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Identifying high-yield genotypes under low water availability is essential for soybean climate-smart breeding. However, a major bottleneck lies in phenotyping, particularly in selecting cost-efficient markers associated with stress tolerance and yield stabilization. Here, we conducted in-depth phenotyping experiments in two soybean genotypes with contrasting drought tolerance, MUNASQA (tolerant) and TJ2049 (susceptible), to better understand soybean stress physiology and identify/statistically validate drought-tolerance and yield-stabilization traits as potential breeding markers. Firstly, at the critical reproductive stage (R5), the molecular differences between the genotype's responses to mild water deficit were explored through massive analysis of cDNA ends (MACE)-transcriptomic and gene ontology. MUNASQA transcriptional profile, compared to TJ2049, revealed significant differences when responding to drought. Next, both genotypes were phenotyped under mild water deficit, imposed in vegetative (V3) and R5 stages, by evaluating 22 stress-response, growth, and water-use markers, which were subsequently correlated between phenological stages and with yield. Several markers showed high consistency, independent of the phenological stage, demonstrating the effectiveness of the phenotyping methodology and its possible use for early selection. Finally, these markers were classified and selected according to their cost-feasibility, statistical weight, and correlation with yield. Here, pubescence, stomatal density, and canopy temperature depression emerged as promising breeding markers for the early selection of drought-tolerant soybeans. [ABSTRACT FROM AUTHOR]

User-generated content based on customer opinions and experience has become a rich source of valuable information for enterprises. The purpose of aspect-based sentiment analysis is to predict the sentiment polarity of specific targets from user-generated content. This study proposes a component focusing multi-head co-attention network model which contains three modules: extended context, component focusing, and multi-headed co-attention, designed to improve upon problems encountered in the past. The extended context module improves the ability of bidirectional encoder representations from transformers to handle aspect-based sentiment analysis tasks, and the component focusing module improves the weighting of adjectives and adverbs, to alleviate the problem of average pooling, which treats every word as an equally important term. The multi-head co-attention network is applied to learn the important words in a multi-word target before acquiring the context representation and performs the attention mechanism on the sequence data. The performance of the proposed model is evaluated in extensive experiments on publicly available datasets. The results show that the performance of the proposed model is better than that of the recent state-of-the-art models. [ABSTRACT FROM AUTHOR]

We introduce a new command, robustate, that executes the inverseprobability weighting estimation and inference for the average treatment effect with robustness against limited overlap (that is, weak satisfaction of the common support condition). This command produces estimates, standard errors, p -values, and confidence intervals for the average treatment effect. The utility of the command is demonstrated with both simulated and real data of right heart catheterization. These illustrations show that the proposed estimator implemented by the robustate command indeed exhibits more robustness against limited overlap than the traditional inverse-probability weighting estimator. The main method of the command is proposed in Sasaki and Ura (2022, Econometric Theory 38: 66–112). [ABSTRACT FROM AUTHOR]

We introduce causal interaction tree (CIT) algorithms for finding subgroups of individuals with heterogeneous treatment effects in observational data. The CIT algorithms are extensions of the classification and regression tree algorithm that use splitting criteria based on subgroup‐specific treatment effect estimators appropriate for observational data. We describe inverse probability weighting, g‐formula, and doubly robust estimators of subgroup‐specific treatment effects, derive their asymptotic properties, and use them to construct splitting criteria for the CIT algorithms. We study the performance of the algorithms in simulations and implement them to analyze data from an observational study that evaluated the effectiveness of right heart catheterization for critically ill patients. [ABSTRACT FROM AUTHOR]

With the rapid development of renewable energy, new energy generation has become one of the main forces of power generation in China. In view of the adaptability of distribution network after distributed energy is connected to the grid, this study proposed a new definition of the adaptability of distribution network. A distribution network adaptability evaluation index system with six first-level indexes, including reliability, load rate, current, power quality, service life and new energy utilization rate is established. Through the subjective and objective weighting method, the combined weight is obtained. Combined with TOPSIS, the expected output value of the evaluation model is determined. This study proposes a distribution network adaptability evaluation model based on improved TOPSIS-PSO-SVM. A distribution network adaptability evaluation model based on improved TOPSIS-PSO-SVM is proposed, and the distribution network in 5 regions of Ningxia is used for example analysis. The results show that the evaluation relative error interval of the TOPSIS-PSO-SVM evaluation model is [0.94%, 1.03%], and the average absolute value of the relative error is 0.885 4%, which indicates that the evaluation model has smaller evaluation error and higher evaluation precision in the adaptability evaluation of distribution network. [ABSTRACT FROM AUTHOR]

Cardiotocography (CTG) is a popular clinical tool to assess foetal health status. Automatic CTG analysis can remove the judgement differences of inter- and intra- doctors. However, absence of CTG database has hindered the development of automatic CTG analysis based on deep learning. Therefore, digitization of CTG signal from clinic report is an important way to enrich CTG database, and in turn promote the automatic CTG analysis. The proposed digitization method extracts digital signal from the commonly used binary CTG paper reports. An adaptive region positioning algorithm based on statistical calculation is used to locate signal regions. Then, by deducing the dynamic minimal weight sum in theory, methods based on statistical calculation and on the weight sum are designed to remove grid lines. Next, according to different breakpoint types and signal line trends, different signal reconstruction methods are formulated. This realize extracting the signal line from binary background grid lines. Finally, a calibration method based on segmented sampling is designed to reduce calibration error due to smartphone lens distortion. The experimental results show that the correlation coefficients of FHR and UC of proposed method both reached 0.98. After three expert gynaecologists's evaluations, there are no clinically relevant differences were identified between the extracted signals and the reference ones. • First time to extract the cardiotocograph signals from the binary clinic paper report. • Adaptive region positioning algorithm based on statistical calculation. • Statistical calculation and weight sum are designed to remove grid lines. • Segmented sampling is designed to reduce calibration error due to smartphone lens distortion. • Digitization of CTG signal is important way to promote the automatic CTG analysis. [ABSTRACT FROM AUTHOR]

Copyright of China Sciencepaper is the property of China Sciencepaper 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.)

Spatial data exhibits the property that nearby points are correlated. This also holds for learnt representations across layers, but not for commonly used weight initialization methods. Our theoretical analysis quantifies the learning behavior of weights of a single spatial filter. It is thus in contrast to a large body of work that discusses statistical properties of weights. It shows that uncorrelated initialization (1) might lead to poor convergence behavior and (2) training of (some) parameters is likely subject to slow convergence. Empirical analysis shows that these findings for a single spatial filter extend to networks with many spatial filters. The impact of (correlated) initialization depends strongly on learning rates and l2-regularization. [ABSTRACT FROM AUTHOR]

Background: Health surveys are commonly somewhat non-representative of their target population, potentially limiting the generalisability of prevalence estimates for health/behaviour characteristics and disease to the population. To reduce bias, weighting methods have been developed, though few studies have validated weighted survey estimates against generally accepted high-quality independent population benchmark estimates.Methods: We applied post-stratification and raking methods to the Australian 45 and Up Study using Census data and compared the resulting prevalence of characteristics to accepted population benchmark estimates and separately, the incidence rates of lung, colorectal, breast and prostate cancer to whole-of-population estimates using Standardised Incidence Ratios (SIRs).Results: The differences between 45 and Up Study and population benchmark estimates narrowed following sufficiently-informed raking, e.g. 13.6% unweighted prevalence of self-reported fair/poor overall health, compared to 17.0% after raking and 17.9% from a population benchmark estimate. Raking also improved generalisability of cancer incidence estimates. For example, unweighted 45 and Up Study versus whole-of-population SIRs were 0.700 (95%CI:0.574-0.848) for male lung cancer and 1.098 (95%CI:1.002-1.204) for prostate cancer, while estimated SIRs after sufficiently-informed raking were 0.828 (95%CI:0.684-0.998) and 1.019 (95%CI:0.926-1.121), respectively.Conclusion: Raking may be a useful tool for improving the generalisability of exposure prevalence and disease incidence from surveys to the population. [ABSTRACT FROM AUTHOR]

It is well known that attentional selection is sensitive to the regularities presented in the display. In the current study we employed the additional singleton paradigm and systematically manipulated the probability that the target would be presented in one particular location within the display (probabilities of 30%, 40%, 50%, 60%, 70%, 80%, and 90%). The results showed the higher the target probability, the larger the performance benefit for high- relative to low-probability locations both when a distractor was present and when it was absent. We also showed that when the difference between high- and low-probability conditions was relatively small (30%) participants were not able to learn the contingencies. The distractor presented at a high-probability target location caused more interference than when presented at a low-probability target location. Overall, the results suggest that attentional biases are optimized to the regularities presented in the display tracking the experienced probabilities of the locations that were most likely to contain a target. We argue that this effect is not strategic in nature nor the result of repetition priming. Instead, we assume that through statistical learning the weights within the spatial priority map are adjusted optimally, generating the efficient selection priorities. [ABSTRACT FROM AUTHOR]

Overlap of primary studies among systematic reviews (SRs) is one of the main methodological challenges when conducting overviews. If not assessed properly, overlapped primary studies may mislead findings, since they may have a major influence either in qualitative analyses or in statistical weight. Moreover, overlapping SRs may represent the existence of duplicated efforts. Matrices of evidence and the calculation of the overall corrected covered area (CCA) are appropriate methods to address this issue, but they seem to be not comprehensive enough. In this article we present Graphical Representation of Overlap for OVErviews (GROOVE), an easy‐to‐use tool for overview authors. Starting from a matrix of evidence, GROOVE provides the number of included primary studies and SRs included in the matrix; the absolute number of overlapped and non‐overlapped primary studies; and an overall CCA assessment. The tool also provides a detailed CCA assessment for each possible pair of SRs (or "nodes"), with a graphical and easy‐to‐read representation of these results. Additionally, it includes an advanced optional usage, incorporating structural missingness in the matrix. In this article, we show the details about how to use GROOVE, what results it achieves and how the tool obtains these results. GROOVE is intended to improve the overlap assessment by making it easier, faster, and more friendly for both authors and readers. The tool is freely available at http://doi.org/10.17605/OSF.IO/U2MS4 and https://es.cochrane.org/es/groovetool [ABSTRACT FROM AUTHOR]

Massively parallel sequencing (MPS) of mitochondrial (mt) DNA allows forensic laboratories to report heteroplasmy on a routine basis. Statistical approaches will be needed to determine the relative frequency of observing an mtDNA haplotype when including the presence of a heteroplasmic site. Here, we examined 1301 control region (CR) sequences, collected from individuals in four major population groups (European, African, Asian, and Latino), and covering 24 geographically distributed haplogroups, to assess the rates of point heteroplasmy (PHP) on an individual and nucleotide position (np) basis. With a minor allele frequency (MAF) threshold of 2%, the data was similar across population groups, with an overall PHP rate of 37.7%, and the majority of heteroplasmic individuals (77.3%) having only one site of heteroplasmy. The majority (75.2%) of identified PHPs had an MAF of 2–10%, and were observed at 12.6% of the nps across the CR. Both the broad and phylogenetic testing suggested that in many cases the low number of observations of heteroplasmy at any one np results in a lack of statistical association. The posterior frequency estimates, which skew conservative to a degree depending on the sample size in a given haplogroup, had a mean of 0.152 (SD 0.134) and ranged from 0.031 to 0.83. As expected, posterior frequency estimates decreased in accordance with 1/n as the sample size (n) increased. This provides a proposed conservative statistical framework for assessing haplotype/heteroplasmy matches when applying an MPS technique in forensic cases and will allow for continual refinement as more data is generated, both within the CR and across the mitochondrial genome. [ABSTRACT FROM AUTHOR]

In Christie et al. (2019), we used simulations to quantitatively compare the bias of commonly used study designs in ecology and conservation. Based on these simulations, we proposed 'accuracy weights' as a potential way to account for study design validity in meta‐analytic weighting methods. Pescott and Stewart (2022) raised concerns that these weights may not be generalisable and still lead to biased meta‐estimates. Here we respond to their concerns and demonstrate why developing alternative weighting methods is key to the future of evidence synthesis.We acknowledge that our simple simulation unfairly penalised randomised controlled trial (RCT) relative to before‐after control‐impact (BACI) designs as we considered that the parallel trends assumption held for BACI designs. We point to an empirical follow‐up study in which we more fairly quantify differences in biases between different study designs. However, we stand by our main findings that before‐after (BA), control‐impact (CI) and after designs are quantifiably more biased than BACI and RCT designs. We also emphasise that our 'accuracy weighting' method was preliminary and welcome future research to incorporate more dimensions of study quality.We further show that over a decade of advances in quality effect modelling, which Pescott and Stewart (2022) omit, highlights the importance of research such as ours in better understanding how to quantitatively integrate data on study quality directly into meta‐analyses. We further argue that the traditional methods advocated for by Pescott and Stewart (2022; e.g. manual risk‐of‐bias assessments and inverse‐variance weighting) are subjective, wasteful and potentially biased themselves. They also lack scalability for use in large syntheses that keep up‐to‐date with the rapidly growing scientific literature.Synthesis and applications. We suggest, contrary to Pescott and Stewart's narrative, that moving towards alternative weighting methods is key to future‐proofing evidence synthesis through greater automation, flexibility and updating to respond to decision‐makers' needs—particularly in crisis disciplines in conservation science where problematic biases and variability exist in study designs, contexts and metrics used. While we must be cautious to avoid misinforming decision‐makers, this should not stop us investigating alternative weighting methods that integrate study quality data directly into meta‐analyses. To reliably and pragmatically inform decision‐makers with science, we need efficient, scalable, readily automated and feasible methods to appraise and weight studies to produce large‐scale living syntheses of the future. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Human Sciences (19922876) is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) 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.)

Regularized regression analysis is a mature analytic approach to identify weighted sums of variables predicting outcomes. We present a novel Coarse Approximation Linear Function (CALF) to frugally select important predictors and build simple but powerful predictive models. CALF is a linear regression strategy applied to normalized data that uses nonzero weights + 1 or − 1. Qualitative (linearly invariant) metrics to be optimized can be (for binary response) Welch (Student) t-test p-value or area under curve (AUC) of receiver operating characteristic, or (for real response) Pearson correlation. Predictor weighting is critically important when developing risk prediction models. While counterintuitive, it is a fact that qualitative metrics can favor CALF with ± 1 weights over algorithms producing real number weights. Moreover, while regression methods may be expected to change most or all weight values upon even small changes in input data (e.g., discarding a single subject of hundreds) CALF weights generally do not so change. Similarly, some regression methods applied to collinear or nearly collinear variables yield unpredictable magnitude or the direction (in p-space) of the weights as a vector. In contrast, with CALF if some predictors are linearly dependent or nearly so, CALF simply chooses at most one (the most informative, if any) and ignores the others, thus avoiding the inclusion of two or more collinear variables in the model. [ABSTRACT FROM AUTHOR]

Intense precipitation is one of the main drivers of landslides around the globe. On a global scale, the occurrence of very wet periods is controlled by different, well-known natural cycles: ENSO, NAO, SUNSPOT, etc. In this paper, we present a spatio-temporal analysis of climate cycles on the island of Majorca (Spain) and their correlation with the landslide inventory. Firstly, using spectral analysis techniques, the main climatic cycles that control the rainy periods on the island have been identified. For this purpose, rainfall data from 62 weather stations have been analysed in a comprehensive manner, with time series of more than 30 years. The cycles with the greatest influence on rainfall, from the point of view of statistical confidence, are ENSO (5.6 y and 3.5 y), as well as NAO (7.5 y) and QBO. Then, using geostatistical methods, the distribution of rainfall during dry, average, and wet years was mapped, as was the spatial representation of the statistical significance of the different natural cycles, in order to define the areas of greatest danger from heavy rainfall. The Serra de Tramuntana is not only the rainiest region of the island, but also the area where the highest values of statistical confidence for the set of climatic cycles are concentrated. The 5 largest landslides of the series are very well located in the areas of highest statistical weight, mainly for the NAO (7.5 y), QBO and HALO (22.4 y) cycles, as well as in the wettest sector for the wet type year. [ABSTRACT FROM AUTHOR]

The recently developed probabilistic genotyping software package MaSTR™ (SoftGenetics LLC) was used to develop statistical weight estimates for a variety of two-person STR mixture profiles with differentially degraded sources of DNA. A total of 864 analyses, on 144 two-person profiles, were performed. Mixture ratios ranged from 1:1 to 1:10, including pristine sources of DNA and various combinations of artificially degraded DNA (average size fragments of 150 or 250 bps). Quantities of DNA template were varied (0.1 to 0.5 ngs of total input) and MaSTR™ analysis was performed with eight chains of 10,000 or 40,000 iterations, with or without a conditioning profile to generate likelihood ratio (LR) values. Overall, the software performed as expected. The resulting log(LR) values for pristine mixture profiles were typically greater than 1030. Lower-quality mixture data associated with sources of DNA at ~ 0.05 ngs for each contributor resulted in peak imbalance and allelic dropout which reduced the weight in support of a contributor. This was exacerbated by higher levels of degradation, with some instances resulting in log(LR) values in support of an exclusion. These studies provide additional support for the use of probabilistic genotyping software solutions in forensic investigations, addressing concerns raised by the President's Council of Advisors on Science and Technology (PCAST). [ABSTRACT FROM AUTHOR]

Propensity score weighting is an important tool for comparative effectiveness research. Besides the inverse probability of treatment weights (IPW), recent development has introduced a general class of balancing weights, corresponding to alternative target populations and estimands. In particular, the overlap weights (OW) lead to optimal covariate balance and estimation efficiency, and a target population of scientific and policy interest. We develop the R package PSweight to provide a comprehensive design and analysis platform for causal inference based on propensity score weighting. PSweight supports (i) a variety of balancing weights, (ii) binary and multiple treatments, (iii) simple and augmented weighting estimators, (iv) nuisance-adjusted sandwich variances, and (v) ratio estimands. PSweight also provides diagnostic tables and graphs for covariate balance assessment. We demonstrate the functionality of the package using a data example from the National Child Development Survey (NCDS), where we evaluate the causal effect of educational attainment on income. [ABSTRACT FROM AUTHOR]

Clinical mastitis (CM) incidence is considerable in terms of cows affected per year, but cases are much less common in terms of detections per cow per milking. From a modeling perspective, where predictions are made every time any cow is milked, low CM incidence per cow day makes training, evaluating, and applying CM prediction models a challenge. The objective of this study was to build models for predicting CM incidence using time-series sensor data and choose models that maximize net return based on a cost matrix. Data collected from 2 university dairy farms, the University of Florida and Virginia Polytechnic Institute and State University, were used to gather representative data, including 110,156 milkings and 333 CM cases. Variables used in the models were milk yield, protein, lactose, fat, electrical conductivity, days in milk, lactation number, and activity as the number of steps, lying time, lying bouts, and lying bout duration. Models that predicted either likelihood of CM caused by gram-negative (GN) or gram-positive (GP) bacteria on each day were derived using extreme gradient boosting with weighting favoring true-positive cases, logistic responses, and log-loss errors. Model accuracies were determined using data randomly held out from the training set on each run. All variables considered were in terms of change (slope) over previous days, including the day CM was visually detected. The GN models had a median sensitivity (Se) of 52.6% and specificity (Sp) of 99.8%, whereas the GP models had a median Se of 37.5% and Sp of 99.9% when tested on the held-out data. In our models optimized to reduce cost from predictions, the Se was much less than Sp, suggesting that CM models might benefit from greater model weighting placed on Sp. Results also highlight the importance of positive predictive value (true positive cases per predicted positive case) along with Sp and Se, as models built on sparse data tend to predict too many false-positive cases. The calculated partial net return of our GN and GP models were −$0.15 and −$0.10 per cow per lactation, respectively, whereas International Organization for Standardization (ISO) standard models with Se of 80% and Sp of 99% would return −$1.32 per cow per lactation. Models chosen that minimized the cost to the farmer differed markedly from models that met ISO guidelines, showing asymmetry in targets between Sp and Se when the disease incidence rate is low. Because of the unique challenges that low-incidence diseases like CM present, we recommend that future CM predictive models consider the economic and practical implications in addition to the traditional model evaluation metrics. [ABSTRACT FROM AUTHOR]

Recent advances in atomistic molecular dynamics (MD) simulations of biomolecules allow us to explore their conformational spaces widely, observing large-scale conformational fluctuations or transitions between distinct structures. To reproduce or refine experimental data using MD simulations, structure ensembles, which are characterized by multiple structures and their statistical weights on the rugged free-energy landscapes, are often used. Here, we summarize weight average approaches for various experimental measurements. Weight average approaches are now applied to hybrid quantum mechanics/molecular mechanics MD simulations to predict fast vibrational motions in a protein with a high accuracy for better understanding of molecular functions from atomic structures. • Conformational dynamics of biomolecules involves fast vibrational motions and slow transitions between distinct structures. • The conformational dynamics is theoretically described via multidimensional free-energy landscapes with many basins. • structural ensemble from molecular dynamics simulations is used to reproduce dynamical properties observed experimentally. • Weight average approaches are useful to analyze not only slow conformational changes but also fast vibrational motions. [ABSTRACT FROM AUTHOR]

Background: As the world's largest coal producer, China was accounted for about 46% of global coal production. Among present coal mining risks, methane gas (called gas in this paper) explosion or ignition in an underground mine remains ever-present. Although many techniques have been used, gas accidents associated with the complex elements of underground gassy mines need more robust monitoring or warning systems to identify risks. This paper aimed to determine which single method between the PCA and Entropy methods better establishes a responsive weighted indexing measurement to improve coal mining safety. Methods: Qualitative and quantitative mixed research methodologies were adopted for this research, including analysis of two case studies, correlation analysis, and comparative analysis. The literature reviewed the most-used multi-criteria decision making (MCDM) methods, including subjective methods and objective methods. The advantages and disadvantages of each MCDM method were briefly discussed. One more round literature review was conducted to search publications between 2017 and 2019 in CNKI. Followed two case studies, correlation analysis and comparative analysis were then conducted. Research ethics was approved by the Shanxi Coking Coal Group Research Committee. Results: The literature searched a total of 25,831publications and found that the PCA method was the predominant method adopted, and the Entropy method was the second most widely adopted method. Two weighting methods were compared using two case studies. For the comparative analysis of Case Study 1, the PCA method appeared to be more responsive than the Entropy. For Case Study 2, the Entropy method is more responsive than the PCA. As a result, both methods were adopted for different cases in the case study mine and finally deployed for user acceptance testing on 5 November 2020. Conclusions: The findings and suggestions were provided as further scopes for further research. This research indicated that no single method could be adopted as the better option for establishing indexing measurement in all cases. The practical implication suggests that comparative analysis should always be conducted on each case and determine the appropriate weighting method to the relevant case. This research recommended that the PCA method was a dimension reduction technique that could be handy for identifying the critical variables or factors and effectively used in hazard, risk, and emergency assessment. The PCA method might also be well-applied for developing predicting and forecasting systems as it was sensitive to outliers. The Entropy method might be suitable for all the cases requiring the MCDM. There is also a need to conduct further research to probe the causal reasons why the PCA and Entropy methods were applied to each case and not the other way round. This research found that the Entropy method provides higher accuracy than the PCA method. This research also found that the Entropy method demonstrated to assess the weights of the higher dimension dataset was higher sensitivity than the lower dimensions. Finally, the comprehensive analysis indicates a need to explore a more responsive method for establishing a weighted indexing measurement for warning applications in hazard, risk, and emergency assessments. [ABSTRACT FROM AUTHOR]

Three-dimensional landscape patterns are an effective means to study the relationship between landscape pattern evolution and eco-environmental effects. This paper selects six districts in Xi'an as the study area to examine the spatial distribution characteristics of the three-dimensional architectural landscape in the city's main urban area using three-dimensional information on the buildings in 2020 with the support of GIS. In this study, two new architectural landscape indices—landscape height variable coefficient and building rugosity index—were employed in landscape pattern analysis, whilst a system of rigorous and comprehensive three-dimensional architectural landscape metrics was established using principal component analysis. A mathematical model of weighted change of landscape metrics based on the objective weighting method was applied to carry out scale analysis of the landscape patterns. Spatial statistical analysis and spatial autocorrelation analysis were conducted to comprehensively study the differentiation of three-dimensional architectural landscape spatial patterns. The results show that the characteristic scale of the three-dimensional landscape pattern in Xi'an's main urban area is around 8 km. Moreover, the three-dimensional landscape of the buildings in this area is spatially positively correlated, exhibiting a high degree of spatial autocorrelation whilst only showing small spatial differences. The layout of the architectural landscape pattern is disorderly and chaotic within the second ring, whilst the clustering of patch types occurs near the third ring. Moreover, the building density in the Beilin, Lianhu, and Xincheng districts is large, the building height types are rich, and the roughness of the underlying surface is high, such that these are key areas to be improved through urban renewal. The height, volume, density, morphological heterogeneity, and vertical roughness of the architectural landscape vary amongst functional areas within the study area. This paper is the first to apply the study of spatial heterogeneity of three-dimensional landscape patterns to Xi'an. It does so in order to provide a quantitative basis for urban landscape ecological design for urban renewal and the rational planning of built-up areas, which will promote the sustainable development of the city's urban environment. [ABSTRACT FROM AUTHOR]

In numerical simulations, a high-fidelity (HF) simulation is generally more accurate than a low-fidelity (LF) simulation, while the latter is generally more computationally efficient than the former. To take advantages of both HF and LF simulations, a multi-fidelity surrogate (MFS) model based on moving least squares (MLS), termed as adaptive MFS-MLS, is proposed. The MFS-MLS calculates the LF scaling factors and the unknown coefficients of the discrepancy function simultaneously using an extended MLS model. In the proposed method, HF samples are not regarded as equally important in the process of constructing MFS-MLS models, and adaptive weightings are given to different HF samples. Moreover, both the size of the influence domain and the scaling factors can be determined adaptively according to the training samples. The MFS-MLS model is compared with three state-of-the-art MFS models and three single-fidelity surrogate models in terms of the prediction accuracy through multiple benchmark numerical cases and an engineering problem. In addition, the effects of key factors on the performance of the MFS-MLS model, such as the correlation between HF and LF models, the cost ratio of HF to LF samples, and the combination of HF and LF samples, are also investigated. The results show that MFS-MLS is able to provide competitive performance with high computational efficiency. [ABSTRACT FROM AUTHOR]

We discuss Tauberian conditions under which the statistical convergence of double sequences of fuzzy numbers follows from the statistical convergence of their weighted means. We also prove some other results which are necessary to establish the main results. [ABSTRACT FROM AUTHOR]

The string method is a useful numerical technique for resolving minimum energy paths in rare-event barrier-crossing problems. However, when applied to systems with relatively small energy barriers, the string method becomes inconvenient since many images trace out physically uninteresting regions where the barrier has already been crossed and recrossing is unlikely. Energy weighting alleviates this difficulty to an extent, but typical implementations still require the string's endpoints to evolve to stable states that may be far from the barrier, and deciding upon a suitable energy weighting scheme can be an iterative process dependent on both the application and the number of images used. A second difficulty arises when treating nucleation problems: for later images along the string, the nucleus grows to fill the computational domain. These later images are unphysical due to confinement effects and must be discarded. In both cases, computational resources associated with unphysical or uninteresting images are wasted. We present a new energy weighting scheme that eliminates all of the above difficulties by actively truncating the string as it evolves and forcing all images, including the endpoints, to remain within and cover uniformly a desired barrier region. The calculation can proceed in one step without iterating on strategy, requiring only an estimate of an energy value below which images become uninteresting. [ABSTRACT FROM AUTHOR]

Background: Prevention of the debris from extruding to the periapical region is an important aspect to be taken care of as its extrusion can result in pain, periapical inflammation, and delayed healing owing to the presence of highly infective components in the debris. Aim: The present trial was aimed to compare and assess the amount of debris extruded apically using the single-file reciprocating system - WaveOne GOLD and single file rotary system - OneShape in root canal preparations in vitro. Materials and Methods: Thirty mandibular premolars were divided into two groups (15 samples each) prepared with a single-file reciprocating system - WaveOne GOLD and single-file rotary system - OneShape. Debris extruded was collected and then stored in an incubator. The final weight was calculated after obtaining the mean of three consecutive weights obtained for each tooth. The data were compared using the ANOVA and Turkey's post hoc analysis. Results: Debris extrusions were recorded in both groups and analysis revealed statistical differences in weight before and after the instrumentation procedures in both groups. When compared, reciprocating single file – WaveOne GOLD extruded more debris than rotary single file system – OneShape with a P < 0.001. Conclusions: The rotary instrumentation systems extrude lesser debris apically than the reciprocating instrumentation system. The difference found was statistically significant. [ABSTRACT FROM AUTHOR]

Probability samples are the preferred method for providing inferences that are generalizable to a larger population. However, in many cases, this approach is unlikely to yield a sample size large enough to produce precise inferences. Our goal here is to improve the efficiency of inferences from a probability sample by combining (or blending) it with a nonprobability sample, which is (by itself) potentially fraught with selection biases that would compromise the generalizability of results. We develop novel methods of statistical weighting that may be used for this purpose. Specifically, we make a distinction between weights that can be used to make the two samples representative of the population individually (disjoint blending) and those that make only the combined sample representative (simultaneous blending). Our focus is on weights constructed using propensity scores, but consideration is also given to calibration weighting. We include simulation studies that, among other illustrations, show the gain in precision provided by the convenience sample is lower in circumstances where the outcome is strongly related to the auxiliary variables used to align the samples. Motivating the exposition is a survey of military caregivers; our interest is focused on unpaid caregivers of wounded, ill, or injured US servicemembers and veterans who served following September 11, 2001. Our work serves not only to illustrate the proper execution of blending but also to caution the reader with respect to its dangers, as invoking a nonprobability sample may not yield substantial improvements in precision when assumptions are valid and may induce biases in the event that they are not. [ABSTRACT FROM AUTHOR]

Since 2001, rough convergence has been discussed in research where the core factor degree of roughness has been treated only as a real number. Instead of analyzing different results based on the same definition, we start with the new flow of ideas to enlarge the spectrum of degree of roughness. As a consequence we invent a new approach to degree of roughness with approximation of neighborhood conception over locally solid Riesz spaces. While we start working with this new definition and scrutinize existing results, many of the earlier results are violated. Naturally our curiosity begins to know that under which condition(s) we may get back either some of the previous results or not? Apart from these, we consider various non-trivial results which enhance and signify the motivation of our paper. [ABSTRACT FROM AUTHOR]

The classic expectation-maximization (EM) algorithm in maximum-likelihood direction finding updates the complete-data sufficient statistics by finding their conditional expectations. Besides, from the perspective of alternating maximization (AM) these sufficient statistics can also be updated by maximizing the complete-data log-likelihood function with respect to only the complete data, based on which both deterministic and stochastic signal models are considered. Theoretical analysis indicates that the proposed AM algorithm is equivalent to the EM algorithm for the deterministic signal model while outperforming the EM algorithm for the stochastic signal model. On this foundation, a sequential AM (SAM) algorithm and two iterative weighting schemes are proposed to improve the convergence of the AM algorithm. Numerical results show that the SAM algorithm yields faster convergence and the two iterative weighting schemes can be used to avoid the convergence of the EM and AM algorithms to an unwanted limit point efficiently. [ABSTRACT FROM AUTHOR]

Considering the scarcity of Uyghur sentiment resources, in this paper proposed a new combined unsupervised sentiment classification method for Uyghur text without any labeled corpora. In the first part, a Uyghur sentiment dictionary, UYSentiDict, was adopted to classify the sentences. For the sentiment vocabulary matching, both the matching of the original word and the stem were considered, and the influence of sentence patterns, negation words, and degree adverbs were further considered as well. Based on different thresholds, the sentences with higher sentiment values were selected from the lexicon-based classification results as a pseudo-labeled dataset. In the second part, different sentiment characteristics were learned from the pseudo-labeled dataset by the machine learning classifier, and the remaining categorical data were further classified. It can be concluded that the method proposed in this paper has good classification efficiency in Uyghur sentiment corpora in four different fields, and some results were performed better than the classification results of machine learning classifier. Moreover, this method is not restricted by the field of data and does not need to be marked in advance with good training corpus, and can solve the resource shortage problem in the field of Uyghur sentiment classification effectively. [ABSTRACT FROM AUTHOR]

Copyright of Iranian Journal of Information Processing & Management is the property of Iranian Journal of Information Processing & Management 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.)

During the multi-objective optimization process, numerous efficient solutions may be generated to form the Pareto frontier. Due to the complexity of formulating and solving mathematical problems, choosing the best point to be implemented becomes a non-trivial task. Thus, this paper introduces a weighting strategy named robust optimal point selection, based on ratio diversification/error, to choose the most preferred Pareto optimal point in multi-objective optimization problems using response surface methodology. Furthermore, this paper proposes to explore a theoretical gap—the prediction variance behavior related to the weighting. The ratios Shannon's entropy/error and diversity/error and the unscaled prediction variance are experimentally modeled using mixture design and the optimal weights for the multi-objective optimization process are defined by the maximization of the proposed measures. The study could demonstrate that the weights used in the multi-objective optimization process influence the prediction variance. Furthermore, the use of diversification measures, such as entropy and diversity, associated with measures of error, such as mean absolute percent error, was determined to be useful in mapping regions of minimum variance within the Pareto optimal responses obtained in the optimization process. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Human Sciences (19922876) is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) 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.)

The growth in size and complexity of convolutional neural networks (CNNs) is forcing the partitioning of a network across multiple accelerators during training and pipelining of backpropagation computations over these accelerators. Pipelining results in the use of stale weights. Existing approaches to pipelined training avoid or limit the use of stale weights with techniques that either underutilize accelerators or increase training memory footprint. This paper contributes a pipelined backpropagation scheme that uses stale weights to maximize accelerator utilization and keep memory overhead modest. It explores the impact of stale weights on the statistical efficiency and performance using 4 CNNs (LeNet-5, AlexNet, VGG, and ResNet) and shows that when pipelining is introduced in early layers, training with stale weights converges and results in models with comparable inference accuracies to those resulting from nonpipelined training (a drop in accuracy of 0.4%, 4%, 0.83%, and 1.45% for the 4 networks, respectively). However, when pipelining is deeper in the network, inference accuracies drop significantly (up to 12% for VGG and 8.5% for ResNet-20). The paper also contributes a hybrid training scheme that combines pipelined with nonpipelined training to address this drop. The potential for performance improvement of the proposed scheme is demonstrated with a proof-of-concept pipelined backpropagation implementation in PyTorch on 2 GPUs using ResNet-56/110/224/362, achieving speedups of up to 1.8X over a 1-GPU baseline. [ABSTRACT FROM AUTHOR]

The two-dimensional principal component analysis (2DPCA) has been one of the basic methods of developing artificial intelligent algorithms. To increase the feasibility, we propose a new general ridge regression model for 2DPCA and variations, with extracting low dimensional features under two projection subspaces. A new relaxed 2DPCA under the quaternion framework is proposed to utilize the label (if known) and color information to compute the essential features of generalization ability with optimization algorithms. The 2DPCA-based approaches for face recognition are also improved by weighting each principle component a scatter measure, which increases efficiently the rate of face recognition. In numerical experiments on well-known standard databases, the R2DPCA approach has high generalization ability and achieves a higher recognition rate than the state-of-the-art 2DPCA-like methods, and has better performance than the basic deep learning methods such as CNNs, DBNs, and DNNs in the small-sample case. [ABSTRACT FROM AUTHOR]

We present an improved method for calculating the parallel and perpendicular velocity correlation functions directly from peculiar velocity surveys using weighted maximum-likelihood estimators. A central feature of the new method is the use of a position-dependent weighting scheme that reduces the influence of nearby galaxies, which are typically overrepresented relative to more distant galaxies in most surveys. We demonstrate that correlation functions calculated in this way are less susceptible to biases due to our particular location in the universe, and thus are more easily comparable to linear theory and between surveys. Our results suggest that the parallel velocity correlation function is a promising cosmological probe, given that it provides a better approximation of a Gaussian distribution than other velocity correlation functions and that its bias is more easily minimized by weighting. Though the position-weighted parallel velocity correlation function increases the statistical uncertainty, it decreases the cosmic variance and is expected to provide more stable and tighter cosmological parameter constraints than other correlation methods in conjunction with more precise velocity surveys in the future. [ABSTRACT FROM AUTHOR]

Reliably assessing the error in an estimated vehicle position is integral for ensuring the vehicle's safety in urban environments. Many existing approaches use GNSS measurements to characterize protection levels (PLs) as probabilistic upper bounds on position error. However, GNSS signals might be reflected or blocked in urban environments, and thus additional sensor modalities need to be considered to determine PLs. In this paper, we propose an approach for computing PLs by matching camera image measurements to a LiDAR‐based 3D map of the environment. We specify a Gaussian mixture model probability distribution of position error using deep neural‐network‐based data‐driven models and statistical outlier weighting techniques. From the probability distribution, we compute PL by evaluating the position error bound using numerical line‐search methods. Through experimental validation with real‐world data, we demonstrate that the PLs computed from our method are reliable bounds on the position error in urban environments. [ABSTRACT FROM AUTHOR]

Does disparity in income and consumption incite terrorism in Africa? To answer this important question, we investigate the empirical linkages between inequality and terrorism by separately regressing income and consumption inequalities on four indicators of terrorism: domestic, transnational, unclear, and total over the period 1980–2012. Employing a negative binomial regression across a panel dataset covering 46 African economies, the following findings are established. First, both income and consumption inequalities have decreasing impacts on all terrorism measures—with the exception of uncertain terrorism (the impact of which is negligible). Second, both income and consumption inequalities exert more statistical influence on transnational terrorism than domestic terrorism. Third, income inequality exerts more statistical weight on terrorism measures than consumption inequality across the model specifications. Last, the non-trivial impact of confounding variables—such as the lagged value of terrorism, surface areas, and conflicts—are validated across the terrorism models. In line with these empirical outcomes, policy implications and suggestions for further studies are offered. [ABSTRACT FROM AUTHOR]

The purpose of this study was to empirically examine corporate environmental performance, climate change mitigation, and green innovation behavior in sustainable finance. Building my argument by drawing on data collected from Barclays, CPI, FiBraS, Finance Watch, GABV, GIZ, KPMG, LAB, Mission 2020, and UK Finance, I performed analyses and made estimates regarding the financial shift to net-zero emissions and climate-resilient growth. Descriptive statistics of compiled data from the completed surveys were calculated when appropriate. This survey employs statistical weighting procedures to clarify deviations in the survey sample from known population features, which is instrumental in correcting for differential survey participation and random variation in samples. Results are estimates and commonly are dissimilar within a narrow range around the actual value. The data was weighted in a multistep process that accounts for multiple stages of sampling and nonresponse that occur at different points in the survey process. The precision of the online polls was measured using a Bayesian credibility interval. Confirmatory factor analysis was employed to test for the reliability and validity of measurement instruments. [ABSTRACT FROM AUTHOR]

Physics-Informed Neural Networks (PINNs) are being applied to forward and inverse problems in various science and engineering disciplines, integrating physical principles into the learning process. In line with this, the previously proposed Physics-Informed Parallel Neural Networks (PIPNNs) framework addresses the inverse structural identification problem of continuous structural systems, particularly for handling inherent discontinuities in the system such as interior supports and dissimilar element properties. The PIPNNs framework accommodates structural discontinuities by dividing the computational domain into subdomains, each uniquely represented through a parallelized and interconnected Neural Network (NN) architecture. However, these parallel NNs pose a challenge due to the increased number of terms in the loss function, making network training more complex. The main contribution of this paper is the development of a Neural Tangent Kernel (NTK)-based self-adaptive loss weighting mechanism within the PIPNNs framework to address the challenges of dissimilar convergence rates of loss terms during the training of multiple NNs, ensuring balanced convergence across various physical constraints and subdomains. Guided by NTK theory, which describes the evolution of fully connected NNs during training by gradient descent, the self-adaptive weighting approach developed here adaptively alters the weights for each loss component throughout the NNs training process. These weights, determined based upon the eigenvalues of the NTK matrix of the PIPNNs, adjust the convergence rates of each loss term to achieve a balanced convergence, while requiring less training data. In the context of the inverse problem and structural identification, the NTK matrix is derived by considering not only the training evolution of the parallel NNs but also the changes in unknown structural parameters during the training process. The NTK-enhanced PIPNNs framework is verified and validated against an alternative independent model, and its accuracy is assessed through the application of numerical examples of several continuous structural systems, including bars, beams, and plates. [ABSTRACT FROM AUTHOR]

ABSTRACT Dams provide a water supply, irrigation and power generation. However, the economic, social and environmental impacts are serious when they rupture. It is important that dams continue to function properly and are constantly evaluated for their safety and functionality. Contrary to general belief, small dams are the ones that present the most problems, because they are often built without adequate design and construction methods. One of the methods to evaluate the safety of a dam is through the Dam Safety Index (DSI), which may not entirely avoid subjectivity in dam safety assessment but does propose balanced individual subjectivities in determining the weights of the index criteria. The DSI can also assist managers with preventive maintenance schedules in a dam safety assessment. This work aims to reassess the importance of the parameters of the DSI based on a new weighting of these criteria. The modification of the DSI shows a new bias in the weighting of the criteria that composes the index, not only considering technical aspects, but also legal and environmental ones.

[Display omitted] • Non-Intrusive approach to assess parameters in Particle-In-Cell (PIC) model. • Three widely different conditions common in engineering applications are studied. • Model parameters show variable sensitivities depending on flow conditions. • Scaling of statistical weight is required for consistency during grid refinement. The limitations in numerical treatment of solids-phase in conventional methods like Discrete Element Model and Two-Fluid Model have facilitated the development of alternative techniques such as Particle-In-Cell (PIC). However, a number of parameters are involved in PIC due to its empiricism. In this work, global sensitivity analysis of PIC model parameters is performed under three distinct operating regimes common in chemical engineering applications, viz. settling bed, bubbling fluidized bed and circulating fluidized bed. Simulations were performed using the PIC method in Multiphase Flow with Interphase eXchanges (MFiX) developed by National Energy Technology Laboratory (NETL). A non-intrusive uncertainty quantication (UQ) based approach is applied using Nodeworks to first construct an adequate surrogate model and then identify the most influential parameters in each case. This knowledge will aid in developing an effective design of experiments and determine optimal parameters through techniques such as deterministic or statistical calibration. [ABSTRACT FROM AUTHOR]

Applications of massively parallel sequencing (MPS) in forensic genetics have allowed to exploit the true variation in core forensic STR loci, discover previously unknown STR alleles, aid mixture interpretation and will increase the statistical weight of the evidences. The worldwide populations database is exceedingly important in order to perform statistical calculations. In this study, we firstly present the population data for 27 autosomal STR loci of 148 Kinh ethnic individual living in Vietnam by using a MPS based on Illumina MiSeq platform. By using the largest number of markers for now, we indicated an increase in number of detected alleles as well. The combined match probability and power of exclusion reached 4.02E-30 and 0.999999999955384, respectively. The used markers are also demonstrated valuable in ancestry tracking of different human origins. [ABSTRACT FROM AUTHOR]

An underground nuclear explosion (UNE) couples mechanical energy into crustal rock, which propagates as seismic and acoustic waves. These different physical phenomena transport, by different pathways, to standoff detectors at varying distances. The transport pathways attenuate the original signal but in different ways. Enabled by correct statistical weighting, signal attenuation models can be used to combine these disparate sensor data to estimate the yield of an UNE. Contemporaneous statistical models, used in yield estimation, can be improved with an advanced partition of error for these physical signal propagation models. We present an advanced multivariate approach to error modelling of multiphenomenology physical signatures. In addition to measurement error, our error model represents physical model biases as random with a physics-based covariance structure. To illustrate this proposed framework, we demonstrate the estimation of explosion yield using openly available seismic and acoustic data from chemical single-point explosions. [ABSTRACT FROM AUTHOR]

This research aimed to compare two levels (medium and high) and three types of fertilization (organic, chemical, and integrated) in both foliar nutritional content and fruit quality of Golden Delicious apple variety. The foliar concentration of macro and micronutrients was affected neither by fertilization level nor fertilization type, with the exception of potassium which reached their highest concentration in the high level of chemical fertilization (T1-HC). Regarding the fruit quality attributes, results showed no statistical differences in weight, size, firmness, pH, and starch content between the level and type of fertilization. With the high level of organic fertilization, the best apple color was found, while the medium level of organic fertilization showed the highest concentration of both soluble solid content and total phenolic compounds. Thus, fertilization level had greater influence than the fertilization type. Results also showed that under organic fertilization management is possible obtaining a foliar nutritional content and fruits quality attributes similar to those treated with chemical fertilizers. Future studies must be carried out for elucidating the impact of long-term organic fertilization on the fruit quality and foliar nutritional content of other crops. [ABSTRACT FROM AUTHOR]