Search

The aim of the paper is to present an analysis of the use of statistical methods and tools in scientific articles related to socio-economic geography and spatial management published in the years 2012-2021. In order to evaluate the intensity and diversity of this phenomenon, a query was carried out using the Web of Sciences electronic academic information database. A preliminary literature search led to the decision to focus on papers published in three selected journals relating to social geography (Geoforum), economic geography (Applied Geography) and spatial management (Landscape and Urban Planning). The paper analyses the variety of the statistical tools used in the studies presented in the aforementioned journals. The frequency and type of the applied statistical methods, computer software and computing tools is correlated with the specificity of the research area. [ABSTRACT FROM AUTHOR]

Introduction: Non-compliance is a common challenge for researchers and may reduce the power of an intention-to-treat analysis. Whilst a per protocol approach attempts to deal with this issue, it can result in biased estimates. Several methods to resolve this issue have been identified in previous reviews, but there is limited evidence supporting their use. This review aimed to identify simulation studies which compare such methods, assess the extent to which certain methods have been investigated and determine their performance under various scenarios. Methods: A systematic search of several electronic databases including MEDLINE and Scopus was carried out from conception to 30th November 2022. Included papers were published in a peer-reviewed journal, readily available in the English language and focused on comparing relevant methods in a superiority randomised controlled trial under a simulation study. Articles were screened using these criteria and a predetermined extraction form used to identify relevant information. A quality assessment appraised the risk of bias in individual studies. Extracted data was synthesised using tables, figures and a narrative summary. Both screening and data extraction were performed by two independent reviewers with disagreements resolved by consensus. Results: Of 2325 papers identified, 267 full texts were screened and 17 studies finally included. Twelve methods were identified across papers. Instrumental variable methods were commonly considered, but many authors found them to be biased in some settings. Non-compliance was generally assumed to be all-or-nothing and only occurring in the intervention group, although some methods considered it as time-varying. Simulation studies commonly varied the level and type of non-compliance and factors such as effect size and strength of confounding. The quality of papers was generally good, although some lacked detail and justification. Therefore, their conclusions were deemed to be less reliable. Conclusions: It is common for papers to consider instrumental variable methods but more studies are needed that consider G-methods and compare a wide range of methods in realistic scenarios. It is difficult to make conclusions about the best method to deal with non-compliance due to a limited body of evidence and the difficulty in combining results from independent simulation studies. PROSPERO registration number: CRD42022370910. [ABSTRACT FROM AUTHOR]

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]

Meta-analysis, as a fundamental aspect of evidence-based practice, synthesizes findings from multiple studies to draw comprehensive conclusions. A critical element of this synthesis is heterogeneity - the variability in outcomes, methodologies, and characteristics across studies. This review provides an in-depth exploration of heterogeneity in meta-analyses, highlighting its types, impact, and evolution over time. It emphasizes the significance of methodological rigor in detecting and managing heterogeneity, which is crucial for the credibility and utility of meta-analytic results. We delve into the methods for evaluating heterogeneity, including statistical tests like Cochran's Q test and the I2 statistic, and visual methods such as forest plots and funnel plots. The paper also discusses strategies for managing identified heterogeneity, such as subgroup analysis, meta-regression, and sensitivity analysis, and the importance of choosing the appropriate statistical model. Advanced developments in heterogeneity evaluation, driven by statistical, technological, and methodological innovations, are also examined. This includes Bayesian approaches, individual participant data (IPD) meta-analysis, and automated tools for literature search and data extraction. The review includes case studies to demonstrate the application of these methods in various research contexts, underscoring the complexity and necessity of continuous advancements in handling heterogeneity. This paper serves as a comprehensive guide for researchers, contributing to the enhancement of meta-analytic practices and the quality of research findings across disciplines. [ABSTRACT FROM AUTHOR]

In this paper, we present a comprehensive study of wavelet theory with a focus on structural damage detection. A new example of the application of operational modal analysis (OMA) techniques to a concrete railway arch bridge located over the Kalix river in Lingforsen, Sweden is presented. Results from the OMA techniques are used for finite element model (FEM) updating of this concrete railway arch bridge. Further, a new case study for sensor placement on Her0ysund bridge located in Nordland, Norway to conduct OMA is discussed in detail. Moreover, artificial intelligence algorithms that can be useful for addressing the problem of missing data sets in structural health monitoring technologies are reviewed. [ABSTRACT FROM AUTHOR]

This review considers issues related to interpreting the mixed legacy of >300 papers published during the past three decades on possible genotoxic effects of exposure of human and animal tissues to radiofrequency electromagnetic fields (RF-EMF). The main paper reviews the evolution of consensus guidelines for genotoxicity testing and the increasing emphasis on systematic reviews for evaluation of scientific studies for use in health risk assessments. An Appendix considers some issues in assessing the bioeffects literature by examining a subset of genotoxicity publications that employed the comet assay. While most studies found no statistically significant effects of exposure, a significant minority of studies (chiefly, in vivo studies) reported statistically significant effects of exposure. The quality of the studies was highly variable; while several studies were meticulously done and documented, none of these studies were compliant with currently accepted guidelines such as those of the Organization for Economic Cooperation and Development (OECD). Evaluation of the studies using risk of bias (RoB) criteria showed that, in this sample of studies, higher quality studies were less likely to find statistically significant results than those of lower quality. The authors conclude that statistical significance should be only one consideration in evaluation of bioeffects studies. Simply listing 'statistically' significant effects identified using null hypothesis testing and the criterion p < 0.05 for statistical significance is misleading and uninformative in assessing health risks of exposure. A careful synthesis of evidence is needed, including assessment of study validity, biological significance of reported effects, and coherence of study results with those of other related studies. The authors recommend that all future RF genotoxicity studies intended for use in human health risk assessments and evaluations of the literature should be done in compliance with accepted quality guidelines, i.e. OECD or equivalent guidelines for genotoxicity screening studies and PRISMA or other accepted guideline for reviews of the literature. The positive studies in this group should be redone with tighter quality control to establish the reliability of the findings. [ABSTRACT FROM AUTHOR]

International capital movement in the form of Foreign Direct Investment (FDI) by multinational enterprises (MNEs) signifies a widely researched phenomenon in comprehensive review of the FDI literature. Its vastness makes it impossible to fathom its depth, as such the paper highlights what the author considers to be mainstream theories in the domain of empirical studies on FDI. This study critically reviews (FDI) literature over a period of 70 years from 1950 to 2020 to provide a theoretical lens for future research beyond the established models. The discussion covers the mode of FDI inflows, statistical methods applied, theoretical models, contributions to paradigms and empirical papers investigating FDI. Furthermore, the papers selected cover heterogenous geographical regions, while in certain papers FDI has been studied as a dependent variable in others as an independent one. The focus lies on the FDI and its resultant activities of MNEs in the form of subsidiaries to carry out market seeking, strategic asset-seeking and efficiency seeking activities. Though, most researchers have narrowed down to four determinants of FDI i.e., motives of MNEs for undertaking FDI, size of MNEs, entry modes into host country and investments sector recently, many works have applied formal theories to provide a framework for market failure such as holdup problems, non-fulfillment of contracts and related agency costs. It is concluded that FDI has evolved into a significant area of empirical investigation. Overall, the gaps and opportunities in existing literature are identified thereby directions for further research emerge. [ABSTRACT FROM AUTHOR]

Microbiome data are characterized by several aspects that make them challenging to analyse statistically: they are compositional, high dimensional and rich in zeros. A large array of statistical methods exist to analyse these data. Some are borrowed from other fields, such as ecology or RNA‐sequencing, while others are custom‐made for microbiome data. The large range of available methods, and which is continuously expanding, means that researchers have to invest considerable effort in choosing what method(s) to apply. In this paper we list 14 statistical methods or approaches that we think should be generally avoided. In several cases this is because we believe the assumptions behind the method are unlikely to be met for microbiome data. In other cases we see methods that are used in ways they are not intended to be used. We believe researchers would be helped by more critical evaluations of existing methods, as not all methods in use are suitable or have been sufficiently reviewed. We hope this paper contributes to a critical discussion on what methods are appropriate to use in the analysis of microbiome data. [ABSTRACT FROM AUTHOR]

Environmental sustainability is defined as maintaining global ecosystems and preserving natural resources in particular to promote health and wellbeing, in the face of the electronics industry’s rapid technological advancement. Several environmental sustainability standards may be applicable depending on the local economic, social, and environmental situations. Exploring people awareness, practices on waste management for environmental sustainability is essential. There are different types of wastes, among them e-waste is very hazardous and has to be handled carefully. Developing countries like India, especially in Karnataka challenges associated with the generation and management of electronic waste is crucial. In this paper, study regarding the challenges in Karnataka state towards the management policies, disposal methods, harmfulness and consequences of mishandling of e-Waste disposal is carried out. In this paper, a survey has been carried out with a set questionnaire. The questionnaire has been formulated based on the age group, type of e-Waste, disposal methods followed, people community, measures taken by the local bodies, rules existing and most importantly on awareness of harmfulness of e-Waste disposals. As a result, it can be concluded that most interviewees in the urban area are familiar with the disposal procedure, however the rural community requires additional exposure. [ABSTRACT FROM AUTHOR]

Copyright of Jordanian Journal of Computers & Information Technology is the property of Jordanian Journal of Computers & Information Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper focuses on a wear evaluation conducted for prototype spur and herringbone gears made from PET-G filament using additive manufacturing. The main objective of this study is to verify if 3D-printed gears can be considered a reliable choice for long-term exploitation in selected mechanical systems, specifically automated retail kiosks. For this reason, two methods were applied, utilizing: (1) vision-based inspection of the gears' cross-sectional geometry and (2) the statistical characterization of the selected kinematic parameters and torques generated by drives. The former method involves destructive testing and allows for identification of the gears' operation-induced geometric shape evolution, whereas the latter method focuses on searching for nondestructive kinematic and torque-based indicators, which allow tracking of the wear. The novel contribution presented in this paper is the conceptual and experimental application of the identification of the changes of 3D-printed parts' geometric properties resulting from wear. The inspected exploited and non-exploited 3D-printed parts underwent encasing in resin and a curing process, followed by cutting in a specific plane to reveal the desired shapes, before finally being subjected to a vision-based geometric characterization. The authors have experimentally demonstrated, in real industrial conditions, on batch production parts, the usefulness of the presented destructive testing technique providing valid indices for wear identification. [ABSTRACT FROM AUTHOR]

Copyright of Valori e Valutazioni is the property of Societa Italiana di Estimo e Valutazione (SIEV) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

New advances in data linkage provide mortality researchers with access to administrative datasets with millions of mortality records and rich demographic covariates. Although these new datasets allow for high-resolution mortality research, administrative mortality records often have technical limitations, such as limited mortality coverage windows and incomplete observation of survivors. We describe a method for fitting truncated distributions that can be used for estimating mortality differentials in administrative data. We apply this method to the CenSoc datasets, which link the United States 1940 Census records to Social Security administrative mortality records. Our approach may be useful in other contexts where administrative data on deaths are available. As a companion to the paper, we release the R package gompertztrunc, which implements the methods introduced in this paper. [ABSTRACT FROM AUTHOR]

This paper provides a review of the present status of the topic dealt with. The contact and non-contact methods used for rock joint roughness measurement are summarized including their salient features, advantages, and disadvantages. A critical review is given of the empirical, statistical, and fractal-based methods used for rock joint roughness quantification identifying their salient features, shortcomings, and strong attributes. The surface topography of rough rock joints is highly erratic. Fractional geometry is better suited than Euclidean geometry in representing highly erratic rock joint surfaces. The influence of non-stationarity on accurate quantification of roughness is discussed. The existence of heterogeneity of natural rock joint roughness and its effect on computed roughness parameters are well illustrated. The controversial findings that have been appearing in the literature on roughness scale effects during the last 40 years have resulted from neglecting the effect of roughness heterogeneity on scale effects. The roughness heterogeneity controls the rock joint roughness scale effect, and it can be either negative, positive, or no scale effect depending on the type and level of the roughness heterogeneity of the rock joint surface. The importance of consideration of the existence of possible anisotropy in the quantification of roughness is well illustrated. The indices available to quantify the level of anisotropy are given. Effects of sampling interval and measurement resolution on the accurate quantification of roughness are discussed. A comparison of results obtained by using different quantification methods is discussed. A few recommendations are given for future research to address the shortcomings that exist on the topic dealt with in the paper. [ABSTRACT FROM AUTHOR]

Estimating the shear strength of a fiber-reinforced polymer (FRP)–reinforced-concrete (RC) beam is a complex task that depends on multiple design variables. The use of FRP bars has emerged as a promising alternative to diminish the corrosion problems that are associated with steel reinforcement in adverse environments; however, an accurate and reliable method of shear strength prediction is needed to ensure the economical use of materials and robust designs. Several optimized design equations are available in the literature; however, when utilizing these equations a substantial difference is observed between the predicted outcome (Vpred) and the experimental shear strength (Vexp) result. Therefore, this paper presented a novel approach toward implementing machine learning (ML) algorithms to accurately estimate the shear strength of FRP–RC beams. A large database that consisted of 302 shear test results on FRP-reinforced slender concrete beams without stirrup was collected from the literature to formulate the most efficient prediction model. The performance of each ML algorithm model was compared with the existing design provisions and models. The model interpretation was performed through feature importance analysis to explain the model output compared with a black box. The proposed data-driven ML models demonstrated a high level of accuracy and excellent performance and were superior to the existing shear strength models. In addition, a simple graphical user interface (GUI) was developed to aid practicing engineers when estimating shear strength without the need for complicated design procedures. The shear strength of FRP–RC beams is calculated using various design codes and guidelines that are heuristically developed based on previous test results. In general, the developed equations are either mechanics-based or empirical. However, this paper demonstrated that data-driven ML algorithms could generate a more reliable and appropriate prediction of the shear strength of FRP–RC beams. Furthermore, as the database increases, it could be automatically updated, which would result in more accurate and reliable results. Designers and practitioners could conveniently use the developed algorithms for the reliable and quick prediction of the shear strength of FRP–RC beams. In addition, the developed GUI is innovative and user-friendly. It allows users to determine the design shear strength without referring to an existing code by employing ML in conjunction with a large, reliable, and authenticated database to ensure accuracy. This could be important for the structural engineering community when assessing the shear capacity of existing FRP–RC beams. [ABSTRACT FROM AUTHOR]

Mathematical models to characterize and forecast the power production of photovoltaic and eolian plants are justified by the benefits of these sustainable energies, the increased usage in recent years, and the necessity to be integrated into the general energy system. In this paper, starting from two collections of data representing the power production hourly measured at a solar plant and a wind farm, adequate time series methods have been used to draw appropriate statistical models for their productions. The data are smoothed in both cases using moving average and continuous time series have been obtained leading to some models in good agreement with experimental data. For the solar power plant, the developed models can predict the specific power of the next day, next week, and next month, with the most accurate being the monthly model, while for wind power only a monthly model could be validated. Using the CUSUM (cumulative sum control chart) method, the analyzed data formed stationary time series with seasonality. The similar methods used for both sets of data (from the solar plant and wind farm) were analyzed and compared. When compare with other studies which propose production models starting from different measurements involving meteorological data and/or machinery characteristics, an innovative element of this paper consists in the data set on which it is based, this being the production itself. The novelty and the importance of this research reside in the simplicity and the possibility to be reproduced for other related conditions even though every new set of data (provided from other power plants) requires further investigation. [ABSTRACT FROM AUTHOR]

The paper studies the soft X-ray data of solar flares and found that the distribution functions of flare fluence are successfully modeled by tapered power law or gamma function distributions whose power exponent is slightly smaller than 2, indicating that the total energy of the flare populations is mostly due to a small number of large flares. The largest possible solar flares in 1000 years are predicted to be around X70 (a peak flux of 70 × 10−4 W m−2) in terms of the GOES (Geostationary Operational Environmental Satellites) flare class. The paper also studies superflares (more energetic than solar flares) from solar-type stars and found that their power exponent in the fitting of the gamma function distribution is around 1.05, which is much flatter than solar flares. The distribution function of stellar flare energy extrapolated downward does not connect to the distribution function of solar flare energy. [ABSTRACT FROM AUTHOR]

Risk management has been a topic of great interest to Michael McAleer. Even as recent as 2020, his paper on risk management for COVID-19 was published. In his memory, this article is focused on bankruptcy risk in financial firms. For financial institutions in particular, banks are considered special, given that they perform risk management functions that are unique. Risks in banking arise from both internal and external factors. The GFC underlined the need for comprehensive risk management, and researchers since then have been working towards fulfilling that need. Similarly, the central banks across the world have begun periodic stress-testing of banks' ability to withstand shocks. This paper investigates the machine-learning and statistical techniques used in the literature on bank failure prediction. The study finds that though considerable progress has been made using advanced statistical and computational techniques, given the complex nature of banking risk, the ability of statistical techniques to predict bank failures is limited. Machine-learning-based models are increasingly becoming popular due to their significant predictive ability. The paper also suggests the directions for future research. [ABSTRACT FROM AUTHOR]

Background: Using four case studies, we aim to provide practical guidance and recommendations for the analysis of cluster randomised controlled trials. Methods: Four modelling approaches (Generalized Linear Mixed Models with parameters estimated by maximum likelihood/restricted maximum likelihood; Generalized Linear Models with parameters estimated by Generalized Estimating Equations (1st order or second order) and Quadratic Inference Function, for analysing correlated individual participant level outcomes in cluster randomised controlled trials were identified after we reviewed the literature. We systematically searched the online bibliography databases of MEDLINE, EMBASE, PsycINFO (via OVID), CINAHL (via EBSCO), and SCOPUS. We identified the above-mentioned four statistical analytical approaches and applied them to four case studies of cluster randomised controlled trials with the number of clusters ranging from 10 to 100, and individual participants ranging from 748 to 9,207. Results were obtained for both continuous and binary outcomes using R and SAS statistical packages. Results: The intracluster correlation coefficient (ICC) estimates for the case studies were less than 0.05 and are consistent with the observed ICC values commonly reported in primary care and community-based cluster randomised controlled trials. In most cases, the four methods produced similar results. However, in a few analyses, quadratic inference function produced different results compared to the generalized linear mixed model, first-order generalized estimating equations, and second-order generalized estimating equations, especially in trials with small to moderate numbers of clusters. Conclusion: This paper demonstrates the analysis of cluster randomised controlled trials with four modelling approaches. The results obtained were similar in most cases, however, for trials with few clusters we do recommend that the quadratic inference function should be used with caution, and where possible a small sample correction should be used. The generalisability of our results is limited to studies with similar features to our case studies, for example, studies with a similar-sized ICC. It is important to conduct simulation studies to comprehensively evaluate the performance of the four modelling approaches. [ABSTRACT FROM AUTHOR]

Machine learning (ML) is the branch of computer science that studies computer algorithms that can learn from data. It is mainly divided into supervised learning, where the computer is presented with examples of entries, and the goal is to learn a general rule that maps inputs to outputs, and unsupervised learning, where no label is provided to the learning algorithm, leaving it alone to find structures. Deep learning is a branch of machine learning based on numerous layers of artificial neural networks, which are computing systems inspired by the biological neural networks that constitute animal brains. In asteroid dynamics, machine learning methods have been recently used to identify members of asteroid families, small bodies images in astronomical fields, and to identify resonant arguments images of asteroids in three-body resonances, among other applications. Here, we will conduct a full review of available literature in the field and classify it in terms of metrics recently used by other authors to assess the state of the art of applications of machine learning in other astronomical subfields. For comparison, applications of machine learning to Solar System bodies, a larger area that includes imaging and spectrophotometry of small bodies, have already reached a state classified as progressing. Research communities and methodologies are more established, and the use of ML led to the discovery of new celestial objects or features, or new insights in the area. ML applied to asteroid dynamics, however, is still in the emerging phase, with smaller groups, methodologies still not well-established, and fewer papers producing discoveries or insights. Large observational surveys, like those conducted at the Zwicky Transient Facility or at the Vera C. Rubin Observatory, will produce in the next years very substantial datasets of orbital and physical properties for asteroids. Applications of ML for clustering, image identification, and anomaly detection, among others, are currently being developed and are expected of being of great help in the next few years. [ABSTRACT FROM AUTHOR]

Asset inventory is one of the essential steps in cyber security analysis and management. It is required for security risk identification. Current information systems are large-scale, heterogeneous, and dynamic. This complicates manual inventory of the assets as it requires a lot of time and human resources. At the same time, an asset inventory should be continuously repeated because continuous modifications of system objects and topology lead to changes in the cyber security situation. Thus, a technique for automated identification of system assets and connections between them is required. The paper proposes a technique for automated inventory of assets and connections between them in different organizations. The developed technique is constructed based on event correlation methods, namely linking the system events to each other. The essence of the technique consists of the investigation of event characteristics and identifying the characteristics that arise solely together. This allows determining system assets via assigning event characteristics to specific asset types. The security risks depend on the criticality of the assets; thus, a discussion of automated calculation of the outlined assets' criticality is provided. Outlined system objects and topology can be further used for restoring possible attack paths and security assessment. The applicability of the developed technique to reveal object properties and types is demonstrated in the experiments. [ABSTRACT FROM AUTHOR]

In this paper, we gather and take stock of the results produced by the Operational Earthquake Forecasting (OEF) system in Italy, during its first 10 yr of operativity. The system is run in real-time: every midnight and after each M L 3.5+ event, it produces the weekly forecast of earthquakes expected by an ensemble model in each cell of a spatial grid covering the entire Italian territory. To evaluate the performance skill of the OEF-Italy forecasts, we consider here standard tests of the Collaboratory for the Study of Earthquake Predictability, which have been opportunely adapted to the case of the overlapped weekly OEF forecasts; then we also adopt new performance measures borrowed from other research fields, like meteorology, specific to validate alarm-based systems by a binary criterion (forecast: yes/no; occurrence: yes/no). Our final aim is to: (i) investigate possible weaknesses and room for improvements in the OEF-Italy stochastic modelling, (ii) provide performance measures that could be helpful for stakeholders who act through a boolean logic (making an action or not) and (iii) highlight possible features in the Italian tectonic seismic activity. [ABSTRACT FROM AUTHOR]

Water resource managers require accurate estimates of the 7-day, 10-year low flow (7Q10) of streams for many reasons, including protecting aquatic species, designing wastewater treatment plants, and calculating municipal water availability. StreamStats, a publicly available web application developed by the United States Geologic Survey that is commonly used by resource managers for estimating the 7Q10 in states where it is available, utilizes state-by-state, locally calibrated regression equations for estimation. This paper expands StreamStats' methodology and improves 7Q10 estimation by developing a more regionally applicable and generalized methodology for 7Q10 estimation. In addition to classical methodologies, namely multiple linear regression (MLR) and multiple linear regression in log space (LTLR), three promising machine learning algorithms, random forest (RF) decision trees, neural networks (NN), and generalized additive models (GAM), are tested to determine if more advanced statistical methods offer improved estimation. For illustrative purposes, this methodology is applied to and verified for the full range of unimpaired, gaged basins in both the northeast and mid-Atlantic hydrologic regions of the United States (with basin sizes ranging from 2–1419 mi2) using leave-one-out cross-validation (LOOCV). Pearson's correlation coefficient (R2), root mean square error (RMSE), Kling–Gupta Efficiency (KGE), and Nash–Sutcliffe Efficiency (NSE) are used to evaluate the performance of each method. Results suggest that each method provides varying results based on basin size, with RF displaying the smallest average RMSE (5.85) across all ranges of basin sizes. [ABSTRACT FROM AUTHOR]

Copyright of Moderna Arhivistika is the property of Maribor Provincial Archives and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Representativeness and quality of collected meteorological data impact accuracy and precision of climate, hydrological, and biogeochemical analyses and predictions. We developed a comprehensive Quality Assurance (QA) and Quality Control (QC) statistical framework, consisting of three major phases: Phase I—Preliminary data exploration, i.e., processing of raw datasets, with the challenging problems of time formatting and combining datasets of different lengths and different time intervals; Phase II—QA of the datasets, including detecting and flagging of duplicates, outliers, and extreme data; and Phase III—the development of time series of a desired frequency, imputation of missing values, visualization and a final statistical summary. The paper includes two use cases based on the time series data collected at the Billy Barr meteorological station (East River Watershed, Colorado), and the Barro Colorado Island (BCI, Panama) meteorological station. The developed statistical framework is suitable for both real-time and post-data-collection QA/QC analysis of meteorological datasets. [ABSTRACT FROM AUTHOR]

The article examines the topical issue of risk management in foreign economic and logistics activities, which is closely related to the enterprise’s chosen strategy and ensures its sustainable development. The study considers the issue of developing a hedging strategy using statistical methods since adequate forecasting allows predicting the impact of external environment factors on the exchange rate, which will allow the enterprise to timely predict and mitigate the risks in foreign economic and logistics activities. The authors have used general scientific and special methods of systemic and structural analysis to clarify the directions of sustainable performance and risk management tools; formulate the goals and steps in choosing a strategy of hedging; compare the methods of foreign exchange rate risk hedging and perform correlation and regression analysis of factors that impact exchange rate under crisis conditions. The purpose of the paper is to study the specifics of ensuring the sustainable performance of an enterprise using statistical methods in risk management for planning foreign economic and logistics activities. The authors have proposed a definition of a hedging strategy based on the concepts, approaches, and ideas of asset and/or investment management with the aim of reducing loss through hedging instruments, whose feasibility can be substantiated by statistical methods. It has been found that the formation and successful implementation of a hedging strategy requires the use of statistical analysis in order to timely predict fluctuations in exchange rates. The findings of the research were tested based on the performance of PrJSC "Linde Gas Ukraine". In the strategy of hedging the exchange rate risks, it has been proposed to simultaneously open a foreign currency deposit and take a loan in the national currency to replenish the company's working capital. The perspective of further research is the implementation of the proposed hedging strategy and assessment of its effectiveness. [ABSTRACT FROM AUTHOR]

Background: Many scientific papers are published each year and substantial resources are spent to develop biomarker-based tests for precision oncology. However, only a handful of tests is currently used in daily clinical practice, since development is challenging. In this situation, the application of adequate statistical methods is essential, but little is known about the scope of methods used. Methods: A PubMed search identified clinical studies among women with breast cancer comparing at least two different treatment groups, one of which chemotherapy or endocrine treatment, by levels of at least one biomarker. Studies presenting original data published in 2019 in one of 15 selected journals were eligible for this review. Clinical and statistical characteristics were extracted by three reviewers and a selection of characteristics for each study was reported. Results: Of 164 studies identified by the query, 31 were eligible. Over 70 different biomarkers were evaluated. Twenty-two studies (71%) evaluated multiplicative interaction between treatment and biomarker. Twenty-eight studies (90%) evaluated either the treatment effect in biomarker subgroups or the biomarker effect in treatment subgroups. Eight studies (26%) reported results for one predictive biomarker analysis, while the majority performed multiple evaluations, either for several biomarkers, outcomes and/or subpopulations. Twenty-one studies (68%) claimed to have found significant differences in treatment effects by biomarker level. Fourteen studies (45%) mentioned that the study was not designed to evaluate treatment effect heterogeneity. Conclusions: Most studies evaluated treatment heterogeneity via separate analyses of biomarker-specific treatment effects and/or multiplicative interaction analysis. There is a need for the application of more efficient statistical methods to evaluate treatment heterogeneity in clinical studies. [ABSTRACT FROM AUTHOR]

Palaeomagnetic investigations often encounter multiple magnetization components, where secondary processes have obscured, partially overprinted or completely replaced the original (primary) remanent magnetization. Identification and separation of primary and secondary magnetizations are generally carried out with principal component analysis of stepwise demagnetization data. However, rocks may contain multiple generations of magnetic minerals with overlapping unblocking ranges that complicate the discrimination of components when applying best-fitting line procedures. Developing a method to differentiate and quantify contributions of overlapping magnetic components using directional data is therefore highly desirable. This paper presents a method to unmix stepwise demagnetization data using an inverse modelling approach. We show that the method is capable of accurately resolving two or three magnetic components with overlapping or superimposed unblocking spectra as well as quantifying absolute component contributions. The method depends on accurate identification and selection of end-member components prior to analysis; in doing so, the method can help palaeomagnetists understand how magnetization components combine to explain their data. We show that the dilution of one component by more than ca. 25 per cent from another component can result in linear demagnetization curves that decay to the origin on orthogonal plots, but whose best-fitting direction can significantly deviate from both end-members. The efficacy of the method is demonstrated through examples of demagnetization data from hematite and/or magnetite-bearing sandstones from China. This method can be broadly applied to all multicomponent magnetization problems in palaeomagnetism. [ABSTRACT FROM AUTHOR]

Accessible Summary: What is known on the subject?: Psychiatric and/or mental health nurses are struggling to measure the outcomes of the nurse–patient relationship.Collecting nurse‐sensitive patient outcomes is a strategy to provide outcomes of a nurse–patient relationship from patients' perspectives.Because there was no validated scale, the Mental Health Nurse‐Sensitive Patient Outcome‐Scale (six‐point Likert‐scale) was recently developed and psychometrically evaluated. What the paper adds to existing knowledge?: This is the first study using the Mental Health Nurse‐Sensitive Patient Outcome‐scale to measure nurse‐sensitive patient outcomes of the nurse–patient relationship in psychiatric hospitals. Moderate to good average scores for the MH‐NURSE‐POS total (4.42) and domains scores (≥4.09). are observed. Especially outcomes related to 'motivation' to follow and stay committed to the treatment received high average scores (≥4.60).Our results are consistent with the patient‐reported effect(s) of relation‐based nursing in qualitative research.The scores generate evidence to support the outcomes of the nurse–patient relationship and implicates that further investment in (re)defining and elaborating nurse–patient relationships in mental healthcare is meaningful and justified.More comparative patient‐reported data can determine how nurse‐sensitive patient outcomes are affected by the patient, nurse, and context. What are the implications for practice?: Demonstrating patient‐reported outcomes of the nurse–patient relationship can be important to enhance the therapeutic alliance between nurses and patients, organize responsive nursing care, and create nursing visibility in mental healthcare. Further nursing staff training on interpersonal competencies, such as self‐awareness and cultural sensitivity, can be pivotal to achieving the patient‐reported outcomes for inpatients with mental health problems. Introduction: Identifying patient‐reported outcomes of the nurse–patient relationship is a priority in inpatient mental healthcare to guide clinical decision‐making and quality improvement initiatives. Moreover, demonstrating nurse‐sensitive patient outcomes can be a strategy to avoid further erosion of the specialism of psychiatric and/or mental health nursing. Aim/Question To measure nurse‐sensitive patient outcomes of the nurse–patient relationship. Method: In a multicentred cross‐sectional study, 296 inpatients admitted to five psychiatric hospitals completed the recently developed and validated Mental Health Nurse‐Sensitive Patient Outcome‐Scale (MH‐NURSE‐POS). The MH‐NURSE‐POS consists of 21 items (six‐point Likert‐scale) in four domains: 'growth', 'expression', 'control', and 'motivation'. Results: Participants displayed moderate to good average scores for the MH‐NURSE‐POS total (4.42) and domain scores (≥4.09). Especially outcomes related to 'motivation' to follow and stay committed to the treatment received high average scores (≥4.60). Discussion The results demonstrate that patients perceive the nurse–patient relationship and the care given by psychiatric and/or mental health nurses as contributing to their treatment. Implications for Practices: Patient‐reported outcomes can guide nurses and managers to provide and organize nursing care and to build a nurse–patient relationship that has a positive impact on these outcomes. Additionally, outcomes can create nursing visibility as a profession in‐ and outside mental healthcare. [ABSTRACT FROM AUTHOR]

To date, there has been relatively little research in the field of credit risk analysis that compares all of the well known statistical, optimization technique (heuristic methods) and machine learning based approaches in a single article. Review on credit risk assessment using sixteen well-known approaches has been conducted in this work. The accuracy of the machine learning approaches in dealing with financial difficulties is superior to that of traditional statistical methods, especially when dealing with nonlinear patterns, according to the findings. Hybrid or Ensemble algorithms, on the other hand have been found to outperform their traditional counterparts – standalone classifiers in the vast majority of situations. Finally, the paper compares the models with nine machine learning classifiers utilizing two benchmark datasets. In this study, we have encountered with 46 datasets, among them 35 datasets have been utilized for once; whereas among the other 11 datasets, Australian, German and Japanese are the three most frequently utilized datasets by the researchers. The study showed that the performance of ensemble classifiers were very much significant. As per the experimental result, for both datasets ensemble classifiers outperformed other standalone classifiers which validate with the prior research also. Although some of these approaches have a high level of accuracy, additional study is required to discover the right parameters and procedures for better outcomes in a transparent manner. Additionally this study is a valuable reference source for analyzing credit risk for both academic and practical domains, since it contains relevant information on the most major machine learning approaches employed so far. [ABSTRACT FROM AUTHOR]

In this paper, we carry out a comparison analysis of the Epidemic Type Aftershock Sequence (ETAS) model for the earthquake process, embedded with the three main exponential-type distributions adopted in practical applications to describe the magnitudes of seismic events, that are, the Gutenberg–Richter (GR), the tapered Gutenberg–Richter (TGR) and the CHaracteristic (CH) frequency–magnitude distributions (FMDs). The first law is a pure-power decreasing function, while both the other two introduce a more rapid decay in the tail of the distribution: a soft taper in the TGR model and a sharp cut-off in the CH one. To perform the comparison, we first investigate some theoretical features of the ETAS model with CH-distributed magnitudes (ETAS-CH), which have not been deeply analysed in the literature as much as for ETAS-TGR and ETAS-GR. In particular, we explicitly compute the branching ratio, we analyse its asymptotics in relation to its parameters, and we derive the proper stability conditions. We then move to the comparison among the three ETAS-GR, ETAS-TGR and ETAS-CH processes, to highlight differences and similarities. This is done by carrying out both a theoretical analysis, mainly focused on the three models' branching ratios and the relative sensitivity, and a simulation analysis of realistic synthetic catalogues to compare the processes' numbers, events' magnitude distribution and temporal evolution. The results we obtained show that the ETAS-TGR and ETAS-CH processes have very similar features. They both have also less restrictive non-explosion conditions than for ETAS-GR; in fact, differently from this latter case, their branching ratios exist for any value of the parameters and are lower than the one of ETAS-GR, to which they converge for large magnitudes. [ABSTRACT FROM AUTHOR]

Copyright of Biuletyn Polskiego Towarzystwa Językoznawczego is the property of Polish Linguistic Society and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The article discusses a comment made by Bourke et al. regarding the method described in Chen et al.'s paper on mapping quantitative trait loci (QTL) in autotetraploids. Chen et al. argue that their comment is correct based on facts and statistical principles. They explain that the method used in Bourke et al.'s paper lacks an essential component, which is genetic linkage analysis between QTLs and surrounding markers for QTL mapping in an outbred autotetraploid segregating population. Chen et al. also address the terminology and methodology used by Bourke et al., pointing out inconsistencies and inaccuracies. They conclude that their own method enables QTL mapping in autotetraploid species under a novel tetrasomic inheritance model and is based on rigorous scientific and statistical principles. [Extracted from the article]

This paper proposes the use of the parametric bootstrap as a simple way of obtaining reliable confidence intervals for the parameters in the space–time epidemic-type aftershock sequence (ETAS) model. Using an earthquake data catalogue spanning almost 20 yr of seismic activity in the Pacific Ocean off the shores of Vancouver Island (British Columbia, Canada), the authors show through simulation that the confidence intervals based on asymptotic maximum-likelihood theory can sometimes be misleading. In contrast, confidence intervals based on empirical quantiles from bootstrap samples have reliable nominal coverage, provided that edge effects are properly taken into account, both in the bootstrap procedure and in simulation studies. An R package called ETASbootstrap , developed by the authors, facilitates the use of this resampling procedure. [ABSTRACT FROM AUTHOR]

In this paper we used statistical methods to understand the genetic information of DNA considered as a statistical system. The alphabet of a DNA sequence is defined by the four nucleotides: adenine, cytosine, guanine, and thymine. The order of nucleotides along the DNA sequences encodes the genetic information. We have analyzed three Cryptosporidium DNA sequences: one DNA sequence isolated and analyzed in our laboratory and two DNA reference sequences from the public database GenBank. Each DNA sequence is considered as a statistical system and is represented by a random variable and an associate probability distribution. The Shannon entropy, Renyi entropy, Onicescu informational energy and square deviation from uniform distribution are used in order to measure the degree of randomness for the three statistical systems. The similarity and difference between the three DNA sequences of the two Cryptosporidium species (Cryptosporidium hominis and Cryptosporidium parvum) were assessed by calculating the statistical distance between the probability distributions associated with each pair of DNA sequences. Each of the three DNA sequences pairs with one of the other two sequences and forms three pairs of sequences. Using the associated probability distributions, the statistical distance between them can be calculated. Bhattacharyya distance measures similarity degree between the two probability distributions. The Kullback-Leiber and the resistor-average distances measure the difference between the two distributions. [ABSTRACT FROM AUTHOR]

In this paper, some indicators covering milk production, weight gain and feeding efficiency are studied, such as the duration of lactation, total amount of milk, daily amount of milk, percentage of fat, protein, lactose in milk, for a representative sample taken from S.C.D.A Simnic. In the first part, a descriptive statistical analysis is made of the indicators in the zootechnical field presented, followed by econometric methods regarding confidence intervals for the average of the entire population, at a threshold of 5%. Also, the correlation matrix between the indicators is analyzed and three validated linear regression models are presented with the interpretation of the results. These results help to optimize management in the zootechnical field by making decisions based on quantitative analyses. [ABSTRACT FROM AUTHOR]