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This paper highlights the tight relationship between the picking and packing processes in warehouse management and the need to consider them as an integrated problem. The study describes and models this integrated problem as a mixed-integer programming model, to optimise overall labour costs by determining the assignment of the subsets of orders, i.e. batches, for picking and packing. To address the issue of model complexity, the paper presents a statistical-based framework for generating approximate models and selecting the optimal one through examination. Based on the examination results, a pair-swapping heuristic is additionally proposed to be combined as a hybrid algorithm. Numerical experiments based on a real-world case demonstrate the effectiveness of the framework-proposed and selected hybrid algorithm by comparison with other framework-proposed approximate models, a solver, and existing heuristics. Our findings indicate that the combined usage of integrated picking and packing processes planning and the hybrid algorithm proposed and selected within the statistical-based framework can effectively reduce the cost of warehouse management. [ABSTRACT FROM AUTHOR]

In order to keep clinical norms aligned with evidence-based practices, dental researchers need to be equipped to read and evaluate meta-analyses. Moreover, clinician scientists in dentistry should be prepared to lead investigations that incorporate meta-analysis. This article provides an overview of the principles and practices that will equip dental researchers to engage in meta-analysis work. Six elements of meta-analysis are addressed: formulating a research question, searching the literature, collecting the data, aggregating the data, analyzing the data, and interpreting results and drawing generalizations. Readers are guided through each of these stages of research so that they can recognize and implement robust, reproducible meta-analysis work. [ABSTRACT FROM AUTHOR]

Results of simulation studies evaluating the performance of statistical methods can have a major impact on the way empirical research is implemented. However, so far there is limited evidence of the replicability of simulation studies. Eight highly cited statistical simulation studies were selected, and their replicability was assessed by teams of replicators with formal training in quantitative methodology. The teams used information in the original publications to write simulation code with the aim of replicating the results. The primary outcome was to determine the feasibility of replicability based on reported information in the original publications and supplementary materials. Replicasility varied greatly: some original studies provided detailed information leading to almost perfect replication of results, whereas other studies did not provide enough information to implement any of the reported simulations. Factors facilitating replication included availability of code, detailed reporting or visualization of data-generating procedures and methods, and replicator expertise. Replicability of statistical simulation studies was mainly impeded by lack of information and sustainability of information sources. We encourage researchers publishing simulation studies to transparently report all relevant implementation details either in the research paper itself or in easily accessible supplementary material and to make their simulation code publicly available using permanent links.

Artificial intelligence techniques are used in many areas today to find solutions to different problems. Scientists are trying to solve some problems in people's daily lives using these techniques. To solve these problems, researchers often use some Machine Learning (ML) algorithms. It is important for researchers to have preliminary information about some metrics of machine learning algorithms in their scientific studies. In this study, k-Nearest Neighbors (k-NN) and Minimum Distance to Means (MDC) ML algorithm, which classifies according to distance measurement methods, were analyzed using the same distance measurement methods. k-NN, which measures distance according to a specified number of distance, and MDC algorithms, which measure distance according to the averages of the classes, were examined in terms of classification performance. The performance comparison of these two algorithms with 5 different distance measurements (Euclidean, Manhattan, Minkowski, Chebyshev, Hellinger) was made using 2 different datasets (Ecoli and Cardiotocography). For Ecoli dataset, the highest train score 100%, test score 84.85% from the k-NN algorithm, and the highest train score 73.78% and test score 76.12% from the MDC algorithm were obtained. For the cardiotocography dataset, the highest train score 99.94%, test score 84.52% from the k-NN algorithm, and the highest train score 69.50% and test score 61.57% from the MDC algorithm were obtained. According to the results of statistical experimental studies, the k-NN algorithm, which classifies according to a certain number, gave better results in both datasets. [ABSTRACT FROM AUTHOR]

Purpose: Mediation analyses allow for exploration of causal mechanisms that explain how a predictor is related to an outcome. Tests of mediation are fundamental to addressing some of the most consequential questions in rehabilitation science. In recent decades, the development of easy-to-use analytical tools has made conducting statistical tests of mediation more accessible to researchers. Unfortunately, there are persistent problems in the conceptual underpinning of many tests of mediation. Even in cases where the statistical analyses are correctly run, problems with the underlying rationale for the mediational analysis will render the results inconsequential, in the best case, or misleading, in the worst case. Method: In this commentary, I summarize the uses of mediation analysis and through a series of six main types of errors provide practical, plain language guidance ("Dos and Don'ts") for conducting a conceptually robust mediation analysis. Results: The "Dos and Don'ts" laid out in this commentary highlight that there are persistent issues with lack of understanding of mediation, confusion about the differences between moderation, mediation, and covariates, lack of strong theoretical justification for mediation, and lack of attention to methodological issues (e.g., measurement) in many mediation analyses. Conclusions: Promoting the use of mediation analysis in rehabilitation research will advance theory and effective practice in our field. Researchers undertaking mediation analysis are encouraged to prioritize developing a strong theoretical framework that justifies use of mediation analysis, ensuring study methodology supports and enables tests of mediation, as well emphasizing a strong statistical approach to conducting the test of mediation. Impact and Implications: Development of new and easy-to-use analytical tools has made conducting statistical tests of mediation more accessible to researchers. The ease with which statistical tests of mediation can now be completed makes it easier for those without a clear understanding of mediation analyses to make errors; most common errors in tests of mediation are not related to the mathematical aspects of running the statistical models, but rather of developing coherent and compelling theoretical models that call for tests of mediation and attending to nonstatistical aspects of the study method, such as measurement of model constructs and sample size/power to detect the mediated effect. Increasing the rigorous application of mediation analyses in rehabilitation research has tremendous potential to provide new insights about mechanisms underlying processes and treatments that are central to researchers and clinicians in the field of rehabilitation psychology. [ABSTRACT FROM AUTHOR]

This paper critically evaluates the conventional insistence on establishing measurement invariance (MI) in cross-cultural psychology. We argue that complex and seemingly arbitrary benchmarks for assessing MI can be unrealistic and effectively prohibit meaningful research. The widespread use of various MI criteria creates unnecessary and often unattainable hurdles for cross-cultural researchers who have made the effort to collect data in multiple cultural contexts. Additionally, the prohibitionist tone of discussions surrounding MI is unhelpful, unscientific, and discouraging. We argue that emerging findings that cultural differences might not be as widespread or profound as once assumed imply that significant cross-cultural differences in measurement should not be the default assumption. Additionally, we advocate a shift towards external validity as a more useful metric of measurement quality. Our overall message is that researchers who go to the considerable trouble of gathering data in more than one country should not be disadvantaged compared to researchers who avoid cross-cultural complications by gathering data only at their home campus. [ABSTRACT FROM AUTHOR]

With urinary proteomics profiling (UPP) as exemplary omics technology, this review describes a workflow for the analysis of omics data in large study populations. The proposed workflow includes: (i) planning omics studies and sample size considerations; (ii) preparing the data for analysis; (iii) preprocessing the UPP data; (iv) the basic statistical steps required for data curation; (v) the selection of covariables; (vi) relating continuously distributed or categorical outcomes to a series of single markers (e.g., sequenced urinary peptide fragments identifying the parental proteins); (vii) showing the added diagnostic or prognostic value of the UPP markers over and beyond classical risk factors, and (viii) pathway analysis to identify targets for personalized intervention in disease prevention or treatment. Additionally, two short sections respectively address multiomics studies and machine learning. In conclusion, the analysis of adverse health outcomes in relation to omics biomarkers rests on the same statistical principle as any other data collected in large population or patient cohorts. The large number of biomarkers, which have to be considered simultaneously requires planning ahead how the study database will be structured and curated, imported in statistical software packages, analysis results will be triaged for clinical relevance, and presented. [ABSTRACT FROM AUTHOR]

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This study applies dynamic mode decomposition (DMD) to three‐dimensional simulation results of urban heat island circulation (UHIC, which is horizontal circulation) and thermals (vertical convections). The aim of this study is to revisit how these phenomena coexist based on the characteristics of temporal changes in the flow field. We used DMD to obtain the dominant spatial patterns and information on temporal changes. One of the modes of horizontal wind, which does not change temporally (no oscillation or amplification), exhibits a spatial UHIC pattern. The unique feature of this UHIC mode is that there are small‐scale striated structures (150–200 m) and large‐scale convergence. The other modes are time‐varying (oscillating and decaying) and represent smaller spatial‐scale phenomena (150–250 m), such as thermals. The frequency of each mode takes various values, some of which are lower than the lifetime of thermals in accordance with the Deardorff convective scale (~10 min). These low‐frequency modes showed striated structures similar to that observed in the UHIC modes. These results suggest that UHIC and thermals deform each other through components that vary in long temporal scales. [ABSTRACT FROM AUTHOR]

ABSTRACT This study investigates the inter‐model spread of climatological extratropical westerly jets in boreal winter, using the historical simulation of 52 Coupled Model Intercomparison Project phase 6 (CMIP6) models from 1851 to 2014. The results show that there is a substantial spread in the latitude of the upper‐tropospheric westerly jet across models, characterised by large inter‐model standard deviations to both the poleward and equatorward sides of the jet axis, although the multi‐model ensemble mean (MME) performs well in simulating meridional position of westerly jets. Furthermore, we detect the consistency of inter‐model jet position spread between the Northern and Southern Hemispheres, based on the inter‐model empirical orthogonal function (EOF) decomposition and correlation of regional‐averaged zonal winds. Specifically, the models that simulate the westerly jets poleward/equatorward relative to the MME position in one hemisphere also tend to simulate the jets poleward/equatorward in the other hemisphere. Accordingly, we define a global jet spread index to depict the concurrence of jet shift in the two hemispheres. The results of inter‐model regression analyses based on this index indicate that the models positioning the jets poleward than the MME tend to simulate a wider Hadley Cell, a poleward‐shifted Ferrel Cell in the Southern Hemisphere, enhanced precipitation in the subtropics and suppressed precipitation in the tropics, and warmer sea surface temperatures in the subtropics and mid‐latitudes. The present results suggest that improving the simulation of jet positions in climate models requires a comprehensive consideration of thermal states in the tropics and subtropics/mid latitudes. [ABSTRACT FROM AUTHOR]

Background: Nursing aims to rely on the best evidence, yet nurses frequently struggle with statistics, impeding the integration of research into practice. The misuse of statistical significance and p values is a pervasive issue, widely debated across disciplines but often overlooked in nursing. Objective: This study seeks to elucidate key arguments surrounding the misuse of p values, assess their relevance to nursing, and propose recommendations for improvement. Approach: Conducting a critical literature review on the interpretation and reporting of statistical significance and its practical implications among nurses, nursing students, and scholars. Results: The literature reveals widespread misunderstandings of probability concepts in classical statistics, leading to the misuse of p values. Some scholars propose abandoning p values altogether due to these challenges. Rather than dismissing p values, this article provides insights into their historical context and proper application. Recommendations are offered for accurately interpreting results and integrating supplementary metrics. Fostering statistical competencies already at the undergraduate level can enhance statistical literacy and alleviate statistics anxiety among nursing students, a prevalent issue. Early exposure to statistical methods in nursing education empowers nurses to actively engage in research. Additional recommendations include preregistering analysis plans to mitigate publication bias and exploring alternative approaches such as Bayesian methods. Conclusions: While p values serve as a valuable tool for preliminary assessment against randomness in nursing research, their widespread misunderstanding necessitates multiple measures for accurate interpretation and understanding of statistical significance. It is imperative for nurse educators and researchers to incorporate these recommendations into their practices. [ABSTRACT FROM AUTHOR]

Objectives: Precision Medicine (PM) uses advanced Machine Learning (ML) techniques and big data to develop personalized treatments, but healthcare still relies on traditional statistical procedures not targeted on individuals. This study investigates the impact of ML on epidemiology. Methods: A quantitative analysis of the articles in PubMed for the years 2000-2019 was conducted to investigate the use of statistical methods and ML in epidemiology. Using structural topic modelling, two groups of topics were identified and analysed over time: topics closer to the clinical side of epidemiology and topics closer to the population side. Results: The curve of the prevalence of topics associated with population epidemiology basically corresponds to the curve of the relative statistical methods, while the more dynamic curve of clinical epidemiology broadly reproduces the trend of algorithmic methods. Conclusion: The findings suggest that a renewed separation between clinical epidemiology and population epidemiology is emerging, with clinical epidemiology taking more advantage of recent developments in algorithmic techniques and moving closer to bioinformatics, whereas population epidemiology seems to be slower in this innovation. [ABSTRACT FROM AUTHOR]

Model ensembles may provide estimates of uncertainties arising from unknown initial conditions and model deficiencies. Often, the ensemble mean is taken as the best estimate, and quantities such as the mean‐squared error between model mean and observations decrease with the number of ensemble members. But the ensemble size is often limited by available resources, and so some idea of how many ensemble members that are needed before the error has saturated would be advantageous. The behaviour with ensemble size is often estimated by producing subsamples from a large ensemble. But this strategy requires that this large ensemble is already available. Fortunately, in many situations, the dependence on ensemble size follows simple analytical relations when the quantity under interest (such as the mean‐squared error between ensemble mean and observations) is calculated over many grid points or time points. This holds both for ensemble means and the related sampling variance. Here, we present such relations and demonstrate how they can be used to estimate the gain of increasing the ensemble. Whereas previous work has mainly focused on the size of the model ensemble, we recognize that uncertainties in observations play a role. We therefore also study the effect of using the mean of an ensemble of reanalyses. We show how the analytical relations can be used to estimate the point where the gain of increasing the size of the model ensemble is dwarfed by the gain of increasing the number of reanalyses. We demonstrate these points using two climate model ensembles: a large multimodel ensemble and a large single‐model initial‐condition ensemble. [ABSTRACT FROM AUTHOR]

With the increasing global demand for renewable energy, biodiesel has become a promising alternative to fossil fuels with significant environmental benefits. This article systematically reviews the latest advances in predictive modeling techniques for estimating the characteristics of biodiesel and its impact on diesel engine performance. Various methods for predicting the key performance of biodiesel and the performance and emissions of diesel engines have been summarized. According to the categories of parameters, research cases in recent years have been listed and discussed separately. This review provides a comprehensive overview and serves as a reference for future research and development of biodiesel. [ABSTRACT FROM AUTHOR]

The primary objective of this study is to evaluate the accuracy of different forecasting models for monthly wind farm electricity production. This study compares the effectiveness of three forecasting models: Autoregressive Integrated Moving Average (ARIMA), Seasonal ARIMA (SARIMA), and Support Vector Regression (SVR). This study utilizes data from two wind farms located in Poland—'Gizałki' and 'Łęki Dukielskie'—to exclude the possibility of biased results due to specific characteristics of a single farm and to allow for a more comprehensive comparison of the effectiveness of both time series analysis methods. Model parameterization was optimized through a grid search based on the Mean Absolute Percentage Error (MAPE). The performance of the best models was evaluated using Mean Bias Error (MBE), MAPE, Mean Absolute Error (MAE), and R2Score. For the Gizałki farm, the ARIMA model outperformed SARIMA and SVR, while for the Łęki Dukielskie farm, SARIMA proved to be the most accurate, highlighting the importance of optimizing seasonal parameters. The SVR method demonstrated the lowest effectiveness for both datasets. The results indicate that the ARIMA and SARIMA models are effective for forecasting wind farm energy production. However, their performance is influenced by the specificity of the data and seasonal patterns. The study provides an in-depth analysis of the results and offers suggestions for future research, such as extending the data to include multidimensional time series. Our findings have practical implications for enhancing the accuracy of wind farm energy forecasts, which can significantly improve operational efficiency and planning. [ABSTRACT FROM AUTHOR]

Detection and separation of the subtle post-seismic deformation signals associated with moderate magnitude earthquakes from interferometric synthetic aperture radar (InSAR) time-series is often challenging. Singular spectrum analysis (SSA) is a statistical non‐parametric technique used to decompose and reconstruct signals from complex time-series data. We show that the SSA analysis effectively distinguished the post-seismic signal associated with the 2019 M w 6 Mirpur earthquake from periodic and noise components. The SSA-derived post-seismic deformation signal is smoother and fits better to an exponential model with a decay time of 34 d. The post-seismic deformation is confined to the southeast of the rupture area and lasted for ∼90 d following the main shock. Inversion of the post-seismic deformation suggests an afterslip mechanism with a maximum slip of ∼0.07 m on the shallow, updip portions of the Main Himalayan Thrust. The 2019 Mirpur earthquake and afterslip together released less than 12 per cent of the accumulated strain energy since the 1555 Kashmir earthquake and implies continued seismic hazard in the future. [ABSTRACT FROM AUTHOR]

Observations of the seafloor Rayleigh ellipticity contribute to seismic imaging in the ocean. To extract such observables from the arbitrarily oriented ocean–bottom seismometer (OBS) data, we develop an orthogonal-regression-based approach to measure the waveform amplitude ratios of the unoriented horizontal and vertical components. The amplitude ratios are then used to calculate the Rayleigh ellipticity (and the sensor orientation angle). The robustness of our method is verified by applications to both the unoriented OBS data and the well oriented on-land seismic data. As we propose to calculate the Rayleigh ellipticity directly from the unoriented three-component data, the measurement process avoids the complexity arising from the surface wave non-great-circle effects and uncertainties of the OBS sensor orientation angles. Overall the Rayleigh ellipticity measurements from our method are systematically higher than those by conventional analysis and show less uncertainties. Our analyses suggest that the Rayleigh ellipticity curve (14–60 s), which could be retrieved from the raw broad-band OBS data, is effective to constrain the oceanic lithosphere structure, and the accurate measurement of Rayleigh ellipticity curve is important. The potential of seafloor Rayleigh ellipticity for seismic imaging in the ocean is evidenced by a case study of the Japan Basin, the Sea of Japan. Considering the insufficient station coverage in the ocean, the single-station measurement of seafloor Rayleigh ellipticity is of significance for OBS community. [ABSTRACT FROM AUTHOR]

In this work, we explore the use of digital technologies and statistical analysis to monitor how Italian secondary school students' writing changes over time and how comparisons can be made across different high school types. We analyzed more than 2,000 exam essays written by Italian high school students over 13 years and in five different school types. Four indicators of writing characteristics were considered—text length, text complexity, and two indicators of source use, all extracted using natural language processing tools—which provided insights into students' citation practices over time and in different school contexts. In particular, we measured the portion of students' essays that included text from source material as well as the amount of copied text that was not properly referenced. We found that student essays became shorter in length over time while also getting more complex. We also found that the tendency to copy uncited text in the essay decreased. High school curricula predict different writing strategies: essays written by students attending scientific and humanistic high schools are longer and less subject to incorrect citations. We argue that such text analysis enables the study of writing features in high school classes and supports the evaluation of curricula. [ABSTRACT FROM AUTHOR]

Accessible Summary: What is known on the subject?: Mental health services report adverse incidents in different ways and the relationship between adverse incidents and the workforce is uncertain. In England, there are national datasets recording all incidents and workforce statistics though there is no peer‐reviewed evidence examining recent trends. What this paper adds to existing knowledge?: Although there has been an overall increase in the number of mental health nurses, more are working in the community and the number of nurses relative to adverse incidents has decreased. There have been service‐provision changes but the role of mental health nurses has not significantly changed in this period, and we can therefore assume that their current practice is saturated with risk or increased reporting. To help understand the relationship between nurses and incidents, we need to transform how incidents are recorded in England. What are the implications for practice?: English mental health services report greater levels of patient‐related factors such as self‐harm or aggression rather than missed or erroneous care. This makes it difficult to understand if a rise in incident frequency is linked to reporting behaviour, patient risk, unsafe/ineffective care or other reasons and therefore planning workforce deployment to improve care quality is problematic. Introduction: There is a paucity of empirical data examining incidents and mental health nurses and the relationship between the two remains uncertain. Aim: Comparison of English national data for incidents and nursing workforce to examine recent trends. Method: Descriptive analysis of two national datasets of incidents and workforce data for England between 2015 and 2022. Results: A 46% increase in incidents was found; the leading causes are self‐harm and aggressive behaviour. Despite the rise in adverse incident reporting, a 6% increase in mental health nurses was found, with more nurses in community settings than hospitals. Discussion: Current services are incident reporting at greater concentrations than in previous years. Patient‐related behaviour continues to be most prominently reported, rather than possible antecedent health services issues that may contribute to reporting. Whilst staffing has increased, this does not seem to have kept pace with the implied workload evident in the increase in incident reports. Implications for Practice: Greater emphasis should be placed on health service behaviour in reporting mechanisms. Self‐harm and aggression should continue to be considered adverse outcomes, but causal health service factors, such as missed care, should be present in pooled reporting to help reduce the occurrence of adverse outcomes. [ABSTRACT FROM AUTHOR]

Despite various interventions in road safety work, fatal and severe road traffic accidents ( R T A s ) remain a significant challenge, leading to human suffering and economic costs. Understanding the multicausal nature of R T A s , where multiple conditions and factors interact, is crucial for developing effective prevention measures in road safety work. This study investigates the multivariate statistical analysis of co-occurring conditions in R T A s , focusing on single-vehicle accidents with single occupancy and personal injury on Austrian roads outside built-up areas from 2012 to 2019. The aim is to detect recurring combinations of accident-related variables, referred to as blackpatterns ( B P s ), using the Austrian R T A database. This study proposes Fisher's exact test to estimate the relationship between an accident-related variable and fatal and severe R T A s (severe casualties). In terms of pattern recognition, this study develops the maximum combination value ( M C V ) of accident-related variables, a procedure to search through all possible combinations of variables to find the one that has the highest frequency. The accident investigation proceeds with the application of pattern recognition methods, including binomial logistic regression and a newly developed method, the PATTERMAX method, created to accurately detect and analyse variable-specific B P s in R T A data. Findings indicate significant B P s contributing to severe accidents. The combination of binomial logistic regression and the PATTERMAX method appears to be a promising approach to investigate severe accidents, providing both insights into detailed variable combinations and their impact on accident severity. [ABSTRACT FROM AUTHOR]

Objective Study Setting and Design Data Sources and Analytic Sample Principal Findings Conclusions To compare theoretical strengths and limitations of common immortal time adjustment methods, propose a new approach using multiple imputation (MI), and provide practical guidance for using MI in precision medicine evaluations centered on a real‐world case study.Methods comparison, guidance, and real‐world case study based on previous literature. We compared landmark analysis, time‐distribution matching, time‐dependent analysis, and our proposed MI application. Guidance for MI spanned (1) selecting the imputation method; (2) specifying and applying the imputation model; and (3) conducting comparative analysis and pooling estimates. Our case study used a matched cohort design to evaluate overall survival benefits of whole‐genome and transcriptome analysis, a precision medicine technology, compared to usual care for advanced cancers, and applied both time‐distribution matching and MI. Bootstrap simulation characterized imputation sensitivity to varying data missingness and sample sizes.Case study used population‐based administrative data and single‐arm precision medicine program data from British Columbia, Canada for the study period 2012 to 2015.While each method described can reduce immortal time bias, MI offers theoretical advantages. Compared to alternative approaches, MI minimizes information loss and better characterizes statistical uncertainty about the true length of the immortal time period, avoiding false precision. Additionally, MI explicitly considers the impacts of patient characteristics on immortal time distributions, with inclusion criteria and follow‐up period definitions that do not inadvertently risk biasing evaluations. In the real‐world case study, survival analysis results did not substantively differ across MI and time distribution matching, but standard errors based on MI were higher for all point estimates. Mean imputed immortal time was stable across simulations.Precision medicine evaluations must employ immortal time adjustment methods for unbiased, decision‐grade real‐world evidence generation. MI is a promising solution to the challenge of immortal time bias. [ABSTRACT FROM AUTHOR]

Over the last decade, a new paradigm for cancer therapies has emerged which leverages the immune system to act against the tumor. The novel mechanism of action of these immunotherapies has also introduced new challenges to drug development. Biomarkers play a key role in several areas of early clinical development of immunotherapies including the demonstration of mechanism of action, dose finding and dose optimization, mitigation and prevention of adverse reactions, and patient enrichment and indication prioritization. We discuss statistical principles and methods for establishing the prognostic, predictive aspect of a (set of) biomarker and for linking the change in biomarkers to clinical efficacy in the context of early development studies. The methods discussed are meant to avoid bias and produce robust and reproducible conclusions. This review is targeted to drug developers and data scientists interested in the strategic usage and analysis of biomarkers in the context of immunotherapies. [ABSTRACT FROM AUTHOR]

This study examines the financial security of the Hungarian adult population. The research is based on surveys regularly conducted by the Organisation for Economic Co-operation and Development (OECD). These surveys measure the financial knowledge, behavior, and attitudes of the population in member states. Using data from the most recent 2023 Hungarian results, this study analyzes financial security from two perspectives: the respondents' sense of security and their actual security. The OECD questionnaire also assesses several demographic characteristics of the respondents. This research primarily compares groups formed based on these characteristics using statistical methods. The findings provide several new insights, often differing from previous literature, and offer a nuanced view of perceived and actual security among Hungarian adults. These new scientific results can help identify the most vulnerable groups in the population and develop practical support methods for them. [ABSTRACT FROM AUTHOR]

Accurate forecasting of electric vehicle (EV) power consumption per unit mileage serves as the cornerstone for determining diurnal variations in EV charging loads. To enhance the prediction accuracy of EV power consumption per unit mileage, this paper proposes a modelling method grounded in an improved Ant Colony Optimization-Support Vector Regression (ACO-SVR) framework. This method integrates the effects of both temperature and speed on the power consumption per unit mileage of EVs. Initially, we analyze the influence mechanism of driving speed and ambient temperature on EV power consumption, elucidating the relationship between power consumption per unit mileage and these factors. Subsequently, we construct an ACO-SVR model utilizing an improved ant colony optimization algorithm, fitting the relationship between power consumption, speed, and temperature to derive the EV power consumption per unit mileage model. Finally, leveraging operational data from EVs in Guangdong, Hong Kong, and Macao as a case study, we validate the energy consumption model of EVs by considering factors such as ambient temperature and driving speed. The results demonstrate that the model proposed in this paper is both accurate and effective. [ABSTRACT FROM AUTHOR]

SUMMARY: Inverse problems occur in many fields of geophysics, wherein surface observations are used to infer the internal structure of the Earth. Given the non-linearity and non-uniqueness inherent in these problems, a standard strategy is to incorporate a priori information regarding the unknown model. Sometimes a solution is obtained by imposing that the inverted model remains close to a reference model and with smooth lateral variations (e.g. a correlation length or a minimal wavelength are imposed). This approach forbids the presence of strong gradients or discontinuities in the recovered model. Admittedly, discontinuities, such as interfaces between layers, or shapes of geological provinces or of geological objects such as slabs can be a priori imposed or even suggested by the data themselves. This is however limited to a small set of possible constraints. For example, it would be very challenging and computationally expensive to perform a tomographic inversion where the subducting slabs would have possible top discontinuities with unknown shapes. The problem seems formidable because one cannot even imagine how to sample the prior space: is each specific slab continuous or broken into different portions having their own interfaces? No continuous set of parameters seems to describe all the possible interfaces that we could consider. To circumvent these questions, we propose to train a Generative Adversarial neural Network (GAN) to generate models from a geologically plausible prior distribution obtained from geodynamic simulations. In a Bayesian framework, a Markov chain Monte Carlo algorithm is used to sample the low-dimensional model space depicting the ensemble of potential geological models. This enables the integration of intricate a priori information, parametrized within a low-dimensional model space conducive to efficient sampling. The application of this approach is demonstrated in the context of a downscaling problem, where the objective is to infer small-scale geological structures from a smooth seismic tomographic image. [ABSTRACT FROM AUTHOR]

Background: Although many health information seeking studies are concerned with longer range outcomes (e.g. patient‐provider communication) the immediate outcomes for the searchers are whether they found the desired information, for whom and how successfully. Objectives: To examine the association between health information seeking via various sources and the reported extent of success in getting the desired information the information needs perspective. Methods: Data were obtained from the 2017 Israel Social Survey and analysed using multinomial regression models. The sample included individuals who reported engaging in seeking health information prior to the survey and mentioned the extent of success in obtaining the desired health information (fully, partially, or not‐at‐all) (N = 2197). Multinominal regression technique served for the multivariable analysis. Discussion: Engagement in health information seeking via friends, family and using various websites (excluding those by Ministry of Health and Health Funds) was associated with the increased likelihood of partial success in getting the desired information. Education level and population group, affected level of success. Conclusions: The (partial) success in meeting health consumers' information needs is associated with the turn to particular sources. Public health professionals and health provider institutions should improve provision and delivery of health information to meet consumer health information needs. [ABSTRACT FROM AUTHOR]

Thin-laminate composites with thicknesses below 200 μm hold significant promise for future, larger, and lighter deployable structures. This paper presents a study of the time-dependent failure behavior of thin carbon-fiber laminates under bending, focusing on establishing a fundamental material-level understanding of this type of failure. A novel test method was developed, enabling in-situ micro-CT imaging during long-term bending. Time-to-rupture experiments revealed the stochastic nature of failure, prompting a statistical approach to account for initial imperfections. The total probability of failure was calculated using separate Weibull functions for instantaneous and delayed time-dependent failures. The resulting function, dependent on curvature and aging time, is a design guideline for the design of future deployable space structures. Time-lapse micro-CT imaging identified kink bands and fiber–matrix debonding as primary failure mechanisms, providing essential insights for the design optimization of composite laminates. [ABSTRACT FROM AUTHOR]

Coastal flooding is a growing hazard. Compound event characterization and uncertainty quantification are critical to accurate flood risk assessment. This study presents univariate, conditional, and joint probabilities for observed water levels, precipitation, and waves. Design events for 10‐ and 100‐year marine water level and precipitation events are developed. A total water level formulation explicitly accounting for wave impacts is presented. Uncertainties associated with sampling method, copula selection, data record length, and utilized rainfall gauge are determined. Eight copulas are used to quantify multivariate uncertainty. Generally, copulas present similar results, except the BB5. Sampling method uncertainty was quantified using four sampling types; annual maximum, annual coinciding, wet season monthly maximum, and wet season monthly coinciding sampling. Annual coinciding sampling typically produced the lowest event magnitude estimates. Uncertainty associated with record length was explored by partitioning a 100‐year record into various subsets. Withholding 30 years of observations (i.e., records of less than 70 years) resulted in substantial variability of both the 10‐ and 100‐year return period estimates. Approximately equidistant rainfall gauges led to large event estimate differences, suggesting microclimatology and gauge selection play a key role in characterizing compound events. Generally, event estimate uncertainty was dominated by sampling method and rainfall gauge selection. [ABSTRACT FROM AUTHOR]

In this study, we aimed to assess the capacity of a physics-based earthquake simulator to improve our understanding of the seismogenic process. In this respect, we applied a previously tested earthquake simulator to two well-known and completely different seismogenic fault systems, namely the Italian Apennines and the Nankai subduction in Japan, for which long historical records of strong earthquakes are available. They are characterized by different fault mechanisms, fault sizes, and slip rates. Because of the difference in slip rates, the time scale of the seismicity patterns is different for the two systems (several hundreds of years for the Apennines and a few tens of years for the Nankai Fault). The results of simulations that produced synthetic catalogues of 100,000 years show these significant long-term seismicity patterns characterizing the seismic cycles for both seismogenic areas as follows: The average stress and the occurrence rate of earthquakes increase in the long term as the next major earthquake approaches; while the average stress increases uniformly, the occurrence rate stops increasing well in advance of the mainshocks; the b-value exhibits a long-term increase before major earthquakes and a fast decrease shortly before the mainshocks. Even if no specific statistical tool was applied for the quantification of the similarities between the seismicity patterns of the two seismic areas, such similarities are clearly justified by the large number of seismic cycles included in the 100,000-year synthetic catalogues. The paper includes a discussion on the capability of the simulation algorithm to reliably represent the real long-term seismogenic process. This question is difficult to answer because the available historical observations are of too short a duration to provide significant statistical results. In spite of the limitations characterizing the use of earthquake simulators for time-dependent earthquake hazard assessment, and the lack of convincing mechanistic explanations of the specific seismic patterns reproduced by our simulator algorithm, our results encourage further investigations into the application of simulators for the development of seismogenic models, including short-term features. [ABSTRACT FROM AUTHOR]

Introduction: Hippocampal atrophy is an established biomarker for conversion from the normal ageing process to developing cognitive impairment and dementia. This study used a novel hypothesis-free machine-learning approach, to uncover potential risk factors of lower hippocampal volume using information from the world's largest brain imaging study. Methods: A combination of machine learning and conventional statistical methods were used to identify predictors of low hippocampal volume. We run gradient boosting decision tree modelling including 2,891 input features measured before magnetic resonance imaging assessments (median 9.2 years, range 4.2–13.8 years) using data from 42,152 dementia-free UK Biobank participants. Logistic regression analyses were run on 87 factors identified as important for prediction based on Shapley values. False discovery rate-adjusted p value <0.05 was used to declare statistical significance. Results: Older age, male sex, greater height, and whole-body fat-free mass were the main predictors of low hippocampal volume with the model also identifying associations with lung function and lifestyle factors including smoking, physical activity, and coffee intake (corrected p < 0.05 for all). Red blood cell count and several red blood cell indices such as haemoglobin concentration, mean corpuscular haemoglobin, mean corpuscular volume, mean reticulocyte volume, mean sphered cell volume, and red blood cell distribution width were among many biomarkers associated with low hippocampal volume. Conclusion: Lifestyles, physical measures, and biomarkers may affect hippocampal volume, with many of the characteristics potentially reflecting oxygen supply to the brain. Further studies are required to establish causality and clinical relevance of these findings. [ABSTRACT FROM AUTHOR]

Abstract Tumours exhibit high genotypic and transcriptional heterogeneity. Both affect cancer progression and treatment, but have been predominantly studied separately in follicular lymphoma. To comprehensively investigate the evolution and genotype-to-phenotype maps in follicular lymphoma, we introduce CaClust, a probabilistic graphical model integrating deep whole exome, single-cell RNA and B-cell receptor sequencing data to infer clone genotypes, cell-to-clone mapping, and single-cell genotyping. CaClust outperforms a state-of-the-art model on simulated and patient data. In-depth analyses of single cells from four samples showcase effects of driver mutations, follicular lymphoma evolution, possible therapeutic targets, and single-cell genotyping that agrees with an independent targeted resequencing experiment.

Abstract Cervical cancer is a common malignant tumor of the female reproductive system and the leading cause of death among women worldwide. The survival prediction method can be used to effectively analyze the time to event, which is essential in any clinical study. This study aims to bridge the gap between traditional statistical methods and machine learning in survival analysis by revealing which techniques are most effective in predicting survival, with a particular emphasis on improving prediction accuracy and identifying key risk factors for cervical cancer. Women with cervical cancer diagnosed between 2013 and 2015 were included in our study using data from the Surveillance, Epidemiology, and End Results (SEER) database. Using this dataset, the study assesses the performance of Weibull, Cox proportional hazards models, and Random Survival Forests in terms of predictive accuracy and risk factor identification. The findings reveal that machine learning models, particularly Random Survival Forests (RSF), outperform traditional statistical methods in both predictive accuracy and the discernment of crucial prognostic factors, underscoring the advantages of machine learning in handling complex survival data. However, for a survival dataset with a small number of predictors, statistical models should be used first. The study finds that RSF models enhance survival analysis with more accurate predictions and insights into survival risk factors but highlights the need for larger datasets and further research on model interpretability and clinical applicability.

Extended Abstract Background: Landslides are among the environmental hazards that annually cause significant human and financial losses in extensive mountainous regions of our country. Therefore, identifying the important and influential factors in the occurrence of this phenomenon can be used as a practical tool to reduce potential damage. Consequently, recognizing the environmental factors affecting the occurrence of landslides in forest road networks is of great importance. Due to its mainly mountainous topography and extensive tectonic activity, Ramsar County has a natural potential for widespread landslides within the communication network area. The objective of this study is to assess the susceptibility of landslide occurrence in the Ramsar County area by considering influential factors, especially forest road networks, and utilizing Geographic Information System (GIS) capabilities. Methods: To achieve this goal, the locations of landslides were determined using field studies, historical reports, and Google Earth images. A total of 95 landslides were identified in the region, which were divided into two groups for modeling (70%) and validation (30%). Sensitivity analysis of landslide occurrence was carried out based on criteria such as slope, slope direction, land use, distance from residential centers, terrain curvature, precipitation, temperature, erodibility, elevation, vegetation cover density, lithological units, soil texture, drainage density, distance from roads, and topographic wetness index. Land cover density and vegetation greenness were calculated using 16-day products from Landsat 8 satellite in Google Earth Engine, and the final output was calculated using weighted regression methods in GIS and Excel software. Receiver Operating Characteristic (ROC) curves were employed for model evaluation and validation. Results: The most significant factors affecting landslide susceptibility include soil type, slope of the terrain, vegetation cover, geology, hydrogeological flows, topography, precipitation, and communication lines. Susceptibility zones and landslide density are more pronounced in peripheral areas of transportation corridors. The construction of extensive structures, side effects of road construction, and the disruption of natural terrain balance for road construction and expansion have led to continuous occurrences in these areas. The correlation of parameters influencing landslide occurrence with the landslide distribution layer indicates that approximately 76% of landslides have occurred in slopes ranging from 11 to 38 degrees. Erosion of slopes by high-velocity runoff flows has caused more than 63% of landslides to occur within 500 meters of watercourses. Alongside the three main factors of soil type, natural slope, and slope erosion, landslide occurrence is influenced by road reconstruction and development without considering slope stability principles. More than half of the observed landslides in the area have occurred in a sequence of rock, shale, sandstone, and volcanic rock, accompanied by coal layers. The discontinuity of the landslide layer with the distance from the road indicates that landslide occurrence up to a distance of 400 meters from the road has been due to direct effects of road construction and embankment activities, disrupting the balance of slopes adjacent to the road, gradually decreasing with the distance from the main road. The results show that the Frequency Ratio model provides an acceptable accuracy with an area under the curve (AUC) of 0.76 for landslide susceptibility mapping. The final susceptibility map indicates that class 5 with very high susceptibility covers 13.06% of the total area, class 4 with high susceptibility covers 16.35%, class 3 with moderate susceptibility covers 22.32%, class 2 with low susceptibility covers 27.46%, and class 1 with very low susceptibility covers 20.79%. Consequently, 31.79% of the total area is at risk of severe landslides. Considering the final map and the close relationship between road presence and landslide occurrence, it can be stated that the northwestern and eastern forest roads of Ramsar County are more exposed to landslide occurrence. Conclusion: It appears that the removal of vegetation cover, alteration of watercourse routes, modification of natural terrain slopes, and excavation of soils and rocks for road construction in the area have led to landslide occurrences around forest roads. The marginal areas of transportation corridors show a significant discrepancy in susceptibility coefficients, highlighting the potential importance of landslide occurrence in these regions. It can be concluded that providing appropriate recommendations and strategies to reduce landslide risks, improving area management, and implementing preventive measures in this region are highly necessary. This study demonstrates that through accurate analyses and alignment with environmental conditions, effective measures can be taken to reduce the effects of landslides and enhance the stability of operational areas. Therefore, a combination of geological, hydrogeological, topographic, and geographical information analyses can be used as an effective tool for studying, predicting, and managing landslide risks in mountainous regions.

We provide a novel algorithm, called Valuto, for the fair valuation of real estate market values. The algorithm is based on the selling prices (or adjusted asking prices) of properties located in well-defined areas (microzones) of a territory. The algorithm takes into consideration the peculiar features of each individual property (cadastral typology, maintenance, presence or absence of an elevator, floor level, energy efficiency rating, gross and conventional surface area) and, based on well-defined conversion tables, recalculates its value. The algorithm could serve a variety of purposes in social applications, from the long sought-after land registry reform to a fair valuation of mortgaged properties. It also has an appealing marketable potential for real estate instant-buyers. In this paper, we describe the full implementation of the algorithm and provide guidelines on its application to the above-mentioned scopes. The algorithm is implemented via a user-friendly, testable web application at www.valuto.org.

In this study, the uncertainty of measurement paths was estimated using selected statistical methods. Specifically, temperature measurements obtained from contact temperature sensors used in a heat transfer test section were investigated. The experiments utilized a drywell temperature calibrator, thermoelements (types K, J, N, and T), and a data acquisition station. Additionally, a certified Pt-100 resistance sensor connected to the temperature meter was considered during measurements. The temperature range for the selected measurement points was 0.3 to 100 °C, covering both increasing and decreasing temperatures. To calculate the expanded uncertainty, both the uncertainty propagation method and the Monte Carlo method were employed. The results were analyzed and found to be similar.

Abstract Migration’s influence in shaping population dynamics in times of impending climate and population crises exposes its crucial role in upholding societal cohesion. As migration impacts virtually all aspects of life, it continues to require attention across scientific disciplines. This study aims to bridge the gap between theoretical understanding and practical application by integrating network analysis and regression methodologies within Migration Studies. In the study we employ network analysis to elucidate migration patterns at various geographical scales-city, country, and global. Additionally, regression analysis is discussed on an exploratory level, where we focus on the underlying factors driving migration, and identifying the key independent variables to enhance predictive accuracy. The study exposes distinct migration network structure and its features, and the consequences these have on conventional regression analysis applications. We conclude on the importance of methodological coherence and disciplinary integration, and highlight the avenues for enhancing the predictive power of migration models.

Solar power forecasting will have a significant impact on the future of large-scale renewable energy plants. Predicting photovoltaic power generation depends heavily on climate conditions, which fluctuate over time. In this research, we propose a hybrid model that combines machine-learning methods with Theta statistical method for more accurate prediction of future solar power generation from renewable energy plants. The machine learning models include long short-term memory (LSTM), gate recurrent unit (GRU), AutoEncoder LSTM (Auto-LSTM) and a newly proposed Auto-GRU. To enhance the accuracy of the proposed Machine learning and Statistical Hybrid Model (MLSHM), we employ two diversity techniques, i.e. structural diversity and data diversity. To combine the prediction of the ensemble members in the proposed MLSHM, we exploit four combining methods: simple averaging approach, weighted averaging using linear approach and using non-linear approach, and combination through variance using inverse approach. The proposed MLSHM scheme was validated on two real-time series datasets, that sre Shagaya in Kuwait and Cocoa in the USA. The experiments show that the proposed MLSHM, using all the combination methods, achieved higher accuracy compared to the prediction of the traditional individual models. Results demonstrate that a hybrid model combining machine-learning methods with statistical method outperformed a hybrid model that only combines machine-learning models without statistical method.

Introduction: The COVID-19 virus has impacted people worldwide, causing significant changes in their lifestyles. Since the emergence of the epidemic, attempts have begun to prepare a vaccine that can eliminate the virus and restore balance to life in the entire world. Over the past two years, countries and specialized companies have competed to obtain a license from the World Health Organization for the vaccines that were discovered. After the appearance of vaccines in the health community, comparisons and fears of their side effects began, but people don’t get an answer to the question of which is the best vaccine. Methods: IEEE Xplore, ScienceDirect, the New England Journal of Medicine, Google Scholar, and PubMed databases were searched for literature on the COVID-19 vaccine and its side effects. we surveyed the literature on the COVID-19 vaccine’s side effects and the sorts of side effects observed after vaccination. Depending on data from the literature, we compared these vaccines in terms of side effects, then we analyzed the gaps and obstacles of previous studies and made proposals to process these gaps in future studies. Results: Overall, 17 studies were included in this scoping systematic review as they fulfilled the criteria specified, the majority of which were cross-sectional and retrospective cross-sectional studies. Most of the side effects were mild, self-limiting, and common. Thus, they usually resolve within 1–3 days after vaccination. Factors associated with higher side effects included advanced age, allergic conditions, those taking other medications (particularly immunosuppressive ones), those with a history of type II diabetes, heart disease, hypertension, COVID-19 infection, and female sex. Our meta‐analyses also found that mRNA vaccines looked to be more effective, while inactivated vaccinations had fewer side effects. Conclusion: This review shows that the COVID-19 vaccine is safe to administer and induces protection.

This study aimed to develop and establish psychometric properties of the End-of-Life Nursing Competency Scale for Clinical Nurses. The initial items were derived from an in-depth literature review and field interviews. The content validation of these items was assessed over three rounds by experts in end-of-life nursing care. The study included 437 clinical nurses from four hospitals in S, E, and D cities in South Korea. The final exploratory factor analysis resulted in a scale consisting of 21 items with the following five factors that explained 68.44% of the total variance: Physical care—imminent end-of-life, legal and administrative processes, psychological care—patient and family, psychological care—nurses' self, and ethical nursing. The final model with these five subscales was validated through confirmatory factor analysis. Both item convergent-discriminant validity and known-group validity, which compared two groups based on clinical experience (p < 0.008) and working department (p < 0.008), were satisfactory. The internal consistency, as measured by Cronbach's α, ranged from 0.62 to 0.89 for the subscales and was 0.91 for the total scale. This scale has been validated as a reliable and effective instrument for clinical nurses to self-assess their end-of-life nursing competencies in a clinical setting. [ABSTRACT FROM AUTHOR]

SUMMARY: The solution of the remote reference method, a frequently used technique in magnetotelluric (MT) data processing, can be viewed as a product of the two-input–multiple-output relationship between the local electromagnetic field and the reference field at a remote station. By applying a robust estimator to the two-input–multiple-output system, one can suppress the influence of outliers in the local magnetic field as well as those in the local electric field based on regression residuals. Therefore, this study develops a new robust remote reference estimator with the aid of robust multivariate linear regression. By applying the robust multivariate regression S-estimator to the multiple-output system, the present work derives a set of equations for robust estimates of the transfer function, noise variances, and the scale of the Mahalanobis distance simultaneously. The noise variances are necessary for the multivariate analysis to normalize the residuals of dependent variables. The Mahalanobis distance, a distance measure for multivariate data, is a commonly used indicator of outliers in multivariate statistics. By updating those robust estimates iteratively, the new robust remote reference estimator seeks the transfer function that minimizes the robust scale estimate of the Mahalanobis distance. The developed estimator can avoid bias in the MT transfer function even if there are significant noises in the reference magnetic field and handle outlying data more robustly than previously proposed robust remote reference estimators. The authors applied the developed method to a synthetic data set and real-world data. The test results demonstrate that the developed method downweights outliers in the local electric and magnetic fields and gives an unbiased transfer function. [ABSTRACT FROM AUTHOR]

This paper presents a statistical analysis of rainfall data through a practical application of statistical tests, with the aim of highlighting climatic variability in the Kébir Rhumel watershed. This analysis uses rainfall data from 1901 to 2021 collected in the Kébir Rhumel watershed (North–East Algeria). The use of the Mann–Kendall and Pettitt tests has allowed us to confirm the existence of a break in 2013 for all stations except Ain Smara and Gerrah, where it was detected in 2020. The SPI (Standardised Precipitation Index) defines five climatic periods that have alternated on the Kébir Rhumel, the wet condition being the most representative with 44% of the series analysed, and the least present is the severe drought (> 1%). The TIA (Trend Innovative Analysis) shows a positive trend for the El Milia, Tadjenant, Grarem Gouga, Guerrah and Ain Smara stations and a negative trend for the Chebabta and Tiberguent stations. Finally, the graph of the climate index of L. Emberger clearly shows the shift of the stations after the break of the sub-humid stage towards the semiarid stage. [ABSTRACT FROM AUTHOR]

This study assessed the research knowledge of postgraduate students across various disciplines at Kakatiya University, Telangana State, India, using a descriptive survey method. A total of 120 students were sampled from commerce, science, and humanities & social sciences, employing a stratified simple random sampling technique. A self-constructed Research Knowledge Test (RKT), covering key areas such as research methods, research tools, sampling techniques, and data processing, was utilized in this study. Descriptive statistics and ANOVA analyses were employed to analyze the data. The findings revealed that postgraduate students possessed a moderate level of research knowledge, with significant differences noted across disciplines. These results underscored the necessity for targeted training programs to enhance research competencies, particularly in areas identified as weaknesses. Recommendations for future educational practices were proposed to foster a more understanding of research principles essential for academic success. [ABSTRACT FROM AUTHOR]

Assessing hospital performance during quality improvement initiatives relies heavily on statistical methodologies aimed at benchmarking. This multi-institutional study evaluates established statistical approaches for their applicability in improving healthcare quality. Using a dataset encompassing 48,521 patients undergoing craniotomy surgery from 2019 to 2023 across 180 hospitals, 6 statistical techniques were analyzed. Non-contraction methods included indirect standardization without hospital effects, with fixed effects, and direct standardization with fixed effects. Contraction methods comprised direct standardization with random effects, indirect standardization with random effects, and Exponential Smoothing methods. Adjusted rates, rankings, and performance outliers are the main emphasis of this study, which evaluates hospital performance concerning the rate of operation deaths and severe complications or deaths among approaches. Contraction methods significantly reduced inter-hospital variation in death rate(adjusted: 1%2%; observed: 0%-10%) and severe complications or death (observed: 3%-35%; adjusted: 7%-17%). Hospital rates were effectively brought closer to the collective mean using these techniques. Direct standardization with hospital random effects resulted in significant changes in death rate quintile rankings for 17% to 39% of hospitals when compared to fixed effects. Indirect standardization with random effects revealed no performance outliers for death rates in compact and moderate-sized hospitals, but logistic and fixed effect approaches revealed outliers. The choice of statistical approach has a considerable impact on hospital rankings and the identification of performance outliers. These insights are critical for appropriately evaluating the hospital's effectiveness in quality improvement initiatives. [ABSTRACT FROM AUTHOR]

Background: Assessing bias (estimated – measured) is key to evaluating glomerular filtration rate (GFR). Stratification by subgroups can indicate where equations perform differently. However, there is a fallacy in the assessment of two instruments (e.g., eGFR and mGFR) when stratifying on the level of only one of those instruments. Here, we present statistical aspects of the problem and a solution for GFR stratification along with an empirical investigation using data from the CKiD study. Methods: Compared and contrasted biases (eGFR relative to mGFR) with 95% confidence intervals within strata of mGFR only, eGFR only, and the average of mGFR and eGFR using data from the Chronic Kidney Disease in Children (CKiD) study. Results: A total of 304 participants contributed 843 GFR studies with a mean mGFR of 48.46 (SD = 22.72) and mean eGFR of 48.67 (SD = 22.32) and correlation of 0.904. Despite strong agreement, eGFR significantly overestimated mGFR when mGFR < 30 (+ 6.2%; 95%CI + 2.9%, + 9.7%) and significantly underestimated when mGFR > 90 (–12.2%; 95%CI − 17.3%, − 7.0%). Significant biases in opposite direction were present when stratifying by eGFR only. In contrast, when stratifying by the average of eGFR and mGFR, biases were not significant (+ 1.3% and − 1.0%, respectively) congruent with strong agreement. Conclusions: Stratifying by either mGFR or eGFR only to assess eGFR biases is ubiquitous but can lead to inappropriate inference due to intrinsic statistical issues that we characterize and empirically illustrate using data from the CKiD study. Using the average of eGFR and mGFR is recommended for valid inferences in evaluations of eGFR biases. [ABSTRACT FROM AUTHOR]

As deep-seated ore deposits become increasingly relevant for mineral exploration, the demand for time-efficient and powerful deep-sounding exploration methods rises. A suitable method for efficiently sensing ores at great depth is airborne electromagnetics (EM) using natural signal of atmospheric origin. The method relates airborne magnetic field recordings in the audio-frequency range to reference magnetic field recordings measured at a ground-based site and can achieve greater penetration depths when compared to controlled source airborne EM techniques. However, airborne natural source EM data are prone to noise caused by platform vibrations especially deteriorating data quality at low frequencies and thus narrowing the depth of investigation. Motional noise manifests as coherent noise on all airborne magnetic field components demanding for a powerful processing tool to remove such kind of noise. Unlike the bivariate approach, which is widely used in natural source EM, the multivariate approach is capable of detecting and reducing the effect of coherent noise. We introduce a robust multivariate processing for airborne natural source EM data and present the code implementation. The code was applied to a large-scale data set from the Kalahari–Copper–Belt in Namibia covering over 1000 km2. We obtained spatially consistent and smooth sounding curves in a frequency range of 10 to 1000 Hz including frequencies with prominent motional noise. Transfer functions are in good agreement with other geophysical and geological information. [ABSTRACT FROM AUTHOR]

Solar power forecasting will have a significant impact on the future of large-scale renewable energy plants. Predicting photovoltaic power generation depends heavily on climate conditions, which fluctuate over time. In this research, we propose a hybrid model that combines machine-learning methods with Theta statistical method for more accurate prediction of future solar power generation from renewable energy plants. The machine learning models include long short-term memory (LSTM), gate recurrent unit (GRU), AutoEncoder LSTM (Auto-LSTM) and a newly proposed Auto-GRU. To enhance the accuracy of the proposed Machine learning and Statistical Hybrid Model (MLSHM), we employ two diversity techniques, i.e. structural diversity and data diversity. To combine the prediction of the ensemble members in the proposed MLSHM, we exploit four combining methods: simple averaging approach, weighted averaging using linear approach and using non-linear approach, and combination through variance using inverse approach. The proposed MLSHM scheme was validated on two real-time series datasets, that sre Shagaya in Kuwait and Cocoa in the USA. The experiments show that the proposed MLSHM, using all the combination methods, achieved higher accuracy compared to the prediction of the traditional individual models. Results demonstrate that a hybrid model combining machine-learning methods with statistical method outperformed a hybrid model that only combines machine-learning models without statistical method. [ABSTRACT FROM AUTHOR]

The rapid development of autonomous vehicle (AV) technology highlights the critical importance of enhancing the reliability of these vehicles. Due to the need to test the reliability of AVs, since 2014, the California Department of Motor Vehicles has permitted autonomous vehicle manufacturers to establish an AV Testing program, enabling them to test automated systems on the transportation network. With this, studies on the reliability of AVs have increased rapidly. The most emphasized issues regarding the reliability of AVs have been disengagements, accidents, and reaction times. In this study, disengagements and reaction times are categorized and explained in detail according to the data type, company, period, and statistical method. The data used in the studies cover the years 20142020. When examining studies on the reliability of AVs, until 2018, inferences were generally made using real data and descriptive statistics, particularly with methods such as correlation analysis and calculation of disengagements per mile, which investigates the relationship between distance traveled and disengagements. However, since 2018, machine learning has gained importance in evaluating AV reliability. It has been observed that regression, classification, and decision trees were frequently used during this period. Techniques such as deep transfer learning, text mining, and natural language processing also stand out. Furthermore, Software Reliability Growth Models are used to measure software reliability, playing an essential role in evaluating, analyzing, and improving the performance and reliability of AVs. This study aims to reveal the development and diversity of the statistical methods used to determine AV reliability. Additionally, this study aims to guide and provide insights to researchers in the field about the statistical approaches they can utilize. [ABSTRACT FROM AUTHOR]

Principal component analysis (PCA) is employed to extract the principal components (PCs) present in nuclear mass models for the first time. The effects from different nuclear mass models are reintegrated and reorganized in the extracted PCs. These PCs are recombined to build new mass models, which achieve better accuracy than the original theoretical mass models. This comparison indicates that using the PCA approach, the effects contained in different mass models can be collaborated to improve nuclear mass predictions. [ABSTRACT FROM AUTHOR]

Estimating permafrost distribution in high‐mountain areas is challenging. In these situations, rock glaciers, provide valuable insights into permafrost distribution and are often used as proxies for identifying permafrost occurrence. Integrating various climatological and topographical conditioning factors with rock glaciers enables inferring the distribution of permafrost in these environments. This study utilized three machine learning models such as random forest (RF), support vector (SVM), and artificial neural network (ANN), and one statistical model, namely, the frequency ratio (FR), to assess the permafrost probability over the northern Kargil region of Indian Himalayas. Among 198 rock glaciers identified through high‐resolution images from Google Earth, 70% are used as training dataset, rest 30% as testing dataset. The study considered eight factors: slope, aspect, elevation, curvature, mean annual land surface temperature (MA‐LST), mean annual normalized difference snow index (MA‐NDSI), mean annual normalized difference water index (MA‐NDWI), and lithology for mapping. Furthermore, the SHapley Additive exPlanations (SHAP) test assessed the variable importance for model performance. The results revealed that the RF model performs best for permafrost probability mapping, followed by the SVM, FR, and ANN models. The study also found that 11% of the total geographic area has a high and very high probability of permafrost occurrence. [ABSTRACT FROM AUTHOR]

Both bisphosphonates and denosumab are the mainstays of treatment for osteoporosis to prevent fractures. However, there are still few trials directly comparing the prevention of fractures and the safety of 2 drugs in the treatment of osteoporosis. We aimed to compare the efficacy and safety between denosumab and bisphosphonates using a nationwide claims database. The database was covered with 10 million, 20% of the whole Korean population sampled by age and sex stratification of the Health Insurance Review and Assessment Service in South Korea. Among 228 367 subjects who were over 50 yr of age and taking denosumab or bisphosphonate from January 2018 to April 2022, the analysis was performed on 91 460 subjects after 1:1 propensity score matching. The primary outcome was treatment effectiveness; total fracture, major osteoporotic fracture, femur fracture, pelvic fracture, vertebral fracture, adverse drug reactions; acute kidney injury, chronic kidney disease, and atypical femoral fracture. Total fracture and osteoporotic major fracture, as the main outcomes of efficacy, were comparable in the denosumab and bisphosphonate group (HR 1.06, 95% CI, 0.98–1.15, P = .14; HR 1.13, 95% CI, 0.97–1.32, P = .12, respectively). Safety for acute kidney injury, chronic kidney disease, and atypical femoral fracture also did not show any differences between the 2 groups. In subgroup analysis according to ages, the denosumab group under 70 yr of age had a significantly lower risk for occurrences of acute kidney injury compared to the bisphosphonate group under 70 yr of age (HR 0.53, 95% CI, 0.29–0.93, P = .03). In real-world data reflecting clinical practice, denosumab and bisphosphonate showed comparable effectiveness for total fractures and major osteoporosis fractures, as well as safety regarding acute kidney injury, chronic kidney disease, and atypical femoral fracture. Lay Summary: This study compared the effectiveness and safety of denosumab and bisphosphonates, 2 primary treatments for osteoporosis, using a large South Korean nationwide claims database. Analysis of data from 91 460 individuals over 50 yr old showed no significant difference in preventing fractures or in safety outcomes such as kidney injury and atypical femoral fractures between the 2 drugs. However, among patients under 70, denosumab was associated with a lower risk of acute kidney injury. Overall, both medications demonstrated similar effectiveness and safety in the real-world treatment of osteoporosis. [ABSTRACT FROM AUTHOR]