Search

• Sea-level trends show a large spatial deviation from the coast to open ocean. • Sea-level variabilities are locally complicated and reveal inconsistent mechanisms. • PDO and ENSO, as remote forcings, show significant influence at the southern sites. The changes in coastal sea levels are now considered a significant concern for densely-populated coastal regions such as Vietnam, which hosts a long coastline and two large deltaic areas. In this study, we used high-resolution (20-Hz) altimetry data from the X-TRACK/ALES product to investigate the trends and variability of sea-level anomalies (SLAs) along the Vietnam coast from 2002 to 2018. We conducted comparisons of SLAs from X-TRACK/ALES with SLAs from four coastal tide gauges, and the results present a good root mean square error (0.03 m on average) and good statistically significant correlation (for p < 0.05) with an average value of 0.66. There is an exception for the Quy Nhon site (r ∼ 0.5) as its closest altimetry points lie in the open ocean. Regarding trend calculations, the tide gauge sites exhibit two-to-three times larger trends than altimetry points, indicating a significant deviation from the coast to the open ocean. We then examined the contribution of various forcing factors by stepwise regression analysis for the 2002 – 2018 and 2008–2018 periods. The stepwise model can explain the observed data in the latter period (about 63 % on average). As local factors, SST and meridional wind stress appear important, whereas PDO and ENSO are significant as remote factors, particularly at the southern sites. However, we could not find the best common forcing factors for all sites suggesting the inconsistent mechanisms causing sea-level variability along the Vietnam coast. Our study demonstrates complicated regional sea-level variability and emphasizes the benefit of high-resolution altimetry data in studying dynamic coastal regions. [ABSTRACT FROM AUTHOR]

Uncertainty quantification is a vital component of any measurement process and is indispensable for comparing results obtained by different methods, instruments, or laboratories. The processing of the measured data often relies on fitting the data by a given function. Common methods such as ordinary nonlinear least squares are not capable of treating general uncertainties and correlations in both dependent and independent variables. A new computation method for nonlinear curve fitting to data with a general covariance structure is introduced. This method is applied to the Oliver-Pharr analysis of unloading curves and differences between different regression methods are addressed. Numerical simulations show that the new method yields parameter estimates in agreement with other methods for simple covariance structures. The obtained uncertainty estimates are in agreement with Monte Carlo studies. • Novel fitting method is suitable for general functions and general covariance matrix of input data. • Novel method yields realistic estimates of uncertainties of fitting parameters. • Correlation of fitting parameters reduces the uncertainty of physical quantities. [ABSTRACT FROM AUTHOR]

Machine learning models are significantly used in organizations for critical business processes and decision-making. The inputs and predictions from these models might drift over time due to several reasons, causing unanticipated behaviour and performance issues. COVID-19 has a significant impact on business resulting in huge dispersions of the data with unstable characteristics and irregular spikes at various time steps. When the data set experiences significant drifts due to the changes in their statistical properties, continuing with existing models based on historical data will lead to poor decision outcomes. It's hard to anticipate all significant future events and create an ML model which can withstand black swan events. Therefore, it's imperative to have an automated process for continuous monitoring in the ML lifecycle to identify the potential issues using drift detection methodologies with proper actions to avoid false predictions in silence. This study is focused on time series where data stream is represented as observations at regular intervals. Many dynamic real-world processes can be modelled using time series where data can flow quickly and change over time which is detrimental to time series analysis and forecasting. The objective of this study is to compare and evaluate different approaches for data drifts in time series models to retain its optimum performance and proposing an integrated approach for handling those drifts in real-time pipelines using timely alerts and re-establishing models. [ABSTRACT FROM AUTHOR]

Yuan et al. developed a predictive model for early response using sub-regional radiomic features from multi-sequence MRI alongside clinical factors. However, biases in feature selection and assessment may lead to misleading conclusions regarding feature importance. This paper elucidates the biases induced by machine learning models and advocates for a robust methodology utilizing statistical techniques, such as Chi-squared tests and p-values, to uncover true associations. By emphasizing the vital distinction between true and model-specific associations, we promote a comprehensive approach that integrates multiple modeling techniques. This strategy enhances the reliability of predictive models in medical imaging, ensuring that outcomes are based on objective relationships and ultimately improving patient care. [ABSTRACT FROM AUTHOR]

• Crack density growth in cross-ply laminates during fatigue loading is analyzed. • Transverse cracking is influenced by local stress ratio in the 90-layer. • Equivalent stress is introduced to include the effect of local stress ratio. • Equivalent stress concept is validated against two different cross-ply layups. Presented test results on transverse cracking in cross-ply laminates upon tension–tension cyclic loading show that the increase of crack density depends not only on the maximum transverse stress in the cycle but also on the local cyclic stress ratio R T loc in the analyzed layer. To include the effect of the R T loc in the model with statistical failure stress distribution for crack initiation (based on Weibull distribution) adapted for fatigue, an equivalent stress is introduced in a similar manner as the equivalent strain energy release rate has been used for delamination crack propagation. The equivalent stress in the layer is defined as a power function of the maximum stress and the stress ratio in the layer. It was found, testing laminates with two different fiber contents that higher the local stress ratio in 90-layer, higher the transverse cracking resistance. Transverse crack density simulation using the developed equivalent stress model has been validated against test results. [ABSTRACT FROM AUTHOR]

Predictive models are widely used in clinical practice. Despite of the increasing number of published AI systems, recent systematic reviews have identified lack of statistical rigor in the development and validation of predictive models. This work reviewed the current literature for predictive performance measures and resampling methods. Furthermore, common pitfalls were discussed. [ABSTRACT FROM AUTHOR]

The given paper considers the problem of generating words of a context-dependent language and the problem of quantitative assessment of the constructed generators properties. The problem is considered in the context of property-bassed unit testing: this paper provides the developer with a method of comparing generators in order to find the most optimal one to ensure the required ratio of time for generating the test data, their complexity, diversity or other characteristics. The article proposes a system of characteristics that can be used to compare generators. The generator of typed λ-terms is considered as an example. [ABSTRACT FROM AUTHOR]

• Recent advances in time-lagged panel modeling may be seen as mimicking clinical reasoning in the focus on temporal sequences. • At the same time, these models are comparatively strong for causal inference. • The above two points together make these methods ideal for bridging the scientist-practitioner gap in psychotherapy research. In this commentary, I advance the view that the scientist-practitioner gap is partly due to the research designs commonly used in psychotherapy research. Specifically, I believe that randomized controlled trials, which are important for establishing treatment efficacy and as leverage when making the case for the value of psychotherapy in relation to various stakeholders, are limited for further development of clinical theories. Instead, I find recent advances in cross-lagged panel modeling to be both clinically intuitive and stronger for causal inference than most other nonexperimental designs. In addition to discussing causal inference and clinical interpretation of cross-lagged panel models, I discuss the potential of improving mediation analysis, personalization of research, and studying issues of clinical timing. Finally, I briefly discuss some limitations of cross-lagged panel models. It is my belief that the use of these data analytic advances can make empirical research better live up to the innovations in Beck's work. [ABSTRACT FROM AUTHOR]

This research reports the BMI status of 176 adults and 134 children from 96 Native American families who are participating in a randomized controlled trial to assess health impacts of home gardens. Analyses include demographic associations with BMI using a novel approach of analyzing BMI status of children and adults together as one population by using LMS-based z scores generated from NHANES data. Results fit national data, with Native Americans more likely to be overweight/obese than other US demographic groups. This, in turn, makes Indigenous communities more vulnerable to chronic diseases. Ending these health inequities requires substantial public health nutrition investments in, for example, restoration of Indigenous foodways. This trial is registered at clinicaltrials.gov as NCT02672748. [ABSTRACT FROM AUTHOR]

Improving prediction computation for time series analysis is still a challenge. Finding a method that combines the benefits of different methodologies is still an open problem. Besides the very efficient prediction combination techniques proposed, there is still a lack of procedures that jointly consider error measure combinations and model constraints. In this work, we propose a new forecast combination procedure based on multi-criteria methods that allows the assignment of weights to different error measures in the objective function and the incorporation of constraints. A real case from the pharmaceutical industry for the sale of a probiotic product is presented to illustrate the performance of the proposal. This method is capable of considering different error measures and non distance based errors, is enriched by the consideration of constraints that consider desirable properties of the solution and is robust with respect to different time series characteristics such as trends, seasonality, etc. Results shows similar accuracy to the best known forecasting methods to date. • Methodology capable of considering different non distance based and error measures. • Methodology that provide information on the behaviour of the prediction direction. • Proposed MNLP enriched by the consideration of constraints. • Criteria's defined within the prediction combination method. • More adaptive to extreme fluctuations or variations in the data. • Robust with respect to different time series characteristics such as trends, etc... [ABSTRACT FROM AUTHOR]

Energy prices are sensitive and volatile due to supply–demand imbalances, policy changes, new environmental targets, and technological breakthroughs. Energy security, sustainability, and affordability are key to policy design and societal advancement, making accurate energy price forecasting essential. Despite the existence of various methods for forecasting energy prices, no single method consistently outperforms the others. In this respect, the Energy Price Index (EPIC) framework is presented with extended forecasting capabilities for 56 different energy products using 33 unique time series. Statistical and machine learning forecasting methods of different natures have been incorporated into the framework, enabling the forecasting of energy prices up to 14 months ahead. Each energy product's historical prices are analyzed for seasonality, trends, and outliers. The results reveal the superiority of deep neural networks over statistical methods, while at the same time highlighting the significant importance of proper tuning of the hyperparameters of a neural network. • How do statistical and machine learning forecasting methods compare in predicting energy prices across various types of energy sources? • To what extent does the Energy Price Index (EPIC) framework enhance the accuracy of energy price forecasts compared to existing methods? • What insights can be gained from the independent study of historical prices for each energy product, particularly in identifying seasonality, patterns, trends, and outliers? • In what ways do deep neural networks outperform traditional statistical forecasting methods in predicting energy prices? [ABSTRACT FROM AUTHOR]

The aim of this work was the development of a procedure for the indirect characterization of 238U and 232Th in ceramic products using the quantification of gross alpha and gross beta activities in a low background proportional counter, which is less time consuming and cheaper than the direct radiochemical methods and gamma spectrometry. The procedure was applied, first, to a reference material used to fine-tune the procedure and statistical techniques, and then, for two types of ceramic products: micronized zircon and zircon flour samples, and spray-dried ceramic powders (atomized). This procedure requires obtaining linear regression models for estimating the activity of 238U and 232Th as a function of specific gross alpha and gross beta measurements. Regression models must be obtained for each type of ceramic material together with the range of application of the model, which depend primarily on the ratio between 238U and 232Th activities. The applications to micronized zircon and zircon flours, and spray-dried ceramic powders demonstrated that the models obtained for the indirect characterization of 238U and 232Th activities explain more than 95% of the variability in the data. In addition, those models were used for predicting the results of new validation samples. Regarding the micronized zircon and zircon flours, predictions were good for the 238U activity with a relative bias of less than 10%, while they were not so accurate for the 232Th activity, with a relative bias of less than 15% for most of the samples. For spray-dried ceramic powders, the predictions were quite good for the 238U activity with a relative bias of less than 20% irrespective of the cluster to which the validation sample belongs, and similarly for the 232Th activity. • Indirect characterization of 238U and 232Th in ceramic products using gross alpha and gross beta activities. • Method tested with reference materials and ceramic products: zircon materials and spray-dried ceramic powders. • Multiple linear regression models proposed depended on the type of material and uranium and thorium ratios. • Relatives bias below 20% were obtained for 238U and 232Th activities. [ABSTRACT FROM AUTHOR]

• Summarizes research results in the MS field and proposes standardized definitions for common clinical courses. • Enhances mutual understanding between clinicians and researchers, crucial for trial recruitment and expected outcomes. • ML techniques, including NNs, prove effective in identifying risk factors and MS-related features. • Provides a comprehensive overview of recent progress in MS research, facilitating comparative analysis of advancements. • Offers a roadmap for future research for mitigating the risks associated with MS. Medical research offers potential for disease prediction, like Multiple Sclerosis (MS). This neurological disorder damages nerve cell sheaths, with treatments focusing on symptom relief. Manual MS detection is time-consuming and error prone. Though MS lesion detection has been studied, limited attention has been paid to clinical analysis and computational risk factor prediction. Artificial intelligence (AI) techniques and Machine Learning (ML) methods offer accurate and effective alternatives to mapping MS progression. However, there are challenges in accessing clinical data and interdisciplinary collaboration. By analyzing 103 papers, we recognize the trends, strengths and weaknesses of AI, ML, and statistical methods applied to MS diagnosis. AI/ML-based approaches are suggested to identify MS risk factors, select significant MS features, and improve the diagnostic accuracy, such as Rule-based Fuzzy Logic (RBFL), Adaptive Fuzzy Inference System (ANFIS), Artificial Neural Network methods (ANN), Support Vector Machine (SVM), and Bayesian Networks (BNs). Meanwhile, applications of the Expanded Disability Status Scale (EDSS) and Magnetic Resonance Imaging (MRI) can enhance MS diagnostic accuracy. By examining established risk factors like obesity, smoking, and education, some research tackled the issue of disease progression. The performance metrics varied across different aspects of MS studies: Diagnosis: Sensitivity ranged from 60 % to 98 %, specificity from 60 % to 98 %, and accuracy from 61 % to 97 %. Prediction: Sensitivity ranged from 76 % to 98 %, specificity from 65 % to 98 %, and accuracy from 62 % to 99 %. Segmentation: Accuracy ranged up to 96.7 %. Classification: Sensitivity ranged from 78 % to 97.34 %, specificity from 65 % to 99.32 %, and accuracy from 71 % to 97.94 %. Furthermore, the literature shows that combining techniques can improve efficiency, exploiting their strengths for better overall performance. [ABSTRACT FROM AUTHOR]

In the ever-evolving field of time series analysis, detecting changes in patterns and dynamics is paramount for accurate forecasting and meaningful insights. This article thoroughly explores several algorithms for detecting and analyzing pattern changes in time series data. The exploration covers a broad spectrum of algorithms, further venturing into their categorization based on functional modalities and the ability to identify complex changes. Recognizing pattern changes in time series data holds pivotal importance as it aids in anticipating future trends, ensuring efficient resource allocation, and mitigating potential challenges. This research goes beyond a basic overview and conducts a thorough comparative analysis, highlighting each algorithm's strengths, drawbacks, and computational complexities. This comparative approach provides practitioners and researchers with the necessary information to select the most suitable algorithm for their requirements. Additionally, this review provides insight into potential future research directions, proposing possible improvements and breakthroughs in the design and application of algorithms. This review also provides a runtime analysis of various pattern change detection algorithms, presenting an in-depth evaluation of the existing methodologies. It serves as a vital reference for individuals dealing with the dynamic nature of time series data. [Display omitted] • Comprehensive analysis of pattern change detection in time series data. • Evaluated methods for identifying shifts in data patterns. • Impact of algorithm selection on detection accuracy. • Insights to improve future pattern change prediction models. • Enhancing real-world applications through robust detection techniques. [ABSTRACT FROM AUTHOR]

Missing outcome data is common in trials, and robust methods to address this are needed. Most trial reports currently use methods applicable under a missing completely at random assumption (MCAR), although this strong assumption can often be inappropriate. To identify and summarise current literature on the analytical methods for handling missing outcome data in randomised controlled trials (RCTs), emphasising methods appropriate for data missing at random (MAR) or missing not at random (MNAR). We conducted a methodological scoping review and identified papers through searching four databases (MEDLINE, Embase, CENTRAL, and CINAHL) from January 2015 to March 2023. We also performed forward and backward citation searching. Eligible papers discussed methods or frameworks for handling missing outcome data in RCTs or simulation studies with an RCT design. From 1878 records screened, our search identified 101 eligible papers. 90 (89%) papers described specific methods for addressing missing outcome data and 11 (11%) described frameworks for overall methodological approach. Of the 90 methods papers, 30 (33%) described methods under the MAR assumption, 48 (53%) explored methods under the MNAR assumption and 11 (12%) discussed methods under a hybrid of MAR and MNAR assumptions. Control-based methods under the MNAR assumption were the most common method explored, followed by multiple imputation under the MAR assumption. This review provides guidance on available analytic approaches for handling missing outcome data, particularly under the MNAR assumption. These findings may support trialists in using appropriate methods to address missing outcome data. [ABSTRACT FROM AUTHOR]

Statistical techniques incorporated with machine-learning algorithms in unison with gene-environment interaction are giving unparalleled understanding of complex diseases. Accurate analysis and intricate capturing of common, rare, and low MAF (Minor Allele Frequency) variants alongside gene-environmental interaction is pivotal whilst concluding reliable and accurate classification of complex diseases. Various complex diseases including genres of diabetes Type 1 and Type 2 alongside the vastly under-researched Lada (Latent Autoimmune Diabetes in Adults) diabetes require further investigation alongside significant machine learning research to gain a deeper understanding of the disease complexities. Despite existing efforts, an ideal combination of statistical techniques with optimal machine-learning algorithms that can accurately capture and model the gene-environment interaction is lacking. Intentionally exploring future and simultaneously exploiting modern-day computational methods in genomic analysis, this paper profoundly investigates both the future and present interaction of statistical analysis techniques and machine-learning algorithms and Ensembles with gene-environmental factors. In this context, this paper firstly presents a conceptual understanding of genomic conventions; secondly, conducts potential future machine learning algorithms alongside an extensive analysis of a range of classification, regression and Ensemble techniques along with exhibiting their imperative relationship and roles in investigating and classifying common, rare variants and a wide array of gene-environmental factors; and thirdly, utilisation of statistical techniques in Genome Wide Association Studies is scrutinised whilst analysing common, rare and MAF variants. As an important contribution, this paper identifies efficient machine-learning algorithms alongside Ensemble models and future potential analysis techniques and exhibits their inherent characteristics that can enhance the reliability and accuracy of the gene-environment classification analysis. [ABSTRACT FROM AUTHOR]

Accurate genetic prediction of complex traits can facilitate disease screening, improve early intervention, and aid in the development of personalized medicine. Genetic prediction of complex traits requires the development of statistical methods that can properly model polygenic architecture and construct a polygenic score (PGS). We present a comprehensive review of 46 methods for PGS construction. We connect the majority of these methods through a multiple linear regression framework which can be instrumental for understanding their prediction performance for traits with distinct genetic architectures. We discuss the practical considerations of PGS analysis as well as challenges and future directions of PGS method development. We hope our review serves as a useful reference both for statistical geneticists who develop PGS methods and for data analysts who perform PGS analysis. Polygenic score (PGS) analysis aggregates association information from genome-wide SNPs to enable genetic prediction of complex traits. PGS analysis is becoming increasingly popular with the abundant availability of genome-wide association studies and the development of PGS methods. Different PGS methods model the polygenic architecture underlying traits in different ways and often make distinct modeling assumptions on the effect size distribution. These modeling assumptions can help to understand the performance of PGS methods across traits with distinct genetic architectures. Recent PGS methods focus on making use of summary statistics as input, specify flexible effect size assumptions, incorporate additional information including SNP functional annotations and pleiotropy association evidence across multiple traits, perform multi-ethnic prediction, and rely on computationally efficient algorithms for scalable inference. The development of PGS methods is closely connected to the development of methods for SNP heritability estimation, and many common methods are shared between the two areas. Experience and lessons learned from SNP heritability estimation can potentially benefit methodological developments for PGS construction. For some diseases, PGS analysis has had initial clinical success and can be especially useful for risk stratification. For the majority of complex traits, however, PGS methods have yet to achieve high prediction accuracy in the general population. [ABSTRACT FROM AUTHOR]

The precision of the measurement of the anisotropy of magnetic susceptibility (AMS) depends, in addition to other factors like accuracy of measurement of directional susceptibility and number of measuring directions, also on the orientation of the measuring design with respect to the specimen's anisotropy. The last factor can be characterized by rotatability coefficient of the measuring design. We investigated through mathematical modelling of measuring process the effect of the rotatability coefficient on the error in the determination of degree of AMS, shape factor and principal directions. The effect is conspicuous in strongly non-rotatable designs with low number of measuring directions. It decreases with increasing rotatability and with increasing number of measuring directions. In designs with high number of directions this effect is small, virtually negligible from the practical point of view. [Display omitted] • Rotatability of design of measuring directions. • Rotatable designs; Error propagation. • Effect of rotatability degree on measuring precision. • Advantage of full rotatability design in case of low number designs. • Decreasing effect of rotatability with increasing number of measuring directions. [ABSTRACT FROM AUTHOR]

The use of classic nonparametric tests (cNPTs), such as the Kruskal–Wallis and Mann–Whitney U tests, in the presence of unequal variance for between-group comparisons of means and medians may lead to marked increases in the rate of falsely rejecting null hypotheses and decreases in statistical power. Yet, this practice remains prevalent in the scientific literature, including nutrition and obesity literature. Some nutrition and obesity studies use a cNPT in the presence of unequal variance (i.e. heteroscedasticity), sometimes because of the mistaken rationale that the test corrects for heteroscedasticity. Herein, we discuss misconceptions of using cNPTs in the presence of heteroscedasticity. We then discuss assumptions, purposes, and limitations of 3 common tests used to test for mean differences between multiple groups, including 2 parametric tests: Fisher's ANOVA and Welch's ANOVA; and 1 cNPT: the Kruskal–Wallis test. To document the impact of heteroscedasticity on the validity of these tests under conditions similar to those used in nutrition and obesity research, we conducted simple simulations and assessed type I error rates (i.e. false positives, defined as incorrectly rejecting the null hypothesis). We demonstrate that type I error rates for Fisher's ANOVA, which does not account for heteroscedasticity, and Kruskal–Wallis, which tests for differences in distributions rather than means, deviated from the expected significance level. Greater deviation from the expected type I error rate was observed as the heterogeneity increased, especially in the presence of an imbalanced sample size. We provide brief tutorial guidance for authors, editors, and reviewers to identify appropriate statistical tests when test assumptions are violated, with a particular focus on cNPTs. [ABSTRACT FROM AUTHOR]

Besides increasing dynamics in market demands, companies strive to avoid short-term changes in their supply chain planning. Therefore, an essential lever to improve supply chain performance is the optimization of the demand forecast. In this regard, artificial intelligence is a widely adopted technique in Industry 4.0 that is associated with high expectations. Against this background, six different forecasting models from statistics and machine learning were evaluated in respect to forecast quality and effort for implementation. The results underline the potential of innovative forecasting models as well as the necessity for an intensive and application-specific evaluation of the advantages and disadvantages of the available approaches. [ABSTRACT FROM AUTHOR]

• Hydrological and water quality variables were investigated to detect human activity. • Mean groundwater level and agricultural water demand were used to justify the results. • Statistical techniques have been applied to recognized trends and change points. • SPI and SDI drought indices were analyzed to evaluate the hydrological regimes. In recent years, climate changeability, hydrologic regime conditions, and human interventions have become crucial issues to be assessed. In this research, two annually recorded datasets were collected to analyse the change in the trend. The first set is comprised of precipitation, streamflow, and water quality variables including Total Dissolved Solids (TDS), pH, cation, and anion and the second one contains the mean groundwater level and agricultural water demand of four main stations of Shahpour River basin in the south of Iran. To recognize the fluctuating patterns, the Mann-Kendall Trend Test (MKTT), KPSS Stationary Test, and Pettit Homogeneity Test (PHT) of statistical methods were utilized at a 5% significance level. The Standardized Precipitation Index (SPI) and Streamflow Drought Index (SDI) were subsequently employed to detect the hydrological drought patterns. According to the statistical analysis, the streamflow and water quality depicted intensive varying trends, while there were slight decreasing trends for the precipitation series. Afterward, the abrupt changing points were identified in the first and second datasets between the years 2004 to 2007. The results of this study clarified that human activity effects (as a major factor) and climate variability (as a minor factor) have been affecting the Shahpour River basin. These effects disrupt the water chemical balance (the relationship between cations and anions) and hydrological regimes (increasing drought drivers) and consequently menace the health of the watershed. [ABSTRACT FROM AUTHOR]

To assess if an integrated longitudinal analysis using all available imaging data affects the precision of estimates of change in patients with axial spondyloarthritis (axSpA), with completers analysis as reference standard. Patients from the DESIR cohort fulfilling the ASAS axSpA criteria were included. Radiographs and MRIs of the sacroiliac joints and spine were obtained at baseline, 1, 2 and 5 years. Each image was scored by 2 or 3 readers in 3 'reading-waves' (or campaigns). Each outcome was analyzed: i. According to a 'combination algorithm' (e.g. '2 out of 3′ for binary scores); and ii. Per reader. Change over time was analyzed with generalized estimating equations by 3 approaches: (a)'integrated-analysis' (all patients with ≥1 score from ≥1 reader from all waves); (b1)Completers-only analysis (patients with 5-year follow-up, using scores from individual readers); (b2)Completers analysis using a 'combination algorithm' (as (b1) but with combined scores). Approaches (b1) and (b2) were considered the 'reference'. In total, 413 patients were included. The 'integrated analysis' was more inclusive with similar levels of precision of the change estimates as compared to both completers analyses. In fact, for low-incident outcomes (e.g.% mNY-positive over 5-years), an increased incidence was 'captured', with more precision, by the 'integrated analysis' compared to the completers analysis with combined scores (% change/year (95%CI): 1.1 (0.7; 1.5) vs 1.2 (0.5; 1.8), respectively). An efficient and entirely assumption-free 'integrated analysis' does not jeopardize precision of the estimates of change in imaging parameters and may yield increased statistical power for detecting changes with low incidence. [ABSTRACT FROM AUTHOR]

• A prior of image patches which can be described by a high dimension Gaussian Mixture Model is built in this paper. • An image patches based nonlocal variational method is proposed to simultaneously inpainting and denoising. • The existence of minimizer for the proposed inpainting model was given in this paper. We consider the inpainting problem for noisy images. It is very challenge to suppress noise when image inpainting is processed. An image patches based nonlocal variational method is proposed to simultaneously inpainting and denoising in this paper. Our approach is developed on an assumption that the small image patches should be obeyed a distribution which can be described by a high dimension Gaussian Mixture Model. By a maximum a posteriori (MAP) estimation, we formulate a new regularization term according to the log-likelihood function of the mixture model. To optimize this regularization term efficiently, we adopt the idea of the Expectation Maximization (EM) algorithm. In which, the expectation step can give an adaptive weighting function which can be regarded as a nonlocal connections among pixels. Using this fact, we built a framework for non-local image inpainting under noise. Moreover, we mathematically prove the existence of minimizer for the proposed inpainting model. By using a splitting algorithm, the proposed model are able to realize image inpainting and denoising simultaneously. Numerical results show that the proposed method can produce impressive reconstructed results when the inpainting region is rather large. [ABSTRACT FROM AUTHOR]

There is an emerging realisation that paediatric reference intervals (RIs) estimated using discrete age-groups may be misleading, especially close to age cut-off values. This limitation has been addressed by estimating RIs that vary continuously with age. This systematic review examines the range of statistical methods used over the past 25 years for estimation of age-specific RIs, and identifies trends in usage and reporting. Literature searches were conducted using predefined search criteria for original publications between 1993 and 2018 on the MEDLINE and Embase databases. Data related to sample size, treatment of age (as categorical or continuous), and statistical methods were extracted from the selected publications. A total of 238 publications were reviewed. Not all publications reported the statistical methods used in different steps. Among the publications, 167 (70%) reported discrete age-group RIs, 54 (23%) reported continuous RIs and 17 (7%) reported both types of RIs. The nonparametric statistical method was commonly used for discrete age-group RIs (64%, n = 117), whereas a wide variety of curve-fitting approaches, including Cole's lambda-mu-sigma method (28%, n = 20), parametric curve-based methods (28%, n = 20), generalised additive model for location, scale and shape method (13%, n = 9) and quantile regression (11%, n = 8) were used for continuous RIs. Improvement in the reporting of statistical methods used for estimating age-specific paediatric RIs is required. There has been insufficient uptake of methods for producing continuous RIs, especially for biomarkers that display strong age-dependence. [ABSTRACT FROM AUTHOR]

• Find of Cordoba's Mosque-Cathedral columns organization system. • 3D photogrammetry of Cordoba's Mosque-Cathedral columns. • Cluster of columns from different buildings inside Cordoba's Mosque-Cathedral • Use of statistical methods in heritage buildings. In this article, we intend to classify the columns of the first constructed sector of the Mosque-Cathedral of Córdoba to analyse possible clusters or to find a systematic organization of the columns. Through the use of statistical methods (multivariate analysis and cluster analysis), topographic surveys and the use of photogrammetry in each column; several clusters of shafts that could belong to different buildings have been found. In addition, a column organization system has been found. This provides important data to the construction system of the first mosque in Córdoba. The results obtained have opened a new line of research inside and outside the building. [ABSTRACT FROM AUTHOR]

Introduction: In the context of an increasing number of publications of trial data analysed by Bayesian methods, clinicians need support to better understand Bayesian statistical methods. The existing checklists are intended for people who already know these methods. We aimed to establish and validate a checklist that contains a group of items considered crucial in interpreting the results of a phase III RCT analysed with Bayesian methods.Methods: A team of biostatisticians created a checklist of previously reported items and additional items identified from a literature review. Using three different articles in three rounds, the items were then validated by residents in anaesthesiology with no skills in statistics.Results: Based on an initial item list, three rounds led to a consensus checklist. Eleven items were considered important information to be specified for understanding the validity of the results. Of these, three were considered essential: specification of the prior, source of the prior (when prior is informative), and the effect size point estimate with its credible interval.Conclusion: The checklist can help clinicians interpret the results of a phase III randomised clinical trial analysed by Bayesian methods, even clinicians with no particular knowledge of statistics, to ensure that the major elements of the statistical section are present and valid. Care should be taken in interpreting the results of a trial analysed by Bayesian methods that are not reported with these three essential items because the validity of the results cannot be established. [ABSTRACT FROM AUTHOR]

• Proposing a comprehensive yet pragmatic model development and validation strategy. • Dealing with key challenges commonly encountered in modelling. • Improving the accuracy, transparancy and robustness of NTCP-models Normal Tissue Complication Probability (NTCP) models can be used for treatment plan optimisation and patient selection for emerging treatment techniques. We discuss and suggest methodological approaches to address key challenges in NTCP model development and validation, including: missing data, non-linear response relationships, multicollinearity between predictors, overfitting, generalisability and the prediction of multiple complication grades at multiple time points. The methodological approaches chosen are aimed to improve the accuracy, transparency and robustness of future NTCP-models. We demonstrate our methodological approaches using clinical data. [ABSTRACT FROM AUTHOR]

Accurate solar power forecasting is essential for grid-connected photovoltaic (PV) systems especially in case of fluctuating environmental conditions. The prediction of PV power output is critical to secure grid operation, scheduling and grid energy management. One of the key elements in PV output prediction is time series analysis especially in locations where the historical solar radiation measurements or other weather parameters have not been recorded. In this work, several time series prediction methods including the statistical methods and those based on artificial intelligence are introduced and compared rigorously for PV power output prediction. Moreover, the effect of prediction time horizon variation for all the algorithms is investigated. Hourly solar power forecasting is carried out to verify the effectiveness of different models. The data utilized in the current work comprises 3640 h of operation data taken from a 20 MW grid-connected PV station in China. • BI-LSTM algorithm is an accurate power prediction model for large scale PV plants. • BI-LSTM algorithm outperforms different NNs and statistical models. • Neural networks are more accurate than statistical models and requires less time. • Time series forecasting for PV plants is only reliable for 1-h ahead prediction. [ABSTRACT FROM AUTHOR]

Objectives: To systematically review the quality of reporting on the application of switching adjustment approaches in published oncology trials and industry submissions to the National Institute for Health and Care Excellence Although methods such as the rank preserving structural failure time model (RPSFTM) and inverse probability of censoring weights (IPCW) have been developed to address treatment switching, the approaches are not widely accepted within health technology assessment. This limited acceptance may partly be a consequence of poor reporting on their application.Methods: Published trials and industry submissions were obtained from searches of PubMed and nice.org.uk, respectively. The quality of reporting in these studies was judged against a checklist of reporting recommendations, which was developed by the authors based on detailed considerations of the methods.Results: Thirteen published trials and 8 submissions to nice.org.uk satisfied inclusion criteria. The quality of reporting around the implementation of the RPSFTM and IPCW methods was generally poor. Few studies stated whether the adjustment approach was prespecified, more than a third failed to provide any justification for the chosen method, and nearly half neglected to perform sensitivity analyses. Further, it was often unclear how the RPSFTM and IPCW methods were implemented.Conclusions: Inadequate reporting on the application of switching adjustment methods increases uncertainty around results, which may contribute to the limited acceptance of these methods by decision makers. The proposed reporting recommendations aim to support the improved interpretation of analyses undertaken to adjust for treatment switching. [ABSTRACT FROM AUTHOR]

This paper outlines the common statistical methods used to analyze four types of observational study: ecological studies, cross-sectional studies, case–control studies and cohort studies. Three statistical methods are considered in detail: correlation coefficients, t-tests for the difference between two means from independent samples and odds ratios. For each method, the need for the same four key outputs is highlighted: the measure of effect, its precision (95% confidence interval), its statistical significance (p-value) and its clinical significance (comparison to the minimum important difference). Where possible, this paper explains the derivation of measures of effect and statistical significance to help readers understand where the numbers come from. The paper also highlights other important aspects of analyzing the quantitative data collected in an observational study, potential biases to consider, and good practice in reporting. Examples from relevant pediatric studies are used where appropriate. [ABSTRACT FROM AUTHOR]

This study analyzes numerically the access of football fans to a typical football stadium through pedestrian flow simulations. With this aim, we introduce a novel framework to address the difficulty of simulating pedestrian dynamics in highly complex geometries with multiple accesses. The framework consists of a combination of the Social Force Model (SFM) and Computational Fluid Dynamics tools to calculate multiple desired velocity maps. The introduction of this framework allows the predefinition of the desired velocity fields for the complex geometries in both the interior and surrounding area of the stadium. We validate the results of the proposed framework using actual entry-rate data from the turnstiles of three gates for 15 matches provided by the football club. Remarkably, the validation process allows us to describe the temporal evolution of the spatial distribution of pedestrians during their arrival with a single set of parameters valid for all the analyzed matches. Finally, we assess the potential fatality risk based on the local pedestrian density varying the total number of attendees and their arrival rate. The outcome evidences permanent clogs emerge at the entrance in the most extreme cases with high attendance or low standard deviation of the arrival rate, indicating the possibility of fatality occurrence. • Simulation of pedestrian access to a stadium from the surrounding streets to specific grandstand seats. • Computational fluid dynamics-based calculation of desired velocity fields in complex geometries. • Reproduction of empirical data of the access of fans through turnstiles. • Analysis of the effect of the total number of attendees and the standard deviation of their arrival on maximum local densities. [ABSTRACT FROM AUTHOR]

Continuous material transportation in the mining industry through conveyor belts is widely efficient. However, the varying conditions of force and operation make these belts susceptible to tears, misalignments, and damages, hampering production. Longitudinal tears, in particular, pose problems due to their potential for accidents and belt breaks. Therefore, it is necessary to develop monitoring systems for early detection of these tears. This study proposes the use of a 2D laser scanner as a cost-effective and highly efficient alternative to identify such tears. Experiments conducted on a test bench with a conveyor belt and a laser scanner demonstrate the effectiveness of three detection algorithms: Three-Sigma, Cumulative Sum Control Chart (CUSUM), and Pruned Exact Linear Time (PELT). The results indicate that these statistical methods can accurately identify longitudinal tears in conveyor belts. • 2D laser scans detect belt tears with efficiency and cost- effectiveness. • Three-Sigma algorithm excel in dynamic and static tear detection. • Statistical algorithms enhance accuracy in conveyor belt tear identification. [ABSTRACT FROM AUTHOR]

The utilization of machine learning (ML) techniques is increasingly prevalent in the domain of building performance evaluation. This trend is primarily driven by ML's capacity to capture intricate relationships between building attributes and performance metrics, such as energy consumption and comfort levels. However, the comparative merits of ML techniques and traditional statistical methods, such as linear and logistic regression, which are typically more cost-effective and interpretable, remains uncertain. This study presents a systematic comparison between ML and statistical methods in the assessment of building performance, considering factors such as model complexity, interpretability, required expertise, performance disparities, and computational costs. Findings indicate that, in most scenarios, ML techniques outperform statistical methods. Nevertheless, there are notable instances where statistical methods can compete, highlighting the context-dependent nature of technique selection. Furthermore, this research introduces a novel Python-based framework with a user-friendly spreadsheet interface designed for the evaluation and benchmarking of ML and statistical methods in research settings. The developed framework can be easily customized for ML evaluation and benchmarking in diverse fields, including production, logistics, supply chain management, and others. [ABSTRACT FROM AUTHOR]

• Hip fracture-related mortality remains an important concern after surgical intervention (5-15% within 30 days). • Two predictive models were developed for risk stratification and patient-specific interventions based on pre and postoperative factors. • LASSO regression outperformed traditional methods in predictive accuracy and identified novel clinical factors associated with mortality. • Targeted interventions to prevent delirium, promote early ambulation, and use of bone protection medication can potentially reduce mortality. Hip fractures are one of the most common injuries experienced by the general population. Despite advances in surgical techniques, postoperative mortality rates remain high. identifying relevant clinical factors associated with mortality is essential to preoperative risk stratification and tailored post-surgical interventions to improve patient outcomes. The purpose of this study aimed to identify preoperative risk factors and develop predictive models for increased hip fracture-related mortality within 30 days post-surgery, using one of the largest patient cohorts to date. Data from the American College of Surgeons National Surgical Quality Improvement Program database, comprising 107,660 hip fracture patients treated with surgical fixation was used. A penalized regression approach, least absolute shrinkage and selection operator was employed to develop two predictive models: one using preoperative factors and the second incorporating both preoperative and postoperative factors. The analysis identified 68 preoperative factor outcomes associated with 30-day mortality. The combined model revealed 84 relevant factors, showing strong predictive power for determining postoperative mortality, with an AUC of 0.83. The study's comprehensive methodology provides risk assessment tools for clinicians to identify high-risk patients and optimize patient-specific care. [ABSTRACT FROM AUTHOR]

Estimating seismic wavelets is crucial in seismic processing and interpretation procedures, such as seismic well-tie and deconvolution. However, extracting the pulse from seismic data is not trivial, and idealized wavelets are commonly applied in seismic interpretation, such as the Ricker, Gabor, or Ormsby wavelets, which can be considered inaccurate. Methods used to estimate wavelets can be distinguished into deterministic – relying on well-log data – or statistical, which involves a geophysical inversion. Here, a deep-learning model based on a Multilayer Perceptron neural network (MLP) was proposed to predict accurate wavelets using only the seismic trace. The MLP is trained using synthetic traces generated based on the convolutional model. The neural network demonstrated that can predict a suitable wavelet in the test dataset with similar frequency content between the predicted and observed wavelet. Next, seismic wavelets were extracted from the 3D seismic cube F3-Block to verify if the NN predicted could be used in a seismic well-tie task. Once the wavelets were obtained, two synthetic traces were generated, one using a Ricker wavelet and the other using the wavelet predicted by the algorithm, and compared with the trace that presents the highest correlation. The synthetic trace created using the predicted wavelet by the NN displays the best result in both cases. • The proposed Multilayer Perceptron accurately predicts zero-phase seismic wavelets. • The method is applicable for wavelet estimation in both synthetic and real datasets. • The estimation results showed improvements when compared to other methods. • The well-tie results also demonstrated improvement in vertical resolution. [ABSTRACT FROM AUTHOR]

In recent years several authors have documented common problems in the use of statistics in nursing research, including failure to consider the effects of multiple testing, inattention to clinical significance, and under-reporting of effect sizes and confidence intervals. More subtle forms of multiple testing are not as widely recognized, and abuse of researcher degrees of freedom has received little attention in the nursing research literature. These and other unsound practices in applying and interpreting statistics are problematic in themselves, and they arguably reflect an insufficiently clear understanding of statistical inference as a method for dealing with randomness among many researchers. The goal of this educational paper is to improve the understanding and practice of inferential statistics among nursing researchers. An accessible explanation of hypothesis testing is provided, including discussion of the crucial concept of repeated sampling. Several pervasive mistakes and misconceptions in statistical inference are examined in detail, including misinterpretation of "non-significant" p -values as evidence for the null hypothesis, failure to account for forms of multiple testing that arise in model selection, abuse of researcher degrees of freedom, and hypothesis testing for baseline differences between arms in randomized trials. Recommendations for better statistical practice are offered. For the foreseeable future classical methods of statistical inference based on the idea of repeated sampling will be the primary tools for quantifying randomness in nursing research. The hypothesis testing framework, despite its limitations, can be helpful in ruling out chance as an explanation for observed effects. Nursing researchers who use quantitative methods, as well as journal reviewers and editors, should understand this framework well. Those involved in educating nursing researchers and those who teach statistics would do well to ask what changes need to be made to raise the level of statistical practice in nursing research. [ABSTRACT FROM AUTHOR]

Chromatin organization remains complex and far from understood. In this article, we review recent statistical methods of extracting biophysical parameters from in vivo single-particle trajectories of loci to reconstruct chromatin reorganization in response to cellular stress such as DNA damage. We look at methods for analyzing both single locus and multiple loci tracked simultaneously and explain how to quantify and describe chromatin motion using a combination of extractable parameters. These parameters can be converted into information about chromatin dynamics and function. Furthermore, we discuss how the timescale of recurrent encounter between loci can be extracted and interpreted. We also discuss the effect of sampling rate on the estimated parameters. Finally, we review a polymer method to reconstruct chromatin structure using crosslinkers between chromatin sites. We list and refer to some software packages that are now publicly available to simulate polymer motion. To conclude, chromatin organization and dynamics can be reconstructed from locus trajectories and predicted based on polymer models. Chromatin organization and dynamics can be reconstructed from loci trajectories. Biophysical parameters are often extracted from single chromatin loci single-particle trajectories (SPTs). Statistical methods are being used to study the dynamics of multiple chromatin loci. Chromatin properties can be described by polymer models. Available packages exist for coarse-graining chromatin reconstruction. [ABSTRACT FROM AUTHOR]

This research investigation explores the dispersion effectiveness of P c (percolation threshold), 0.5, 1.0, 1.5 and 2.0 vol fraction of MWCNTs (Multiwalled Carbon Nanotubes) in epoxy using UV–Vis (Ultraviolet Visible Region) spectroscopy, their effect on the rheological properties of the solvent and mechanical properties of the fabricated NCs (nanocomposites). Addition of higher amount of MWCNTs increased the absorptivity of the solution in UV–Vis region depicting higher number of untangled and individual MWCNTs in the solution. However, post ultrasonication reagglomeration of these nanoparticles was found to be inevitable and its rate depended upon the amount of nanotubes in the epoxy. Addition of MWCNTs increased the viscosity of the polymer but they were found to be unable to diminish its shear thinning behaviour. Once proper dispersion of the nanotubes in the epoxy was confirmed by UV–Vis spectroscopy, their NCs with (three-phased) and without (two-phased) glass fibers were prepared. Tensile tests asserted that an increase in MWCNT content in the matrix increase toughness of the prepared nanocomposites. Individual CNTs contributed to the ultimate strength of the laminates and their aggregates enhanced the strain to failure. Addition of MWCNTs in GFRP (glass fiber reinforced polymer) laminates not only increased their cycles to failure but also imparted large coefficient of variation in their fatigue life. Reliability analysis and survival probability of the concerned laminates were included for their design considerations. [ABSTRACT FROM AUTHOR]

Pulsed plasma thrusters have been widely used all over the world in the past several decades. A new and effective model to calculate the ablated mass bit (the mass ablated from the propellant in each discharge) of ablative pulsed plasma thrusters is presented in this paper. The model is based on the random degradation of molecular chains in polytetrafluoroethylene (PTFE) and statistical methods. New expressions are obtained for the ablated PTFE mass bit as a function of the energy used in PTFE ablation, the ratio of the energy used to successfully break the carbon-carbon bonds to the energy used in PTFE ablation and the ratio of the mass that breaks away from the ablation surface of the PTFE to the total ablation mass. The introduction of statistical methods allows us to define a simplified model that is useful for the prediction of the ablated mass bit and for preliminary design guidance. The obtained theoretical results for the mass bit show very good agreement with the results from experimental studies. The range of the average relative error is approximately −15.7% to 12.4%. Finally, this new model can be used to evaluate the ablated mass bit, in addition to predicting the lifetime and guiding the design of new ablative pulsed plasma thrusters. • A new ablation model for APPT was proposed based on the random degradation of PTFE. • The mechanism of the ablation process in PTFE was detailed analyzed. • A series of experiments were carried out to verify the validity and feasibility. • The obtained theoretical results show good agreement with the experimental results. [ABSTRACT FROM AUTHOR]

In the present study, ultra-high performance concrete (UHPC) mixtures reinforced with varying dosage of steel fibers were considered for studying the effects of exposure duration and fiber content on mechanical properties of the mixtures subjected to a sustained pre-spalling elevated temperature of 300 °C for different durations. Test results showed an increase in compressive strength and modulus of toughness even after exposing to elevated temperature for 5 h. However, modulus of elasticity and flexural strength decreased significantly with increase in exposure duration. The experimental data were statistically analyzed using analysis of variance (ANOVA) method to examine the effects of fiber content and exposure duration on the mechanical properties. Empirical equations were obtained with excellent degrees of fit correlating the mechanical properties of UHPC mixtures with fiber content and exposure duration. [ABSTRACT FROM AUTHOR]

As Automated Vehicles (AVs) enter the fleet at lower levels of automated (SAE, 2018), the need for human drivers to remain engaged in the driving task will continue. Thus, understanding driver distraction and estimating the reduction in risk associated with removing distractions is important as AV technology develops. While previous research (e.g., Dingus et al., 2016) has estimated large odds ratios (i.e., 3–4) for using cell-phones while driving, countermeasures directed at reducing cell-phone use have not realized large crash reductions. One reason may be that drivers may replace cell-phone use with other risky activities and that odds ratios (ORs) have often compared cell-phone use to ideal driving rather than a realistic reference. Using data from the second Strategic Highway Research Program (SHRP2), we developed two cell-phone propensity models, one with age and one without, to develop weights for events without cell phone use. Using these weights, we estimated the probability of engagement in a variety of tasks in place of cell-phone use. We also estimated weighted odds ratios for cell-phone use (all uses) and cell-phone talking only. Weighted ORs are lower than unweighted ORs and much lower than ORs compared to ideal driving. This is consistent with the idea that in practice, even if cell-phone bans are effective at reducing cell-phone use, they may not greatly reduce risk because drivers may replace cell-phone use with other distracting activities in the same situations in which they normally use cell phones while driving. We also discuss the influence of young drivers on our results. Younger drivers in the dataset are more likely to use cell phones and thus are influential in the propensity model results. [ABSTRACT FROM AUTHOR]

Objective: The fields of medicine and public health are undergoing a data revolution. An increasing availability of data has brought about a growing interest in machine-learning algorithms. Our objective is to present the reader with an introduction to a knowledge representation and machine-learning tool for risk estimation in medical science known as Bayesian networks (BNs).Study Design: In this article we review how BNs are compact and intuitive graphical representations of joint probability distributions (JPDs) that can be used to conduct causal reasoning and risk estimation analysis and offer several advantages over regression-based methods. We discuss how BNs represent a different approach to risk estimation in that they are graphical representations of JPDs that take the form of a network representing model random variables and the influences between them, respectively.Methods: We explore some of the challenges associated with traditional risk prediction methods and then describe BNs, their construction, application, and advantages in risk prediction based on examples in cancer and heart disease.Results: Risk modeling with BNs has advantages over regression-based approaches, and in this article we focus on three that are relevant to health outcomes research: (1) the generation of network structures in which relationships between variables can be easily communicated; (2) their ability to apply Bayes's theorem to conduct individual-level risk estimation; and (3) their easy transformation into decision models.Conclusions: Bayesian networks represent a powerful and flexible tool for the analysis of health economics and outcomes research data in the era of precision medicine. [ABSTRACT FROM AUTHOR]

Highlights • ImPACT can be used serially over a one-hour period. • Neurocognitive scores did not differ across administrations. • A small percentage of scores were outside RCI cutoffs, suggesting sufficient one-hour reliability. Abstract Background Computerized Neurocognitive Testing (CNT) is frequently used for serial assessment of athletes following concussion. The Immediate Post-Concussion Assessment for Cognitive Testing (ImPACT) is a commonly used CNT with well-documented test–retest reliability in healthy samples for intervals ranging from one month to two years. However, previous research has not explored use of ImPACT for other serial testing methodologies such as immediately before and after an experimental trial/intervention where administration on the same day may be necessary. Purpose To examine the suitability of ImPACT for short-term, serial assessment of neurocognitive functioning. Study design Prospective, repeated measures research design. Methods Forty-two healthy, college-aged individuals completed ImPACT twice, with a one-hour break between assessments. Reliability was assessed using Pearson correlation coefficients, intraclass correlation coefficients (ICCs), reliable change indices (RCIs) and regression-based methods (RBM). Results No significant increases in mean ImPACT composite or symptom scores were observed between assessments. ICCs ranged from 0.34 to 0.74 (single)/0.51 to 0.85 (average). Across two test administrations, 92–100% and 93–98% of participants' change scores fell within cutoffs when utilizing the RCI and RBM, respectively. Conclusion The ICCs for ImPACT composite and factor scores across the one-hour administration were consistent with previous studies. Only a small percentage of scores fell outside of RCI and RBM cutoffs. These statistical metrics suggest that ImPACT has sufficient reliability when repeating administration within one-hour. [ABSTRACT FROM AUTHOR]

Highlights • Tensile behaviour of different GFRP laminates under elevate temperatures has is investigated. • Laminates with continuous unidirectional fibers showed the best tensile performance. • Laminates with chopped strand fibers showed the weakest tensile performance. • Laminates with continuous woven fibers performed better than chopped strand and weaker than unidirectional laminates. Abstract This study was aimed at gaining an improved understanding of the behavior of glass fiber-reinforced polymer laminates at elevated temperatures by means of testing laminate specimens with unidirectional, woven, and randomly distributed (chopped strand mat) fibers. The testing parameters were temperature, the type of fiber, and the thickness of the laminates. The failure modes of the specimens and their elasticity moduli at ambient temperature were investigated, and analysis of variance was conducted to determine the contribution of each parameter to the behavioral test results. The findings showed that among the parameters, an increase in temperature exerted the strongest effect on the specimens. The unidirectional laminate specimens exhibited the best performance, maintaining nearly 40% of their loading capacity at 550 °C. At this temperature, the woven laminate specimens could not carry any tensile load, and at 400 °C, the laminate specimens containing randomly distributed fibers lost all their strength. [ABSTRACT FROM AUTHOR]

Abstract Multiple self-potential (SP) anomalies are analyzed by using a Genetic-Price Algorithm (GPA), which has been recently introduced for the inversion of SP data. The proposed approach is tested on multiple synthetic anomalies, which are modeled by horizontal cylinders. First, a forward modeling is used to analyze the resolution of such anomalies by varying all model parameters. Then, GPA is applied to invert synthetic multiple SP anomalies. The numerical analyses show that the proposed approach is able to fully characterize the anomaly sources by providing the correct values of the model parameters as well as the number of sources, even if Gaussian random noise is added to the synthetic data. Furthermore, to show the computational efficiency of GPA, the results of a comparative analysis with the Very Fast Simulated Annealing algorithm are given. The validity of the GPA approach is confirmed by its application to three examples of self-potential field data from mineral exploration and groundwater investigations, which are presented and discussed in relation to other inversion approaches. Finally, the quantitative interpretation of multiple anomalies along a SP profile crossing the Mt. Somma-Vesuvius volcano caldera (southern Italy) is provided. Highlights • A global optimization algorithm is applied for multiple SP anomaly interpretation. • The effectiveness of the proposed approach is tested on synthetic SP data. • Applications of the algorithm to field anomalies of multiple sources are presented. • The algorithm is shown to be competitive with PSO and VFSA optimization methods. [ABSTRACT FROM AUTHOR]

Highlights • We present an empirical statistical model to estimate the topographic contribution to measured microwave images acquired by Near-field Scanning Microwave Microscopy (NSMM). Since topographic and material property variations can contribute to any NSSM measurement, our approach is critical for determination of the material property contribution to any NSSM measurement. • The prediction model parameters are determined from either reference sample or from the sample of interest by robust regression methods. • We apply our methods to thin Perovskite photovoltaic material samples and a GaN nanowire. • For the GaN, we show that our statistical prediction model is dramatically better than a prediction based on a simple physical model. Abstract We develop empirical models to predict the contribution of topographic variations in a sample to near-field scanning probe microwave microscopy (NSMM) images. In particular, we focus on | S 11 | images of a thin Perovskite photovoltaic material and a GaN nanowire. The difference between the measured NSMM image and this prediction is our estimate of the contribution of material property variations to the measured image. Prediction model parameters are determined from either a reference sample that is nearly free of material property variations or directly from the sample of interest. The parameters of the prediction model are determined by robust linear regression so as to minimize the effect of material property variations on results. For the case where the parameters are determined from the reference sample, the prediction is adjusted to account for instrument drift effects. Our statistical approach black is fully empirical black and thus complementary to current approaches based on physical models that are often overly simplistic. [ABSTRACT FROM AUTHOR]