Search

Tasks in which learners are asked to compare two data sets using box plots and decide which distribution contains more observations above a given threshold have already been investigated in research. There are indications that these tasks are solved schema-based and that different (correct and erroneous) schemas are used depending on the arrangement of the quartiles around the threshold. Erroneous schemas can cause systematic errors and are often based on typical misconceptions. For example, if learners did not complete the conceptual change and assume that in box plots - like in most other statistical representations (e.g., bar or circle diagrams) - more (box) area also represents more observations, they decide the task according to which box plot shows more box area above the threshold. However, this can lead to incorrect answers, as the box area does not represent frequency but the range of the middle half of the data (interquartile range) and thus a measure of variability. So far, these schema-based reasoning processes have mainly been investigated via differences in solution rates of congruent and incongruent items. The present study investigates whether eye-tracking data can help to better understand which information is processed in the different schemas. Our research interest is based on hypotheses specifying which box plot components are significantly involved in the different schemas. We assume that the gaze patterns of learners using different schemas differ both regarding the number and duration of fixations on the relevant box plot components (areas of interest) and in terms of the number of transitions between them. We asked N = 14 participants to solve congruent and incongruent items and simultaneously collected eye movement data. In the analysis, we first used the solution rates to assign the schemas most likely used. Subsequently, the eye-tracking data were analyzed regarding differences in line with our hypotheses. We found hypothesis-compliant effects in all schemas regarding the number of fixations and transitions, but not regarding fixation duration. These results not only validate the schemas identified in previous studies, but also indicate that the schemas differ primarily in terms of which quartile is focused. [ABSTRACT FROM AUTHOR]

Planning target volume (PTV) to deliver the desired dose to the clinical target volume (CTV) accounts for systematic (∑) and random (σ) errors during the planning and execution of intensity modulated radiation therapy (IMRT). As these errors vary at different departments, this study was conducted to determine the 3-dimensional PTV (PTV 3D) margins for head and neck cancer (HNC) at our center. The same was also estimated from reported studies for a comparative assessment. A total of 77 patients with HNCs undergoing IMRT were included. Of these, 39 patients received radical RT and 38 received postoperative IMRT. An extended no action level protocol was implemented using on-board imaging. Shifts in the mediolateral (ML), anteroposterior (AP), and superoinferior (SI) directions of each patient were recorded for every fraction. PTV margins in each direction (ML, AP, SI) and PTV 3D were calculated using van Herk's equation. Weighted PTV 3D was also computed from the ∑ and σ errors in each direction published in the literature for HNC. Our patients were staged T 2-4 (66/77) and N 0 (39/77). In all, 2280 on-board images were acquired, and daily shifts in each direction were recorded. The PTV margins in the ML, AP, and SI directions were computed as 3.2 mm, 2.9 mm, and 2.6 mm, respectively. The PTV 3D margin was estimated to be 6.5 mm. This compared well with the weighted median PTV 3D of 7.2 mm (range: 3.2 to 9.9) computed from the 16 studies reported in the literature. To ensure ≥95% CTV dose coverage in 90% of HNC patients, PTV 3D margin for our department was estimated as 6.5 mm. This agrees with the weighted median PTV 3D margin of 7.2 mm computed from the 16 published studies in HNCs. Site-specific PTV 3D margin estimations should be an integral component of the quality assurance protocol of each department to ensure adequate coverage of dose to CTV during IMRT. [ABSTRACT FROM AUTHOR]

Evaluation metrics play a pivotal role in the calibration process of hydrological models, serving as objective functions that directly influence the final values of model parameters and significantly affect users' perceptions of model performance. However, the choice and interpretation of evaluation metrics are subjective; therefore, this study provides a more objective framework for assessing model performance. This paper initially explored the applicability of various commonly used evaluation metrics, providing an overview of their limitations. Following this, we decomposed errors by analysing their physical significance and geometric representation in scatter plots, categorizing them into systematic and unsystematic errors. Through the decomposition and derivation of the Nash–Sutcliffe efficiency (NSE) formula, we established the quantitative relationship among various evaluation metrics. The soil and water assessment tool (SWAT) model was utilized to simulate monthly runoff in the Baishan basin (China), for the period 1994–2017, with NSE serving as the objective function for calibration. Our findings are consistent with previous studies, indicating that the model tends to slightly underestimate high flows while significantly overestimating low flows. Further analysis through error decomposition and the examination of relationships among various evaluation metrics revealed that unsystematic errors were dominant during the spring snowmelt runoff period, while systematic errors prevailed in the dry season. By evaluating the runoff series based on the magnitude of runoff or by categorizing it according to seasons and months, a more stringent assessment of the model's performance was achieved. These findings not only highlight the necessity for careful selection of evaluation metrics but also underscore the significance of our methodological advancements in enhancing hydrological model precision and reliability. [ABSTRACT FROM AUTHOR]

I argue that dimensional analysis provides an answer to a skeptical challenge to the theory of model mediated measurement. The problem arises when considering the task of calibrating a novel measurement procedure, with greater range, to the results of a prior measurement procedure. The skeptical worry is that the agreement of the novel and prior measurement procedures in their shared range may only be apparent due to the emergence of systematic error in the exclusive range of the novel measurement procedure. Alternatively: what if the two measurement procedures are not in fact measuring the same quantity? The theory of model mediated measurement can only say that we assume that there is a common quantity. In contrast, I show that the satisfaction of dimensional homogeneity across the metrological extension is independent evidence for the so-called assumption. This is illustrated by the use of dimensional analysis in high pressure experiments. This results in an extension of the theory of model mediated measurement, in which a common quantity in metrological extension is no longer assumed, but hypothesized. [ABSTRACT FROM AUTHOR]

Frequency is one of the most significant indicators of power system, which needs accurate estimation to provide a reliable basis for monitoring, control, and protection. Owning to the efficiency, DFT is widely used in the frequency measurement of power systems. However, the inherent defects, that is, the spectral leakage and picket fence effect, decrease the accuracy of DFT. One of the variants, named the windowed phase difference method (WPD), addresses the problem using the phase variation of two windowed sequences in the frequency domain and achieves accurate frequency estimation. Suffering from the ignored negative component and contamination of noise, there are still undesired errors in WPD-based frequency estimation. To reduce the influence of the undesired errors, this paper first analyzes the systematic error of WPD introduced by spectrum leakage and proposes an improved WPD (IWPD) through systematic error compensation to achieve more accurate frequency estimations. Then, the windowing effect on IWPD-based frequency estimation of a noisy signal is studied by deducing the theoretical expression of frequency estimation variance. Finally, the proposed IWPD is validated by extensive computer simulations and practical experiments. [ABSTRACT FROM AUTHOR]

The clade Syndermata includes the endoparasitic Acanthocephala, the epibiotic Seisonidea, and the free-living Bdelloidea and Monogononta. The phylogeny of Syndermata is highly debated, hindering the understanding of the evolution of morphological features, reproductive modes, and lifestyles within the group. Here, we use publicly available whole-genome data to re-evaluate syndermatan phylogeny and assess the credibility of alternative hypotheses, using a new combination of phylogenomic methods. We found that the Hemirotifera and Pararotatoria hypotheses were recovered under combinations of datasets and methods with reduced possibility of systematic error in concatenation-based analyses. In contrast, the Seisonidea-sister and Lemniscea hypotheses were recovered under dataset combinations with increased possibility of systematic error. Hemirotifera was further supported by whole-genome microsynteny analyses and species-tree methods that use multi-copy orthogroups after removing distantly related outgroups. Pararotatoria was only partially supported by microsynteny-based phylogenomic reconstructions. Hence, Hemirotifera and partially Pararotatoria were supported by independent phylogenetic methods and data-evaluation approaches. These two hypotheses have important implications for the evolution of syndermatan morphological features, such as the gradual reduction of locomotory ciliation from the common ancestor of Syndermata in the stem lineage of Pararotatoria. Our study illustrates the importance of combining various types of evidence to resolve difficult phylogenetic questions. [ABSTRACT FROM AUTHOR]

Objectives The critical problem to be solved in the construction of a high-precision seafloor control network is to reduce the influence of systematic errors on the coordinate accuracy of seafloor control points during acoustic positioning.Methods First, we analyze two positioning models: Non-difference and difference between epochs. Second, due to the change of sound velocity in seawater is affected by the long period term error and the short period term error, the system error caused by the shift in sound velocity in the measurement also has the characteristics of periodic change.Results and Conclusions Based on this premise, a symmetric epoch differential positioning model was constructed. The results of simulation analysis and measured data analysis show that when the observation duration is an even multiple of the system error variation period, the symmetric differential positioning model can effectively reduce the influence of the system error on the positioning results. Still, when the observation duration is not an even multiple of the system error variation period, the model is no longer applicable. [ABSTRACT FROM AUTHOR]

The aim of this study is to develop a practical method for bivariate z-score analysis which can be applied to the survey of an external quality assessment programme. To develop the bivariate z-score analysis, the results of four surveys of the international D-Dimer external quality assessment programme of 2022 of the ECAT Foundation were used. The proposed methodology starts by identifying the bivariate outliers, using a Supervised Sequential Hotelling T2 control chart. The outlying data are removed, and all the remaining data are used to provide robust estimates of the parameters of the assumed underlying bivariate normal distribution. Based on these estimates two nested homocentric ellipses are drawn, corresponding to confidence levels of 95 and 99.7 %. The bivariate z-score plot described provides the laboratory with an indication of both systematic and random deviations from zero z-score values. The bivariate z-score analysis was examined within survey 2022-D4 across the three most frequently used methods. The number of z-score pairs included varied between 830 and 857 and the number of bivariate outliers varied between 20 and 28. The correlation between the z-score pairs varied between 0.431 and 0.647. The correlation between the z-score pairs for the three most frequently used varied between 0.208 and 0.636. The use of the bivariate z-score analysis is of major importance when multiple samples are distributed around in the same survey and dependency of the results is likely. Important lessons can be drawn from the shape of the ellipse with respect to random and systematic deviations, while individual laboratories have been informed about their position in the state-of-the-art distribution and whether they have to deal with systematic and/or random deviations. [ABSTRACT FROM AUTHOR]

Tasks in which learners are asked to compare two data sets using box plots and decide which distribution contains more observations above a given threshold have already been investigated in research. There are indications that these tasks are solved schema-based and that different (correct and erroneous) schemas are used depending on the arrangement of the quartiles around the threshold. Erroneous schemas can cause systematic errors and are often based on typical misconceptions. For example, if learners did not complete the conceptual change and assume that in box plots – like in most other statistical representations (e.g., bar or circle diagrams) - more (box) area also represents more observations, they decide the task according to which box plot shows more box area above the threshold. However, this can lead to incorrect answers, as the box area does not represent frequency but the range of the middle half of the data (interquartile range) and thus a measure of variability. So far, these schema-based reasoning processes have mainly been investigated via differences in solution rates of congruent and incongruent items. The present study investigates whether eye-tracking data can help to better understand which information is processed in the different schemas. Our research interest is based on hypotheses specifying which box plot components are significantly involved in the different schemas. We assume that the gaze patterns of learners using different schemas differ both regarding the number and duration of fixations on the relevant box plot components (areas of interest) and in terms of the number of transitions between them. We asked N = 14 participants to solve congruent and incongruent items and simultaneously collected eye movement data. In the analysis, we first used the solution rates to assign the schemas most likely used. Subsequently, the eye-tracking data were analyzed regarding differences in line with our hypotheses. We found hypothesis-compliant effects in all schemas regarding the number of fixations and transitions, but not regarding fixation duration. These results not only validate the schemas identified in previous studies, but also indicate that the schemas differ primarily in terms of which quartile is focused.

The traditional method of joint calibration of fixed baseline interferometer with multiple errors requires more than three calibration emitters to achieve real-time calibration of the interferometer in orbit, but the performance of the traditional method will be reduced or even failed when the number of calibration emitters is insufficient. For the situation that the number of calibration emitters is insufficient, a single or double emitters calibration method for fixed baseline interferometer is proposed. Under the condition of relative motion between the interferometer and the calibration emitters, a set of phase differences of calibration signals is obtained firstly by multiple measurements, and a set of equations about the systematic errors of the interferometer is listed by using the obtained phase differences, finally the systematic errors are solved by the Penrose inverse matrix assisted iterative least squares method. The simulation results verify the feasibility of the proposed method. On this basis, the influence of the emitter selection on the positioning performance of the interferometer after calibration is analyzed qualitatively by means of numerical simulation, decomposing the parameters of the emitter selection. The analysis results show that the calibration emitter located near the sub-star in the single emitter case or two calibration emitters located far apart both can make the interferometer obtain better positioning performance after calibration.

Unlike classical systems based on the use of morphological data, modern phylogenetic analyses use genetic information to construct phylogenetic trees. Ongoing research in the field of phylogenetics is evaluating the accuracy of phylogenetic estimation results and the reliability of phylogenetic trees to explain evolutionary relationships. Recently, the probability of stochastic errors in large-scale phylogenetic datasets has decreased, while the probability of systematic errors has increased. Therefore, before constructing a phylogenetic tree, it is necessary to assess the causes of systematic bias to improve the accuracy of phylogenetic estimates. We performed analyses of three datasets (Terebelliformia, Daphniid, and Glires clades) using bioinformatics software to assess systematic error and improve phylogenetic tree accuracy. Then, we proposed a combination of systematic biases capable of discerning the most suitable gene markers within a series of taxa and generating conflicting phylogenetic topologies. Our findings will help improve the reliability of phylogenetic software to estimate phylogenies more accurately by exploiting systematic bias. [ABSTRACT FROM AUTHOR]

In engineering practice, anuloid surfaces are produced that are subject to strict requirements in terms of accuracy (e.g., in the bearing industry). Roundness is also often measured. The accuracy of the roundness measurement does not only depend on the accuracy of a particular metrological system. The setting of the component on the measuring device can also have a significant influence on the accuracy of the measurement. The axis of the measured surface must be perpendicular to the section in which the roundness is measured. Tilting the measured surface causes a systematic measurement error, which depends on the size of the tilt angle and the parameters of the measured surface. This study presents mathematical relations for the calculation of this error when measuring roundness on an anuloid surface. Mathematical relations for the internal and external surfaces formed by the inner and outer parts of the anuloid are derived herein. There is also a theoretical analysis of the influence of inclination and the parameters of the measured surface on the size of this error. Theoretically calculated values were compared with practically measured values of roundness. The error had a negative effect on the accuracy of the roundness measurement. It affected the roundness value, the second harmonic component, and also the roundness profile itself. [ABSTRACT FROM AUTHOR]

To address the problem of system instability during vehicle low-speed driving, we propose improving the visual odometer using ORB (Oriented FAST and Rotated BRIEF) features. The homogeneity of ORB features leads to poor corner point properties of some feature points. When the environmental texture lacks richness, it leads to poor matching performance and low matching accuracy of the feature points. We solve the problem of the corner point properties of feature points using weight calculation for regions with different textures. When the vehicle speed is too low, the continuous frames captured by the camera will overlap significantly, causing large fluctuations in the system error. We use motion model estimation to solve this problem. Meanwhile, experimental validation using the KITTI dataset achieves good results. [ABSTRACT FROM AUTHOR]

Introduction: The phylogenetic relationships within Pancrustacea (including Crustacea and Hexapoda) remain elusive despite analyses using various molecular data sets and analytical approaches over the past decade. The relationship between the major lineages of Allotriocarida, which includes Hexapoda, the most species-rich animal taxon, is particularly recalcitrant. Methods: To investigate and resolve the root of phylogenetic ambiguity in Pancrustacea, we re-evaluated the evolutionary relationships of major pancrustacean clades using a phylogenetically informed orthology approach and assessed the effect of systematic errors, with a major focus on long branch attraction (LBA) and incomplete lineage sorting (ILS). A data set comprising 1086 orthologs from 106 species representing all major extant classes of pancrustaceans was assembled and used in gene tree and species tree construction after various filtering processes. Results and discussion: Regardless of the filtering criteria and phylogenetic analyses, the resulting trees consistently supported (1) a sister relationship of Remipedia and Hexapoda (hence rejecting the monophyly of Xenocarida, i.e. Remipedia + Cephalocarida), and (2) refuted the monophyly of Multicrustacea, as Copepoda is either sister to or nested within Allotriocarida. Examination of gene trees reveals that the grouping of Multicrustacea and Xenocarida in previous phylogenetic studies may represent LBA artefacts. Phylogenetic signal analyses suggest a low resolution and an incidence of strong conflicting signals at the deep splits. Further analyses indicate a partial contribution of incomplete lineage sorting (ILS) to the contradictory signal in the allotriocaridan phylogeny, leading to limited support for any potential relationships between Branchiopoda, Cephalocarida and Copepoda. This study suggests the need for further examination of other potential sources of signal discordance, such as introgression and gene tree estimation error to fully understand the evolutionary history of Pancrustacea. [ABSTRACT FROM AUTHOR]

Copyright of Aero Weaponry is the property of Aero Weaponry Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Geodetic coordinate information and attitude information of the observation platform are necessary for multi-UAV position alignment and target tracking. In a complex sea environment, the navigation equipment of a UAV is susceptible to interference. High-precision geodetic coordinate information and attitude information are difficult to obtain. Aiming to solve the above problems, a low-precision geodetic coordinate real-time systematic spatial registration algorithm based on multi-UAV observation and an improved robust fusion tracking algorithm of multi-UAV to sea targets considering attitude error are proposed. The spatial registration algorithm obtains the observation information of the same target based on the mutual observation information. Then, geodetic coordinate systematic error is accurately estimated by establishing the systematic error estimation measurement equation. The improved robust fusion tracking algorithm considers the influence of UAV attitude error in the observation. The simulation experiment and practical experiment show that the algorithm can not only estimate systematic error accurately but also improve tracking accuracy. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Ordnance Equipment Engineering is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Postmarket safety surveillance is an integral part of mass vaccination programs. Typically relying on sequential analysis of real‐world health data as they accrue, safety surveillance is challenged by sequential multiple testing and by biases induced by residual confounding in observational data. The current standard approach based on the maximized sequential probability ratio test (MaxSPRT) fails to satisfactorily address these practical challenges and it remains a rigid framework that requires prespecification of the surveillance schedule. We develop an alternative Bayesian surveillance procedure that addresses both aforementioned challenges using a more flexible framework. To mitigate bias, we jointly analyze a large set of negative control outcomes that are adverse events with no known association with the vaccines in order to inform an empirical bias distribution, which we then incorporate into estimating the effect of vaccine exposure on the adverse event of interest through a Bayesian hierarchical model. To address multiple testing and improve on flexibility, at each analysis timepoint, we update a posterior probability in favor of the alternative hypothesis that vaccination induces higher risks of adverse events, and then use it for sequential detection of safety signals. Through an empirical evaluation using six US observational healthcare databases covering more than 360 million patients, we benchmark the proposed procedure against MaxSPRT on testing errors and estimation accuracy, under two epidemiological designs, the historical comparator and the self‐controlled case series. We demonstrate that our procedure substantially reduces Type 1 error rates, maintains high statistical power and fast signal detection, and provides considerably more accurate estimation than MaxSPRT. Given the extensiveness of the empirical study which yields more than 7 million sets of results, we present all results in a public R ShinyApp. As an effort to promote open science, we provide full implementation of our method in the open‐source R package EvidenceSynthesis. [ABSTRACT FROM AUTHOR]

Performing quality assurance checks on hydrochemical data prior to interpreting these data is important as shown in this case study. The informative hydrochemistry paper authored by Mora et al. (Environ Monit Assess 189:394–408, 2017. https://doi.org/10.1007/s10661-017-6096-y) presents a significant amount of hydrogeochemical data for 30 large-yield wells, interpretation (including advanced statistics), findings, and conclusions for the Monterrey metropolitan area (MMA), Mexico. The author concludes that the probable analytical inaccuracy (notable bias and imprecision) and data conversion errors contained in the Mora et al. (2017) paper do not appreciably impact negatively their hydrogeochemical findings and conclusions. The author performed the cation–anion balance (CAB) quality assurance (QA) check method and discovered systematic error (bias) ranging from − 20.8% (Well S1) to 16.5% (Well S9), with 14 well analyses associated with CAB errors ≥ 5.0%. Coupling this simple but effective QA check with other QA algorithms, the author determined for nine well major-ion analyses, HCO3 concentrations were notably low, and 10 well analyses were identified as possessing anomalously low Ca concentrations. These QA check methods will enhance: (1) the accuracy and defensibility of analytical results, and (2) potentially the interpretation and findings of hydrochemical investigations. Additionally, this paper provides new hydrogeochemical evaluation, interpretation, and findings for the MMA, i.e., Na/Cl molar ratios ≤ 0.88 for six studied wells strongly suggest that the groundwater supplying these wells has been impacted by seawater intrusion most likely sourced by the Western Interior Seaway. At least four of these wells occur in an area underlain by marine sedimentary rocks. [ABSTRACT FROM AUTHOR]

A sample of 1452 adult respondents in the United States was drawn using a highly respected commercial Internet-based service provider. The 85-question survey addressed issues related to anti-consumption behavior among consumers. The instrument incorporated four quality control checks designed to identify inattentive respondents and cleanse the resultant data set. These quality control (QC) checks were a uniform identifier (where one specific response should result), an instructional manipulation check. A common knowledge question, and a time check. A total of 514 (35.4%) respondents failed at least one of the four QC checks with nine respondents failing all four. Comparisons of inattentive respondents to those who passed all four QC checks consistently identified statistically significant differences on each of the metrics used to determine the potential impact of inattentive respondents. These results indicate that inattentiveness does not produce "random noise" as some authors have stated; rather the inattentive respondents "create both random and systematic measurement error which impacts estimates of the reliability and validity" of the metrics used to assess various consumer phenomena. The results of this study are important, not only in projects designed to help marketers, but also for research in other fields where accuracy is of paramount importance. [ABSTRACT FROM AUTHOR]

The aim of research is to find the “truth” in medicine. The researcher should be aware of the causes and characteristics of conscious and unconscious errors and know how to prevent them in order to reflect the “truth”. The value determined in epidemiological studies is the sum of: 1) Truth, 2) Random error, 3) Systematic error (Bias). In order to declare that the result is correct, errors must be excluded. Errors may arise at several stages in epidemiological research. Systematic errors lead to bias. In controlled studies, systematic errors often cause significant difference of one study group. However, “random error” or the chance factor does not cause bias. Since this type of error causes a deviation in both groups in the same direction, its effect is canceled, however the sensitivity of the results may decrease. Bias may be conscious or unconscious. A conscious bias is an intentional unethical act, made in order to achieve an intended result, ignoring scientific rules. Unconscious bias is due to inexperience of the researchers who do not consider the factors that may affect the results. Various errors may be made during epidemiological measurements. Since errors can never be eliminated, one should aim to minimize errors. Minimizing systematic errors increases the validity, and minimizing random errors increases power and accuracy of the studies. Bias results in incorrect diagnosis and management proposals, and waste of resources in studies that are carried out to diagnose health problems and to develop management strategies.

It is necessary to plat the multiple-map sheet data sets into a single map when processing the geochemical data from the entire map sheet and compiling a large-scale geochemical map, but step effects might readily arise where different map sheets converge. There are currently few papers that compare and optimize various methods, and most systematic error-related studies focus on a specific way to correct and assess the step effect of geochemical data in a particular area. With the map sheet from the Yuanyi, Bajiabielichier, Goulixiang, and Zhiyi in Gouli region of Qinghai Province as a case study, this paper applies the framing standardization method, normalized method, and contrast method to perform systematic error correction and method optimization on 1∶50 000 stream sediment geochemical data and provides the effective method reference for the systematic error processing of the geochemical prospecting data of the rock cuttings. The results indicate that these three methods have distinct effects on systematic error correction.After the original data are corrected, the background difference between adjacent map sheets is basically eliminated, the step effect is effectively weakened, the geochemical zoning is continuous, the strong anomalies are appropriately suppressed, and the weak anomalies are appropriately enhanced and represented in the background. After the same element is corrected with various methods, there are differences in the anomalous area, form of anomalies, and goodness of fit with the mineral deposit (point). The goodness of fit between anomalies and known deposits (points) after correction with the framing standardization method and normalized method is 75%; the anomalies are evenly distributed throughout the region, but individual ore-induced anomalies are weakened, and the influence of invalid anomalies cannot be avoided. The nonmineral anomaly in the high background area can be effectively suppressed by the contrast method, and the mineral-induced anomaly in the low background area can be strengthened. The anomaly's goodness of fit with the deposit (point) is 87.5%, the invalid anomaly's influence is minimal, and the geological background has the maximum goodness of fit. Combined with the study on the physical geological body conditions in the region of interest, the contrast method is more suitable for the correction of systematic errors in stream sediment geochemical survey data on different map sheets in the Gouli Region and can be selected and applied in geochemical data processing under similar topographic and geomorphic conditions in Northwest China.

IntroductionThe phylogenetic relationships within Pancrustacea (including Crustacea and Hexapoda) remain elusive despite analyses using various molecular data sets and analytical approaches over the past decade. The relationship between the major lineages of Allotriocarida, which includes Hexapoda, the most species-rich animal taxon, is particularly recalcitrant.MethodsTo investigate and resolve the root of phylogenetic ambiguity in Pancrustacea, we re-evaluated the evolutionary relationships of major pancrustacean clades using a phylogenetically informed orthology approach and assessed the effect of systematic errors, with a major focus on long branch attraction (LBA) and incomplete lineage sorting (ILS). A data set comprising 1086 orthologs from 106 species representing all major extant classes of pancrustaceans was assembled and used in gene tree and species tree construction after various filtering processes.Results and discussionRegardless of the filtering criteria and phylogenetic analyses, the resulting trees consistently supported (1) a sister relationship of Remipedia and Hexapoda (hence rejecting the monophyly of Xenocarida, i.e. Remipedia + Cephalocarida), and (2) refuted the monophyly of Multicrustacea, as Copepoda is either sister to or nested within Allotriocarida. Examination of gene trees reveals that the grouping of Multicrustacea and Xenocarida in previous phylogenetic studies may represent LBA artefacts. Phylogenetic signal analyses suggest a low resolution and an incidence of strong conflicting signals at the deep splits. Further analyses indicate a partial contribution of incomplete lineage sorting (ILS) to the contradictory signal in the allotriocaridan phylogeny, leading to limited support for any potential relationships between Branchiopoda, Cephalocarida and Copepoda. This study suggests the need for further examination of other potential sources of signal discordance, such as introgression and gene tree estimation error to fully understand the evolutionary history of Pancrustacea.

Systematic error from selection bias, uncontrolled confounding, and misclassification is ubiquitous in epidemiologic research but is rarely quantified using quantitative bias analysis (QBA). This gap may in part be due to the lack of readily modifiable software to implement these methods. Our objective is to provide computing code that can be tailored to an analyst's dataset. We briefly describe the methods for implementing QBA for misclassification and uncontrolled confounding and present the reader with example code for how such bias analyses, using both summary-level data and individual record-level data, can be implemented in both SAS and R. Our examples show how adjustment for uncontrolled confounding and misclassification can be implemented. Resulting bias-adjusted point estimates can then be compared to conventional results to see the impact of this bias in terms of its direction and magnitude. Further, we show how 95% simulation intervals can be generated that can be compared to conventional 95% confidence intervals to see the impact of the bias on uncertainty. Having easy to implement code that users can apply to their own datasets will hopefully help spur more frequent use of these methods and prevent poor inferences drawn from studies that do not quantify the impact of systematic error on their results. [ABSTRACT FROM AUTHOR]

An important component of many Adaptive Instructional Systems (AIS) is a 'Learner Model' intended to track student learning and predict future performance. Predictions from learner models are frequently used in combination with mastery criterion decision rules to make pedagogical decisions. Important aspects of learner models, such as learning rate and item difficulty, can be estimated from prior data. A critical function of AIS is to have students practice new content once the AIS predicts that they have 'mastered' current content or learned it to some criterion. For making this prediction, individual student parameters (e.g., for learning rate) are frequently unavailable due to having no prior data about a student, and thus population-level parameters or rules-of-thumb are typically applied instead. In this paper, we will argue and demonstrate via simulation and data analysis that even in best-case scenarios, learner models assuming equal learning rates for students will inevitably lead to systematic errors that result in suboptimal pedagogical decisions for most learners. This finding leads us to conclude that systematic errors should be expected, and mechanisms to adjust predictions to account for them should be included in AIS. We introduce two solutions that can adjust for student differences "online" in a running system: one that tracks systemic errors of the learner model (not the student) and adjusts accordingly, and a student-level performance adaptive feature. We demonstrate these solutions' efficacy and practicality on six large educational datasets and show that these features improved model accuracy in all tested datasets. [ABSTRACT FROM AUTHOR]

Copyright of Bulletin of Legal Medicine / Adli Tıp Bülteni is the property of Galenos Yayinevi Tic. LTD. STI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In engineering practice, anuloid surfaces are produced that are subject to strict requirements in terms of accuracy (e.g., in the bearing industry). Roundness is also often measured. The accuracy of the roundness measurement does not only depend on the accuracy of a particular metrological system. The setting of the component on the measuring device can also have a significant influence on the accuracy of the measurement. The axis of the measured surface must be perpendicular to the section in which the roundness is measured. Tilting the measured surface causes a systematic measurement error, which depends on the size of the tilt angle and the parameters of the measured surface. This study presents mathematical relations for the calculation of this error when measuring roundness on an anuloid surface. Mathematical relations for the internal and external surfaces formed by the inner and outer parts of the anuloid are derived herein. There is also a theoretical analysis of the influence of inclination and the parameters of the measured surface on the size of this error. Theoretically calculated values were compared with practically measured values of roundness. The error had a negative effect on the accuracy of the roundness measurement. It affected the roundness value, the second harmonic component, and also the roundness profile itself.

To address the problem of system instability during vehicle low-speed driving, we propose improving the visual odometer using ORB (Oriented FAST and Rotated BRIEF) features. The homogeneity of ORB features leads to poor corner point properties of some feature points. When the environmental texture lacks richness, it leads to poor matching performance and low matching accuracy of the feature points. We solve the problem of the corner point properties of feature points using weight calculation for regions with different textures. When the vehicle speed is too low, the continuous frames captured by the camera will overlap significantly, causing large fluctuations in the system error. We use motion model estimation to solve this problem. Meanwhile, experimental validation using the KITTI dataset achieves good results.

Geodetic coordinate information and attitude information of the observation platform are necessary for multi-UAV position alignment and target tracking. In a complex sea environment, the navigation equipment of a UAV is susceptible to interference. High-precision geodetic coordinate information and attitude information are difficult to obtain. Aiming to solve the above problems, a low-precision geodetic coordinate real-time systematic spatial registration algorithm based on multi-UAV observation and an improved robust fusion tracking algorithm of multi-UAV to sea targets considering attitude error are proposed. The spatial registration algorithm obtains the observation information of the same target based on the mutual observation information. Then, geodetic coordinate systematic error is accurately estimated by establishing the systematic error estimation measurement equation. The improved robust fusion tracking algorithm considers the influence of UAV attitude error in the observation. The simulation experiment and practical experiment show that the algorithm can not only estimate systematic error accurately but also improve tracking accuracy.

The TanDEM-X Digital Elevation Model (DEM) is limited by the radar side-view imaging mode, which still has gaps and anomalies that directly affect the application potential of the data. Many methods have been used to improve the accuracy of TanDEM-X DEM, but these algorithms primarily focus on eliminating systematic errors trending over a large area in the DEM, rather than random errors. Therefore, this paper presents the least-squares collocation-based error correction algorithm (LSC-TXC) for TanDEM-X DEM, which effectively eliminates both systematic and random errors, to enhance the accuracy of TanDEM-X DEM. The experimental results demonstrate that TanDEM-X DEM corrected by the LSC-TXC algorithm reduces the root mean square error (RMSE) from 6.141 m to 3.851 m, resulting in a significant improvement in accuracy (by 37.3%). Compared to three conventional algorithms, namely Random Forest, Height Difference Fitting Neural Network and Back Propagation in Neural Network, the presented algorithm demonstrates a reduction in the RMSEs of the corrected TanDEM-X DEMs by 6.5%, 7.6%, and 18.1%, respectively. This algorithm provides an efficient tool for correcting DEMs such as TanDEM-X for a wide range of areas. [ABSTRACT FROM AUTHOR]

We investigate the error in developing a technique for measuring the background concentration of methane by the passive location method using solar radiation as a source. Background concentration of methane is measured along three separate trajectories in a region northeast of Moscow. The first route passes through an urbanized area with developed infrastructure, industry and high population density; the second and third routes pass through areas with a lower population density and buildings, and have many forests and reservoirs. When processing measurement results, it was found that, along with random errors, it is necessary to take into account the systematic error due to the influence of external factors on measuring the methane background concentration during sounding under small angles to the horizon, when the path length noticeably increases. It is shown that such influencing factors can include the scattering of light by aerosols and other impurity particles in the atmosphere. Compensation adjustments have been made which reduced the systematic error to almost zero. A monitoring technique has been proposed that makes it possible to obtain a relative error in units of percent when measuring the background concentration of methane over a long duration. The results of the performed measurements are consistent with the results of global measurements and confirm the growth in the background concentration of methane in the Earth's atmosphere in recent years. [ABSTRACT FROM AUTHOR]

Serial Dependence is a ubiquitous visual phenomenon in which sequentially viewed images appear more similar than they actually are, thus facilitating an efficient and stable perceptual experience in human observers. Although serial dependence is adaptive and beneficial in the naturally autocorrelated visual world, a smoothing perceptual experience, it might turn maladaptive in artificial circumstances, such as medical image perception tasks, where visual stimuli are randomly sequenced. Here, we analyzed 758,139 skin cancer diagnostic records from an online app, and we quantified the semantic similarity between sequential dermatology images using a computer vision model as well as human raters. We then tested whether serial dependence in perception occurs in dermatological judgments as a function of image similarity. We found significant serial dependence in perceptual discrimination judgments of lesion malignancy. Moreover, the serial dependence was tuned to the similarity in the images, and it decayed over time. The results indicate that relatively realistic store-and-forward dermatology judgments may be biased by serial dependence. These findings help in understanding one potential source of systematic bias and errors in medical image perception tasks and hint at useful approaches that could alleviate the errors due to serial dependence. [ABSTRACT FROM AUTHOR]

Accurate state of charge (SOC) estimation is essential for the battery management system (BMS). In engineering, inappropriate selection of equivalent circuit model (ECM) and model parameters is common for lithium-ion batteries. This can result in systematic errors (i.e., modeling errors) in the state-space equation, thus affecting the SOC estimation accuracy. To address this problem, this paper proposes a self-calibration method. In the method, a novel state-space equation containing an unknown systematic error term is developed based on the Thevenin model. A self-calibration unscented Kalman filter (SC-UKF) algorithm is then introduced for recursive SOC estimation. The algorithm can automatically recognize and calibrate the unknown systematic error in the state equation, while also reducing the random noise effect through data fusion with the measurement equation. Test results demonstrate that the method can effectively correct the Thevenin modeling error and improve SOC estimation accuracy. Furthermore, the proposed method is computationally simple and convenient for engineering applications without increasing model complexity.

In order to improve the positioning accuracy of the micromanipulation system, a comprehensive error model is first established to take into account the microscope nonlinear imaging distortion, camera installation error, and the mechanical displacement error of the motorized stage. A novel error compensation method is then proposed with distortion compensation coefficients obtained by the Levenberg–Marquardt optimization algorithm combined with the deduced nonlinear imaging model. The compensation coefficients for camera installation error and mechanical displacement error are derived from the rigid-body translation technique and image stitching algorithm. To validate the error compensation model, single shot and cumulative error tests were designed. The experimental results show that after the error compensation, the displacement errors were controlled within 0.25 μm when moving in a single direction and within 0.02 μm per 1000 μm when moving in multiple directions. [ABSTRACT FROM AUTHOR]

The Integrated Multisatellite Retrievals for GPM (IMERG) is designed to derive precipitation by merging data from all the passive microwave (PMW) and infrared (IR) sensors. While the input source errors originating from the PMW and IR sensors are important, their structure, characteristics, and algorithm improvement remain unclear. Our study utilized a four-component error decomposition (4CED) method and a systematic and random error decomposition method to evaluate the detectability of IMERG dataset and identify the precipitation errors based on the multi-sensors. The 30 min data from 30 precipitation stations in the Tunxi Watershed were used to evaluate the IMERG data from 2018 to 2020. The input source includes five types of PMW sensors and IR instruments. The results show that the sample ratio for IR (Morph, IR + Morph, and IR only) is much higher than that for PMW (AMSR2, SSMIS, GMI, MHS, and ATMS), with a ratio of 72.8% for IR sources and a ratio of 27.2% for PMW sources. The high false ratio of the IR sensor leads to poor detectability performance of the false alarm ratio (FAR, 0.5854), critical success index (CSI, 0.3014), and Brier score (BS, 0.1126). As for the 4CED, Morph and Morph + IR have a large magnitude of high total bias (TB), hit overestimate bias (HOB), hit underestimate bias (HUB), false bias (FB), and miss bias (MB), which is related to the prediction ability and sample size. In addition, systematic error is the prominent component for AMSR2, SSMIS, GMI, and Morph + IR, indicating some inherent error (retrieval algorithm) that needs to be removed. These findings can support improving the retrieval algorithm and reducing errors in the IMERG dataset. [ABSTRACT FROM AUTHOR]

Recent technological advances have allowed the possibility of performing patient-specific quality assurance (QA) without time-intensive measurements. The objectives of this study are to: (1) compare how well the log file-based Mobius QA system agrees with measurement-based QA methods (ArcCHECK and portal dosimetry, PD) in passing and failing plans, and; (2) evaluate their error sensitivities. To these ends, ten phantom plans and 100 patient plans were measured with ArcCHECK and PD on VitalBeam, while log files were sent to Mobius for dose recalculation. Gamma evaluation was performed using criteria 3%/2 mm, per TG218 recommendations, and non-inferiority of the Mobius recalculation was determined with statistical testing. Ten random plans were edited to include systematic errors, then subjected to QA. Receiver operating characteristic curves were constructed to compare error sensitivities across the QA systems, and clinical significance of the errors was determined by recalculating dose to patients. We found no significant difference between Mobius, ArcCHECK, and PD in passing plans at the TG218 action limit. Mobius showed good sensitivity to collimator and gantry errors but not MLC bank shift errors, but could flag discrepancies in treatment delivery. Systematic errors were clinically significant only at large magnitudes; such unacceptable plans did not pass QA checks at the TG218 tolerance limit. Our results show that Mobius is not inferior to existing measurement-based QA systems, and can supplement existing QA practice by detecting real-time delivery discrepancies. However, it is still important to maintain rigorous routine machine QA to ensure reliability of machine log files. [ABSTRACT FROM AUTHOR]

In order to reduce the dependence of the two-stage harvesting of fiber flax on weather conditions, it is advisable to use a rippling apparatus to remove the seeds from the stems. The high efficiency of the rippling process can be ensured only after correcting the errors of horizontal copying of the curvature of the flax plant swaths by a picking-up mechanism. The solution to this problem is possible with the help of an alignment device mounted between the pick-up portion and the rippling apparatus of the pickup rippling machine. The operation of the device is based on shifting the plant swath on an inclined table under the action of gravity and/or by an alignment conveyor. The purpose of the study is to substantiate the parameters and modes of operation of the alignment device for the oriented feeding of the plant swath into the rippling apparatus during the two-stage harvesting of fiber flax. It has been analytically established that the alignment device operates most effectively with dry plants at a conveyor speed of 1.5 m/s and with table angle of inclination of 60 degrees. When working with moist plants, an increase in the width of the conveyors will contribute to the activation of the process under study. The obtained results are implemented in the design of the POL-1.5K pickup-rippling machine. The effectiveness of the alignment device was evaluated in real production conditions by video recording and correlation-spectral analysis of the processes of changing the ordinates of the point(top)-end of the flax swath at the infeed (as it enters) and discharge (as it leaves) of the device. Positive changes in the internal structure of the processes under study were recorded in terms of a decrease in mean squared deviation from 7.22 cm at the infeed to 3.33 cm at the discharge from the alignment device. At the same time, the nature of oscillations at the discharge became smoother (with an increase in the correlation interval from 2.1 to 7.8 s) and narrowband with dispersion spectrum filtering in the frequency range from 0.3 to 3.3 s–1. The filtered oscillations were the result of errors of copying the flax swath by the pickup mechanism. The quality of the alignment device operation is assessed as positive since the actual values of the mean squared deviation and the coefficient of variation of the discharge process are within acceptable limits. [ABSTRACT FROM AUTHOR]

BackgroundInstitute of Modern Physics, Chinese Academy of Sciences is developing a high-precision calibration device for lead-bismuth flowmeters based on the static mass method. In static mass method-based calibration devices, the uncertainty component of the diverter is the main component of the uncertainty of the flowmeter calibration device.PurposeThis study aims to predict the system error of a cylinder-driven diverter due to the asymmetry of cylinder forward and reverse strokes in the calibration process of lead-bismuth flowmeters, realize the a priori analysis of class B uncertainty components of a cylinder-driven commutator, and investigate whether the class B uncertainty can accurately reflect the magnitude of the relative system error introduced by the diverter.MethodsFirstly, a two-way coupling calculation method based on computational fluid dynamics (CFD) was developed for the diverter, and the SST k-ω turbulence model and Euler multiphase flow model were employed for calculation. Then, the stroke difference method was employed to obtain the class B uncertainty components for the diverter under different flow conditions and torque drives, and the calibration relative system errors under different timing methods and velocity distributions at the commutator nozzle outlet were calculated. Finally, the operating characteristics of the cylinder-driven diverter were analyzed.Results & ConclusionsComputation results show that the greater the thrust of the cylinder, the smaller is the type B uncertainty of the diverter. Compared to using the start or end of stroke as the timing moment, the calibration error caused by the diverter is the lowest when the diverter baffle is moved to the midpoint of the stroke as the timing moment, and the error is approximately 1/7⁓1/30 times those of the other two timing methods. The magnitude of diverter class B uncertainty reflects the envelope value of the relative systematic error caused by the diverter when using different timing moments.

YZ gas field group in the Pearl River Mouth Basin is located at the edge of the continental shelf, and the gas reservoir is buried deeper than 3 000 m. Structural research faces multiple problems, such as large changes in water depth, large changes in the thickness of the surface low-velocity mudstone, and small-scale shallow gas. It is difficult to predict the depth of gas reservoirs precisely, which brings risks to the implementation of the overall development plan in the gas fields. Aiming at this problem, a research idea of integration of processing and interpretation is proposed: firstly, dense velocity analysis is carried out along the target layer and its upper seismic reflection layer to improve the accuracy of seismic velocity analysis; secondly, in order to eliminate the systematic error of well-seismic velocity, the macro correction of seismic velocity is carried out by using the calibrated velocity of synthetic seismic records, and the corrected velocity is used to perform time-depth conversion to obtain the initial depth structure; the main source of the residual error at the well point is clarified by the correlation analysis method, and with the correlation trend surface as the constraint, the error grid is edited in combination with the residual error of the well to correct the initial depth structure. Finally, a higher-precision depth structure is obtained by correcting the small error at the well location. The practice of gas field exploitation shows that the error between the structural depth predicted by this method and the actual depth of development wells has been reduced from 10-40 m to less than 10 m, which has successfully improved the accuracy of structural prediction, effectively guided the design and implementation of development wells, and reduced the risk of gas field exploitation. For oil and gas fields under similar geological conditions, the detailed study of the structure is not only based on data-driven velocity analysis and seismic imaging, identifying and eliminating the influencing factors of the structure is also the key.

Effective control of pipeline systems requires adequate mathematical models. To match an object with the model it is necessary to analyze data on physical system structure and parameters and on operating processes (or the fluid flow regimes). Operating parameter measurements serve as data source for operational control. Incorrect SCADA readings bring about erroneous simulation results and unfounded dispatch decisions. Sometimes it is rather easy to identify information system failures and tell the cause. But sometimes as in the case of stage-by-stage failure such as sensor drift failure identification becomes really difficult. To detect and eliminate a progressive defect it is advisable to use maximum of available data, that is all measurements of the operating parameters (pressure, flow rate and, possibly, temperature) transmitted during the last few SCADA sessions. This approach takes into account the interdependence of operating parameters within the same session as well as specific parameter measurements during a number of sessions. The suggested model of identifying the failed device and the failure moment is built on a stochastic basis. The measurement results are considered random variables, which distribution obeys error theory standard assumptions. • Information processing on a set of the operating parameters measurements of the pipeline system. • Models for failures recognizing of pressure gauges and flow meters. • Determination of the failure moment and place at the metrological complex. • Potential applicability of the methodology to unaccounted for gas. [ABSTRACT FROM AUTHOR]

Purpose: Propensity score matching (PSM) is subject to limitations associated with limited degrees of freedom and covariate overlap. Cardinality matching (CM), an optimization algorithm, overcomes these limitations by matching directly on the marginal distribution of covariates. This study compared the performance of PSM and CM. Methods: Comparative cohort study of new users of angiotensin‐converting enzyme inhibitor (ACEI) and β‐blocker monotherapy identified from a large U.S. administrative claims database. One‐to‐one matching was conducted through PSM using nearest‐neighbor matching (caliper = 0.15) and CM permitting a maximum standardized mean difference (SMD) of 0, 0.01, 0.05, and 0.10 between comparison groups. Matching covariates included 37 patient demographic and clinical characteristics. Observed covariates included patient demographics, and all observed prior conditions, drug exposures, and procedures. Residual confounding was assessed based on the expected absolute systematic error of negative control outcome experiments. PSM and CM were compared in terms of post‐match patient retention, matching and observed covariate balance, and residual confounding within a 10%, 1%, 0.25% and 0.125% sample group. Results: The eligible study population included 182 235 (ACEI: 129363; β‐blocker: 56872) patients. CM achieved superior patient retention and matching covariate balance in all analyses. After PSM, 1.6% and 28.2% of matching covariates were imbalanced in the 10% and 0.125% sample groups, respectively. No significant difference in observed covariate balance was observed between matching techniques. CM permitting a maximum SMD <0.05 was associated with improved residual bias as compared to PSM. Conclusion: We recommend CM with more stringent balance criteria as an alternative to PSM when matching on a set of clinically relevant covariates. [ABSTRACT FROM AUTHOR]

One hypothesis for why humans enjoy musical rhythms relates to their prediction of when each beat should occur. The ability to predict the timing of an event is important from an evolutionary perspective. Therefore, our brains have evolved internal mechanisms for processing the progression of time. However, due to inherent noise in neural signals, this prediction is not always accurate. Theoretical considerations of optimal estimates suggest the occurrence of certain systematic errors made by the brain when estimating the timing of beats in rhythms. Here, we tested psychophysically whether these systematic errors exist and if so, how they depend on stimulus parameters. Our experimental data revealed two main types of systematic errors. First, observers perceived the time of the last beat of a rhythmic pattern as happening earlier than actual when the inter-beat interval was short. Second, the perceived time of the last beat was later than the actual when the inter-beat interval was long. The magnitude of these systematic errors fell as the number of beats increased. However, with many beats, the errors due to long inter-beat intervals became more apparent. We propose a Bayesian model for these systematic errors. The model fits these data well, allowing us to offer possible explanations for how these errors occurred. For instance, neural processes possibly contributing to the errors include noisy and temporally asymmetric impulse responses, priors preferring certain time intervals, and better-early-than-late loss functions. We finish this article with brief discussions of both the implications of systematic errors for the appreciation of rhythm and the possible compensation by the brain's motor system during a musical performance. [ABSTRACT FROM AUTHOR]

Abstract The internal validity of conclusions about effectiveness or impact in systematic reviews, and of decisions based on them, depends on risk of bias assessments being conducted appropriately. However, a random sample of 50 recently-published articles claiming to be quantitative environmental systematic reviews found 64% did not include any risk of bias assessment, whilst nearly all that did omitted key sources of bias. Other limitations included lack of transparency, conflation of quality constructs, and incomplete application of risk of bias assessments to the data synthesis. This paper addresses deficiencies in risk of bias assessments by highlighting core principles that are required for risk of bias assessments to be fit-for-purpose, and presenting a framework based on these principles to guide review teams on conducting risk of bias assessments appropriately and consistently. The core principles require that risk of bias assessments be Focused, Extensive, Applied and Transparent (FEAT). These principles support risk of bias assessments, appraisal of risk of bias tools, and the development of new tools. The framework follows a Plan-Conduct-Apply-Report approach covering all stages of risk of bias assessment. The scope of this paper is comparative quantitative environmental systematic reviews which address PICO or PECO-type questions including, but not limited to, topic areas such as environmental management, conservation, ecosystem restoration, and analyses of environmental interventions, exposures, impacts and risks.