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Purpose: Understanding disease prevalence can inform treatment and resource needs across populations. This study aimed to identify the prevalence of sleep apnea (OSA) among veterans and nonveterans. Design: The national Comparative Health Assessment Interview Study, cross-sectional survey using probability-based sampling frames. Setting: Surveys completed by Internet or phone. Subjects: 15,166 veterans (40% response rate) and 4,654 nonveterans (57% response rate). Measures: Self-report of healthcare provider-based diagnosis of OSA. Analysis: Calculation of prevalence of OSA using statistical weighting to allow for direct comparison between veterans and nonveterans. Secondary analyses evaluated OSA by deployment status among veterans and compared average age of OSA diagnosis and differences in OSA prevalence among veterans and nonveterans stratified by gender, marital status, race/ethnicity, and posttraumatic stress disorder diagnosis. Results: OSA diagnosis was more than twice as prevalent among veterans (21%, 95% CI 20%-22%) than nonveterans (9%, 95% CI 8%-10%; aOR: 2.56, 95% CI 2.22-2.95, P <.001). Deployment was associated with higher odds of OSA among veterans (aOR: 1.64, 95% CI 1.43-18.7, P < 001.) Veterans were diagnosed with OSA on average 5 years earlier than nonveterans. Conclusion: Veterans have a high prevalence rate of OSA, highlighting the importance of veterans' access to treatment. OSA is likely underdiagnosed in nonveterans, particularly among racial/ethnic minoritized groups. Future research should investigate disparities in access to diagnostic testing for racial/ethnic minority nonveterans and/or risk factors for OSA among racial/ethnic minority veterans. The increased odds of OSA among those with PTSD highlights in the importance of early referral for OSA testing by providers as well as development of trauma-informed strategies to promote OSA treatment adherence. Limitations include a bias toward underestimation of true disease prevalence due to self-report of diagnosis. [ABSTRACT FROM AUTHOR]

This research presents an innovative approach in text mining based on rough set theory. This study fundamentally utilizes the concept of symmetry from rough set theory to construct indiscernibility matrices and model uncertainties in data analysis, ensuring both methodological structure and solution processes remain symmetric. The effective management and analysis of large-scale textual data heavily relies on automated text classification technologies. In this context, term weighting plays a crucial role in determining classification performance. Particularly, supervised term weighting methods that utilize class information have emerged as the most effective approaches. However, the optimal representation of class–term relationships remains an area requiring further research. This study proposes the Rough Multivariate Weighting Scheme (RMWS) and presents its mathematical derivative, the Square Root Rough Multivariate Weighting Scheme (SRMWS). The RMWS model employs rough sets to identify information-carrying documents within the document–term–class space and adopts a computational methodology incorporating α, β, and γ coefficients. Moreover, the distribution of the term among classes is again effectively revealed. Comprehensive experimental studies were conducted on three different datasets featuring imbalanced-multiclass, balanced-multiclass, and imbalanced-binary class structures to evaluate the model's effectiveness. The results show that RMWS and its derivative SRMWS methods outperform existing approaches by exhibiting superior performance on balanced and unbalanced datasets without being affected by class imbalance and number of classes. Furthermore, the SRMWS method is found to be the most effective for SVM and KNN classifiers, while the RMWS method achieves the best results for NB classifiers. These results show that the proposed methods significantly improve the text classification performance. [ABSTRACT FROM AUTHOR]

In this paper, the Fibonacci sequence, renowned for its significance across various fields, its ability to illuminate numerical concepts, and its role in uncovering patterns in mathematics and nature, forms the foundation of this research. This study introduces innovative concepts of weighted density, weighted statistical summability, weighted statistical convergence, and weighted statistical Cauchy, uniquely defined via the Fibonacci sequence and modulus functions. Key theorems, relationships, examples, and properties substantiate these novel principles, advancing our comprehension of sequence behavior. Additionally, we extend the notion of statistical cluster points within a broader framework, surpassing traditional definitions and offering deeper insights into convergence in a statistical context. Our findings in this paper open avenues for new applications and further exploration in various mathematical fields. [ABSTRACT FROM AUTHOR]

Background: The concept of the population is of fundamental importance in epidemiology and statistics. In some instances, it is not possible to sample directly from the population of interest. Weighting is an established statistical approach for making inferences when the sample is not representative of this population. Methods: The effective sample size (ESS) is a descriptive statistic that can be used to accompany this type of weighted statistical analysis. The ESS is an estimate of the sample size required by an unweighted sample that achieves the same level of precision as the weighted sample. The ESS therefore reflects the amount of information retained after weighting the data and is an intuitively appealing quantity to interpret, for example by those with little or no statistical training. Results: The conventional formula for calculating ESS is derived under strong assumptions, for example that outcome data are homoscedastic. This is not always true in practice, for example for survival data. We propose three new approaches to compute the ESS, that are valid for any type of data and weighted statistical analysis, and so can be applied more generally. Conclusion: We illustrate all methods using an example and conclude that our proposals should accompany, and potentially replace, the existing approach for computing the ESS. [ABSTRACT FROM AUTHOR]

This research presents an innovative method for constructing composite indicators: the Subjective–objective method of maximizing extracted variance (Sommev). Sommev's hybrid weighting approach fills an important gap within a highly controversial area of the composite indicators' literature, which criticizes the statistical assignment of weights disconnected from theory and the errors and judgmental biases inherent in the expert opinion-based weighting approach. These innovations contribute to a more coherent and consistent operationalization of the theoretical framework of multidimensional phenomena, reconciling the non-compensability between sub-indicators and the maximum retention of original information through statistically defined weights, in which the expert's opinion is considered, but does not determine the sub-indicator's weights. Twenty simulations were carried out to analyze the application of the method in representing social exclusion in a Brazilian city. Composite indicators constructed by Sommev retain twice as much information as those constructed with equal weights or weights defined by experts. This increased informational capacity favors a more comprehensive representation of the multidimensional phenomenon, having a high potential for application in solving problems of a multidimensional nature in the social, economic, and environmental areas. [ABSTRACT FROM AUTHOR]

We consider seven-vertex two-dimensional integrable statistical model. With the help of intertwining vector method we construct its counterpart integrable model of SOS type. More general models of both types are constructed by means of fusion procedure. For SOS models we calculate the Boltzmann weights in terms of terminating hypergeometric series 9F8. Then using the similarity transformation for R-operators we construct a new family of vertex models containing the 11-vertex model as a simplest representative. For this new set of models the vertex-SOS correspondence is constructed: we find the intertwining vectors and show that they do not depend on spectral parameter and the SOS statistical weights are similar to those obtained from the 7-vertex model. [ABSTRACT FROM AUTHOR]

In the present study, we aimed to gain a comprehensive understanding of the Eileh 1 iron ore deposit located in Taybad City, Razavi Khorasan Province. A three-dimensional (3D) model was developed using specialized software, which incorporated lithological logs from boreholes, cross-sectional lithological profiles, and assay estimations covering the entire ore deposit area. The dominant lithology of the region consists of altered limestone and dolomite, as well as altered sediments, including sandstone, siltstone, and shale. To estimate the reserves, we employed both the classical inverse distance weighting (IDW) method and the geostatistical kriging method, tailored to the specific conditions of the ore deposit. Variogram analysis indicated that data values varied with distance and direction, revealing geometric anisotropy within the deposit. The maximum search radius in the direction of 0° azimuth was 42.6 meters, the average search radius at 90° azimuth was 10.6 meters, and the minimum search radius in the vertical direction (0° azimuth) was 32 meters. The dataset included topographic information and data from 177 boreholes, totaling 6,936 meters deep. Following geometric modeling and constructing a block model for the deposit, we estimated reserves for various iron-grade thresholds and compared the results of both methods. The findings indicate that the reserve estimates from the two methods were quite similar. While the inverse distance weighting method is recognized as one of the most accurate classical techniques for reserve estimation, the kriging method demonstrated greater precision and reliability. This is attributed to Kriging's ability to account for the spatial structure of the deposit, its unbiased nature, and its lower estimation variance. The findings indicate that the reserve estimates from the two methods were quite similar. While the inverse distance weighting method is recognized as one of the most accurate classical techniques for reserve estimation, the kriging method demonstrated greater precision and reliability. This is attributed to Kriging's ability to account for the spatial structure of the deposit, its unbiased nature, and its lower estimation variance. IntroductionThe evaluation of mineral reserves is conducted using various methods, which differ in calculation algorithms, accuracy, speed, the state of the mineral, and the characteristics of exploration activities (Madani, 1997; Ahmadi, 2010). All estimation methods require exploratory data analysis, and numerous statistical techniques can be employed to analyze this data. However, due to the detrimental effects of uncertainty on investment risk, it is essential to utilize the most effective estimation method grounded in precise data analysis techniques to minimize estimation error. Mineral reserve estimation methods can generally be categorized into two groups: classical (geometric) methods and geostatistical (statistical weighting) methods (Madani, 1997; Hassani Pak and Sharafodin, 2001; Ahmadi, 2010). Classical reserve estimation methods rely on traditional statistics and geometric calculations, while geostatistical methods, such as those based on regional variables, are grounded in spatial structure analysis and utilize the Kriging method. Among classical estimation techniques, the inverse distance weighting (IDW) method is recognized for its accuracy. For this study, both the ordinary Kriging method and the classical estimation method, specifically inverse distance weighting, were employed using two reserve estimation software programs: Surpac and Rockwork. The choice of methods is influenced by the state of the mineral, the extent and nature of exploratory work, and the available exploratory information, with estimation parameters selected through appropriate filtering techniques. In these methods, the deposit is divided into blocks with a square base, and mineral characteristics in each block are calculated based on the data distance within and surrounding it. Blocks must be designed to ensure that data is present within them. The statistical weight of each data point is considered in proportion to the inverse of its distance from the center of the block; thus, data closer to the center carry more statistical weight than those further away. The power of the distance typically varies from 1 to 3, with a common consideration of 2, which is why the method is termed inverse distance weighting.The studied area is part of the Eileh 1 iron ore mine complex, located 49 km southwest of Taibad city in Razavi Khorasan province, covering an area of 240 hectares. The area is defined by longitudes ranging from 60° 22' 40' to 60° 31' 24' and latitudes from 34° 36' 40' to 34° 41' 45' (Figure 1). In the study area, a total of 177 exploratory boreholes were conducted using various methods, including reverse circulation (RC) (35 boreholes) and coring (12 boreholes), with an overall lenth of 6,936 meters. Material and methodsThe final step in the reserve evaluation process involves estimating the grade throughout the entire estimation space in three dimensions and calculating the reserve amount. The principles underlying reserve calculation methods are consistent; however, the primary differences among various methods lie in how the area is divided into segments and how their thickness and average grade are calculated. A comprehensive understanding of a deposit necessitates a series of processing and modeling ofaations (Hassani Pak, 2000). Due to the complexities involved and the lenthy, time-consuming calculations, this work is typically faformed using specialized software, which offers greater accuracy, speed, and ease of use. Grade estimation methods can be categorized into two types: geometric and distance-based. Each of these methods has its advantages and disadvantages, and the selection of a suitable estimation method should be based on the underlying assumptions. In geometric methods, assumptions regarding the spatial distribution of grade within the deposit are taken into account; for instance, the variability of grade or thickness may be assumed to be linear. The software utilized in this research includes Rockworks and Surpac, both of which are comprehensive and powerful tools for imaging, modeling, and analyzing geological information and exploratory data, as well as for faforming various modeling tasks based on the type and amount of data available. Additionally, the preparation of different sections from the mineral material was conducted by geologists and enineers. By employing these software programs, and through the drawing of boreholes, creation of cross-sectional profiles of lithology and grade, and development of a three-dimensional model of lithology, a thorough understanding of both the surface and depth of the deposit has been achieved. Furthermore, to calculate the mineral material reserve, the average density of the mineral material was considered to be 3.8 g/cm³.The data investigated for modeling and reserve estimation are purely hypothetical and cover an area of 2.4 square kilometers. The existing drilling network in the region is based on local outcrops, with varying distances and trends. These distances have been minimized when addressing mineral matter and conducting more detailed investigations. A total of 177 boreholes have been drilled, of which 164 have encountered minerals, resulting in a cumulative borehole lenth of 6,936 meters. Among all drilled boreholes, the maximum depth is 96 meters, attributed to reverse circulation (RC) boreholes No. E1-RC-BH-9.2 and E1-RC-BH-27, while the minimum depth is 9 meters, associated with Rasol borehole No. E1-R-BH-57. The sampling intervals for iron are set at 50 meters. Discussion and conclusionThe process of reserve estimation and the preparation of a block model for a mineral deposit involves distinct steps that must be executed deliberately and precisely. This approach ensures an accurate assessment and a suitable model, utilizing default values for reserve calculations within the software. However, certain applications may yield suboptimal results. Consequently, this research considers the mineral's shape as a series of correct hollow shells, determining the maximum exploration radius, the minimum and maximum number of points used in block estimation, and the method of point exploration through range division.In this study, three-dimensional quantitative modeling of mineralization (grading) and mineral reserves of the Eileh 1 iron deposit was conducted, utilizing data from well logs of exploratory boreholes and the grading of their drilling cores to evaluate the results. The estimation accuracy was assessed by comparing the results of reserve estimation using two classical methods: inverse distance weighting (IDW) with Rockwork software and kriging geostatistics with Surpac software. Figure 17 illustrates the grade-tonnage curve of the Eileh 1 iron deposit derived from these two methods. As anticipated, the figure demonstrates that as the limit grade increases, the deposit amount decreases while the average grade rises.The findings of this research in the field of grading data modeling and reserve estimation for the Eileh 1 iron deposit, utilizing distance squared and kriging methods in Surpac and Rock Works software for a grade limit of 17% (as shown in Table 2), indicate that the calculated reserve amounts derived from different methods and software show no significant differences overall. As illustrated in Figure 17, the graph of the average grade calculated by both methods and across the two software platforms aligns closely, with variations in tonnage charts primarily reflected in the slope of the graph line at specific grades. Although the results from these two methods are largely consistent, the inverse distance weighting method is recognized as one of the most accurate classical reserve estimation techniques. While this method offers numerous advantages, such as high accuracy due to the blocking of the deposit, it does have a drawback: in the case of low-grade deposits, the mixing of ore and tailings within the blocks tends to be high, leading to reserves being estimated slightly above their actual value. Conversely, the geostatistical method of kriging, which considers the spatial structure of the region, is more precise and boasts a higher degree of reliability due to its unbiased nature and minimal estimation variance. Vairographic studies, conducted by plotting longitudinal variograms in various directions, revealed that the deposit exhibits geometric anisotropy, with the highest elongation observed along the X-axis and the lowest along the Y-axis (Figures 9c and 10).Based on the results obtained from the modeling of the Eileh 1 deposit and the available information regarding areas that were inaccessible during the initial excavation but will be accessible in the future, it is recommended to drill new boreholes in a regular grid pattern. This approach will facilitate a more accurate determination of the mine reserves. The design and implementation of this drilling strategy should be carefully planned to optimize resource assessment. [ABSTRACT FROM AUTHOR]

Small bubbles in fluids rise to the surface due to Archimede's force. Remarkably, in turbulent flows this process is severely hindered by the presence of vortex filaments, which act as moving potential wells, dynamically trapping light particles and bubbles. Quantifying the statistical weights and roles of vortex filaments in turbulence is, however, still an outstanding experimental and computational challenge due to their small scale, fast chaotic motion, and transient nature. Here we show that, under the influence of a modulated oscillatory forcing, the collective bubble behavior switches from a dynamically localized to a delocalized state. Additionally, we find that by varying the forcing frequency and amplitude, a remarkable resonant phenomenon between light particles and small-scale vortex filaments emerges, likening particle behavior to a forced damped oscillator. We discuss how these externally actuated bubbles can be used as a type of microscopic probe to investigate the space-time statistical properties of the smallest turbulence scales, allowing to quantitatively measure physical characteristics of vortex filaments. We develop a superposition model that is in excellent agreement with the simulation data of the particle dynamics which reveals the fraction of localized/delocalized particles as well as characteristics of the potential landscape induced by vortices in turbulence. Our approach paves the way for innovative ways to accurately measure turbulent properties and to the possibility to control light particles and bubble motions in turbulence with potential applications to oceanography, medical imaging, drug/gene delivery, chemical reactions, wastewater treatment, and industrial mixing. [ABSTRACT FROM AUTHOR]

A highly versatile evaluation method is proposed for transient plasmas based on statistical physics. It would be beneficial in various industrial sectors, including semiconductors and automobiles. Our research focused on low-energy plasmas in laboratory settings, and they were assessed via our proposed method, which incorporates relative entropy and fractional Brownian motion, based on a revised collisional–radiative model. By introducing an indicator to evaluate how far a system is from its steady state, both the trend of entropy and the radiative process' contribution to the lifetime of excited states were considered. The high statistical weight of some excited states may act as a bottleneck in the plasma's energy relaxation throughout the system to a steady state. By deepening our understanding of how energy flows through plasmas, we anticipate potential contributions to resolving global environmental issues and fostering technological innovation in plasma-related industrial fields. [ABSTRACT FROM AUTHOR]

The problems of generalization and transportation of treatment effect estimates from a study sample to a target population are central to empirical research and statistical methodology. In both randomized experiments and observational studies, weighting methods are often used with this objective. Traditional methods construct the weights by separately modeling the treatment assignment and study selection probabilities and then multiplying functions (e.g., inverses) of their estimates. In this work, we provide a justification and an implementation for weighting in a single step. We show a formal connection between this one-step method and inverse probability and inverse odds weighting. We demonstrate that the resulting estimator for the target average treatment effect is consistent, asymptotically Normal, multiply robust, and semiparametrically efficient. We evaluate the performance of the one-step estimator in a simulation study. We illustrate its use in a case study on the effects of physician racial diversity on preventive healthcare utilization among Black men in California. We provide R code implementing the methodology. [ABSTRACT FROM AUTHOR]

Deuterium fractionation in the closest vicinity of a protostar is important in understanding its potential heritage to a planetary system. Here, we have detected the spectral line emission of CH3OH and its three deuterated species, CH2DOH, CHD2OH, and CH3OD, toward the low-mass protostellar source B335 at a resolution of 0.″03 (5 au) with Atacama Large Millimeter/submillimeter Array. They have a ring distribution within the radius of 24 au with the intensity depression at the continuum peak. We derive the column densities and abundance ratios of the above species at six positions in the disk/envelope system as well as the continuum peak. The D/H ratio of CH3OH is ∼[0.03–0.13], which is derived by correcting the statistical weight of 3 for CH2DOH. The [CHD2OH]/[CH2DOH] ratio is derived to be higher ([0.14–0.29]). On the other hand, the [CH2DOH]/[CH3OD] ratio ([4.9–15]) is higher than the statistical ratio of 3 and is comparable to those reported for other low-mass sources. We study the physical structure on a few astronomical unit scales in B335 by analyzing the CH3OH (183,15 − 182,16, A) and HCOOH (120,12 − 110,11) line emission. Velocity structures of these lines are reasonably explained as the infalling-rotating motion. The protostellar mass and the upper limit to the centrifugal barrier are thus derived to be 0.03–0.07 M ⊙ and <7 au, respectively, showing that B335 harbors a young protostar with a tiny disk structure. Such youth of the protostar may be related to the relatively high [CH2DOH]/[CH3OH] ratio. [ABSTRACT FROM AUTHOR]

As a result of an imbalance of innovation, several countries' increase in productivity has stopped, which is why Education 4.0 promotes development. A world-class system of education that develops outstanding learners and cutting-edge concepts is essential, and talented teachers serve a key role in development--results from the current study on Innovative Work Behaviour (IWB) at Traditional Chinese Medicine University. Professors are discussed online. These research outcomes show that HE institutions significantly impact Organisation Commitment (OC) and IWB, with OC functioning as an initial step towards IWB. The paper intended to provide insight into how Higher Education (HE) institutions in the nation of China employ the principles of Traditional Chinese Medical Education Innovative Work Behaviour (TCMEWB) along with factors that might impact IWB analysis. The medical education on-the-job teachers' TCMEWB is investigated regarding their respective emotions, strengths, and adaptability. Similar to previous studies and current hypotheses, outcomes show that stress and unreasonable assumptions affect the relationship between self-confidence and TCMEWB, with both theoretical and practical impacts. The investigators in the southwest region of China analysed 500 teachers (F=75.5%; M = 24.5%) and reported that IWB and the impact of OC dramatically improved academic achievement. The results of the study were examined using Multiple Linear Regression (MLR) and SPSS. Studies from the two statistical tests of weight (t-test and analysis of deviation) indicate that the mean OC of teachers at China's Traditional Chinese Medicinal Higher Education Institutions depends on demographic factors such as Experience, Education, and Salary. In order to figure out the level of OC among teachers, investigators evaluated several factors, including emotions, strength, and adaptability. The correlation and coefficients for these factors vary from 0.24 to 0.57. [ABSTRACT FROM AUTHOR]

The unpredictable nature of our world can introduce a variety of errors in our actions, including sensory prediction errors (SPEs) and task performance errors (TPEs). SPEs arise when our existing internal models of limb-environment properties and interactions become miscalibrated due to changes in the environment, while TPEs occur when environmental perturbations hinder achievement of task goals. The precise mechanisms employed by the sensorimotor system to learn from such limb- and task-related errors and improve future performance are not comprehensively understood. To gain insight into these mechanisms, we performed a series of learning experiments wherein the location and size of a reach target were varied, the visual feedback of the motion was perturbed in different ways, and instructions were carefully manipulated. Our findings indicate that the mechanisms employed to compensate SPEs and TPEs are dissociable. Specifically, our results fail to support theories that suggest that TPEs trigger implicit refinement of reach plans or that their occurrence automatically modulates SPE-mediated learning. Rather, TPEs drive improved action selection, that is, the selection of verbally sensitive, volitional strategies that reduce future errors. Moreover, we find that exposure to SPEs is necessary and sufficient to trigger implicit recalibration. When SPE-mediated implicit learning and TPE-driven improved action selection combine, performance gains are larger. However, when actions are always successful and strategies are not employed, refinement in behavior is smaller. Flexibly weighting strategic action selection and implicit recalibration could thus be a way of controlling how much, and how quickly, we learn from errors. Humans learn from errors in their movements. This study shows that learning mechanisms triggered by limb-related sensory prediction errors and task-related performance errors are dissociable. While prediction errors drive implicit updates to motor plans, performance errors trigger deliberative selection of error-reducing actions. [ABSTRACT FROM AUTHOR]

This interlaboratory comparison was conducted to evaluate the performance of the Latin-American Biodosimetry Network (LBDNet) in analyzing digitized images for scoring dicentric chromosomes from in vitro irradiated blood samples. The exercise also assessed the use of weighted robust algorithms to compensate the uneven expertise among the participating laboratories. Three sets of coded images obtained through the dicentric chromosome assay from blood samples irradiated at 1.5 Gy (sample A) and 4 Gy (sample B), as well as a non-irradiated whole blood sample (sample C), were shared among LBDNet laboratories. The images were captured using the Metafer4 platform coupled with the AutoCapt module. The laboratories were requested to perform triage scoring, conventional scoring, and dose estimation. The dose estimation was carried out using either their laboratory calibration curve or a common calibration curve. A comparative statistical analysis was conducted using a weighted robust Hampel algorithm and z score to compensate for uneven expertise in dicentric analysis and dose assessment among all laboratories. Out of twelve laboratories, one had unsatisfactory estimated doses at 0 Gy, and two had unsatisfactory estimated doses at 1.5 Gy when using their own calibration curve and triage scoring mode. However, all doses were satisfactory at 4 Gy. Six laboratories had estimated doses within 95% uncertainty limits at 0 Gy, seven at 1.5 Gy, and four at 4 Gy. While the mean dose for sample C was significantly biased using robust algorithms, applying weights to compensate for the laboratory's analysis expertise reduced the bias by half. The bias from delivered doses was only notable for sample C. Using the common calibration curve for dose estimation reduced the standard deviation (s*) estimated by robust methods for all three samples. The results underscore the significance of performing interlaboratory comparison exercises that involve digitized and electronically transmitted images, even when analyzing non-irradiated samples. In situations where the participating laboratories possess different levels of proficiency, it may prove essential to employ weighted robust algorithms to achieve precise outcomes. [ABSTRACT FROM AUTHOR]

Point cloud registration is a technology that aligns point cloud data from different viewpoints by computing coordinate transformations to integrate them into a specified coordinate system. Many cutting-edge fields, including autonomous driving, industrial automation, and augmented reality, require the registration of point cloud data generated by millimeter-wave radar for map reconstruction and path planning in 3D environments. This paper proposes a novel point cloud registration algorithm based on a weighting strategy to enhance the accuracy and efficiency of point cloud registration in 3D environments. This method combines a statistical weighting strategy with a point cloud registration algorithm, which can improve registration accuracy while also increasing computational efficiency. First, in 3D indoor spaces, we apply PointNet to the semantic segmentation of the point cloud. We then propose an objective weighting strategy to assign different weights to the segmented parts of the point cloud. The Iterative Closest Point (ICP) algorithm uses these weights as reference values to register the entire 3D indoor space's point cloud. We also show a new way to perform nonlinear calculations that yield exact closed-form answers for the ICP algorithm in generalized 3D measurements. We test the proposed algorithm's accuracy and efficiency by registering point clouds on public datasets of 3D indoor spaces. The results show that it works better in both qualitative and quantitative assessments. [ABSTRACT FROM AUTHOR]

The impact of common and rare variants in COVID-19 host genetics has been widely studied. In particular, in Fallerini et al. (Human genetics, 2022, 141, 147-173), common and rare variants were used to define an interpretable machine learning model for predicting COVID-19 severity. First, variants were converted into sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. After that, the Boolean features, selected by these logistic models, were combined into an Integrated PolyGenic Score (IPGS), which offers a very simple description of the contribution of host genetics in COVID-19 severity. IPGS leads to an accuracy of 55%-60% on different cohorts, and, after a logistic regression with both IPGS and age as inputs, it leads to an accuracy of 75%. The goal of this paper is to improve the previous results, using not only the most informative Boolean features with respect to the genetic bases of severity but also the information on host organs involved in the disease. In this study, we generalize the IPGS adding a statistical weight for each organ, through the transformation of Boolean features into "Boolean quantum features," inspired by quantum mechanics. The organ coefficients were set via the application of the genetic algorithm PyGAD, and, after that, we defined two new integrated polygenic scores (IPGS¹ph and IPGS²ph). By applying a logistic regression with both IPGS, (IPGS²ph (or indifferently IPGS¹ph) and age as inputs, we reached an accuracy of 84%-86%, thus improving the results previously shown in Fallerini et al. (Human genetics, 2022, 141, 147-173) by a factor of 10%. [ABSTRACT FROM AUTHOR]

It is well known that shortened modeling of particle trajectories with the use multiplicative statistical weights, as a rule, increases the efficiency of the program (in terms of accuracy/time ratio). This trick is often used in non-branching schemes simulating transfer processes without multiplication (for example, the transfer of X-ray radiation), in which it is sufficient to confine ourselves to studying only the average values of the field characteristics. With an increase in energy, however, multiplication processes begin to play a significant role (the production of electron-photon pairs by gamma quanta with energies above 1.022 MeV, etc.), when the resulting trajectory is not just a broken curve in the phase space, but a branched tree. This technique is also applicable to this process, but only if the study of statistical fluctuations and correlations is not the purpose of the calculation. The present review contains the basic concepts of the Monte Carlo method as applied to the theory of particle transport, demonstration of the weighting method in non-branching processes, and ends with a discussion of unbiased estimates of the second moment and covariance of additive functionals. [ABSTRACT FROM AUTHOR]

In recent decades, food sovereignty (FS) proposals have posed a challenge to the theoretical and practical foundations of food security and globalized agri-food systems. The general statement of FS focuses on people's right to nutritious and culturally appropriate food, as well as on strengthening sustainable production systems and short marketing chains. Although this approach has been widely adopted in discourse, significant methodological gaps have been identified in measuring FS at the rural household level. Given the complexity of the concept, it is approached as a complex variable, and a methodological proposal is put forward based on a series of indicators to measure FS in peasant agroecosystems from a socioecological perspective. Based on an extensive review of the current literature, the dimensions and categories of analysis were determined, along with proposed indicators to measure FS, including sustainability and resilience indicators previously validated in rural contexts. From 469 scientific articles, 5 analytical approaches and 6 dimensions of FS were identified. A list of 1,335 indicators was obtained, which, after undergoing a triple validation process, was narrowed down to 89, grouped into 21 categories linked to the proposed dimensions. This study confirms that FS is a complex and dynamic concept encompassing diverse spatial, temporal, and social scales. It was found that evaluating the proposed indicators requires a transdisciplinary approach, a multidisciplinary team, and collaborative work, as well as appropriate statistical weighting due to the quantitative and qualitative nature of the scales used. Additionally, the importance of fieldwork in measuring FS is highlighted. [ABSTRACT FROM AUTHOR]

The spectrum of schizophrenia disorders (SSD) is a severe mental disorder. It is one of the main medical causes of disability that generates high health and social costs. Objective: To analyze the factors associated with clinical recovery (CR) (symptomatic remission-SR and functional recovery-FR) and personal recovery (PR) in people diagnosed with SSD. 14 meta-analyses focused on recovery were reviewed following the PRISMA model statements. 95 % of CI was established. Shorter Duration of Untreated Psychosis (Zr = 0.24, [0.17, 0.30]) and total Duration of Untreated Illness (Zr = 0.34, [0.20, 0.48]) were related to greater SR and general functioning, respectively. Resilience was the variable with the greatest effect on FR (Zr = 0.67, [0.63, 0.71]). Premorbid adjustment (Zr = 0.34, [0.18, 0.49]) and physical intervention (Zr = 0.71, [0.55, 0.86]) had the greatest effect on occupational and social functioning, respectively. Less severe affective symptoms were related to greater PR (Zr = 0.46, [0.42, 0.50]). There are differences between affective SR and the other types of SR (Zr (SR-A - SR−) = 0.13, Qb = 6.51, p = 0.011), (Zr (SR-A - SR+) = 0.20, Qb = 8.52, p = 0.004), (Zr (SR-A – SR) = 0.18, Qb = 19.29, p = 0.0001). In all, resilience was associated with greater recovery (Zr = 0.67, [0.53, 0.80]), with the global effect being greater on PR than on CR (Zr (PR-CR) = 0.07, Qb = 3.45, p = 0.05). Resilience was the variable most strongly associated with recovery. Symptomatic or functional improvement obtained less statistical weight. • Recovery is the sum of symptomatic remission, functional recovery and personal perception of recovery. • Mental health recovery transcends symptoms. • Resilience is the most important factor associated to recovery. • Personal, clinical, functional and cognitive factors have an impact on recovery. [ABSTRACT FROM AUTHOR]

Fires are a growing problem even in temperate climate areas, such as those in Central Italy, due to climate change leading to longer and longer periods of drought. Thus, knowing the fire susceptibility of an area is crucial for good planning and taking appropriate countermeasures. In this context, it was decided to use only causal factors of a geomorphological and environmental nature in order to obtain a fire susceptibility analysis that can also be applied to climatically under-sampled areas. Vector data of fires in Central Italy from 2005 to 2023 were collected, and the correct areal extent was calculated for each. At the same time, six factors were selected that could have an influence on fire development, such as ecological units, topographic wetness index (TWI), geology, slope, exposure, and altitude. The model was obtained by means of the weight of evidence statistical method, which takes into account past data by reinterpreting them in a future-oriented way on the basis of the identified factors and classes. The model was validated with a test sample and shows an area under the curve (AUC) value of 0.72 with a reliability that can be described as good considering the total absence of climatic factors that are known to play a major role in fire development. Furthermore, the identified causal factors were divided into classes, and these were carefully weighted in order to define their relative influence in the study area. Particularly Ecological Units with characteristic and well-defined contrast (C) values, which could lead to a more complete definition of forests that tend to increase fire susceptibility and those that tend to decrease it, allowing the latter to be exploited as a hazard mitigation agent. [ABSTRACT FROM AUTHOR]

Under a federated learning environment, the training samples are generally collected and stored locally on each client's device, which makes the machine learning procedure not meet the requirement of independent and identical distribution (IID). Existing federated learning methods to deal with non-IID data generally assume that the data is globally balanced. However, real-world multi-class data tend to exhibit long-tail distribution, where the majority of samples are in a few head classes and a large number of tail classes only have a small amount of data. This paper, therefore, focuses on addressing the problem of handling non-IID and globally long-tailed data in a federated learning scenario. Accordingly, we propose a new federated learning method called Federated meta re-weighting networks (FedReN), which assigns weights during the local training process from the class-level and instance-level perspectives, respectively. To deal with data non-IIDness and global long-tail, both of the two re-weighting functions are globally trained by the meta-learning approach to acquire the knowledge of global long-tail distribution. Experiments on several long-tailed image classification benchmarks show that FedReN outperforms the state-of-the-art federated learning methods. The code is available at https://github.com/pxqian/FedReN. [ABSTRACT FROM AUTHOR]

Multi-UAV cluster technology is a hot topic in modern electronic countermeasures research. Based on the "OODA loop" theory of electronic countermeasures, this paper analyzes the effectiveness evaluation method of multi-UAV collaborative electronic countermeasures. We establish an electronic countermeasures correlation state analysis model based on CRITIC weighting and improved gray correlation theory. This method combines multi-UAV collaborative scene examples and use the gray state correlation analysis method based on the dynamic resolution coefficient. We simulate the effects of electronic countermeasures and various influencing factors on the expected task objectives and verify the effectiveness of the proposed method. The results show that this method can better adapt to the dynamic electronic countermeasures scenarios of multi-UAV clusters and has certain participation value for comprehensively and systematically improving the effectiveness evaluation of electronic countermeasures. [ABSTRACT FROM AUTHOR]

Learning individualized treatment rules is an important topic in precision medicine. Current literature mainly focuses on deriving individualized treatment rules from a single source population. We consider the observational data setting when the source population differs from a target population of interest. Compared with causal generalization for the average treatment effect that is a scalar quantity, individualized treatment rule generalization poses new challenges due to the need to model and generalize the rules based on a prespecified class of functions that may not contain the unrestricted true optimal individualized treatment rule. The aim of this paper is to develop a weighting framework to mitigate the impact of such misspecification, and thus facilitate the generalizability of optimal individualized treatment rules from a source population to a target population. Our method seeks covariate balance over a nonparametric function class characterized by a reproducing kernel Hilbert space and can improve many individualized treatment rule learning methods that rely on weights. We show that the proposed method encompasses importance weights and overlap weights as two extreme cases, allowing for a better bias-variance trade-off in between. Numerical examples demonstrate that the use of our weighting method can greatly improve individualized treatment rule estimation for the target population compared with other weighting methods. [ABSTRACT FROM AUTHOR]

The object of the research consists of testing the suitability of the vector normalization procedure (NP) in the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) method. One of the most problematic steps of the Multi-Criteria Decision Making (MCDM) process is related to the application of NPs by default to transform different measurement units of criteria into a comparable unit. This is because of the absence of a universal agreement that defines which NP is the most suitable for a given MCDM method. In the literature, there are thirty-one available NPs, each one of them has its strengths and weaknesses and, accordingly, can efficiently be applied to an MCDM method and even worst to another. Let’s note that many NPs (e. g., NPs of sum, max-min, vector, and max) have been used by default (i. e., without suitability study) in the TOPSIS method. Consequently, outcomes of multi-criteria evaluation and rankings of alternatives considered in the decision problems could have led to inconsistent solutions, and, therefore, decision-makers could have made irrational or inappropriate decisions. That’s why suitability studies of NPs become indispensable. Moreover, a description of the methodology, proposed in this research, is outlined as follows: 1) method of weighting based on an ordinal ranking of criteria and Lagrange multiplier (for determining criteria weights); 2) TOPSIS method (for ranking considered alternatives); 3) a statistical approach with 3-estimate (for comparing effects generated by the used NPs). In the research, twelve different NPs are compared to each other in the TOPSIS method via a numerical example, which deals with the wheel loader selection problem. The results of the comparison indicate that, amongst the twelve different NPs analyzed in this suitability study, vector NP has the lesser effect on the considered alternatives’ evaluation outcomes, when used with the TOPSIS method. The vector NP-TOPSIS approach can therefore be applied to solve multi-criteria decision problems. Its application further allows the decision-makers and users to better select efficient solutions and, consequently, to make conclusive decisions. [ABSTRACT FROM AUTHOR]

Objective: The Mental and Substance Use Disorders Prevalence Study (MDPS) builds upon previous epidemiologic studies to provide estimates of prevalence and treatment rates of mental and substance use disorders among adults aged 18–65 in the U.S. The study background and methods are described. Method: The MDPS employed novel techniques such as the inclusion of household, prison, homeless and state psychiatric hospital populations, a semi‐structured clinical interview administered by trained clinical interviewers to assess disorders, the assessment of both past year and lifetime schizophrenia spectrum disorder (SSD) using full Diagnostic and Statistical Manual 5 criteria, as well as other mental and substance use disorders, and video‐based interviewing. Population specific and combined sample weights were developed to estimate nationally representative prevalence and treatment rates. Results: Data collection was conducted between October 2020 and October 2022 resulting in 5679 clinical interviews. The statistical weighting and analytic plan are described. Weighted response rates and reasons for non‐response are provided for each study population. Conclusions: The MDPS successfully developed and employed novel techniques to estimate the prevalence and treatment rates of mental and substance use disorders in both household and non‐household populations, including some of the most impairing disorders such as SSD. [ABSTRACT FROM AUTHOR]

Extracting maximal information from experimental data requires access to the likelihood function, which however is never directly available for complex experiments like those performed at high energy colliders. Theoretical predictions are obtained in this context by Monte Carlo events, which do furnish an accurate but abstract and implicit representation of the likelihood. Strategies based on statistical learning are currently being developed to infer the likelihood function explicitly by training a continuous-output classifier on Monte Carlo events. In this paper, we investigate the usage of Monte Carlo events that incorporate the dependence on the parameters of interest by reweighting. This enables more accurate likelihood learning with less training data and a more robust learning scheme that is more suited for automation and extensive deployment. We illustrate these advantages in the context of LHC precision probes of new Effective Field Theory interactions. [ABSTRACT FROM AUTHOR]

To address the unknown spatial relationship between source and target domain labels, which leads to poor fault diagnosis accuracy, a contrastive universal domain adaptation model and rolling bearing fault diagnosis approach are proposed. The approach introduces bootstrap your own latent network to mine the data-specific structure of the target domain and proposes rejecting unknown class samples using an entropy separation strategy. Simultaneously, a source class weighting mechanism is designed to improve the transferable semantics augmentation method by assigning various class-level weights to source categories, which improves the alignment of the feature distributions in the shared label space to further construct fault diagnosis models. Experimental validation on two rolling bearing datasets confirmed the superior fault diagnosis accuracy of the proposed method under diverse working conditions. • The utilization of contrastive learning for uncovering the unique structure within target domain data is proposed. • A source class weighting mechanism is proposed. • A contrastive universal domain adaptation model is proposed. • The effectiveness of the proposed method is validated with two rolling bearing datasets. [ABSTRACT FROM AUTHOR]

The precise allocation of weights to criteria plays a fundamental role in multicriteria decision-making, exerting a significant influence on the obtained results. Ensuring an appropriate weighting of criteria is crucial for conducting a fair and accurate evaluation of various alternatives. In this context, we present an innovative solution that addresses the allocation of weights to attributes in datasets, aiming to overcome limitations and challenges associated with expert consultation in multicriteria problems. The proposed method is grounded in an objective approach and adopts a hybrid perspective by integrating the mathematical principles of Principal Component Analysis with the application of the Entropy Method. This method was implemented along with the exponential weighted sum model in a case study related to the classification of neighborhoods in Mexico City based on the level of marginalization. Results were compared with the marginalization index reported in official sources, using evaluation metrics MAE and MAPE with values of 0.24 and 11.3%, respectively. This research demonstrates the efficiency of the proposed method, which integrates techniques used for attribute weighting, providing a robust and reliable tool for decision-making. [ABSTRACT FROM AUTHOR]

In this study, the existence of anchoring bias—people's tendency to rely on, evaluate, and decide based on the first piece of information they receive—is examined in two multi‐attribute decision‐making (MADM) methods, simple multi‐attribute rating technique (SMART), and Swing. Data were collected from university students for a transportation mode selection. Data analysis revealed that the two methods, which have different starting points, display different degrees of anchoring bias. Statistical analyses of the weights obtained from the two methods show that, compared to Swing (with a high anchor), SMART (with a low anchor) produces lower weights for the least important attributes, while for the most important attributes, the opposite is true. Despite their differences in anchoring bias, analytical approaches supported by empirical studies suggest that both methods (SMART and Swing) overweigh the less important attributes and underweigh the more important attributes. As such, we examined whether the best‐worst method (BWM), which has two opposite anchors in its procedure (a possible promising anchoring debiasing strategy), could produce results that are less prone to anchoring bias. Our findings show that the BWM is indeed able to produce lower weights (compared to SMART and Swing) for the less important attributes and higher weights for the more important attributes. This study shows the vulnerability of MADM methods with a single anchor and supports the idea that MADM methods with multiple (opposite) anchors, like BWM, are less prone to anchoring bias. [ABSTRACT FROM AUTHOR]

We study the random weighting method for M-test in a linear model with dependent errors. The asymptotic distribution of this test statistic is derived under some conditions. This article shows that the random weighting method can be used for deciding the critical value for the M-test regardless of whether the null hypothesis is true or not. Finally, the performance of these results is evaluated by a simulation study. [ABSTRACT FROM AUTHOR]

Karaisa [29] presented Jakimovski- Leviatan- Durrmeyer type operators by means of Appell polynomials. In a similar manner, Wani et al. [43] proposed a sequence of Jakimovski-Leviatan-Durrmeyer type operators involving Brenke type polynomials which include Appell polynomials and Hermite polynomials. We note that the definitions of the operators given in both these papers are not correct. In the present article, we introduce a Stancu variant of the operators considered in [43] after correcting their definition. The definition of the operator proposed in [29] may be similarly corrected. We establish the Korovkin type approximation theorem and the rate of convergence by means of the usual modulus of continuity, Peetre's K-functional and the class of Lipschitz type functions for our operators. Next, we discuss the Voronovskaja and Gr$ \ddot{u} $ss Voronovskaja type asymptotic theorems. Finally, we study the convergence of these operators in a weighted space and the Korovkin type weighted statistical approximation theorem. [ABSTRACT FROM AUTHOR]

This research offers a solution to a highly recognized and controversial problem within the composite indicator literature: sub-indicators weighting. The research proposes a novel hybrid weighting method that maximizes the discriminating power of the composite indicator with objectively defined weights. It considers the experts' uncertainty concerning the conceptual importance of sub-indicators in the multidimensional phenomenon, setting maximum and minimum weights (constraints) in the optimization function. The hybrid weighting scheme, known as the SAW-Max-Entropy method, avoids attributing weights that are incompatible with the multidimensional phenomenon's theoretical framework. At the same time, it reduces the influence of assessment errors and judgment biases on composite indicator scores. The research results show that the SAW-Max-Entropy weighting scheme achieves greater discriminating power than weighting schemes based on the Entropy Index, Expert Opinion, and Equal Weights. The SAW-Max-Entropy method has high application potential due to the increasing use of composite indicators across diverse areas of knowledge. Additionally, the method represents a robust response to the challenge of constructing composite indicators with superior discriminating power. [ABSTRACT FROM AUTHOR]

Rotational temperatures in the Mesosphere‐Lower Thermosphere region are estimated by utilizing the OH(6,2) Meinel band nightglow data obtained with an Ebert‐Fastie spectrometer (EFS) operated at Arecibo Observatory (AO), Puerto Rico (18.35°N, 66.75°W) during February‐April 2005. To validate the estimated rotational temperatures, a comparison with temperatures obtained from a co‐located Potassium Temperature Lidar (K‐Lidar) and overhead passes of the Sounding of the Atmosphere by Broadband Emission Radiometry (SABER) instrument onboard NASA's Thermosphere Ionosphere Mesosphere Energetics and Dynamics (TIMED) satellite is performed. Two types of weighting functions are applied to the K‐Lidar temperature profiles to compare them with EFS temperatures. The first type has a fixed peak altitude and a fixed full width at half maximum (FWHM) for the whole night. In the second type, the peak altitude and FWHM vary with the local time. SABER measurements are utilized to estimate the OH(6,2) band peak altitudes and FWHMs as a function of local time and considerable temporal variations are observed in both the parameters. The average temperature differences between the EFS and K‐Lidar obtained with both types of weighting functions are comparable with previously published results from different latitude‐longitude sectors. We found that the temperature comparison improves when the time‐varying weighting functions are considered. Comparison between EFS, K‐Lidar, and SABER temperatures reveal that on average, SABER temperatures are lower than the other two instruments and K‐Lidar temperatures compare better with SABER in comparison to EFS. Such a detailed study using the AO EFS data has not been carried out previously. Plain Language Summary: In the Earth's atmosphere, a strong band of near‐infrared (NIR) emissions exists at ∼90 km altitude due to the vibrational‐rotational excitation of the Hydroxyl (OH) molecules. The intensity of these OH emissions depends on the temperatures of their rotational states. Under the assumption of thermodynamic equilibrium, the rotational temperatures should represent the background atmospheric temperatures. A scanning spectrometer of the Ebert‐Fastie configuration (EFS) was operated on the nights of February‐April 2005 at Arecibo Observatory (AO), Puerto Rico (18.35°N, 66.75°W) to record the intensity of the OH(6,2) band which is used to estimate rotational temperatures in this work. During the same period, a co‐located Potassium temperature Lidar (K‐Lidar) utilized resonance scattering from the atmospheric K‐atoms to measure temperatures in the 80–100 km altitude range. By nature, the EFS‐derived temperatures are an altitude‐integrated quantity while the K‐Lidar provides altitude‐resolved temperatures. First, we followed a commonly used methodology to compare the EFS and K‐Lidar temperatures by using the SABER data onboard NASA Thermosphere Ionosphere Mesosphere Energetics and Dynamics satellite. We found good agreement between our results and previously published studies. Then, we propose a modified methodology with the help of SABER data to show that it provides a better comparison between the EFS and K‐Lidar temperatures. Key Points: Good inter‐comparison is obtained between Arecibo Observatory spectrometer and Lidar temperatures, and results are similar to previous studiesThe temperature comparison improves when time‐varying weighting functions are used instead of a single weighting functionRotational temperature estimation from the spectrometer data depends strongly on the choice of Einstein coefficients for the P1 line transitions [ABSTRACT FROM AUTHOR]

Skin disease prediction using artificial intelligence has shown great potential in improving early diagnosis and treatment outcomes. However, the presence of class imbalance within skin disease datasets poses a significant challenge for accurate prediction, particularly for rare diseases. This study proposes a novel approach to address class imbalance through data balancing using classes weighting, coupled with transfer learning techniques, to enhance the performance of skin disease prediction models. Two experiments were conducted using a tuned EfficientNetV2L based classifier. In the first experiment, a default dataset structure was utilized for training and testing. The second experiment involved employing classes weighting approach to balance the dataset. The effectiveness of the proposed approach is evaluated using the ISIC 2018 dataset, which comprises a diverse collection of skin lesion images. By assigning appropriate weights to different classes based on their prevalence, the proposed method aims to balance the representation of rare disease classes. To evaluate the performance of the proposed methodology, several performance evaluation metrics, including accuracy, precision, and recall, were employed. These findings revealed that the balanced dataset achieved enhanced generalization, mitigating the biases associated with class imbalance. As a result, the efficacy of artificial intelligence models is enhanced. [ABSTRACT FROM AUTHOR]

Global Climate Models (GCMs) are globally accepted simulations used for modeling and forecasting of precipitation and other parameters of climate. However, discrepancies among GCMs reduce the scope of individual models. In this regard, a multimodel ensemble provides a more accurate picture than individual models. However, the presence of extreme values and outliers in GCMs makes ensemble inappropriate for statistical analysis and reanalysis. In this article, we propose a novel weighting scheme that integrates the Exponentially Weighted Moving Average (EWMA) chart to obtain optimum weights for ensemble means of multiple GCMs – EWMA-based Ensemble (EWMABE). To investigate the validity of EWMABE, we used precipitation data of eighteen GCMs from the Coupled Model Intercomparison Project Phase 6 (CMIP6). In this study, the performance of EWMABE is compared with Multi-Model Simple Averaging Ensemble (MMSAE). The comparative statistics show that the EWMABE scheme has significantly improved the multimodel estimates. These results suggests the potential candidacy of EWMABE for the estimation of multimodel ensemble. [ABSTRACT FROM AUTHOR]

In this paper, we introduce new summability methods for sequences of sets named Riesz summability of order α and weighted statistical convergence of order α, where α = (0; 1]. Also, we examine some properties of these methods and give some inclusion theorems. [ABSTRACT FROM AUTHOR]

For decades, in-person data collection has been the standard modality for nationally and sub-nationally representative socio-economic survey data in low- and middle-income countries. As the COVID-19 pandemic rendered in-person surveys impossible and unethical, the urgent need for rapid monitoring necessitated researchers and statistical agencies to turn to phone surveys. However, apart from pandemic-related factors, a variety of other reasons can render large segments of a population inaccessible for in-person surveys, including political instability, climatic shocks, and remoteness. Such circumstances currently prevail in Myanmar, a country facing civil conflict and political instability since the February 2021 military takeover. Moreover, Myanmar routinely experiences extreme weather events and is characterized by numerous inaccessible and remote regions due to its mountainous geography. We describe a novel approach to sample design and statistical weighting that has been successfully applied in Myanmar to obtain nationally and sub-nationally representative phone survey data. We use quota sampling and entropy weighting to obtain a better geographical distribution compared to recent in-person survey efforts, including reaching respondents in areas of active conflict. Moreover, we minimize biases towards certain household and respondent characteristics that are usually present in phone surveys, for example towards well-educated or wealthy households, or towards men or household heads as respondents. Finally, due to the rapidly changing political and economic situation in Myanmar in 2022, the need for frequent and swift monitoring was critical. We carried out our phone survey over four quarters in 2022, interviewing more than 12,000 respondents in less than three months each survey. A survey of this scale and pace, though generally of much shorter duration than in-person interviews, could only be possible on the phone. Our study proves the feasibility of collecting nationally and sub nationally representative phone survey data using a non-representative sample frame, which is critical for rapid monitoring in any volatile economy. [ABSTRACT FROM AUTHOR]

Although the guided image filter (GIF) is an excellent edge-preserving filter, it generally suffers from halo artifacts due to the local property and the fixed regularization parameter. To address the problem, a weighted guided image filter (WGIF) was proposed by incorporating an edge-aware weighting into the GIF. In the filtering process, WGIF employs an averaging strategy for edge-aware weighting. Although the averaging strategy is a highly efficient method, it is susceptible to extreme values and tends to obscure critical factors, so it often leads to inaccurate results. Consequently, the output results quality of the WGIF is often degraded. To remedy the deficiency, a weighted guided image filter with entropy evaluation weighting (EEW-WGIF) is proposed in this paper. EEW-WGIF employs an edge-aware weighting strategy based on entropy evaluation method to detect edges more accurately, and incorporates an explicit constraint based on the gradient variation to better preserve edges. To verify the filtering effectiveness of the EEW-WGIF, it was applied to edge-preserving smoothing filtering, exposure images fusion, single image detail enhancement, structure-transferring filtering and image denoising. Experimental results show that the proposed filter can achieve excellent performance in both visual quality and objective evaluation. [Display omitted] • An edge-aware weighting strategy based on entropy evaluation method is proposed, which is more reliable in calculating the importance of each edge-aware factor. • The proposed EEW-WGIF incorporates an explicit constraint based on gradient variation and hyperbolic function to handle the edges so that the edges can be better preserved. • The EEW-WGIF was applied to edge-preserving smoothing filtering, exposure images fusion, single image detail enhancement, structure-transferring filtering and image denoising. [ABSTRACT FROM AUTHOR]

Pigs are raised on a global scale for commercial or research purposes and often experience pain as a by product of management practices and procedures performed. Therefore, ensuring pain can be effectively identified and monitored in these settings is critical to ensure appropriate pig welfare. The Unesp-Botucatu Pig Composite Acute Pain Scale (UPAPS) was validated to diagnose pain in pre-weaned and weaned pigs using a combination of six behavioral items. To date, statistical weighting of supervised and unsupervised algorithms was not compared in ranking pain-altered behaviors in swine has not been performed. Therefore, the aim of this study was to verify if supervised and unsupervised algorithms with different levels of complexity can improve UPAPS pain diagnosis in pigs undergoing castration. The predictive capacity of the algorithms was evaluated by the area under the curve (AUC). Lower complexity algorithms containing fewer pain-altered behaviors had similar AUC (90.1–90.6) than algorithms containing five (89.18–91.24) and UPAPS (90.58). In conclusion, utilizing a short version of the UPAPS did not influence the predictive capacity of the scale, and therefore it may be easier to apply and be implemented consistently to monitor pain in commercial and experimental settings. [ABSTRACT FROM AUTHOR]

The recently introduced Gaussian Process State (GPS) provides a highly flexible, compact, and physically insightful representation of quantum many-body states based on ideas from the zoo of machine learning approaches. In this work, we give a comprehensive description of how such a state can be learned from given samples of a potentially unknown target state and show how regression approaches based on Bayesian inference can be used to compress a target state into a highly compact and accurate GPS representation. By application of a type II maximum likelihood method based on relevance vector machines, we are able to extract many-body configurations from the underlying Hilbert space, which are particularly relevant for the description of the target state, as support points to define the GPS. Together with an introduced optimization scheme for the hyperparameters of the model characterizing the weighting of modeled correlation features, this makes it possible to easily extract physical characteristics of the state such as the relative importance of particular correlation properties. We apply the Bayesian learning scheme to the problem of modeling ground states of small Fermi–Hubbard chains and show that the found solutions represent a systematically improvable trade-off between sparsity and accuracy of the model. Moreover, we show how the learned hyperparameters and the extracted relevant configurations, characterizing the correlation of the wave function, depend on the interaction strength of the Hubbard model and the target accuracy of the representation. [ABSTRACT FROM AUTHOR]

We present a new iterative scheme for potential energy surface (PES) construction, which relies on both physical information and information obtained through statistical analysis. The adaptive density guided approach (ADGA) is combined with a machine learning technique, namely, the Gaussian process regression (GPR), in order to obtain the iterative GPR–ADGA for PES construction. The ADGA provides an average density of vibrational states as a physically motivated importance-weighting and an algorithm for choosing points for electronic structure computations employing this information. The GPR provides an approximation to the full PES given a set of data points, while the statistical variance associated with the GPR predictions is used to select the most important among the points suggested by the ADGA. The combination of these two methods, resulting in the GPR–ADGA, can thereby iteratively determine the PES. Our implementation, additionally, allows for incorporating derivative information in the GPR. The iterative process commences from an initial Hessian and does not require any presampling of configurations prior to the PES construction. We assess the performance on the basis of a test set of nine small molecules and fundamental frequencies computed at the full vibrational configuration interaction level. The GPR–ADGA, with appropriate settings, is shown to provide fundamental excitation frequencies of an root mean square deviation (RMSD) below 2 cm−1, when compared to those obtained based on a PES constructed with the standard ADGA. This can be achieved with substantial savings of 65%–90% in the number of single point calculations. [ABSTRACT FROM AUTHOR]

Incomplete data, whether realized from nonresponse in survey data or counterfactual outcomes in observational studies, may lead to biased estimation of study variables. Nonresponse and selection bias may be mitigated with techniques that weight the incomplete data to match characteristics of the partially unobserved complete data. Inverse probability weighting is a widely used method in causal inference that relies on a propensity model to construct adjusted weights; whereas calibration is a common method used by survey statisticians to use constrained optimization to construct adjusted weights. This article reviews inverse probability weighting and entropy balancing calibration by distinguishing them in the statistical sense of variable balancing, extending propensity score construction to include generalized boosting models, and demonstrating the use of inverse probability weighting and calibration separately and together through a widely cited simulation study evaluation. This article is categorized under:Statistical and Graphical Methods of Data Analysis > SamplingData: Types and Structure > Categorical DataStatistical Learning and Exploratory Methods of the Data Sciences > Modeling Methods [ABSTRACT FROM AUTHOR]

Background and Aims: Dengue is endemic in Thailand and imposes a high burden on the health system and society. We conducted a prospective cohort study in Umphang District, Tak Province, Thailand, to investigate the share of dengue cases with long symptoms and their duration. Here we present the results of the enrollment process during the COVID‐19 pandemic with implications and challenges for research and policy. Methods: In a prospective cohort study conducted in Umphang District, Thailand, we examined the prevalence of persistent symptoms in dengue cases. Clinically diagnosed cases were offered free laboratory testing, We enrolled ambulatory dengue patients regardless of age who were confirmed through a highly sensitive laboratory strategy (positive NS1 and/or IgM), agreed to follow‐up visits, and gave informed consent. We used multivariate logistic regressions to assess the probability of clinical dengue being laboratory confirmed. To determine the factors associated with study enrollment, we analyzed the relationship of patient characteristics and month of screening to the likelihood of participation. To identify underrepresented groups, we compared the enrolled cohort to external data sources. Results: The 150 clinical cases ranged from 1 to 85 years old. Most clinical cases (78%) were confirmed by a positive laboratory test, but only 19% of those confirmed enrolled in the cohort study. Women, who were half as likely to enroll as men, were underrepresented in the cohort. Conclusions: The Thai physicians' clinical diagnoses at this rural district hospital had good agreement with laboratory diagnoses. By identifying underrepresented groups and disparities, future studies can ensure the creation of statistically representative cohorts to maximize their scientific value. This involves recruiting and retaining underrepresented groups in health research, such as women in this study. Promising strategies for meaningful inclusion include multi‐site enrollment, offering in‐home or virtual services, and providing in‐kind benefits like childcare for underrepresented groups. Key points: Cohort studies are a powerful tool for examining correlates of major diseases, such as dengueThe validity of the resulting data, however, depends on the representativeness of the underlying cohortDuring the COVID‐19 pandemic, only a small share of eligible patients were willing to join the cohort study in Thailand's Tak ProvinceComparisons with external data showed that adult women were particularly underrepresented in the cohort due to their household and childcare responsibilitiesThe analyses showed how cohort studies could be improved by identifying underrepresented groups, offering targeted support services and performing statistical weighting [ABSTRACT FROM AUTHOR]

Background: Time-restricted eating (TRE), a feasible form of intermittent fasting, has been proven to benefit metabolic health in animal models and humans. To our knowledge, specific guidance on the appropriate period for eating during TRE has not yet been promoted. Therefore, to compare and assess the relative effectiveness estimates and rankings of TRE with different eating windows on human metabolic health, we conducted a systematic review and network meta-analysis (NMA). Method: PubMed, EMBASE and the Cochrane Library were searched for randomized controlled trials that compared different eating windows on human metabolic health for adults. A Bayesian NMA was used to compare direct and indirect effects to determine the best different eating windows, and scientific evidence using GRADE. Results: Twenty-seven RCTs comparing TRE with different eating windows on human metabolic health were reviewed, and all were included in the NMA. Compared with the normal diet group (non-TRE), the TRE group has certain benefits in reducing weight and fasting insulin. In terms of reducing fasting insulin, the 18:6 group (eating time = 6 h) was better than the 14:10 group (eating time = 10 h) and 16:8 group (eating time = 8 h) (P < 0.05); The < 6 group (eating time < 6 h) was better than the 14:10 group (P < 0.05). In terms of reducing fasting glucose, the < 6 group was better than the 14:10 group (P < 0.05). There were no statistical variations in weight, HDL, TG, and LDL across the different modes of TRE (P > 0.05). Conclusions: Our research showed that no particular metabolic advantages of various eating windows were found. Therefore, our results suggested that different eating windows could promote similar benefits for metabolic parameters. [ABSTRACT FROM AUTHOR]

Inverse probability weighting can be used to correct for missing data. New estimators for the weights in the nonmonotone setting were introduced in 2018. These estimators are the unconstrained maximum likelihood estimator (UMLE) and the constrained Bayesian estimator (CBE), an alternative if UMLE fails to converge. In this work we describe and illustrate these estimators, and examine performance in simulation and in an applied example estimating the effect of anemia on spontaneous preterm birth in the Zambia Preterm Birth Prevention Study. We compare performance with multiple imputation (MI) and focus on the setting of an observational study where inverse probability of treatment weights are used to address confounding. In simulation, weighting was less statistically efficient at the smallest sample size and lowest exposure prevalence examined (n = 1500, 15% respectively) but in other scenarios statistical performance of weighting and MI was similar. Weighting had improved computational efficiency taking, on average, 0.4 and 0.05 times the time for MI in R and SAS, respectively. UMLE was easy to implement in commonly used software and convergence failure occurred just twice in >200 000 simulated cohorts making implementation of CBE unnecessary. In conclusion, weighting is an alternative to MI for nonmonotone missingness, though MI performed as well as or better in terms of bias and statistical efficiency. Weighting's superior computational efficiency may be preferred with large sample sizes or when using resampling algorithms. As validity of weighting and MI rely on correct specification of different models, both approaches could be implemented to check agreement of results. [ABSTRACT FROM AUTHOR]

In this article, we intend to extend the investigation for statistical boundedness of sequences and study Δ-deferred Cesàro and deferred Riesz statistical bounded sequences of order γ. Some interesting inclusion relations are presented. Further, with the aid of interesting examples, we investigate some relationships among these concepts. Some characterizations of Δ-deferred weighted statistical boundedness are developed in this paper. Finally, we apply the notion of Δ-deferred weighted statistical boundedness with a view to prove the decomposition theorem. [ABSTRACT FROM AUTHOR]

This study aims to efficiently assist decision makers in evaluating global third-party logistics (3PL) providers from the perspectives of economic, social, and environmental sustainability and explore the determinants of the 3PL providers' performance. In doing so, an integrated framework for an MCDM-based entropy objective weighting method is proposed for the first time in a logistics industry assessment. In the first stage, the entropy method defines the weight of the decision criteria based on real data collected from the top 15 global 3PL providers. This study lists the prominent quantitative evaluation criteria, taking into consideration the sustainability perspective. The advantage of the entropy method is that it reduces the subjective impact of decision makers and increases objectivity. In the second stage, the measurement of alternatives and ranking according to compromise solution (MARCOS) method is used to rank the 3PL providers according to their performance on the basis of these criteria. Sensitivity analysis and comparative analysis are implemented to validate the results. The current research work is devoted to the emerging research topic of sustainable development in the logistics industry and supply chain management. The proposed model identifies key performance indicators in the logistics industry and determines the most efficient 3PL providers. Consequently, the results show that the carbon dioxide emissions (20.50%) factor is the most important criterion for the competitiveness of global logistics companies. The results of this study can help inefficient 3PL providers make strategic decisions to improve their performance. However, this study only focuses on 15 companies due to a lack of data. The integration of these two techniques provides a novel way to evaluate global 3PL providers which has not been addressed in the logistics industry to date and as such remains a gap that needs to be investigated. [ABSTRACT FROM AUTHOR]

Simple Summary: Wet Distillers' Grains plus Solubles (WDGS) is characterized by a high concentration of crude protein and crude fat on a dry matter basis (DM), which makes them a valuable feed source for pigs. Because of that, WDGS can replace some amounts of the protein components and cereal grains of feed. Preservation based on ensiling improves the nutritional value of WDGS through enhancing its digestibility. This study reveals that utilizing 20% WDGS may have a detrimental effect on feed intake. WDGS did not affect growth performance or the quality and the nutritional value of the pork. The purpose of this study was to determine the nutritional suitability of WDGS in pigs' feeding and production. Pigs were liquid fed and divided into 3 groups. Pigs in the control group were fed diets based on cereal grains, while the experimental groups were also given 10% or 15% WDGS, which partially replaced their cereal grains. During this study, the average daily gains (ADG), feed intake, chemical composition of meat, fatty acid profile of meat, and quality parameters of the carcass and meat were examined. The highest statistical weight gains were detected for the group WDGS 10% during the first stage of the fattening period. No statistical differences were detected for the final body weight, carcass traits, chemical composition of the meat or the composition of fatty acids such as SFAs, PUFAs, and MUFAs, with the exception of eicosenoic acid (C20:1n9). Pigs fed on 10% WDGS exhibited lower peroxidation of lipids (TBARS) than the control group or WDGS 15%. Similarly, water holding capacity (WHC) was the lowest for the group WDGS 10%. Of the meat coloration, redness (a*), yellowness (b*), and chroma (C*) were affected by the WDGS' inclusion, where the highest values were observed for the group WDGS 10%. In conclusion, WDGS can be utilized in the liquid feeding of pigs for up to 15% of their DM. [ABSTRACT FROM AUTHOR]

Researchers have addressed uncertainty in multicriteria decision making from the perspective of the performance values of the alternatives, weighting of the evaluation criteria, and the evaluation methods. Still, they are yet to address the uncertainty caused by the normalization approach. In this paper, we show that different normalization methods, namely sum normalization, min–max normalization, vector normalization, and maximization normalization, can result in different rankings of the alternatives while the performance values and weights are unchanged. We applied the grey system theory to address the problem of uncertainty in this study from three aspects: alternative performance values measurement, criteria weighting, and decision matrix/table normalization within a period. The grey hybrid normalization method is proposed as the main contribution in this paper. Then, we present the rankings of 48 cities under uncertainty to decide the location of a branch office of a Chinese electric vehicle manufacturer as a practical example based on the grey entropy weighting method and grey relational analysis with positive and negative references (GRA-PNR) within the period from the year 2019 to 2021. The research results using this approach ranked New York City the best, with a stock market capitalization of economy validity as the top contributor in terms of weighting. Finally, we used simple additive weighting with grey value and the technique for order of preference by similarity to ideal solution with grey value methods to validate the study results. [ABSTRACT FROM AUTHOR]