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The article discusses the navigating manuscript rejection. Topics include the study demonstrating how common manuscript rejection is among faculty of all academic ranks in hopes to normalize rejection and open conversations about rejection in mentorship; approach, innovations related to the study design, and reason for the rejection of the study to quantify on the fate of manuscripts.

In the literature, modifying the baseline distributions and proposing novel approach of the existing probability models play an important role in the analysis of real-world phenomena as well as the development of new distributions essentially stems from the need to adequately characterize environmental and lifetime events. In this paper, we introduce a new model by applying maximum compound technique to the baseline distributions for modeling more types data used in different case of studies including symmetric, asymmetric, skewed and complex data sets. The proposed model called univariate maximum Harris extended Rayleigh distribution and three parameters characterize it, and it is regarded as strong competitor for widely applied symmetrical and non symmetrical models. The paper offers varied fundamental distributional and mathematical properties of the suggested distribution, such as conditional probability and its expectation, as well as quantile and moment-generating functions. The model's parameters are estimated by applying numerous procedures including maximum likelihood estimation, Expectation–Maximization iterative method and Bayesian approach. Bayesian estimation is performed via various loss functions such as square error, Linex and general entropy functions. As well as, approximate and Bootstrap methods are used to construct the confidence interval for the unknown parameters of the proposed model. Additionally, we illustrated different extensive simulation experiments to see the potential of the suggested estimation methods which give satisfactory performance results. In the end, we applied two real data sets for illustrative purposes. In our illustration, we have compared the practicality of the recommended model with several well-known competing distributions. [ABSTRACT FROM AUTHOR]

Evidence indicates that the use of sedative–hypnotics, including benzodiazepines and z-drugs, is linked to an increased risk of falls and fractures. Nonetheless, the potential exacerbation of this risk by orexin receptor antagonists, which are novel therapeutic agents for treating insomnia, remains uncertain despite their escalating prevalence in clinical practice. We systematically searched four electronic databases from inception to April 17, 2024. In addition, we performed a quality assessment; calculated pooled odds ratios (ORs) to assess the relationship between the use of orexin receptor antagonists and the occurrence of falls or fractures; evaluated heterogeneity across the included studies; and conducted sensitivity analyses. The meta-analysis encompassed eight papers, comprising a total of 46,636 subjects. These papers included 5 case-control studies and 3 randomized controlled trials (RCTs), collectively encompassing ten studies. Analysis of the included case-control studies (pooled adjusted OR = 0.75, 95% confidence interval [CI] = 0.00–1.50, I2 = 66.2%, k = 3) and RCTs (OR = 0.68, 95% CI = 0.31–1.50, I2 = 45.9%, k = 5) indicated that the use of orexin receptor antagonists did not elevate the risk of falls. Similarly, analysis of the included case-control studies revealed no significant increase in the risk of fractures associated with the use of orexin receptor antagonists (pooled adjusted OR = 1.01, 95% CI = 0.82–1.20, I2 = 40.1%, k = 2). This meta-analysis suggests that the use of orexin receptor antagonists for treating insomnia does not escalate the risk of falls or fractures, although the data for lemborexant and daridorexant are limited. • The utilization of orexin receptor antagonists did not heighten the risk of falls. • The use of suvorexant did not elevate the risk of falls. • The use of orexin receptor antagonists did not increase the risk of fractures. [ABSTRACT FROM AUTHOR]

False alarms are detrimental to the safety of industrial processes and are often removed by alarm deadbands. However, most of the existing methods design alarm deadbands based on historic data samples under the assumption of stationarity, and may not achieve the desired performance in reducing false alarms for non-stationary process variables. This paper proposes an adaptive method to update alarm deadbands for non-stationary process variables to remove false alarms. Two technical challenges are addressed in this paper. First, time instants of statistically significant changes in process variables are detected based on mean values of maximum amplitude deviations between process variables and alarm trippoints. Second, when to update an alarm deadband width after detecting a mean change is determined based on a confidence interval of the designed width developed from Bayesian estimation rule. Numerical and industrial examples are provided to illustrate the proposed method and compare with alarm deadbands with fixed widths. [ABSTRACT FROM AUTHOR]

The prevalence of depression related to precarious employment (PE) has become a significant public health concern, given the declining trend of the standard employment relationship. Research has focused on the mental health detrimental effects of employment conditions, whereas there is scarce evidence concerning the burden of depression that could be prevented by targeting precariousness. This paper estimates the impact of PE on the risk of depression and the attributable fraction within the active and working salaried population in Spain. Observational cross-sectional on data drawn from the Spanish portion of European Health Survey 2020. After applying selection criteria and descriptives, binary logistic regression models stratified by sex are used to examine the associations between a 9-categories combination of employment precariousness and occupational social class, and depressive symptoms. There is a higher risk of depression among individuals in PE and among those who are unemployed, with a notable gradient based on occupational social class for women. Adjusting by sex, age and foreign-born origin, we estimate that approximately 15.0% (95% confidence interval [CI]: 1.0%–26.2%) of depression cases among the working population and 33.3% (95% CI: 23.2%-43.2) among the active population can be attributed to PE. These findings highlight the public health impact of PE on mental health, provide evidence to estimate the economic burden linked to employment-related mental health, and underscore the need for policy changes and interventions at the level of labour markets and workplaces to mitigate the detrimental effects of PE. [ABSTRACT FROM AUTHOR]

Design of efficient test plans to assessing product reliability rapidly, is a critical step to obtain product reliability accurately. In this paper, we are adopted the problem of statistical inference of two generalized inverted exponential populations in a competing duration. To save the balance between the optimal test time and the number of failures needing for statistical inference, joint adaptive type-II hybrid progressive censoring scheme is applied. The model parameters are estimated with classical methods (maximum likelihood and bootstrap) for point estimate and the corresponding confidence intervals. Also, Bayes estimators of model parameters are computed under importance sample technique with the corresponding Bayes credible intervals. A real data sets are analyzed for illustrating purposes. The results are compared and assessed through Monte Carlo simulation study. [ABSTRACT FROM AUTHOR]

Heterogeneity in agents' preferences is generally analysed through mixed logit models, which assume taste parameters are distributed in the population according to a certain mixing distribution. As a result, if the utility function is linear in attributes, the willingness to pay is the ratio of two random parameters and is itself random. This paper proposes a technique built on the Delta method, partly analytical and partly based on simulations, to obtain the sampling distribution of the willingness to pay, accounting for both heterogeneity and sampling error. The paper contributes to the literature by: (i) redressing some imprecisions in Bliemer and Rose (2013) that produce biased results; (ii) proposing a faster estimation process, compared to the Krinsky and Robb (1986, 1990) method that, relying on simulation only, proves computationally more demanding; (iii) comparing the performance of different methods using both synthetic and real data sets. The paper shows, via a Monte Carlo study, that the method we develop and the Krinsky and Robb one produce similar results, while outperforming that proposed by Bliemer and Rose. • Uncertainty in estimating Willingness To Pay under Mixed Logit Model arises from both heterogeneity and sampling error. • Delta method can be used, in combination with normal mixtures, to estimate both sources of uncertainty. • Both confidence and prediction intervals can be computed for Willingness To Pay by means of Delta method. • Delta method, being partly analytical, proves time-saving when compared to Krinsky and Robb method. [ABSTRACT FROM AUTHOR]

Cervical fusion for degenerative disorders carries a known risk of adjacent segment disease (ASD), a complication that often requires surgical intervention to relieve symptoms. Proposed risk factors for development of ASD include both clinical and radiographic patient characteristics. However, the true impact of these risk factors is less understood due to limitations in sample sizes and loss to follow-up in individual studies. To review and critically examine current literature on the clinical risk factors associated with development of ASD in the cervical spine following ACDF. Systematic Review and Meta-Analysis. We systematically reviewed the literature in December 2019 according to the PRISMA guidelines. Methodological quality of included papers and quality of evidence were evaluated according to MINORS and GRADE framework. Meta-analysis was performed to compute the odds ratio(OR)with corresponding 95% confidence interval(CI)for dichotomous data, and mean difference(MD) with 95% CI for continuous variables. 6,850 records were obtained using database query. Title/abstract screening resulted in 19 articles for full review, from which 10 papers met the criteria for analysis. There were no significant differences in gender (OR 0.99, 95% CI 0.75–1.30), BMI (MD -0.09, 95% CI -0.46 to 0.29), smoking (OR 1.13, 95% CI 0.80–1.59), alcohol (OR 1.07, 95% CI 0.70–1.64), diabetes (OR 0.85, 95% CI 0.56–1.31), number of segments fused (OR 0.86, 95% CI 0.64–1.16), and preoperative JOA (MD -0.50, 95% CI -1.04 to 0.04). Age (MD 3.21, 95% CI 2.00–4.42), congenital/developmental stenosis (OR 1.94, 95% CI 1.06–3.56), preoperative NDI (MD 4.18, 95% CI 2.11 to 6.26), preoperative VAS (neck) (MD 0.54 95% CI 0.09–0.99), and preoperative VAS (arm) (MD 0.98, 95% CI 0.43–1.34) were found to be statistically significant risk factors. Patients with congenital stenosis, advanced age, and high preoperative NDI are at increased risk of developing ASD. [ABSTRACT FROM AUTHOR]

Rapid internet penetration and technological advancements have facilitated accessibility to internet-enabled devices globally. Since Asia lacks comprehensive data on internet addiction among college students, this review aims to estimate its pooled prevalence. This was a systematic review and meta-analysis. Searches were conducted in PubMed, Scopus, CINAHL, and MEDLINE from their inception through August 2022 using terms appropriate to internet addiction and Asian countries/territorial for publications in English peer-reviewed journals. The studies included those done on current college students and provided unambiguous indicators of the threshold of internet addiction. The pooled prevalence was calculated through a random-effects meta-analysis by RStudio software, and I 2 statistic was used to assess heterogeneity. The Joanna Briggs Institute critical appraisal checklist was used for quality assessment. Overall, between 2007 and 2021, 39 papers comprising 45 effect sizes and totaling 58,058 participants met the inclusion criteria. The pooled prevalence of internet addiction among Asian college students was 24.3% (95% confidence interval: 19.8%–29.5%, Q = 6234, df = 44, τ2 = 0.79, I 2 = 99.29%), and strikingly, this percentage increased over time. A high degree of heterogeneity was seen, and no publication bias was found. To our knowledge, this is the first comprehensive review report on Asian college students, which found that one-fifth suffer from internet addiction. The findings emphasize the significance of multidisciplinary prevention and management strategies to mitigate the harmful effects of internet addiction. Further research is warranted to develop a standardized diagnostic tool for a more precise estimation of internet addiction among this population. [ABSTRACT FROM AUTHOR]

Cerebral palsy (CP) is a group of neuromotor diseases that develops as a result of damage to the developing central nervous system during the perinatal period. The condition is usually accompanied by musculoskeletal problems resulting in movement disorders. Gait improvement therefore, is an important part of its treatment. Roboticassisted gait training (RAGT) is a new potential rehabilitation tool for CP patients, however there is no clear evidence for the effectiveness of this method. Can robotic-assisted gait training improve walking function in children with CP? A systematic search was performed in five databases: MEDLINE (via PubMed), Cochrane Central Register of Controlled Trials (CENTRAL), Embase, Scopus, and Web of Science. Eligible studies were randomized controlled trials (RCT) with CP patients under the age of 18. Gross motor function and kinematic gait parameters of patients were the main outcomes. Two authors determined the risk of bias of the RCTs independently using the revised Risk of Bias 2 (ROB 2) tool. Mean Differences (MDs) along with their 95% Confidence Interval (CI) were calculated when at least three studies were present for an outcome, subgroup analysis was performed based on the treatment of the control group. Of the 7363 screened articles, 13 papers met our inclusion criteria and among them, 7 studies could be used in our meta-analyses. The results related to RAGT suggest nonsignificant improvement in standing and walking function (Gross Motor Function Measure D, E), moreover changes in gait speed, step length, and in cadence were also only comparable to controls. The results indicate that there is a trend in some gait parameters where the improvement was higher in the intervention group than in control group. The therapeutic effect of RAGT was probably not superior to physiotherapy combined with treadmill training. • Robotic-assisted gait training is a promising new treatment in cerebral palsy. • Effects and significance are not proven yet. • This review found a tendency for gait improvement after robotic training. • Straightforward evidence for effectiveness is still missing. [ABSTRACT FROM AUTHOR]

Strong relation between reliability studies and product quality. And reliability analysis deals with failure time of units and there are many well-known lifetime distributions. In this paper, we adopt the statistical inference of one more flexible one parameter distribution called Suja lifetime distribution. We primarily take into account four methods to estimate the distributional parameter other than the maximum likelihood estimation. The approximate confidence interval and two bootstrap confidence intervals have also been determined. The point and interval estimators of the Suja' parameter are assessed and compared throughout the Monte Carlo simulation study. Finally, the Suja distribution is used to modeled and analyzed three real data sets. [ABSTRACT FROM AUTHOR]

Background Noncommunicable disease development is related to impairments in glycemic and insulinemic responses, which can be modulated by fiber intake. Fiber's beneficial effects upon metabolic health can be partially attributed to the production of SCFAs via microbial fermentation of fiber in the gastrointestinal tract. Objectives We aimed to determine the effects of SCFAs, acetate, propionate, and butyrate on glycemic control in humans. Methods The CENTRAL, Embase, PubMed, Scopus, and Web of Science databases were searched from inception to 7 December 2021. Papers were included if they reported a randomized controlled trial measuring glucose and/or insulin compared to a placebo in adults. Studies were categorized by the type of SCFA and intervention duration. Random-effects meta-analyses were performed for glucose and insulin for those subject categories with ≥3 studies, or a narrative review was performed. Results We identified 43 eligible papers, with 46 studies within those records (n  = 913), and 44 studies were included in the meta-analysis. Vinegar intake decreased the acute glucose response [standard mean difference (SMD), −0.53; 95% CI, −0.92 to −0.14; n  = 67] in individuals with impaired glucose tolerance or type 2 diabetes and in healthy volunteers (SMD, −0.27; 95% CI, −0.54 to 0.00; n  = 186). The meta-analyses for acute acetate, as well as acute and chronic propionate studies, showed no significant effect. Conclusions Vinegar decreased the glucose response acutely in healthy and metabolically unhealthy individuals. Acetate, propionate, butyrate, and mixed SCFAs had no effect on blood glucose and insulin in humans. Significant heterogeneity, risks of bias, and publication biases were identified in several study categories, including the acute vinegar glucose response. As evidence was very uncertain, caution is urged when interpreting these results. Further high-quality research is required to determine the effects of SCFAs on glycemic control. [ABSTRACT FROM AUTHOR]

Examine the relationship between nutrition knowledge, diet quality, and eating behavior among active-duty US Army Soldiers. Cross-sectional study with data collection in February 2018 via paper surveys during the validation of the Military Eating Behavior Survey. Among 440 US Army Soldiers, nutrition knowledge was positively and significantly associated with diet quality (b = 0.29, P < 0.001). For every 1-point increase in nutrition knowledge, the Healthy Eating Index-2015 score was expected to increase by 0.29 points. Nutrition knowledge was not significantly associated with skipping breakfast (odds ratio, 1.01; 95% confidence interval, 0.98–1.04) or dining out (odds ratio, 1.01; 95% confidence interval, 0.98–1.03). The outcomes of this study warrant further investigation to determine what interventions provide the strongest outcomes for improving nutrition knowledge and diet quality, as well as create and support an environment that enhances healthy behaviors regarding nutrition that lead to improved diet quality among active-duty Soldiers. [ABSTRACT FROM AUTHOR]

In this paper, we study fluctuations and precise deviations of cumulative INAR time series, both in a non-stationary and in a stationary regime. The theoretical results are based on the recent mod- ϕ convergence theory as presented in Féray et al., 2016. We apply our findings to the construction of approximate confidence intervals for model parameters and to quantile calculation in a risk management context. [ABSTRACT FROM AUTHOR]

Sequential (or adaptive) designs are common in acceptance sampling and pharmaceutical trials. This is because they can achieve the same type 1 and type 2 error rate with fewer subjects on average than fixed sample trials. After the trial is completed and the test result decided, we need full inference on the main parameter Δ. In this paper, we are interested in exact one-sided lower and upper limits. Unlike standard trials, for sequential trials there need not be an explicit test statistic, nor even p -value. This motivates the more general approach of defining an ordering on the sample space and using the construction of Buehler (1957). This is guaranteed to produce exact limits, however, there is no guarantee that the limits will agree with the test. For instance, we might reject Δ ≤ Δ 0 at level α but have a lower 1 − α limit being less then Δ 0. This paper gives a very simple condition to ensure that this unfortunate feature does not occur. When the condition fails, the ordering is easily modified to ensure compatibility. • For sequential tests, it is unclear how to define a p-value which can be inverted to a confidence limit. • Consequently, confidence limits can conflict with the results of a sequential test. • For discrete endpoints, standard approximate limits have poor accuracy so we seek exact limits. • Exact limits also need not agree with the results of an exact sequential test. • We give a simple easily checked condition that ensures agreement. [ABSTRACT FROM AUTHOR]

Object detection is one of the most important tasks involved in intelligent agriculture systems, especially in pest detection. This paper focuses on a most devastated agricultural disaster: grasshopper plagues. Grasshopper detection and monitoring is of paramount importance in preventing grasshopper plagues. This paper proposes a probabilistic faster R-CNN algorithm with stochastic region proposing, where a probabilistic region proposal network, an image classification network, and an object detection network are integrated to detect and locate grasshoppers. More specifically, in the proposed framework, the probabilistic region proposal network considers attributes (e.g. size, shape) of region proposals and the image classification network identifies the existence of grasshoppers while the object detection network scores recognition confidence for a region proposal. By integrating these three networks, the uncertainty can be passed from end to end, and the final confidence is obtained for each region proposal can be explicitly quantified. To enhance algorithm robustness, a stochastic region proposing algorithm is developed to screen region proposals rather than using a predetermined threshold. The proposed algorithm is validated by recently collected grasshopper datasets. The experimental results demonstrate that the proposed algorithm not only outperforms competing algorithms in terms of average precision (0.91), average missed rate (0.36), and maximum F 1 -score (0.9263), but also reduces the false positive rate of recognising the existence of grasshoppers in an open field. [ABSTRACT FROM AUTHOR]

• Analytical developments support the understanding of ELM variability. • A special attention is paid to the impact of the input weight randomness. • Variance estimates for homoskedastic, heteroskedastic, regularized cases are given. • The possibility of constructing accurate confidence intervals is discussed. • A new Python library allows the computation of the proposed estimates. Uncertainty quantification is crucial to assess prediction quality of a machine learning model. In the case of Extreme Learning Machines (ELM), most methods proposed in the literature make strong assumptions on the data, ignore the randomness of input weights or neglect the bias contribution in confidence interval estimations. This paper presents novel estimations that overcome these constraints and improve the understanding of ELM variability. Analytical derivations are provided under general assumptions, supporting the identification and the interpretation of the contribution of different variability sources. Under both homoskedasticity and heteroskedasticity, several variance estimates are proposed, investigated, and numerically tested, showing their effectiveness in replicating the expected variance behaviours. Finally, the feasibility of confidence intervals estimation is discussed by adopting a critical approach, hence raising the awareness of ELM users concerning some of their pitfalls. The paper is accompanied with a scikit-learn compatible Python library enabling efficient computation of all estimates discussed herein. [ABSTRACT FROM AUTHOR]

OBJECTIVE: The purpose of this paper was to understand associations between low sleep duration (< 8 hours) and positive mental health screens among adolescents (ages 13-18) seen for preventive visits in primary care. METHODS: Data were from two randomized controlled trials testing the efficacy of an electronic health risk behavior screening and feedback tool for adolescent preventive visits. Participants (n = 601) completed screeners at baseline, 3 months, and 6 months which included sleep duration in hours and the Patient Health Questionnaire 9 and Generalized Anxiety Disorder 7 screeners for depression and anxiety, respectively. Main analyses included adjusted logistic regressions testing associations between low sleep duration and positive mental health screens. RESULTS: Adjusted models showed that low sleep duration was associated with significantly greater odds of a positive depression screen (OR = 1.58, 95% CI: 1.06-2.37) but not with a positive anxiety screen or co-occurring positive depression and anxiety screens. However, follow-up analyses indicated an interaction between sleep duration and anxiety in the association with a positive depression screen, such that the association between low sleep and a positive depression screen was driven by those who did not screen positive for anxiety. CONCLUSIONS: As pediatric primary care guidelines for sleep continue to evolve, further research, training, and support for sleep screening are warranted to ensure effective early intervention for sleep and mental health problems during adolescence. [ABSTRACT FROM AUTHOR]

BACKGROUND: The Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) promotes and supports breastfeeding for low-income women and children. A prior review reported negative associations of WIC with breastfeeding outcomes. WIC food package changes in 2009 increased breastfeeding support. OBJECTIVE: The objectives of this systematic review were to 1) evaluate evidence on WIC participation and breastfeeding outcomes and 2) evaluate breastfeeding outcomes of WIC participants before versus after the 2009 food package. DATA SOURCES: PubMed, Embase®, CINAHL, ERIC, SCOPUS, PsycINFO, and the Cochrane Central Register of Controlled Trials for papers published January 2009 to April 2022. ELIGIBILITY CRITERIA: Included studies compared breastfeeding outcomes (initiation, duration, exclusivity, early introduction of solid foods) of WIC participants with WIC-eligible nonparticipants, or among WIC participants before versus after the 2009 package change. STUDY APPRAISAL METHODS: Two independent reviewers evaluated each study and assessed risk of bias using EHPHP assessment. RESULTS: From 13 observational studies we found: 1) moderate strength of evidence (SOE) of no difference in initiation associated with WIC participation; 2) insufficient evidence regarding WIC participation and breastfeeding duration or exclusivity; 3) low SOE that the 2009 food package change is associated with greater breastfeeding exclusivity; 4) low SOE that WIC breastfeeding support services are positively associated with initiation and duration. LIMITATIONS: Only observational studies, with substantial risk of bias and heterogeneity in outcomes and exposures. CONCLUSIONS AND IMPLICATIONS OF KEY FINDINGS: WIC participation is not associated with a difference in breastfeeding initiation compared to WIC-eligible nonparticipants, but the 2009 food package change may have improved breastfeeding exclusivity among WIC participants and receipt of breastfeeding support services may have improved breastfeeding initiation and duration. [ABSTRACT FROM AUTHOR]

The aim of this paper is to better define the clinical features and outcomes of young patients with non-rhabdomyosarcoma soft tissue sarcoma (NRSTS) with regional and distant lymph node (LN) metastases treated in a standardised fashion, we analysed LN involvement in COG study ARST0332, which evaluated a risk-based treatment strategy for young patients with all stages of NRSTS. Patients <30 years old with newly diagnosed NRSTS and LN metastases enrolled on ARST0332 were studied. Regional LN sampling was required for those with epithelioid sarcoma, clear cell sarcoma or clinically/radiographically enlarged LNs. Tumour features and extent of pre-enrolment resection determined treatment, including chemotherapy, radiotherapy, and delayed surgery. Recommendations for LN metastases included LN dissection at the time of primary tumour resection and dose-adapted radiotherapy based on extent of LN resection. Twenty of 529 eligible and evaluable ARST0332 patients with NRSTS had LN metastases; epithelioid sarcoma had the highest incidence (18%, 5 of 28). Pre-treatment imaging identified LN enlargement in 19 of 20 patients; 1 had no pre-treatment LN imaging. At 6.9 years median follow-up for surviving patients, 5-year overall survival was 85.7% (95% CI: 33.4%, 97.9%) for seven patients with isolated LN metastases and 15.4% (95% CI: 2.5%, 38.8%) for 13 patients with additional extranodal metastases. LN recurrence occurred in only one patient without LNs sampled at initial diagnosis. LN metastases occur in about 4% of paediatric/young adult NRSTS, are limited to a few histologic subtypes, and are rare in patients who did not have clinical or imaging evidence of lymphadenopathy, suggesting that biopsies of non-enlarged LNs are not necessary to identify occult involvement. Patients with isolated LN metastases have high 5-year overall survival (∼85%) and should be treated with curative intent. NCT00346164. • Large prospective study of NRSTS with lymph node (LN) involvement in young patients. • Occult lymph node involvement is exceedingly rare. • Isolated LN metastases have a favourable prognosis so should be treated aggressively. • Complete enlarged LN excision and nodal bed radiation correlate with better outcome. [ABSTRACT FROM AUTHOR]

Using progressive Type-II censoring data, this study deals with the estimation of parameters of the Unit-Weibull distribution using two classical methods and the Bayesian method. In the classical methods, maximum likelihood and the maximum product of spacing (MPS) methods are used to obtain the parameters of the model by utilizing the Newton–Raphson method. On the basis of observed Fisher information matrix, approximate confidence intervals for the unknown parameters are obtained. In addition, two bootstrap methods are used to obtain confidence intervals for the unknown parameters of the model. In the Bayesian estimation, we have considered both likelihood function as well as product of spacing function to estimate the model parameters. Bayes estimators are obtained under squared error loss function using independent gamma density priors for the unknown model parameters. Since closed-form of the Bayes estimators is not available, the Metropolis–Hastings algorithm is proposed to approximate the Bayes estimates. In addition, highest posterior density credible intervals are obtained. Further, using different optimally criteria, an optimal scheme has been proposed. A simulation study is conducted to assess the statistical performance of all the estimators. To demonstrate the proposed methodology a real data analysis is provided to illustrate all the statistical inferential procedures developed in the paper. [ABSTRACT FROM AUTHOR]

• Explored a novel finite mixture modelling approach to deal with dependent competing risks data. • Assumed a more realistic scenario of competing risks data with multiple causes of failure. • Considered a flexible progressive first failure censoring scheme. • Different cause-specific lifetime distributions are assumed to belong to proportional hazard family of distributions. • A Diabetic Retinopathy dataset is analysed in details assuming some important members of the proportional hazard family. This paper proposes a flexible mixture modeling approach to analyze competing risks data with multiple causes of failure under a progressive first-failure censoring scheme where the causes may not be independent. The failure time distributions corresponding to different causes of failure are assumed to belong to the flexible proportional hazard family of distributions, some important members of which are Weibull, Burr XII and Gompertz distributions. We address the problem here from a classical viewpoint. The point estimates of the parameters can be easily obtained by employing the maximum likelihood principle. For interval estimation purposes, we resort to using the asymptotic confidence intervals. An extensive simulation study is carried out to validate our proposed model. Finally, a diabetic retinopathy treatment dataset is used as an application to study the effectiveness of our proposed model assuming some important members of the proportional hazard family. [ABSTRACT FROM AUTHOR]

In edge computing (EC), when the edge server (ES) is processing tasks delivered by the mobile devices (MDs), the MDs move outside the coverage of the ES, where task migration is required to ensure service continuity. Most current research on task migration ignores inter-task dependencies and uncertain computing environments, and it focuses mainly on migration scenarios where MDs have a one-to-one or many-to-one relationship with ESs. Aiming at the problem of workflow migration with multi-MDs and multi-ESs in uncertain environments, this paper proposes an interval many-objective optimized workflow migration in uncertain environments (I-MaOWMUE) model that considers transforming uncertainty factors into interval parameters for processing, along with the migration delay, maximum completion time, energy consumption, and load balancing as an objective function, and at the same time, utilize real-time priority scheduling strategies to achieve the fast response of the tasks. Considering the dependency of tasks and the changing characteristics of ES load in a migration environment, this paper designs a migration-based interval many-objective evolutionary algorithm (MI-MaOEA), which adopts an interval confidence strategy to improve algorithm convergence and formulates an objective-value-dominated hierarchical sorting and dual-migration selection strategy based on the migration delay and the success rate of the migration to improve the diversity of the populations. Simulation results show that MI-MaOEA optimizes 27%, 35%, 14%, and 80% in solving the four objective values of I-MaOWMUE, and enables the solution to have faster converse speed and better distribution. [ABSTRACT FROM AUTHOR]

Wind power point and interval prediction plays an important role in dispatching. However, for obtaining both point estimations and prediction intervals (PIs), the existing models like constructing the probability density function are too complicated. This paper proposes a novel multi-objective upper and lower bound and point estimation (MOULPE) model. It constructs a neural network (NN) with double outputs to directly estimate the prediction intervals (PIs) and the median of PIs is calculated as point estimation. Considering wide decision-making space, the problem formulation of MOULPE is defined as three objectives which covers both evaluation indices of PIs and point prediction. Furthermore, based on elite opposition-based learning (EOBL), this paper improves non-dominated fast sort genetic algorithm-III (INSGA-III) to search the optimal front. Two criteria called prediction interval nominal confidence (PINC) and point prediction nominal error (PPNE) are adopted to pick out the best solution. According to the general requirements in literature, four examples of real wind power data are conducted. Compared with some state-of-the-art methods, the coverage probability of PIs constructed by the proposed model not only reaches the preset PINC, but the average width is also the lowest. Similarly, the point estimation error of the proposed method is less than PPNE. • MOULPE is proposed to obtain PIs and PP results through one RWNN. • Using the median of PI as the PP result in MOULPE to improve the optimization effect. • An improved NSGA-III is proposed for optimizing the weights of NN in MOULPE. • MOULPE can provide a set of decision schemes with different standards for operators. • The proposed MOULPE shows superiority on four real-world datasets. [ABSTRACT FROM AUTHOR]

In this paper, we adopted the competing risks model considering that, the failure time is distributed with the Chen lifetime distribution, causes of failure are partially observed and the individuals are tested under type-I censoring scheme. The model parameters are estimated by maximum likelihood and Bayes estimations. Also, the asymptotic confidence intervals as well as credible interval are constructed. The proposed model is used to analyze two sets of data, real and simulate lifetime data sets for illustrative purposes. [ABSTRACT FROM AUTHOR]

It is challenging to obtain maximum power output from a Solar Photovoltaic System (SPVS) under uncertain weather conditions due to rapid change in temperature and irradiance. It is achievable through an application of an appropriate Maximum Power Point Tracking (MPPT) technique. The precise tracking of Maximum Power Point (MPP) is crucial for randomly varying irradiance and temperature due to sudden occurrence of clouds and rain during uncertain variable weather conditions. In this paper, an approach based on Intersection Point Determination Method (IPDM) is proposed to effectively track the MPP under such uncertain climatic conditions. It is an iterative method which starts with formation of a rectangle by drawing straight lines parallel to voltage and current axes of I–V curve, subsequently identifies the point of intersection of its diagonals. This identified intersection point is used as the initial point for the next iteration until the convergence is achieved i.e. the new intersection point superimposes the previous point in two successive iterations. The computer simulations have been carried out on a stand-alone (MSX-60) solar PVS of 60W along with a boost converter designed in MATLAB/Simulink. The performance of proposed IPDM technique is tested and compared with other well established MPPT techniques in four distinct theoretical test cases comprises all possible variable weather conditions to ensure its vast applicability. Further, an experimental setup is established in laboratory environment to also validate its performance for the real-life input variables. In which, the maximum and average power outputs are achieved to 50.5598 W and 30.6338 W respectively, for the unfavourable real-life weather conditions i.e. mostly a cloudy day with rainfall and sunshine for short durations. All these simulation results as obtained by all five MPPT methods are statistically validated through t-Tests with 5% of confidence of interval. The statistical analysis proves the superiority of the proposed IPDM technique as compared to the other four competing MPPT techniques. The proposed method is promising in terms of better convergence, fast tracking speed, negligible oscillations, and maximum power output in all possible weather conditions. [ABSTRACT FROM AUTHOR]

Dual programmed cell death-1 and vascular endothelial growth factor pathway inhibition is the novel standard of care for patients with unresectable hepatocellular carcinoma. Direct comparisons between first-line treatments are lacking. We conducted a literature search in MEDLINE (https://pubmed.ncbi.nlm.nih.gov), the Cochrane library (https://www.cochranelibrary.com) and Embase (www.embase.com) between January 2007 and February 2022. We included phase III randomised controlled trials that tested immune-checkpoint inhibitors or tyrosine kinase inhibitors, including sorafenib, lenvatinib and donafenib, and evaluated as primary end-point overall survival (OS) or progression-free survival (PFS). Studies testing loco-regional therapies were excluded. The primary end-point was to compare the efficacy of first-line options in terms of OS and PFS. We extracted Hazard ratios (HR) and 95% confidence intervals (95% CI) for OS and PFS and performed a frequentist network meta-analysis with fixed effect multivariable meta-regression models. The research protocol was registered in PROSPERO, an international prospective register of systematic reviews (registration code CRD42022312489). Literature review yielded 13709 results, after duplicates removal and exclusion of not relevant studies, 70 papers were available for screening. After full-text review, 9 studies were eligible for analysis. Atezolizumab plus bevacizumab reduced the risk of death compared to placebo (HR 0·40; 95% CI 0·28-0·57), sorafenib (HR 0·58; 95% CI 0·42-0·80), lenvatinib (HR 0·63; 95% CI 0·44-0·89), atezolizumab plus cabozantinib (HR 0·64; 95% CI 0·43-0·97), nivolumab (HR 0·68; 95% CI 0·48-0·98) and donafenib (HR 0·69; 95% CI 0·48-0·99). Atezolizumab plus bevacizumab was not statistically superior to durvalumab plus tremelimumab (HR 0·74; 95% CI 0·52-1·06) and sintilimab plus IBI305 (HR 1·02; 95% CI 0·67-1·55) in reducing the risk of death. Efficacy was associated with a higher risk of grade 3 adverse events. • Immune-checkpoint inhibitor plus anti-vascular endothelial growth factor agent provide the highest reduction in the risk of death in hepatocellular carcinoma. • The efficacy of immune-checkpoint inhibitor plus anti-vascular endothelial growth factor is similar to that of dual checkpoint inhibition. • The efficacy of combination strategies comes at the expense of higher toxicity. • Nivolumab is associated with the lowest risk of adverse events. [ABSTRACT FROM AUTHOR]

To reduce smoking uptake in adolescents, the medical students' network Education Against Tobacco (EAT) has developed a school-based intervention involving a face-aging mobile app (Smokerface). A two-arm cluster-randomized controlled trial was conducted, evaluating the 2016 EAT intervention, which employed the mobile app Smokerface and which was delivered by medical students. Schools were randomized to intervention or control group. Surveys were conducted at baseline (pre-intervention) and at 9, 16, and 24 months post-intervention via paper & pencil questionnaires. The primary outcome was the difference in within-group changes in smoking prevalence between intervention and control group at 24 months. Overall, 144 German secondary schools comprising 11,286 pupils participated in the baseline survey, of which 100 schools participated in the baseline and at least one of the follow-up surveys, yielding 7437 pupils in the analysis sample. After 24 months, smoking prevalence was numerically lower in the intervention group compared to control group (12.9 % vs. 14.3 %); however, between-group differences in change in smoking prevalence between baseline and 24-months follow-up (OR=0.83, 95 %-CI: 0.64–1.09) were not statistically significant (p = 0.176). Intention to start smoking among baseline non-smokers declined non-significantly in the intervention group (p = 0.064), and remained essentially unchanged in the control group, but between-group differences in changes at the 24-months follow-up (OR=0.88, 0.64–1.21) were not statistically significant (p = 0.417). While a trend towards beneficial effects of the intervention regarding smoking prevalence as well as intention to start smoking among baseline non-smokers was observed, our smoking prevention trial demonstrated no significant effect of the intervention. • A face-aging app intervention aimed to reduce adolescent smoking uptake. • Two-arm cluster-randomized controlled trial with 144 German secondary schools. • No significant difference in smoking prevalence change at 24 months (p = 0.176). • Subgroup analyses showed a tendency towards benefits among boys and grammar school pupils. • Intention to start smoking declined non-significantly in the intervention group (p = 0.064). [ABSTRACT FROM AUTHOR]

• Multi-domain features characterize different stages of damage. • VAE for dimensionality reduction and fusion of high-dimensional features. • The proposed semi-supervised model accurately predicts and quantifies damage. • Hyperparameter value functions improve the accuracy and robustness of the model. Accurate monitoring of the damage state of composite structures is crucial. However, uncertainties such as complicated dispersion processes make it difficult to monitor the damage state. In this paper, a semi-supervised model integrating variational auto-encoder (VAE) and Bayesian neural network (BNN) is proposed to predict the damage state. A high-dimensional damage feature set is constructed by extracting different features from Lamb wave signals, which are then fused and downscaled by the VAE model's hidden layer. To avoid overfitting, noise signals are added into the VAE hidden layer to generate diversity samples. Finally, the VAE hidden layer is fed into the BNN network to predict the damage state of the composite plate, and the model's uncertainty is measured with 95% confidence intervals. Experimental validation results on composite plate demonstrate that all experimental measurements fall within the 95% confidence interval, achieving accurate prediction of the damage state of the composite plate. [ABSTRACT FROM AUTHOR]

Recent studies indicate an association between immunosuppression for immune-related adverse events (irAEs) and impaired survival in patients who received immune checkpoint inhibitors. Whether this is related to corticosteroids or second-line immunosuppressants is unknown. In the largest cohort thus far, we assessed the association of immunosuppressant type and dose with survival in melanoma patients with irAEs. Patients with advanced melanoma who received immunosuppressants for irAEs induced by first-line anti-PD-1 ± anti-CTLA-4 were included from 18 hospitals worldwide. Associations of cumulative and peak dose corticosteroids and use of second-line immunosuppression with survival from start of immunosuppression were assessed using multivariable Cox proportional hazard regression. Among 606 patients, 404 had anti-PD-1 + anti-CTLA-4-related irAEs and 202 had anti-PD-1-related irAEs. 425 patients (70 %) received corticosteroids only; 181 patients (30 %) additionally received second-line immunosuppressants. Median PFS and OS from starting immunosuppression were 4.5 (95 %CI 3.4–8.1) and 31 (95 %CI 15-not reached) months in patients who received second-line immunosuppressants, and 11 (95 %CI 9.4–14) and 55 (95 %CI 41–not reached) months in patients who did not. High corticosteroid peak dose was associated with worse PFS and OS (HR adj 1.14; 95 %CI 1.01–1.29; HR adj 1.29; 95 %CI 1.12–1.49 for 80vs40mg), while cumulative dose was not. Second-line immunosuppression was associated with worse PFS (HR adj 1.32; 95 %CI 1.02–1.72) and OS (HR adj 1.34; 95 %CI 0.99–1.82) compared with corticosteroids alone. High corticosteroid peak dose and second-line immunosuppressants to treat irAEs are both associated with impaired survival. While immunosuppression is indispensable for treatment of severe irAEs, clinicians should weigh possible detrimental effects on survival against potential disadvantages of undertreatment. irAE guidelines advise starting with 1-2 mg/kg prednisone for most ≥grade 3 irAEs. Previous papers suggest that immunosuppression for irAEs impairs survival. Whether this is due to steroid dose or second-line immunosuppression is unknown. We show that both steroid peak dose and second-line immunosuppression are associated with survival. This suggest that it may be better to start with lower steroid doses whenever possible. [ABSTRACT FROM AUTHOR]

This paper aimed to examine the psychometric properties of a modified version of the Self-Efficacy in Clinical Teaching Scale with a sample of undergraduate nursing students. Enhancing nursing students' self-efficacy in clinical teaching is important for developing their clinical teaching capabilities, which ultimately contributes to safe quality healthcare. Little is known about nursing students' self-efficacy in clinical teaching, potentially due to a lack of validated instruments to measure their self-efficacy in clinical teaching. This study used a cross-sectional survey design. In 2022, a convenience sample of Year 2 and Year 3 nursing students in an Australian Bachelor of Nursing Program completed the modified Self-Efficacy in Clinical Teaching Scale. The scale's construct validity was assessed using known-group measures (year level and peer teaching experience) and confirmatory factor analysis. Concurrent validity was established through correlation with a visual analogue scale and reliability was tested with Cronbach's alpha. Three hundred and nineteen surveys were analysed. The mean total self-efficacy in clinical teaching score for Year 2 participants was significantly lower than that of Year 3 participants (mean difference = −11.13, 95 % CI [-16.33, −5.93]). Participants with peer teaching experience had a significantly higher mean total score compared with those without peer teaching experience (mean difference = 15.32, 95 % CI [8.74, 21.91]). The scale's relative chi-square test was acceptable (CMIN/df = 4.69); other fit indices were within or just outside acceptable parameters (RMSEA =.11; SRMR =.039; NFI =.89; RFI =.88; IFI =.91; TLI =.90; CFI =.91). There was a strong correlation between participants' total self-efficacy in clinical teaching scores and their visual analogue scale scores (r =.83, n = 314, p <.001 [two-tailed]). Cronbach's alpha for the total scale was.98. The modified Self-Efficacy in Clinical Teaching Scale was sensitive to student year level and peer teaching experience. When subjected to confirmatory factor analysis, the scale's model was an acceptable fit across several indices. These findings support an argument for the scale's construct validity. Total self-efficacy scores were strongly correlated with visual analogue scales, suggesting concurrent validity. Using Cronbach's alpha, the scale was found to be reliable, however may benefit from a reduction of item numbers. The modified Self-Efficacy in Clinical Teaching Scale may support future investigations of undergraduate nursing students' self-efficacy in clinical teaching and contribute to an understanding of health professional students' self-efficacy in clinical teaching across disciplines. [ABSTRACT FROM AUTHOR]

Many nations and jurisdictions have legalized non-medical adult use of cannabis, or are considering doing so. This paper contributes to knowledge of adult use legalization's associations with cannabis use disorder (CUD) treatment utilization. This study collected data from a dataset of all publicly funded substance use disorder treatment delivered in California from 2010 to 2021 (1,460,066 episodes). A logistic regression model estimates adult use legalization's impacts on CUD treatment utilization using an individual-level pre-post time series model, including individual and county-level characteristics and county and year-fixed effects. Adult use legalization was associated with a significant decrease in the probability of admission to CUD treatment (average marginal effect (AME): −0.005, 95 % CI: −0.009, 0.000). Adult use legalization was also associated with a decrease in the probability of admission to CUD treatment for males (AME: −0.025, 95 % CI: −0.027, −0.023) Medi-Cal beneficiaries (AME: −0.025, 95 % CI: −0.027, −0.023) adults ages 21+ (AME: −0.011, 95 % CI: −0.014, −0.009) and Whites (AME: −0.012, 95 % CI: −0.015, −0.010), and an increase in the probability of admission to CUD treatment for patients referred from the criminal justice system (AME: 0.017, 95 % CI: 0.015, 0.020) and Blacks (AME: 0.004, 95 % CI: 0.000, 0.007) and Hispanics (AME: 0.009, 95 % CI: 0.006, 0.011). Adult use legalization is associated with declining CUD treatment admissions, even though cannabis-related problems are becoming more prevalent. Policies and practices that protect public health, and engage people with CUD in treatment are needed. • Pre-legalization, 19 % of SUD admissions were CUD, compared to 9 % post-legalization. • Adult use legalization was associated with a decrease in CUD treatment admissions. • Differences existed in legalization's associations on different demographic groups. [ABSTRACT FROM AUTHOR]

• There is weak interobserver reliability of interpreting clinical difference based on observed difference and the confidence interval. • The interpretation of the p-value did not differentiate physician reported clinical relevance. • The novel OD/CI ratio, showed a significant difference for differentiating clinical relevance. • The OD/CI maximized the sensitivity (SN) and specificity (SP) for identifying clinical relevance. A measure of effect size, such as observed difference (OD) and its 95% confidence interval (CI), is necessary to determine clinical relevance (CR) of research findings. The purpose of this paper is to (1) determine the interobserver reliability (IOR) of determining CR when presented with only the OD and CI and (2) to determine if a ratio of OD over CI (OD/CI) had a stronger association with CR than the p -value. A survey including the OD and CI results from 21 studies was sent to 36 physicians, of which 21 responded. Respondents were asked to determine if the results were clinically relevant or not clinically relevant. Twenty-one (58%) physicians responded. The IOR of interpreting CR based on OD and the CI was weak (kappa=0.13, CI 0.10 to 0.15). The p-value did not differ between CR and non-CR results (median difference -0.001, CI -0.005 to 0.0, p = 0.07). The OD/CI however, was greater for CR vs. non-CR results (median difference 0.5, CI 0.09 to 0.95, p = 0.02). The area under the curve for the p-value and OD/CI receiver-operator characteristic curve was 0.70 and 0.80. The p-value and OD/CI that maximized the sensitivity (SN) and specificity (SP) for identifying CR was 0.001 (SN 88%, SP 59%) and 0.95 (SN 88%, SP 84%). Determining CR from the OD and CI alone had weak interobserver reliability. The OD/CI ratio had a stronger association with CR than the p-value making it potentially useful in evaluating the CR of research findings. [ABSTRACT FROM AUTHOR]

The paper aims to evaluate the efficacy and safety of 223Ra in patients who progressed after first-line androgen deprivation therapy. EXCAAPE (NCT03002220) was a multicentre, single-arm, open-label, non-controlled phase IIa trial in 52 patients with metastatic castration-resistant prostate cancer and asymptomatic bone metastases who have progressed on abiraterone acetate or enzalutamide, up to six doses of 223Ra (55 kBq/kg of body weight per month). The primary end-point was radiographic progression-free survival (rPFS). Secondary end-points included rPFS based on androgen receptor splice variant 7 (AR-V7) expression in circulating tumour cells (CTCs), overall survival, and safety. Median rPFS was 5.5 months (95% CI 5.3–5.5). Median rPFS of patients with AR-V7(−) CTCs was longer than that of patients with AR-V7(+) CTCs (5.5 versus 2.2 months, respectively; P = 0.056). Median overall survival was 14.8 months (95% CI 11.2–not reached) and was significantly greater for AR-V7(−) patients than for AR-V7(+) patients (14.8 months versus 3.5 months, respectively; P < 0.01). 223Ra was well tolerated; anaemia and thrombocytopenia were the most common grade 3/4 adverse events (5.8% and 11.5%, respectively). 223Ra seems to be a reasonable treatment for patients with metastatic castration-resistant prostate cancer and asymptomatic bone metastases progressing on novel hormonal therapy and had an acceptable safety profile. • 223Ra had a promising antitumour activity in patients with metastatic castration-resistant prostate cancer with asymptomatic bone metastases. • 223Ra showed an acceptable safety profile similar to that reported in other studies. • Overall survival of androgen receptor splice variant 7 (−) patients was significantly higher than androgen receptor splice variant 7 (+) patients. [ABSTRACT FROM AUTHOR]

Overdiagnosis of invasive breast cancer (BC) is a contentious issue. The aim of this paper is to estimate the overdiagnosis rate of invasive BC in an organised BC screening program and to evaluate the impact of age and follow-up time. The micro-simulation model SiMRiSc was calibrated and validated for BC screening in Flanders, where women are screened biennially from age 50 to 69. Overdiagnosis rate was defined as the number of invasive BC that would not have been diagnosed in the absence of screening per 100,000 screened women during the screening period plus follow-up time (which was set at 5 years and varied from 2 to 15 years). Overdiagnosis rate was calculated overall and stratified by age. The overall overdiagnosis rate for women screened biennially from 50 to 69 was 20.1 (95%CI: 16.9–23.2) per 100,000 women screened at 5-year follow-up from stopping screening. Overdiagnosis at 5-year follow-up time was 12.9 (95%CI: 4.6–21.1) and 74.2 (95%CI: 50.9–97.5) per 100,000 women screened for women who started screening at age 50 and 68, respectively. At 2- and 15-year follow-up time, overdiagnosis rate was 98.5 (95%CI: 75.8–121.3) and 13.4 (95%CI: 4.9–21.9), respectively, for women starting at age 50, and 297.0 (95%CI: 264.5–329.4) and 34.2 (95%CI: 17.5–50.8), respectively, for those starting at age 68. Sufficient follow-up time (≥10 years) after screening stops is key to obtaining unbiased estimates of overdiagnosis. Overdiagnosis of invasive BC is a larger problem in older compared to younger women. • Sufficient follow-up time is key to obtaining unbiased estimates of overdiagnosis. • Estimates of overdiagnosis need ≥10 years of follow-up after screening stops. • Overdiagnosis of invasive breast cancer is<1 in 1000 women biennially screened. • Overdiagnosis is a larger problem in older compared to younger women. • Overdiagnosis is most evident in prevalence mammography. [ABSTRACT FROM AUTHOR]

We study variance estimation and associated confidence intervals for parameters characterizing genetic effects from genome-wide association studies (GWAS) in misspecified mixed model analysis. Previous studies have shown that, in spite of the model misspecification, certain quantities of genetic interests are consistently estimable, and consistent estimators of these quantities can be obtained using the restricted maximum likelihood (REML) method under a misspecified linear mixed model. However, the asymptotic variance of such a REML estimator is complicated and not ready to be implemented for practical use. In this paper, we develop practical and computationally convenient methods for estimating such asymptotic variances and constructing the associated confidence intervals. Performance of the proposed methods is evaluated empirically based on Monte-Carlo simulations and real-data application. • We propose variance estimators for restricted maximum likelihood estimators of heritiability and variance components in genome-wide association studies under misspecified mixed model analysis. • We also present associated confidence intervals. • Our proposed method and the genome-based restricted maximum likelihood (GREML) method under genome-wide complex trait analysis have similar performance in simulation studies and data application, which may provide justification for GREML. [ABSTRACT FROM AUTHOR]

Nonlinear regression is frequently used to fit nonlinear relations between response variables and regressors, for process data. The procedure involves the minimization of the square norm of the residuals with respect to the model parameters. Nonlinear least squares may lead to parametric collinearity, multiple optima and computational inefficiency. One of the strategies to handle collinearity is model reparameterization, i.e. the replacement of the original set of parameters by another with increased orthogonality properties. In this paper we propose a systematic strategy for model reparameterization based on the response surface generated from a carefully chosen set of points. This is illustrated with the support points of locally K-optimal experimental designs, to generate a set of analytical equations that allow the construction of a transformation to a set of parameters with better orthogonality properties. Recognizing the difficulties in the generalization of the technique to complex models, we propose a related alternative approach based on first-order Taylor approximation of the model. Our approach is tested both with linear and nonlinear models. The Variance Inflation Factor and the condition number as well as the orientation and eccentricity of the parametric confidence region are used for comparisons. • The paper considers the model reparametrization to decrease ill-condition in nonlinear regression. • The support points of K-optimal designs are used for constructing a family of surface curves. • Semidefinite Programming is used to find K-optimal designs. • Two different approaches to handle the parameter's reversion are presented. • The methodology is demonstrated with various challenging nonlinear regression models. [ABSTRACT FROM AUTHOR]

In this paper, we present a new method to get the value of claims and relations of an argumentation graph. By the method, the values of claims and relations are determined by the average of the solutions of the differential equation derived from the argumentation graph. We show that the overhead of obtaining multiple solutions can be reduced by parallel execution and the values obtained by the method agree with our intuition. We can get a confidence interval of each value and a rough grouping of claims and relations by the method. We show the results for toy graphs, large artificial graphs, and a natural language argumentation graph. [ABSTRACT FROM AUTHOR]

OBJECTIVE: Since its inception, the Pediatric Quality Measures Program has focused on the development and implementation of new and innovative pediatric quality measures (PQM) for both public and private use. Building the evidence base related to measure usability and feasibility is central to increasing measure uptake and, thereby, to increased performance monitoring and quality improvement (QI) for children in Medicaid or the Children's Health Insurance Program. This paper describes key stakeholder insights focused on measure implementation and increasing the uptake of PQM. METHODS: The PQMP Learning Collaborative conducted semistructured interviews with 9 key informants (KIs) representing states, health plans, and other potential end users of the measures. The interviews focused on gaining KIs' perspectives on 6 research questions focused on assessing the feasibility and usability of PQM and strengthening the connection between measurement and improvement. RESULTS: Our synthesis identified insights that highlight facilitators and barriers from the KIs' experience and the strategies they employ when using measures to drive improvement "on-the-ground." Importantly, while the KIs agreed on how essential the research questions are to measure implementation and uptake, they uniformly acknowledged the complexity of the issues raised and pinpointed multiple unresolved issues. DISCUSSION: The views expressed by these stakeholders point to several key issues - including incorporation of socio-economic status into quality measures and performance comparisons, use of benchmark data, and criteria for QI versus accountability - for developing a real-world research agenda to guide the future direction of quality measurement and implementation to improve children's health care. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive statistical framework in order to rigorously derive the correlation coefficient distribution between two independent chemical data sets. The novel approach not only allows for unequivocal interpretation of distribution parameters and confidence intervals but also the generalization of previous results for fixed size comparisons into the new and experimentally important case of variable dimension data assessments. Original theoretical results include an objective criterion ("number of nines") for the coefficient of multiple determination in mathematical model fitting (e.g. calibration curve acceptance in validation protocols) and the requirement of a minimum correlation coefficient match of approximately 70% in variable size evaluations. Significance tests have been applied to the drug profile linkage of 532 real case cocaine samples seized by the National Police of Peru between the years of 2021 and 2022. • A novel and comprehensive mathematical framework to rigorously derive the correlation coefficient distribution between independent data sets. • Generalization of fixed dimension comparisons into the new and experimentally important case of variable size assessments. • Explicit derivation of critical correlation coefficient values in terms of Excel® statistical functions. • Minimum correlation coefficient value of approximately 70% for variable size data evaluations. • Coefficient of multiple determination criterion for calibration curve acceptance in validation protocols. [ABSTRACT FROM AUTHOR]

• First trends analysis of data from the national Canadian Cannabis Survey. • Past-year cannabis use increased since non-medical cannabis legalization in Canada. • The proportion of past-year consumers using cannabis frequently is unchanged. • Past-year use of edibles, drinks and vape pens/cartridges has increased. • Legal purchasing is displacing the illicit cannabis market in Canada. Cannabis use for non-medical purposes was legalized and regulated in Canada through the Cannabis Act in October 2018. This paper examined trends in use of cannabis for non-medical purposes and related indicators from pre- to post-legalization in Canada (2018–2022). Data from 5 years of the Canadian Cannabis Survey, an annual web-based survey administered to Canadians 16 years of age or older, were used in the analysis (n 2018 =12,952; n 2019 =11,922; n 2020 =10,821; n 2021 =10,733; n 2022 =10,048). Cannabis measures include questions about use, types of products, sources, risk perceptions and beliefs, and exposure to public education campaigns and health warnings. Adjusted logistic regression models tested differences in outcomes over time. Past 12-month cannabis consumption increased among Canadians from 22 % in 2018 to 27 % in 2022 (AOR=1.41;99 % CI:1.28–1.54). Similarly, daily/almost daily (DAD) consumption increased from 5 % in 2018 to 7 % in 2022 (AOR=1.36;99 % CI:1.16–1.59). Consumption of dried flower, hash/kief, and concentrates/extracts (e.g., wax, shatter, budder) decreased since 2018, whereas consumption of edibles, beverages and vape pens/cartridges increased (p < 0.001). Legal purchasing increased from 4 % in 2018 to 69 % in 2022, while accessing cannabis through social and illegal sources decreased over time (p < 0.001). More Canadians are reporting cannabis consumption since legalization and regulation of cannabis for non-medical purposes, continuing a pre-existing trend despite an increase in awareness of the risks of consuming cannabis. Trends in product use indicate a transition from dried flower and concentrates/extracts towards consumption of cannabis foods, drinks and vape pens/cartridges. The legal market is increasingly displacing the illicit cannabis market in Canada. [ABSTRACT FROM AUTHOR]

• Compared to passive control, dance improves mental health and quality of life in individuals with Parkinson's disease. • Non-partnered dance is more effective in improving mental health than partnered dance. • Higher total dosage of dance intervention negatively correlates with the effects on mental health. Parkinson's disease (PD) is a neurodegenerative disease that affects millions of individuals worldwide. Dance has emerged as a comprehensive intervention for enhancing well-being in this population. This meta-analysis aimed to assess the effectiveness of dance on mental health and quality of life among individuals with PD. Three databases were searched in December 2022. Research papers comparing the effects of dance with a non-dance control on the quality of life or mental health of individuals with PD were included. Two authors independently screened the studies, extracted data, and assessed methodological quality of eligible studies. To address the interdependence of effect sizes within studies, the three-level meta-analysis approach was employed to analyze the data. Thirteen trials involving a total of 496 participants were included, with 11 being subjected to statistical analysis. The results indicated that dance had a positive impact on mental health (g = 0.43, 95 % CI = [0.11, 0.75]) and quality of life (g = 0.46, 95 % CI = [-0.04, 0.95]) when compared to passive control groups. Moderator analyses revealed that non-partnered dance and dance interventions with lower total dosages were particularly beneficial for mental health. Dance interventions are an effective lifestyle activity for enhancing mental health and quality of life in individuals with PD. A theoretical framework is proposed to explain the impact of dance on well-being from neurological, social, physical, and psychological perspectives. [ABSTRACT FROM AUTHOR]

The misinformation prevention (MP) problem, which aims to find anchor nodes (P-seeds) to start an anti-spread of positive information, has attracted abundant research attention due to its practical application in reducing the risk of social unrest. Existing MP researches ideally assume that the M-seeds (nodes start misinformation spread) are known in advance. However, the misinformation spread usually occurs abruptly so that M-seeds are not easy to be identified as prior knowledge. In this paper, we study the MP problem with uncertain M-seeds. We assume that only a set of suspicious nodes (S-nodes) is known. Each S-node has a transition probability (T-prob) for becoming an M-seed. With different T-prob availability, we propose and study two new research problems: (1) the probabilistic MP (PMP) problem given exactly all the T-probs, and (2) the robust MP (RMP) problem given a confidence interval for each T-prob. For the PMP problem, we propose the prefixed Hybrid sampling based Misinformation Prevention (pHMP) algorithm that return a (1 − 1 / e − ɛ) -approximate solution. For the RMP problem, we optimize the worst-case ratio between the current solution and the optimal one. Two bi-criteria algorithms are proposed that achieve both δ absolute error and (1 − 1 / e − ɛ) 2 (1 − η) and (1 − 1 / e − ɛ) (1 − η) relative error respectively. We conduct extensive experiments on several real world datasets. The results show the proposed algorithms are superior over the baseline algorithm on all datasets, and robust against the changing of different application conditions. [ABSTRACT FROM AUTHOR]

The current paper presents a data-driven hierarchical Bayesian model (HBM) for predicting maximum lateral wall deflections in deep excavations in clay. The presented approach can address a number of challenges, including handling missing input parameters, incorporating the observational method, and accounting for site uniqueness. A new database, EXCA-CLAY/11/901, comprising 302 excavation sites worldwide, is compiled to train the HBM. The trained HBM is applied to a real case study in downtown Shanghai. The HBM can be applied to predict the maximum lateral wall deflection at each excavation stage independent of measured deflections from past stages (non-observational approach) or the more typical observational approach that includes past deflection measurements. Extensive cross-validation analysis validates the performance of the HBM and compares it with three existing regression models. The results show that the regression models exhibit slightly better performance within their applicable range when compared to the non-observational HBM prediction, but are outperformed by the observational HBM. The HBM is shown to offer several advantages over the conventional regression models, including the ability to produce 95% confidence intervals, handle missing input parameters, accommodate a diverse database, and continuously refine predictions as new information becomes available. [ABSTRACT FROM AUTHOR]

• The peak plasma ionospheric frequency (f o F2) from ionosonde stations located at Islamabad and Sonmiani is analyzed. • The 30 days running median technique to detect the abnormality in f o F2 over the epicenter of impending earthquake. • The major reason of these seismo-ionospheric anomalies is the distance between the ionosonde station and epicenter. • The evidence supports that these seismo-ionospheric precursors are probably due to the lithospheric-ionospheric coupling. The rapid advancement in ground and space based ionospheric measurements provide an opportunity to work on different earthquake precursors for lithosphere-ionosphere coupling hypothesis. In this paper, the peak plasma ionospheric frequency (f o F2) for 90 days before/after the main shock of September 24, 2019 (M5.6) earthquake in Pakistan are studied for earthquake precursors from ionosonde stations located at Islamabad and Sonmiani. We implement the 30 days running median technique to detect the abnormality in f o F2 over the epicenter of impending earthquake. A comprehensive analysis of these anomalies on two stations is performed in order to extract the maximum of these abnormalities in their respective regions. The deviation in hourly data in Sonmiani station shows significant variation within 10–20 days before the main shock, as most of the values within 5–10 days' window are within the confidence limits. On the other hand, positive and negative deviations in the analysis of Islamabad station may be revealed as a possible signature of seismo-ionospheric anomalies. The major reason of these seismo-ionospheric anomalies is the distance between the ionosonde station and epicenter, where Islamabad station is close to the epicenter as compared to Sonmiani. It is noteworthy that both negative and positive deviations are observed before the M5.6 earthquake; however, the intensity of positive anomalies is more than negative anomalies. Moreover, severe positive deviation occurs within 10–20 days before the earthquake at the Sonmiani station. Also, there is no geomagnetic storm within 10 days before the earthquake which opposes the existence of seismo-ionospheric anomalies. The evidence supports that these seismo-ionospheric precursors are probably due to the lithospheric-ionospheric coupling. [ABSTRACT FROM AUTHOR]

This paper aims to predict the peak response of concrete-filled steel tubes (CFSTs) under blast loads using regression-based machine learning (ML) algorithms considering the variability of input parameters. Twelve regression metrics that are commonly used are utilized to evaluate and compare the efficiency and effectiveness of the proposed ML models. The Monte Carlo approach is used to propagate the variability in the input space to the predicted output. The results showed that the optimal method varies with perspectives. The XGBoost algorithm has the highest final score, and the SVM algorithm exhibits the highest total score for standard deviation. Furthermore, the confidence interval increases with the peak response, and the single model presents a higher accuracy in predicting large displacements. However, ensemble models do the opposite. Sensitivity and uncertainty analyses are performed, indicating that the scaled distance, section size and ultimate moment capacity exhibit the highest contribution to the peak response of the CFST. Finally, a simple formula is developed based on the MLP model, which can cover three input parameters. The findings of this paper can be used for computer-aided dynamic response design of CFST columns subjected to blast loads. • MLP and SVM are more suitable for predicting large displacement response than AdaBoost and XGBoost. • XGBoost has the highest prediction accuracy whereas SVM has the best robustness. • The confidence interval of ML regression algorithm increases with peak displacement response. • A simple formula whose R2 = 0.989 and a 10-index = 0.524 is developed. [ABSTRACT FROM AUTHOR]

This paper newly proposed a computer vision-based crack quantification algorithm using a statistical approach. Recently, high-resolution digital images have been effectively utilized for automated crack width and length evaluation on concrete structures. However, cracks are often difficult to accurately measure by randomly distributed and complex shapes. To overcome the technical limitation, a novel statistical crack quantification algorithm is proposed and experimentally validated through concrete structures in this paper. First, cracks on digital images are automatically detected using a deep semantic segmentation network. Then, multi-branched cracks are separated into single cracks through crack map generation. Each separated crack length and width is calculated by the Euclidean distance transform algorithm. Finally, crack width is presented as a representative value with a statistical confidence interval. The quantitative crack evaluation results for width and length were successfully compared with the actual field measurement values by average differences of 18.07% and − 26.28%, respectively. • Computer vision-based statistical crack quantification algorithm is proposed for precise crack evaluation. • Pixel-level damage detection is accomplished using a deep semantic segmentation network. • Multi-branched cracks are separated through crack map generation. • The separated crack length and width are quantitatively estimated using Euclidean distance transform. • Crack width is quantitatively represented with a statistical confidence interval. [ABSTRACT FROM AUTHOR]

The addition of green spaces (GS) in cities is perceived as an efficient solution to combat climate change and biodiversity loss while also improving human health. Quantitative health impact assessment (QHIA) is a powerful tool to assess the health benefits of GS and support policy-making decisions. In France, a preliminary analysis of the literature led to the decision of developing guidance for QHIA applied to GS and mortality. This paper focuses on the choice of exposure-response functions (ERF) for those QHIA. Literature review and analysis of the key steps of QHIA. Articles providing ERF for all-cause, cardiovascular and respiratory mortality in relation to GS were identified through a literature review and ranked based on a quality score. ERF from the articles with the highest scores was pooled in meta-analyses. In total, 13 ERF were selected for all-cause mortality, 10 for cardiovascular mortality and 5 for respiratory mortality. Meta-risk for a 0.1 increase in the normalised differential vegetation index were, 0.96 (95% confidence interval [CI] 0.94; 0.97), 0.98 (95% CI 0.96; 0.99) and 0.97 (95% CI 0.92; 1.02) for all-cause, cardiovascular and respiratory mortality, respectively. While current knowledge makes it possible to use QHIA on GS and mortality, interdisciplinary research is still needed to clarify the shape of the relationship and its temporality and to assess exposure in a meaningful way for decision-making. [ABSTRACT FROM AUTHOR]