporal trends in PP in recent decades as a consequence of climate change. However, a number of studies have reported that currently operational chlorophyll a algorithms perform poorly in the Arctic Ocean (AO), largely due to the interference of colored and detrital material (CDM) with the phytoplankton signal in the visible part of the spectrum. To determine how and to what extent CDM biases the estimation of Chl, we evaluated the performances of eight currently available ocean-color algorithms: OC4v6, OC3Mv6, OC3V, OC4L, OC4P, AO.emp, GSM01 and AO.GSM. Our results suggest that the empirical AO.emp algorithm performs the best overall, but, for waters with high CDM acdm(443) > 0.067 m−1), a common scenario in the Arctic, the two semi-analytical GSM models yield better performance. In addition, sensitivity analyses using a spectrally and vertically resolved Arctic primary-production model show that errors in Chl mostly propagate proportionally to PP estimates, with amplification of up to 7%. We also demonstrate that, the higher level of CDM in relation to Chl in the water column, the larger the bias in both Chl and PP estimates. Lastly, although the AO.GSM is the best overall performer among the algorithms tested, it tends to fail for a significant number of pixels (16.2% according to the present study), particularly for waters with high CDM. Our results therefore suggest the ongoing need to develop an algorithm that provides reasonable Chl estimates for a wide range of optically complex Arctic waters. [ABSTRACT FROM AUTHOR]

Oblique bifactor models, where group factors are allowed to correlate with one another, are commonly used. However, the lack of research on the statistical properties of oblique bifactor models renders the statistical validity of empirical findings questionable. Therefore, the present study took the first step to examine the statistical properties of oblique bifactor models through Monte Carlo simulations. Study 1 showed that the classic oblique bifactor measurement models had severe convergence issues in many conditions. Even for converged replications, both factor loading and group factor correlation estimates were severely biased. Study 2 further showed that the classic oblique bifactor predictive models still had serious convergence problems, and structural parameters suffered from issues of severe estimation bias and low power. Fortunately, the augmentation approach, where one or multiple indicators are specified to load onto only the general factor but not any of the group factors, was useful in ameliorating these issues in both oblique bifactor measurement and predictive models. Tentative recommendations regarding the selection between oblique and orthogonal bifactor models and the approaches to finding augmenting indicators were provided. [ABSTRACT FROM AUTHOR]

The article discusses the potential use of renin as a biomarker in sepsis. Renin is a component of the renin-angiotensin system (RAS) and has been associated with sepsis outcomes in previous studies. The authors conducted a post hoc analysis of patients enrolled in a randomized controlled trial and found that higher renin levels were associated with increased 30-day mortality. However, further research is needed to fully understand the role of renin as a prognostic biomarker in sepsis. [Extracted from the article]

With the continuous development of global navigation satellite systems (GNSS) modernization, the code bias presented in the form of observation-specific biases (OSB) is more flexible and effective when processing modern multi-frequency and multi-GNSS signals, and therefore widely accepted by the GNSS community. However, the newly reported signal distortion biases (SDB) are ignored in the existing OSB estimation methods, which may affect the reliability of the final OSB estimation. In addition, the existing studies about SDB estimation are independent of OSB estimation, and thus a set of SDB products is generated independent of OSB products, which undoubtedly brings more complicated correction operations to GNSS users. In view of it, this study proposes an approach to code OSB estimation for multi-frequency and multi-GNSS arbitrary signals by reparameterization, which considers the SDB parameter. The aim of this study is to generate a set of code OSB products that take into account SDB parameters for the GNSS users to use. Due to considering the influence of SDB on raw code observations, the OSB estimation obtained by the proposed method is related to specific satellite-receiver pairs, which makes it different from the traditional OSB estimation methods. To achieve this, an extended model for multi-frequency and multi-GNSS arbitrary signal is first established based on the undifferenced and uncombined model, in which the SDB parameter is taken into account by re-parameterization. Based on the proposed model, three types of code bias that contain the OSB information can be obtained for the subsequent OSB estimation. Then, the three types of code bias in the first step are used as virtual observations for multi-frequency and multi-GNSS arbitrary signal OSB estimation through a two-step method. The proposed method is validated with a whole month of real measured data from 418 stations in MGEX and GA CORS to generate OSBs for GPS, GLONASS, and Galileo. The comparison between the estimated OSBs and the OSB products from CAS indicates the reliability of the proposed method. Besides, all types of OSB for four receiver groups are generated based on the proposed method to analyze the relationship between OSB and receiver groups. Results indicate that the estimated OSBs are related to receiver groups, and specific differences exist between different satellites and GNSS signals. It also proves that the OSB estimation is biased by the inconsistencies of the deviation in different receiver groups, which is more obvious for GLONASS. [ABSTRACT FROM AUTHOR]

The influence of chief executive officer (CEO) power on innovation has only briefly been the subject of study thus far creating a need for further exploration. The purpose of this research is to provide more evidence of the impact of CEO power on innovation as a business strategy. We also address the moderating effect that national culture has on the relationship between CEO power and innovation. The Thomson Reuters database provided the data for this research. The cohort of firms represents different countries, specifically, a sample of firms from 37 countries. To estimate the model, we used the generalised method of moments (GMM) procedure, an estimator that allows the researcher to control for unobservable heterogeneity and endogeneity. GMM also attenuates estimation bias. Our findings reveal that CEO power has a positive effect on innovation. In turn, the dimensions of national culture used here do not have the same moderating effect on the relationship between CEO power and innovation. Power distance and uncertainty avoidance negatively moderate the positive association between CEO power and innovation; individualism and indulgence reinforce the positive effect of CEO power on innovation; masculinity and long‐term orientation do not affect the relationship. [ABSTRACT FROM AUTHOR]

The ongoing growth of cities due to better job opportunities is leading to increased labour-related commuter flows in several countries. On the one hand, an increasing number of people commute and move to the cities, but on the other hand, the labour market indicates higher unemployment rates in urban areas than in the surrounding areas. We investigate this phenomenon on regional level by an alternative definition of unemployment rates in which commuting behaviour is integrated. We combine data from the Labour Force Survey with dynamic mobile network data by small area models for the federal state North Rhine-Westphalia in Germany. From a methodical perspective, we use a transformed Fay–Herriot model with bias correction for the estimation of unemployment rates and propose a parametric bootstrap for the mean squared error estimation that includes the bias correction. The performance of the proposed methodology is evaluated in a case study based on official data and in model-based simulations. The results in the application show that unemployment rates (adjusted by commuters) in German cities are lower than traditional official unemployment rates indicate. [ABSTRACT FROM AUTHOR]

We provide evidence that suggests that a reduction in outside wage options reduces wage increases in retained jobs. We use the natural experiment of a reform that reduced outside wage options for employees in deregulated crafts occupations in comparison to employees in not reformed crafts occupations. To avoid estimation biases from general reform effects on wages, we concentrate on employees active in crafts occupations who worked for employers in the industry and commerce sectors and exclude employees in the crafts sector. Four years after the reform, the wages of treated employees in deregulated crafts were 5 per cent lower than wages of employees in not reformed occupations (control group). The reform, therefore, led to wage differentiation between comparable employees. The wage effects are concentrated in employers with high general wage increases after the reform and they can be found even at individual employers. [ABSTRACT FROM AUTHOR]

Background: Several approaches are commonly used to estimate the effect of diet on changes of various intermediate disease markers in prospective studies, including "change-score analysis", "concurrent change-change analysis" and "lagged change-change analysis". Although empirical evidence suggests that concurrent change-change analysis is most robust, consistent, and biologically plausible, in-depth dissection and comparison of these approaches from a causal inference perspective is lacking. We intend to explicitly elucidate and compare the underlying causal model, causal estimand and interpretation of these approaches, intuitively illustrate it with directed acyclic graph (DAG), and further clarify strengths and limitations of the recommended concurrent change-change analysis through simulations. Methods: Causal model and DAG are deployed to clarify the causal estimand and interpretation of each approach theoretically. Monte Carlo simulation is used to explore the performance of distinct approaches under different extents of time-invariant heterogeneity and the performance of concurrent change-change analysis when its causal identification assumptions are violated. Results: Concurrent change-change analysis targets the contemporaneous effect of exposure on outcome (measured at the same survey wave), which is more relevant and plausible in studying the associations of diet and intermediate biomarkers in prospective studies, while change-score analysis and lagged change-change analysis target the effect of exposure on outcome after one-period timespan (typically several years). Concurrent change-change analysis always yields unbiased estimates even with severe unobserved time-invariant confounding, while the other two approaches are always biased even without time-invariant heterogeneity. However, concurrent change-change analysis produces almost linearly increasing estimation bias with violation of its causal identification assumptions becoming more serious. Conclusions: Concurrent change-change analysis might be the most superior method in studying the diet and intermediate biomarkers in prospective studies, which targets the most plausible estimand and circumvents the bias from unobserved individual heterogeneity. Importantly, careful examination of the vital identification assumptions behind it should be underscored before applying this promising method. [ABSTRACT FROM AUTHOR]

In this study, a similarity coefficient generation code system was established using the Monte Carlo Code for Advanced Reactor Design (McCARD) transport code and the MIG multi-correlated input sampling code. We considered the adjoint-based sensitivity and uncertainty (S/U) and stochastic sampling (S.S.) approaches to the generation of the ck similarity coefficient. To examine the code system, the ck similarity coefficients of 23 relevant critical experiments and the System-Integrated Modular Advanced Reactor (SMART) small modular reactor (SMR) target system were generated using ENDF/B-VII.1 covariance data with the Los Alamos National Laboratory (LANL) 30-group energy group structure. Our results show that the similarity coefficients between the 16 LEU thermal-spectrum-based critical experiments and SMART are more than 0.90, which is the recommended criterion of the U.S. Nuclear Regulatory Commission (NRC). These results are very helpful for licensees and can be used to justify the determination of critical experiment benchmarks for computational bias estimations of the SMART target system. To examine the discrepancy in the similarity coefficient, ck, due to the covariance data, similarity analyses for a 24 × 24 benchmark matrix were performed using ENDF-VIII.0, JENDL-5.0, and JEFF-3.3 covariance data. The results show that the selection of the covariance data used for ck generation significantly impacts the similarity coefficient. Moreover, it was observed that the current results for the SCALE 6.1 covariance data show a consistent trend with the results reported in earlier studies. [ABSTRACT FROM AUTHOR]

Background: Surrogate endpoints, such as those of interest in chronic kidney disease (CKD), are often evaluated using Bayesian meta-regression. Trials used for the analysis can evaluate a variety of interventions for different sub-classifications of disease, which can introduce two additional goals in the analysis. The first is to infer the quality of the surrogate within specific trial subgroups defined by disease or intervention classes. The second is to generate more targeted subgroup-specific predictions of treatment effects on the clinical endpoint. Methods: Using real data from a collection of CKD trials and a simulation study, we contrasted surrogate endpoint evaluations under different hierarchical Bayesian approaches. Each approach we considered induces different assumptions regarding the relatedness (exchangeability) of trials within and between subgroups. These include partial-pooling approaches, which allow subgroup-specific meta-regressions and, yet, facilitate data adaptive information sharing across subgroups to potentially improve inferential precision. Because partial-pooling models come with additional parameters relative to a standard approach assuming one meta-regression for the entire set of studies, we performed analyses to understand the impact of the parameterization and priors with the overall goals of comparing precision in estimates of subgroup-specific meta-regression parameters and predictive performance. Results: In the analyses considered, partial-pooling approaches to surrogate endpoint evaluation improved accuracy of estimation of subgroup-specific meta-regression parameters relative to fitting separate models within subgroups. A random rather than fixed effects approach led to reduced bias in estimation of meta-regression parameters and in prediction in subgroups where the surrogate was strong. Finally, we found that subgroup-specific meta-regression posteriors were robust to use of constrained priors under the partial-pooling approach, and that use of constrained priors could facilitate more precise prediction for clinical effects in trials of a subgroup not available for the initial surrogacy evaluation. Conclusion: Partial-pooling modeling strategies should be considered for surrogate endpoint evaluation on collections of heterogeneous studies. Fitting these models comes with additional complexity related to choosing priors. Constrained priors should be considered when using partial-pooling models when the goal is to predict the treatment effect on the clinical endpoint. [ABSTRACT FROM AUTHOR]

Research on dynamic treatment regimes has enticed extensive interest. Many methods have been proposed in the literature, which, however, are vulnerable to the presence of misclassification in covariates. In particular, although Q‐learning has received considerable attention, its applicability to data with misclassified covariates is unclear. In this article, we investigate how ignoring misclassification in binary covariates can impact the determination of optimal decision rules in randomized treatment settings, and demonstrate its deleterious effects on Q‐learning through empirical studies. We present two correction methods to address misclassification effects on Q‐learning. Numerical studies reveal that misclassification in covariates induces non‐negligible estimation bias and that the correction methods successfully ameliorate bias in parameter estimation. [ABSTRACT FROM AUTHOR]

To inform environmental policy and practice, researchers estimate effects of interventions/exposures by conducting primary research (e.g., impact evaluations) or secondary research (e.g., evidence reviews). If these estimates are derived from poorly conducted/reported research, then they could misinform policy and practice by providing biased estimates. Many types of bias have been described, especially in health and medical sciences. We aimed to map all types of bias from the literature that are relevant to estimating causal effects in the environmental sector. All the types of bias were initially identified by using the Catalogue of Bias (catalogofbias.org) and reviewing key publications (n = 11) that previously collated and described biases. We identified 121 (out of 206) types of bias that were relevant to estimating causal effects in the environmental sector. We provide a general interpretation of every relevant type of bias covered by seven risk-of-bias domains for primary research: risk of confounding biases; risk of post-intervention/exposure selection biases; risk of misclassified/mismeasured comparison biases; risk of performance biases; risk of detection biases; risk of outcome reporting biases; risk of outcome assessment biases, and four domains for secondary research: risk of searching biases; risk of screening biases; risk of study appraisal and data coding/extraction biases; risk of data synthesis biases. Our collation should help scientists and decision makers in the environmental sector be better aware of the nature of bias in estimation of causal effects. Future research is needed to formalise the definitions of the collated types of bias such as through decomposition using mathematical formulae. [ABSTRACT FROM AUTHOR]

We didactically derive a correlated traits correlated (methods – 1) [CTC(M – 1)] multitrait‐multimethod (MTMM) model for dyadic round‐robin data augmented by self‐reports. The model is an extension of the CTC(M – 1) model for cross‐classified data and can handle dependencies between raters and targets by including reciprocity covariance parameters that are inherent in augmented round‐robin designs. It can be specified as a traditional structural equation model. We present the variance decomposition as well as consistency and reliability coefficients. Moreover, we explain how to evaluate fit of a CTC(M – 1) model for augmented round‐robin data. In a simulation study, we explore the properties of the full information maximum likelihood estimation of the model. Model (mis)fit can be quite accurately detected with the test of not close fit and dynamic root mean square errors of approximation. Even with few small round‐robin groups, relative parameter estimation bias and coverage rates are satisfactory, but several larger round‐robin groups are needed to minimize relative parameter estimation inaccuracy. Further, neglecting the reciprocity covariance‐structure of the augmented round‐robin data does not severely bias the remaining parameter estimates. All analyses (including data, R scripts, and results) and the simulation study are provided in the Supporting Information. Implications and limitations are discussed. [ABSTRACT FROM AUTHOR]

Müller-Lyer (ML) figures bias size estimation consistently, yet different methods can lead to different degrees of illusory bias. Autistic individuals may also be less likely to perceive illusory biases with varying levels of autistic trait expression proposed to modulate reported illusory biases. The Autism-Spectrum Quotient (AQ) and Systemizing Quotient (SQ) are self-report measures that quantify autistic trait expression and systemizing ability in neurotypical individuals. The current study sought to determine if perceptions of illusory size bias negatively correlate with autistic trait expression and the extent to which varying methods of illusion presentation change the magnitude of illusory bias. Thirty neurotypical adults completed both questionnaires as well as four size estimation tasks. Two tasks involved perceptual discrimination of ML figures by concurrent and successive presentation, where participants selected the longer figure by keypress. For Tasks 3 and 4, participants adjusted the size of a non-illusory line (Task 3) or complementary illusory figure (Task 4) to match the perceived length. Overall, task performance was not correlated with autistic trait expression. One exception was a negative correlation with AQ when adjusting a complementary illusory ML figure in Task 4. Illusory biases were also stronger when two illusory figures were presented concurrently. Given these results, illusion susceptibility to the ML is suggested to be reduced with increases in AQ, but only when the method of illusion measurement is adjustment of concurrent illusory figures. Taken together the results provide evidence that traits associated with autism in a neurotypical population may systematically modulate perception. [ABSTRACT FROM AUTHOR]

We propose a framework to directly estimate the gradient in multivariate nonparametric regression models that bypasses fitting the regression function. Specifically, we construct the estimator as a linear combination of adjacent observations with the coefficients from a vector‐valued difference sequence, so it is more flexible than existing methods. Under the equidistant designs, closed‐form solutions of the optimal sequences are derived by minimizing the estimation variance, with the estimation bias well controlled. We derive the theoretical properties of the estimators and show that they achieve the optimal convergence rate. Further, we propose a data‐driven tuning parameter‐selection criterion for practical implementation. The effectiveness of our estimators is validated via simulation studies and a real data application. [ABSTRACT FROM AUTHOR]

Track‐to‐track association methods based on machine learning and deep learning have greatly improved the association results, but the scope of application is limited by the poor interpretability and manual association labelling. To enhance the interpretability of the neural networks, enhance the credibility of association decisions, and reduce the consumption for labelling associated track pairs, the authors estimate and counteract radar bias by homography estimation to achieve track‐to‐track association. The proposed model is composed of a mixing extraction module and a homography estimation module. Aiming at the interaction of temporal and spatial features of tracks, the spatial‐temporal mixing features are extracted by a mixing extraction module. Focusing on attaining explainable discriminant factors, the homography matrix is generated by the homography estimation module. Targeting at the unsupervised learning, the radar bias and association matrix are estimated jointly so that the labelled track association pairs are not demanded. Finally, a track from one radar is transformed into the other radar, and the homography matrix that counteracts the radar bias can provide explainable discriminant factors and make the association decision more credible. Extensive experiments demonstrated that the proposed method can achieve better association results and the association results can be well interpreted. [ABSTRACT FROM AUTHOR]

Purpose: The aim is to study the impact of schooling on between-groups wage inequality beyond the lens of the standard approach in the literature. Design/methodology/approach: Simple econometric theory is used to make the main point of the paper. Supporting empirical evidence is also presented. Findings: Disregarding the persistence of current earnings implies a bias in the estimation of the wage return to schooling both at labour-market entry and in the rest of the working life. Research limitations/implications: The use of current earnings as a dependent variable in wage-schooling models may be problematic and requires specific handling. Social implications: The impact of schooling on the between-groups dimension of wage inequality may be different than previously thought. Originality/value: The paper is the first to show that, when current earnings are used as a dependent variable, the identification of a wage-schooling model with the standard (time-invariant external instrument-variable) approach may lead to misleading conclusions. [ABSTRACT FROM AUTHOR]

We consider a situation where the distribution of a random variable is being estimated by the empirical distribution of noisy measurements of that variable. This is common practice in, for example, teacher value-added models and other fixed-effect models for panel data. We use an asymptotic embedding where the noise shrinks with the sample size to calculate the leading bias in the empirical distribution arising from the presence of noise. The leading bias in the empirical quantile function is equally obtained. These calculations are new in the literature, where only results on smooth functionals such as the mean and variance have been derived. We provide both analytical and jackknife corrections that recenter the limit distribution and yield confidence intervals with correct coverage in large samples. Our approach can be connected to corrections for selection bias and shrinkage estimation and is to be contrasted with deconvolution. Simulation results confirm the much-improved sampling behavior of the corrected estimators. An empirical illustration on heterogeneity in deviations from the law of one price is equally provided. [ABSTRACT FROM AUTHOR]

The mechanical behavior of block-in-matrix materials is heavily dependent on their block content. This parameter is in most cases obtained through visual analyses of the ground through digital imagery, which provides the areal block proportion (ABP) of the area analyzed. Nowadays, computer vision models have the capability to extract knowledge from the information stored in these images. In this research, we analyze and compare classical feature-detection algorithms with state-of-the-art models for the automatic calculation of the ABP parameter in images from surface and underground outcrops. The outcomes of this analysis result in the development of a framework for ABP calculation based on the Segment Anything Model (SAM), which is capable of performing this task at a human level when compared with the results of 32 experts in the field. Consequently, this model can help reduce human bias in the estimation of mechanical properties of block-in-matrix materials as well as contain underground technical problems due to mischaracterization of rock block quantities and dimensions. The methodology used to obtain the ABP at different outcrops is combined with estimates of the rock matrix properties and other characterization techniques to mechanically characterize the block-in-matrix materials. The combination of all these techniques has been applied to analyze, understand and try, for the first time, to model Roman gold-mining strategies in an archaeological site in NW Spain. This mining method is explained through a 2D finite-element method numerical model. [ABSTRACT FROM AUTHOR]

While satellite-derived global vegetation structure products are powerful and easy to use, their utility for studying spatial patterns within heterogeneous landscapes such as forest-savanna mosaics has not been extensively evaluated. We explored the application of global vegetation structure products in heterogeneous landscapes by comparing them with Airborne Laser Scanning. Specifically, we assessed the accuracy and bias of two fractional cover products, MODIS Vegetation Continuous Fields (VCF) and Hansen Global Forest Change (GFC), and one global canopy height model, Global Forest Canopy Height Model (CHM), in comparison with the same variables derived from local ALS point clouds. We found that there were limitations to all three products. MODIS VCF was less accurate than its reported accuracy by at least 3%, and GFC was 10% less accurate than MODIS VCF. While Global CHM had a similar magnitude of error to its reported product accuracy, product agreement was much lower (R2 0.19 vs. R2 0.61). We also found that the context of the analysis is important when choosing whether to use one fractional product over the other. Global products should be applied with caution in heterogeneous landscapes. Increased training and validation from these landscapes could improve the performance of these products and their utility for landscape-scale ecological research. [ABSTRACT FROM AUTHOR]

The sparse regularization (SReg) model has been prove to be an effective tool for the structural damage identification. However, the SReg model overly penalizes the larger components in the damage parameter leading to extra estimation bias, and it ignores the similarity information among different measurements that is useful for improving the performance of damage identification. To further improve the accuracy of damage identification, this study proposes a joint fraction function regularization model by jointing multiple fraction function regularization models, where fraction function regularizers are utilized to overcome the excessive penalty drawback, and the similarity of different measurements is employed through a data fusion technique. The numerical and experimental study show that, compared with the previous SReg models, the damage identification errors of the proposed model are reduced by 4.15% and 2.12% on average, respectively. [ABSTRACT FROM AUTHOR]

In this study, a distributed primal‐dual bandit feedback method for online convex optimization with time‐varying coupled inequality constraints on unbalanced directed graphs is proposed. A multiagent network is considered in which agents exchange the estimations of the dual optimizer and the scaling variable with their neighbors. The scaling variable is used to resolve the bias of the estimations caused by a directed communication network. Each agent does not have prior knowledge of the loss function, and its value at a queried point is sequentially disclosed to each agent. Each agent performs a projected subgradient‐based primal‐dual algorithm to estimate the optimal solution. It is confirmed that both the expected dynamic regret of the loss function and the cumulative error of the constraint violation achieve sublinearity using the proposed online algorithm with the two‐point bandit feedback. [ABSTRACT FROM AUTHOR]

Recently, Abbott Diagnostics marketed a new generation of Alinity enzyme assays, introducing a multiparametric calibrator [Consolidated Chemistry Calibrator (ConCC)] in place of or in addition to factor-based calibrations. For alkaline phosphatase (ALP), both calibration options are offered, i.e., with ConCC (ALP2) and with an experimental calibration factor (ALP2F). Both options are declared traceable to the 2011 IFCC reference measurement procedure (RMP). Before to replace the old generation (ALP1) with the new one, we decided to validate the trueness of ALP2/ALP2F. Three approaches were employed: (a) preliminary comparison on 48 native frozen serum samples with ALP1, of which traceability to RMP was previously successfully verified; (b) examination of three banked serum pools (BSP) with values assigned by RMP; (c) direct comparison with RMP on a set of 24 fresh serum samples. Bias estimation and regression studies were performed, and the standard measurement uncertainty associated with ALP measurements on clinical samples (uresult) was estimated and compared with established analytical performance specifications (APS). ConCC commutability was also assessed. A positive proportional bias was found with both ALP2 and ALP2F when compared to ALP1 and RMP. This positive bias was confirmed on BSP: in average, +13.1 % for ALP2 and +10.0 % for ALP2F, respectively. uresult were 13.28 % for ALP2 and 10.04 % for ALP2F, both not fulfilling the minimum APS of 4.0 %. Furthermore, ConCC was not commutable with clinical samples. Our results unearth problems in the correct implementation of traceability of Alinity ALP2/ALP2F, with the risk for the new assay to be unfit for clinical purposes. [ABSTRACT FROM AUTHOR]

With the development of electronic game technology, the content of electronic games presents a larger number of units, richer unit attributes, more complex game mechanisms, and more diverse team strategies. Multi-agent deep reinforcement learning shines brightly in this type of team electronic game, achieving results that surpass professional human players. Reinforcement learning algorithms based on Q-value estimation often suffer from Q-value overestimation, which may seriously affect the performance of AI in multi-agent scenarios. We propose a multi-agent mutual evaluation method and a multi-agent softmax method to reduce the estimation bias of Q values in multi-agent scenarios, and have tested them in both the particle multi-agent environment and the multi-agent tank environment we constructed. The multi-agent tank environment we have built has achieved a good balance between experimental verification efficiency and multi-agent game task simulation. It can be easily extended for different multi-agent cooperation or competition tasks. We hope that it can be promoted in the research of multi-agent deep reinforcement learning. [ABSTRACT FROM AUTHOR]

Cohen's d – a common effect size – contains a positive bias. The traditional bias correction, based on strict distribution assumption, does not always work for a small study with limited data. The non-parametric bootstrapping is not limited by distribution assumption and can be used to remove the bias in Cohen's d. A real example is included to illustrate the implementation of bootstrap bias estimation and the removal of sizable bias in Cohen's d. [ABSTRACT FROM AUTHOR]

Satellite Laser Ranging (SLR) has become an invaluable core technique in numerous geodetic applications. SLR measurements to passive spherical satellites essentially contribute to the determination of geocenter coordinates and global scale in the International Terrestrial Reference Frame (ITRF) realizations. In addition, SLR measurements to active satellites in Low Earth Orbit (LEO) are up to now mostly used for an independent validation of orbit solutions, usually derived by microwave tracking techniques based on Doppler Orbitography and Radiopositioning Integrated by Satellite (DORIS) or Global Navigation Satellite Systems (GNSS). This allows for the analysis of systematic orbit errors (e.g., originating from poorly known non-gravitational perturbations or sensor offsets) not only in the radial direction (key to satellite altimetry missions), but in three dimensions. Major obstacles to reach the millimeter accuracy and stability goals of 0.1 mm / y (at decadal time scales) of the Global Geodetic Observing System (GGOS) are station-satellite measurement biases and on-ground/board coordinate offsets. Among the observatories of the International Laser Ranging Service (ILRS) a large diversity of measurement qualities exists, and the calibration of station-dependent errors is now necessary to further exploit SLR data for present and future climate-driven needs. We demonstrate that the analysis of SLR data to active LEO satellites equipped with DORIS or GNSS receivers is a promising means to characterize SLR biases and their stability. Using two independent selections of Earth observation missions in LEOs (consisting of six altimetry, three magnetic field and two gravity field satellites) with three different analysis software packages (Bernese, ZOOM, Napeos), we estimate SLR range biases for all involved tracking stations on a yearly basis. We find that for many of the stations independently estimated sets of biases agree on a few-mm level and that the inclusion of satellites from multiple missions allows rendering the bias estimation more robust and in particular less prone to geographically correlated orbit errors. This shows that microwave-derived orbits of active LEO satellites, nowadays of very high quality due to numerous advances in modeling and analysis techniques, can serve as interesting sources for SLR station calibration in demanding climate applications. [ABSTRACT FROM AUTHOR]

Recent advances in deep learning frameworks have established valuable tools for analyzing the long-timescale behavior of complex systems, such as proteins. In particular, the inclusion of physical constraints, e.g., time-reversibility, was a crucial step to make the methods applicable to biophysical systems. Furthermore, we advance the method by incorporating experimental observables into the model estimation showing that biases in simulation data can be compensated for. We further develop a new neural network layer in order to build a hierarchical model allowing for different levels of details to be studied. Finally, we propose an attention mechanism, which highlights important residues for the classification into different states. We demonstrate the new methodology on an ultralong molecular dynamics simulation of the Villin headpiece miniprotein. [ABSTRACT FROM AUTHOR]

In this paper, we first used the expected utility function to explore the benefits of urban park landscape construction on residents’ health. In order to reduce the estimation bias caused by omitted variables, the structural equation model with multiple mediators was used to test the multiple health behavior paths of the built environment of urban parks affecting residents’ health. Secondly, landscape ecology was used to reveal the changing law of landscape space patterns in urban parks, and different pattern indices were selected to quantitatively describe the changes in landscape space patterns in parks. Finally, three different types of parks, A, B and C, were selected as case studies to investigate the differences in the health benefits of different types of urban park landscape space for residents. The results showed that the utility model adjusted R2 = 0.2745 and the standardized coefficient beta=0.089, i.e., for every 1 gradient increase in the landscape pattern index of urban park landscapes, the health benefits of residents increased by 0.089 gradients. This study is of great significance for enhancing the health of residents and achieving the strategic goal of ‘Healthy China’.

Satellite-based detection of methane (CH4) point sources is crucial in identifying and mitigating anthropogenic emissions of CH4, a potent greenhouse gas. Previous studies have indicated the presence of CH4 point source emissions from coal mines in Shanxi, China, an important source region with large CH4 emissions, but a comprehensive survey has remained elusive. This study aims to conduct a survey of CH4 point sources over Shanxi's coal mines based on observations of the Advanced HyperSpectral Imager (AHSI) on board the Gaofen-5B satellite (GF-5B/AHSI) between 2021 and 2023. The spectral shift in center wavelength and change in full-width-half-maximum (FWHM) are estimated for all spectra channels, which are used as inputs for retrieving the enhancement of column-averaged dry-air mole fraction of CH4 (ΔXCH4) using a matched-filter based algorithm. Our results show that the spectral calibration on GF-5B/AHSI reduced estimation biases of emission flux rate by up to 5.0%. We applied the flood-fill algorithm to automatically extract emission plumes from ΔXCH4 maps. We adopted the integrated mass enhancement (IME) model to estimate the emission flux rate values from each CH4 point source. Consequently, we detected CH4 point sources in 32 coal mines with 93 plume events in Shanxi province. The estimated emission flux rate ranges from 857.67 ± 207.34 kg·h-1 to 14333.02 ± 5249.32 kg·h-1. The total emission flux rate reaches 13.26 t·h-1 in Shanxi, assuming all point sources emit simultaneously. Our results show that wind speed is the dominant source of uncertainty contributing about 84.84% to the total uncertainty in emission flux rate estimation. Interestingly, we found a number of false positive detections due to solar panels that are widely spread in Shanxi. This study also evaluates the accuracy of wind fields in ECMWF ERA5 reanalysis by comparing with ground-based meteorological station. We found large discrepancy, especially in wind direction, suggesting incorporating local meteorological measurements into the study CH4 point source are important to achieve high accuracy. The study demonstrates that GF-5B/AHSI possesses capabilities for monitoring large CH4 point sources over complex surface characteristics in Shanxi. [ABSTRACT FROM AUTHOR]

Satellite-based detection of methane (CH4) point sources is crucial in identifying and mitigating anthropogenic emissions of CH4, a potent greenhouse gas. Previous studies have indicated the presence of CH4 point source emissions from coal mines in Shanxi, China, an important source region with large CH4 emissions, but a comprehensive survey has remained elusive. This study aims to conduct a survey of CH4 point sources over Shanxi's coal mines based on observations of the Advanced HyperSpectral Imager (AHSI) on board the Gaofen-5B satellite (GF-5B/AHSI) between 2021 and 2023. The spectral shift in center wavelength and change in full-width-half-maximum (FWHM) are estimated for all spectra channels, which are used as inputs for retrieving the enhancement of column-averaged dry-air mole fraction of CH4 (ΔXCH4) using a matched-filter based algorithm. Our results show that the spectral calibration on GF-5B/AHSI reduced estimation biases of emission flux rate by up to 5.0 %. We applied the flood-fill algorithm to automatically extract emission plumes from ΔXCH4 maps. We adopted the integrated mass enhancement (IME) model to estimate the emission flux rate values from each CH4 point source. Consequently, we detected CH4 point sources in 32 coal mines with 93 plume events in Shanxi province. The estimated emission flux rate ranges from 857.67 ± 207.34 kg·h-1 to 14333.02 ± 5249.32 kg·h-1. The total emission flux rate reaches 13.26 t·h-1 in Shanxi, assuming all point sources emit simultaneously. Our results show that wind speed is the dominant source of uncertainty contributing about 84.84 % to the total uncertainty in emission flux rate estimation. Interestingly, we found a number of false positive detections due to solar panels that are widely spread in Shanxi. This study also evaluates the accuracy of wind fields in ECMWF ERA5 reanalysis by comparing with ground-based meteorological station. We found large discrepancy, especially in wind direction, suggesting incorporating local meteorological measurements into the study CH4 point source are important to achieve high accuracy. The study demonstrates that GF-5B/AHSI possesses capabilities for monitoring large CH4 point sources over complex surface characteristics in Shanxi. [ABSTRACT FROM AUTHOR]

Streaming data sequences arise from various areas in the era of big data, and it is challenging to explore efficient online models that adapt to them. To address the potential heterogeneity, we introduce a new online estimation procedure to analyze the constantly incoming streaming datasets. The underlying model structures are assumed to be the generalized linear models with dynamic regression coefficients. Our key idea lies in introducing a vector of unknown parameters to measure the differences between batch-specific regression coefficients from adjacent data blocks. This is followed by the usage of the adaptive lasso penalization methodology to accurately select nonzero components, which indicates the existence of dynamic coefficients. We provide detailed derivations to demonstrate how our proposed method not only fits within the online updating framework in which the old estimator is recursively replaced with a new one based solely on the current individual-level samples and historical summary statistics but also adaptively avoids undesirable estimation biases coming from the potential changes in model parameters of interest. Computational issues are also discussed in detail to facilitate implementation. Its practical performance is demonstrated through both extensive simulations and a real case study. In summary, we contribute to a novel online method that efficiently adapts to streaming data environment, addresses potential heterogeneity, and mitigates estimation biases from changes in coefficients. [ABSTRACT FROM AUTHOR]