Robust Weight Research Articles

Biweight midcorrelation-based CRITIC method: a case study for electrification of BIPV in Iran

Criteria weights are essential in various multi-criteria decision-making (MCDM) approaches. The amount of weight indicates the criterion's importance, and the performance of MCDM techniques depends on the correct estimation of criteria weights and criteria amount in the decision matrix. The distance correlation-based CRiteria Importance Through Inter-criteria Correlation (CRITIC) technique is a modified version of the CRITIC method. Since Pearson's correlation is sensitive to noisy and outlier data, this paper proposes using the biweight midcorrelation as a robust alternative to Pearson's correlation. The weights obtained from the proposed biweight midcorrelation method make using the full range of MCDM techniques possible. This study examined the performance of the proposed method in comparison with eight criteria weighting techniques on the electric supply for a Building-Integrated PhotoVoltaic (BIPV) problem. Results from three different tests (i.e. distance correlation, Spearman rank-order correlation, and symmetric mean absolute percentage error (sMAPE)) indicated that the proposed method could produce more valid criteria weights and ranks than other methods. The value of the sMAPE criterion for the proposed method is 0.1096. Sensitivity analysis also revealed that the proposed method provides more robust criteria weights and ranks with a larger decision matrix.

Graph Representation Learning enhanced Semi-supervised Feature Selection

Feature selection is a key step in machine learning by eliminating features that are not related to the modeling target to create reliable and interpretable models. By exploring the potential complex correlations among features of unlabeled data, recently introduced self-supervision-enhanced feature selection greatly reduces the reliance on the labeled samples. However, they are generally based on the autoencoder with sample-wise self-supervision, which can hardly exploit the relations among samples. To address this limitation, this paper proposes Graph representation learning enhanced Semi-supervised Feature Selection (G-FS) which performs feature selection based on the discovery and exploitation of the non-Euclidean relations among features and samples by translating unlabeled “plain” tabular data into a bipartite graph. A self-supervised edge prediction task is designed to distill rich information on the graph into low-dimensional embeddings, which remove redundant features and noise. Guided by the condensed graph representation, we propose a batch-attention feature weight generation mechanism that generates more robust weights according to batch-based selection patterns rather than individual samples. The results show that G-FS achieves significant performance edges in fourteen datasets compared to twelve state-of-the-art baselines, including two recent self-supervised baselines. The source code is public available at https://github.com/Icannotnamemyselff/G-FS_Graph_enhacned_feature_selection .

A hybrid deep learning skin cancer prediction framework

Skin cancer, a critical health concern, necessitates accurate early detection and classification to mitigate its impact. However, the limited availability of medical datasets and the challenge of optimizing learnable parameters in deep learning strategies for medical images pose significant hurdles. To address these challenges, we propose an innovative solution—a hybrid artificial intelligence (AI) framework for skin cancer prediction. This framework consists of two pivotal steps: firstly, a comprehensive skin cancer dataset is curated by combining diverse public datasets encompassing multiple disease types. This facilitates robust weight tuning of Convolutional Neural Networks (CNNs) by enhancing the diversity of the learning process. Subsequently, the optimization of deep learning is meticulously executed through the artificial Bee Colony (ABC) strategy, effectively mitigating the potential adverse effects of initial parameter randomness on AI model performance. Rigorous evaluation of the hybrid framework against eight prominent CNN models, including DenseNet121, InceptionResNetV2, and others, underscores its superior predictive capabilities. The optimized hybrid framework achieves exceptional results, boasting an accuracy of 93.04%, recall of 92.0%, precision of 93.0%, and an impressive F1-score of 93.12%. In summary, our proposed hybrid solution demonstrates a substantial advancement in multi-disease skin cancer classification. It outperforms existing AI models and holds promise as a pragmatic smart solution with far-reaching implications.

A growth-differentiation factor 15 receptor agonist in randomized placebo-controlled trials in healthy or obese persons.

Growth Differentiation Factor 15 (GDF15), a divergent member of the TGF-β superfamily, signals via the hindbrain glial-derived neurotrophic factor receptor alpha-like and rearranged during transfection receptor co-receptor (GFRAL-RET) complex. In nonclinical species, GDF15 is a potent anorexigen leading to substantial weight loss. MBL949 is a half-life extended recombinant human GDF15 dimer. MBL949 was evaluated in multiple nonclinical species and then in humans in two randomized and placebo-controlled clinical trials. In the Phase 1 first-in-human, single ascending dose trial MBL949 or placebo was injected subcutaneously to overweight and obese healthy volunteers (n=65) at doses ranging from 0.03 to 20 mg. In Phase 2, MBL949 or placebo was administered subcutaneously every other week for a total of 8 doses to obese participants (n=126) in five different dose regimens predicted to be efficacious based on data from the Phase 1 trial. In nonclinical species, MBL949 was generally safe and effective with reduced food intake and body weight in mice, rats, dogs, and monkeys. Weight loss was primarily from reduced fat, and metabolic endpoints improved. A single ascending dose study in overweight or obese healthy adults demonstrated mean terminal half-life of 18-22 days, and evidence of weight loss at the higher doses. In the Phase 2, weight loss was minimal following biweekly dosing of MBL949 for 14 weeks. MBL949 was safe and generally tolerated in humans over the dose range tested, adverse events of the gastrointestinal system were the most frequent observed. The prolonged half-life of MBL949 supports biweekly dosing in patients. MBL949 had an acceptable safety profile. The robust weight loss observed in nonclinical species did not translate to weight loss efficacy in humans. ClinicalTrials.gov NCT05199090.

Robust Bluetooth AoA Estimation for Indoor Localization Using Particle Filter Fusion

With the growing demand for positioning services, angle-of-arrival (AoA) estimation or direction-finding (DF) has been widely investigated for applications in fifth-generation (5G) technologies. Many existing AoA estimation algorithms only require the measurement of the direction of the incident wave at the transmitter to obtain correct results. However, for most cellular systems, such as Bluetooth indoor positioning systems, due to multipath and non-line-of-sight (NLOS) propagation, indoor positioning accuracy is severely affected. In this paper, a comprehensive algorithm that combines radio measurements from Bluetooth AoA local navigation systems with indoor position estimates is investigated, which is obtained using particle filtering. This algorithm allows us to explore new optimized methods to reduce estimation errors in indoor positioning. First, particle filtering is used to predict the rough position of a moving target. Then, an algorithm with robust beam weighting is used to estimate the AoA of the multipath components. Based on this, a system of pseudo-linear equations for target positioning based on the probabilistic framework of PF and AoA measurement is derived. Theoretical analysis and simulation results show that the algorithm can improve the positioning accuracy by approximately 25.7% on average.

Iterative robust peak-aware guided filter for signal smoothing

Due to the imperfection of experimental measurements, the measured spectrum signals are inevitably corrupted by random error (noise). It is very useful in applications to remove the noise in the measured data while preserving some significant features (such as peaks) by a smoothing filtering process. This paper presents a peak-preserving smoothing technique in the framework of guided filtering for spectrum signals, called robust peak-aware guided filter (RPAGF). Firstly, the robust peak-aware weight (RPAW) is introduced by means of hyperbolic tangent with threshold parameter, where the parameter is estimated by the median of absolute deviations from the median of local variances of the signal under consideration. Then the RPAGF is presented by incorporating the RPAW of a guidance signal into the cost function of guided filter as the regularization coefficient. The RPAGF uses the Gaussian-weighted averaging to deal with the problem of overlapping windows involved in computing filtering output, while the guidance signal is generated by a time fractional diffusion filtering from the input signal. Finally, the self-iteration scheme of RPAGF called iterative RPAGF (IRPAGF) is designed to provide a more faithful guidance signal for RPAGF at each iteration. The IRPAGF is evaluated by experiments on simulated and real spectrum signals, in comparison to the relevant state-of-the-art methods. Results show that the IRPAGF achieves higher performance in terms of peak-preserving smoothing of spectrum signals.

Robust kernel extreme learning machines with weighted mean of vectors and variational mode decomposition for forecasting total dissolved solids

A stable and accurate forecast of water quality parameters is crucial for planning and managing future investment programs. A well-known water quality indicator is the total dissolved solids (TDS), which measures the number of metals, minerals, and salts dissolved in a particular volume of water. This paper introduces an innovative approach, which combines the kernel extreme learning machine (KELM) with the robust weight mean of vectors (INFO) algorithm. The proposed technique stands out for its strategic integration of Boruta-XGBoost (B-XGB) as an exceptional feature selection method, along with variational mode decomposition (VMD) to decompose the input variables and enhance the accuracy of forecasting. The integrated model is termed V-KELM-INFO. INFO is an essential component that incorporates a complex exploration process. It utilizes a weighted mean of vectors, a vector combining operator to enhance population variety, and a localized search operator to expedite convergence. INFO, a potent tool, is included in the V-KELM training stage. It efficiently extracts optimum parameters and dramatically improves the accuracy of monthly TDS predictions at the Idenak station in southwest Iran. Also, the multi-criteria decision-making (MCDM) approach of weighted aggregated sum product assessment (WASPAS) is used to rank models. The V-KELM-INFO model was chosen based on the WASPAS results and statistical metrics (R = 0.962, RMSE = 57.84, WHD = 7.01, and U95 = 160.74) as the best model to forecast the TDS. In addition, the superiority of the proposed model was assessed against the metaheuristic-based V-KELM models, comprising the V-KELM grey wolf optimizer (V-KELM-GWO), V-KELM slime mold algorithm (V-KELM-SMA), and V-KELM equilibrium optimizer (V-KELM-EO). This work provided insight into improving the accuracy of modeling-based methodologies and spurred water quality modeling technology to develop sustainable and clean practices.

An adaptive iterative reweighted filtering methodology for urban MLS dataset

ABSTRACT This study presents a novel filtering methodology for Mobile Laser Scanning (MLS) data using robust iterative reweighting. Initially, 3D point clouds are projected onto a 2D grid to create surfaces from the lowest points. Weights are assigned based on the Height Above Ground (HAG) of these points. Ground points are distinguished by applying a surface function to the dataset via iterative reweighting. Among the tested four robust weight functions, the Denmark and Beaton-Tukey functions outperformed others, achieving total error values of 2.30 and 2.32 across three test areas, respectively. This method efficiently filters MLS data, irrespective of ground point proportions.

A robust time-varying weight combined model for crude oil price forecasting

Crude oil plays a vital role in industrial and social development and has become an integral part of the economic development. However, influenced by policies, wars, etc., it is hard to capture the trend of complex and volatile crude oil price if only one model is used, which often leads to poor forecasts. To enhance the prediction accuracy and robustness of forecasting models, a novel combined forecasting method with time-varying weights, i.e., Jaynes weight hybrid (JWH) model incorporating the Shannon information entropy and several forecasting methods is proposed in this paper. In the selection of the baseline models, the autoregressive integrated moving average in classical statistical forecasting strategies, back propagation neural network, extreme learning machine in neural network and long short-term memory neural network in deep learning models are chosen to fit the crude oil price. Four datasets and three experiments are constructed to verify the prediction ability of the novel combined forecasting model. Empirical results are calculated by five measurement criteria, suggesting that the prediction accuracy of the novel combined method is significantly higher than several comparison models and the mean absolute percentage error of the model has arrived 2.81 % in detail. Particularly, the proposed model has achieved satisfactory performances in Covid-19 and the War in Ukraine, further verifying the robustness of the combined methodology.

Insurance Scheme: Inequality in Untreated Caries and Tooth Loss

Objectives: In Thailand, access to dental care services varies based on different insurance schemes. Our objective was to determine the association of untreated caries and tooth loss with insurance schemes among adult population in Thailand. Methods: This is a cross-sectional study. Secondary data from oral examinations and a questionnaire administered during Thailand’s National Oral Health Survey 2017 were analyzed. Untreated caries and tooth loss were used as continuous dependent variables. Based on the insurance schemes, participants were categorized into four groups: Universal Coverage Scheme (UCS), Civil Servant Medical Benefit Scheme (CSMBS), Social Security Scheme (SSS), and “others” (uninsured, do not use, do not know). Poisson regression with robust variance and sampling weights was used to calculate the ratio of means (RM), and 95% confidence interval (CI) was used for untreated caries and tooth loss, with adjustments for age, gender, and location. Results: A total of 4,534 participants were included. The mean age and number of untreated caries and tooth loss were 39.6±2.9 years, 0.9±1.7 teeth, and 2.2±3.1 teeth, respectively. In covariate-adjusted models, participants under the UCS showed a significantly higher chance of untreated caries compared to those under the CSMBS (RM=1.23, 95% CI=1.04–1.45). Regarding tooth loss, participants under the others category had a substantially higher chance of tooth loss compared with those with the CSMBS. (RM=1.37, 95% CI=1.02–1.85). Conclusions: Insurance schemes are predictors of untreated caries and tooth loss. Expansion of coverage of all insurance schemes to facilitate access to dental services is required.

Synthesis of Mechanically Robust Very High Molecular Weight Polyisoprene Particle Brushes by Atom Transfer Radical Polymerization.

Linear polyisoprene (PI) and SiO2-g-PI particle brushes were synthesized by both conventional and activators regenerated by electron transfer (ARGET) atom transfer radical polymerization (ATRP). The morphology and solution state study on the particle brushes by transmission electron microscopy (TEM) and dynamic light scattering (DLS) confirmed the successful grafting of PI ligands on the silica surface. The presence of nanoparticle clusters suggests low grafting density (associated with the limited initiation efficiency of ARGET for PI). Nevertheless, particle brushes with very high molecular weights, Mn > 300,000, were prepared, which significantly improved the dispersion of silica nanoparticles and also contributed to excellent mechanical performance. The reinforcing effects of SiO2 nanofillers and very high molecular weight PI ligands were investigated by dynamic mechanical analysis (DMA) as well as computational simulation for the cured linear PI homopolymer/SiO2-g-PI particle brush bulk films.

Preoperative DLco and FEV1 are correlated with postoperative pulmonary complications in patients after esophagectomy

Limited information is available regarding the association between preoperative lung function and postoperative pulmonary complications (PPCs) in patients with esophageal cancer who undergo esophagectomy. This is a retrospective cohort study. Patients were classified into low and high lung function groups by the cutoff of the lowest fifth quintile of forced expiratory volume in 1 s (FEV1) %predicted (%pred) and diffusing capacity of the carbon monoxide (DLco) %pred. The PPCs compromised of atelectasis requiring bronchoscopic intervention, pneumonia, and acute lung injury/acute respiratory distress syndrome. Modified multivariable-adjusted Poisson regression model using robust error variances and inverse probability treatment weighting (IPTW) were used to assess the relative risk (RR) for the PPCs. A joint effect model considered FEV1%pred and DLco %pred together for the estimation of RR for the PPCs. Of 810 patients with esophageal cancer who underwent esophagectomy, 159 (19.6%) developed PPCs. The adjusted RR for PPCs in the low FEV1 group relative to high FEV1 group was 1.48 (95% confidence interval [CI] = 1.09–2.00) and 1.98 (95% CI = 1.46–2.68) in the low DLco group relative to the high DLco group. A joint effect model showed adjusted RR of PPCs was highest in patients with low DLco and low FEV1 followed by low DLco and high FEV1, high DLco and low FEV1, and high DLco and high FEV1 (Reference). Results were consistent with the IPTW. Reduced preoperative lung function (FEV1 and DLco) is associated with post-esophagectomy PPCs. The risk was further strengthened when both values decreased together.

Age and Agency: Evidence from a Women’s Empowerment Program in Tanzania

Understanding the effectiveness of programs designed to empower women is important for development policy, and critically important in places with historically unequal gender norms. In this paper, we evaluate the impact of a multidimensional program on women’s empowerment in northern Tanzania, where our study sample is among the pastoralist and traditionally patriarchal Maasai tribe. The multidimensional approach of the program included various components: microcredit groups, business skills, livestock management, water improvements, women’s rights discussions, and nutrition education. To measure the effect of the program, we employ a difference-in-differences approach, with doubly robust weighting. We use the livestock-enhanced project-level Women’s Empowerment in Agriculture Index (pro-WEAI), a new quantitative measurement of women’s empowerment designed for communities where livestock is a main productive asset. Our analysis of the program impact shows that the multidimensional program led to higher levels of empowerment for young women, specifically more group membership, access to credit, increased control over income, and a larger input into household productive decisions. Notably, the improvement in empowerment for young women does not reflect any change in attitudes towards intimate partner violence. Despite the positive findings for young women, we do not find any change in empowerment for the full sample of women (all ages). Our results demonstrate that programming focused on women’s empowerment has differential effects over a woman’s life cycle.

Glucocorticoid use in acute respiratory failure from pulmonary causes and association with early changes in the systemic host immune response

BackgroundGlucocorticoids are commonly used in patients with or at-risk for acute respiratory distress syndrome (ARDS), but optimal use remains unclear despite well-conducted clinical trials. We performed a secondary analysis in patients previously enrolled in the Acute Lung Injury and Biospecimen Repository at the University of Pittsburgh. The primary aim of our study was to investigate early changes in host response biomarkers in response to real-world use of glucocorticoids in patients with acute respiratory failure due to ARDS or at-risk due to a pulmonary insult. Participants had baseline plasma samples obtained on study enrollment and on follow-up 3 to 5 days later to measure markers of innate immunity (IL-6, IL-8, IL-10, TNFr1, ST2, fractalkine), epithelial injury (sRAGE), endothelial injury (angiopoietin-2), and host response to bacterial infections (procalcitonin, pentraxin-3). In our primary analyses, we investigated the effect of receiving glucocorticoids between baseline and follow-up samples on host response biomarkers measured at follow-up by doubly robust inverse probability weighting analysis. In exploratory analyses, we examined associations between glucocorticoid use and previously characterized host response subphenotypes (hyperinflammatory and hypoinflammatory).Results67 of 148 participants (45%) received glucocorticoids between baseline and follow-up samples. Dose and type of glucocorticoids varied. Regimens that used hydrocortisone alone were most common (37%), and median daily dose was equivalent to 40 mg methylprednisolone (interquartile range: 21, 67). Participants who received glucocorticoids were more likely to be female, to be on immunosuppressive therapy at baseline, and to have higher baseline levels of ST-2, fractalkine, IL-10, pentraxin-3, sRAGE, and TNFr1. Glucocorticoid use was associated with decreases in IL-6 and increases in fractalkine. In exploratory analyses, glucocorticoid use was more frequent in participants in the hyperinflammatory subphenotype (58% vs 40%, p = 0.05), and was not associated with subphenotype classification at the follow-up time point (p = 0.16).ConclusionsGlucocorticoid use varied in a cohort of patients with or at-risk for ARDS and was associated with early changes in the systemic host immune response.

Dietary intervention reverses molecular markers of hepatocellular senescence in the GAN diet-induced obese and biopsy-confirmed mouse model of NASH.

Hepatocellular senescence may be a causal factor in the development and progression of non-alcoholic steatohepatitis (NASH). The most effective currently available treatment for NASH is lifestyle intervention, including dietary modification. This study aimed to evaluate the effects of dietary intervention on hallmarks of NASH and molecular signatures of hepatocellular senescence in the Gubra-Amylin NASH (GAN) diet-induced obese (DIO) and biopsy-confirmed mouse model of NASH. GAN DIO-NASH mice with liver biopsy-confirmed NASH and fibrosis received dietary intervention by switching to chow feeding (chow reversal) for 8, 16 or 24 weeks. Untreated GAN DIO-NASH mice and chow-fed C57BL/6J mice served as controls. Pre-to-post liver biopsy histology was performed for within-subject evaluation of NAFLD Activity Score and fibrosis stage. Terminal endpoints included blood/liver biochemistry, quantitative liver histology, mitochondrial respiration and RNA sequencing. Chow-reversal promoted substantial benefits on metabolic outcomes and liver histology, as demonstrated by robust weight loss, complete resolution of hepatomegaly, hypercholesterolemia, elevated transaminase levels and hepatic steatosis in addition to attenuation of inflammatory markers. Notably, all DIO-NASH mice demonstrated ≥ 2 point significant improvement in NAFLD Activity Score following dietary intervention. While not improving fibrosis stage, chow-reversal reduced quantitative fibrosis markers (PSR, collagen 1a1, α-SMA), concurrent with improved liver mitochondrial respiration, complete reversal of p21 overexpression, lowered γ-H2AX levels and widespread suppression of gene expression markers of hepatocellular senescence. Dietary intervention (chow reversal) substantially improves metabolic, biochemical and histological hallmarks of NASH and fibrosis in GAN DIO-NASH mice. These benefits were reflected by progressive clearance of senescent hepatocellular cells, making the model suitable for profiling potential senotherapeutics in preclinical drug discovery for NASH.

Low-Light Enhancement Method Based on a Retinex Model for Structure Preservation

Enhancing low-light image visibility is a critical task in computer vision since it helps to improve input for high-level algorithms. High-quality images typically have clear structural information. In previous studies, due to the lack of proper structural guidance, restored images had some problems, such as unclear structural areas and overexposed or underexposed local areas. To address the above problems, in this paper, we introduce a coefficient of variation (COV) with excellent performance in maintaining structural information, and then we propose a low-light image enhancement method that utilizes the COV to extract structural information from images. First, we apply a traditional retinex model to estimate both reflectance and illumination. Second, we use the COV to indicate the degree of dispersion of the input sample, which enables us to obtain a robust structure-distinguishing weight map for low-light images. The weight map is adaptively divided to obtain a structural weight map, which is then used to enhance the gradient image. This process is applied before the reflectance layer of the retinex model. Finally, the result is obtained by using the block coordinate descent method. According to extensive experiments, outstanding results can be achieved by our proposed method in terms of both subjective and objective evaluation metrics in comparison with other state-of-the-art methods. The source code is available at our website <xref ref-type="fn" rid="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">¹</xref> <fn id="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><label>¹</label> <uri>https://github.com/bbxavi/spcv22</uri> </fn> .

Low-Complexity Uncertainty-Set-Based Robust Adaptive Beamforming for Passive Sonar

Recent work has highlighted the potential benefits of exploiting ellipsoidal uncertainty-set-based robust Capon beamformer (RCB) techniques in passive sonar. Regrettably, the computational complexity required to form RCB weights is cubic in the number of adaptive degrees of freedom, which is often prohibitive in practice. For this reason, several low-complexity techniques for computing RCB weights, or equivalent worst case robust adaptive beamformer weights, have recently been developed. These techniques, whose complexities are only quadratic in the number of adaptive degrees of freedom, use gradient-based, reduced-dimension Krylov-subspace or Kalman-filtering methods. In this work, we review these techniques for passive sonar, analyzing their complexities and evaluating them initially on simulated data. The best performing methods are then evaluated on two in-water recorded passive sonar data sets. One set, containing a strong controlled acoustic source, demonstrates the ability of the algorithms to protect against signal cancellation when pointing at the source, and their ability to reject the source when pointing away from it. The other data set, recorded during a period when the boat was accelerating, demonstrates the ability of the algorithms to operate in the presence of speed-induced noises.

Developing non-response weights to account for attrition-related bias in a longitudinal pregnancy cohort

BackgroundProspective cohorts may be vulnerable to bias due to attrition. Inverse probability weights have been proposed as a method to help mitigate this bias. The current study used the “All Our Families” longitudinal pregnancy cohort of 3351 maternal-infant pairs and aimed to develop inverse probability weights using logistic regression models to predict study continuation versus drop-out from baseline to the three-year data collection wave.MethodsTwo methods of variable selection took place. One method was a knowledge-based a priori variable selection approach, while the second used Least Absolute Shrinkage and Selection Operator (LASSO). The ability of each model to predict continuing participation through discrimination and calibration for both approaches were evaluated by examining area under the receiver operating curve (AUROC) and calibration plots, respectively. Stabilized inverse probability weights were generated using predicted probabilities. Weight performance was assessed using standardized differences of baseline characteristics for those who continue in study and those that do not, with and without weights (unadjusted estimates).ResultsThe a priori and LASSO variable selection method prediction models had good and fair discrimination with AUROC of 0.69 (95% Confidence Interval [CI]: 0.67–0.71) and 0.73 (95% CI: 0.71–0.75), respectively. Calibration plots and non-significant Hosmer-Lemeshow Goodness of Fit Tests indicated that both the a priori (p = 0.329) and LASSO model (p = 0.242) were well-calibrated. Unweighted results indicated large (> 10%) standardized differences in 15 demographic variables (range: 11 − 29%), when comparing those who continued in the study with those that did not. Weights derived from the a priori and LASSO models reduced standardized differences relative to unadjusted estimates, with the largest differences of 13% and 5%, respectively. Additionally, when applying the same LASSO variable selection method to develop weights in future data collection waves, standardized differences remained below 10% for each demographic variable.ConclusionThe LASSO variable selection approach produced robust weights that addressed non-response bias more than the knowledge-driven approach. These weights can be applied to analyses across multiple longitudinal waves of data collection to reduce bias.

An empirical semiparametric one‐sided confidence bound for lower quantiles of distributions with positive support

AbstractIn many industries, the reliability of a product is often determined by a quantile of a distribution of a product's characteristics meeting a specified requirement. A typical approach to address this is to assume a parametric model and compute a one‐sided confidence bound on the quantile. However, this can become difficult if the sample size is too small to reliably estimate such a parametric model. Linear interpolation between order statistics is a viable nonparametric alternative if the sample size is sufficiently large. In most cases, linear extrapolation from the extreme order statistics can be used, but can result in inconsistent coverage. In this work, we perform an empirical study to generate robust weights for linear extrapolation that greatly improves the accuracy of the coverage across a feasible range of distribution families with positive support. Our method is applied to two industrial datasets.

Long COVID Prevalence and the Impact of the Third SARS-CoV-2 Vaccine Dose: A Cross-Sectional Analysis from the Third Follow-Up of the Borriana Cohort, Valencia, Spain (2020-2022).

In March 2020, a COVID-19 outbreak linked to mass gathering dinners at the Falles Festival in Borriana, Spain, resulted in an estimated attack rate of 42.6% among attendees. In June 2022, we conducted a cross-sectional follow-up study of 473 adults aged 18 to 64 who attended the dinners at the Falles Festival in 2020, examining the cumulative experience after SARS-CoV-2 infection and vaccination responses. Data included demographic details, lifestyle habits, medical history, infection records, and vaccinations from a population-based vaccine registry. Blood samples were analyzed for SARS-CoV-2 antibodies and cellular immunity. We employed a doubly robust inverse-probability weighting analysis to estimate the booster vaccine dose's impact on long COVID prevalence and symptom count. A total of 28.1% of participants met the WHO criteria for long COVID, with older individuals showing higher rates. Long COVID diagnosis was less likely with factors including O blood group, higher occupational status, physical activity, three vaccine doses, strong SARS-CoV-2-S-reactive IFNγ-producing-CD8+ response, and infection during the Omicron period. Increased age, high or low social activity, underlying health conditions, a severe initial COVID episode, and reinfection were associated with higher long COVID likelihood. A booster dose, compared to one or two doses, reduced long COVID risk by 74% (95% CI: 56% to 92%) and symptom count by 55% (95% CI: 32% to 79%). Long COVID was prevalent in a significant portion of those who contracted COVID-19, underscoring the need for sustained follow-up and therapeutic strategies. Vaccinations, notably the booster dose, had a substantial beneficial effect on long-term infection outcomes, affirming the vaccination's role in mitigating SARS-CoV-2 infection consequences.