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Physiologists often express the change in the value of a measurement made on two occasions as a ratio of the initial value. This is usually motivated by an assumption that the absolute change fails to capture the true extent of the alteration that has occurred in attaining the final value – if there is initial variation among individual cases. While it may appear reasonable to use ratios to standardize the magnitude of change in this way, the perils of doing so have been widely documented. Ratios frequently have intractable statistical properties, both when taken in isolation and when analysed using techniques such as regression. A new method of computing a standardized metric of change, based on principal components analysis (PCA), is described. It exploits the collinearity within sets of initial, absolute change and final values. When these sets define variables subjected to PCA, the standardized measure of change is obtained as the product of the loading of absolute change onto the first principal component (PC1) and the eigenvalue of PC1. It is demonstrated that a sample drawn from a population of these standardized measures: approximates a normal distribution (unlike the corresponding ratios); lies within the same range; and preserves the rank order of the ratios. It is also shown that this method can be used to express the magnitude of a physiological response in an experimental condition relative to that obtained in a control condition. Key points: The intractable statistical properties of ratios and the perils of using ratios to standardize the magnitude of change are well known.A new method of computing a standardized metric, based on principal components analysis (PCA), is described, which exploits the collinearity within sets of initial, absolute change and final values.A sample drawn from a population of these PCA‐derived measures: approximates a normal distribution (unlike the corresponding ratios); lies within the same range as the ratios; and preserves the rank order of the ratios.The method can also be applied to express the magnitude of a physiological response in an experimental condition relative to a control condition. [ABSTRACT FROM AUTHOR]

Most classical statistical tests assume data are normally distributed. If this assumption is not met, researchers often turn to non-parametric methods. These methods have some drawbacks, and if no suitable non-parametric test exists, a normal distribution may be used inappropriately instead. A better option is to select a distribution appropriate for the data from dozens available in modern software packages. Selecting a distribution that represents the data generating process is a crucial but overlooked step in analysing data. This paper discusses several alternative distributions and the types of data that they are suitable for. [ABSTRACT FROM AUTHOR]

Local polynomial fitting exhibits numerous compelling statistical properties, particularly within the intricate realm of multivariate analysis. However, as functional data analysis gains prominence as a dynamic and pertinent field in data science, the exigency arises for the formulation of a specialized theory tailored to local polynomial fitting. We explored the intricate task of estimating the regression function operator and its partial derivatives for stationary mixing random processes, denoted as (Yi, Xi), using local higher-order polynomial fitting. Our key contributions include establishing the joint asymptotic normality of the estimates for both the regression function and its partial derivatives, specifically in the context of strongly mixing processes. Additionally, we provide explicit expressions for the bias and the variance-covariance matrix of the asymptotic distribution. Demonstrating uniform strong consistency over compact subsets, along with delineating the rates of convergence, we substantiated these results for both the regression function and its partial derivatives. Importantly, these findings rooted in reasonably broad conditions that underpinned the underlying models. To demonstrate practical applicability, we leveraged our results to compute pointwise confidence regions. Finally, we extended our ideas to the nonparametric conditional distribution, and obtained its limiting distribution. [ABSTRACT FROM AUTHOR]

Regular nilpotent Hessenberg varieties form an important family of subvarieties of the flag variety, which are often singular and sometimes not normal varieties. Like Schubert varieties, they contain distinguished points called permutation flags. In this paper, we give a combinatorial characterization for a permutation flag of a regular nilpotent Hessenberg variety to be a singular point. We also apply this result to characterize regular nilpotent Hessenberg varieties which are normal algebraic varieties. [ABSTRACT FROM AUTHOR]

In just a few decades, the normative world we inhabit is comprehensively different to the one in which S.H. Foulkes lived. As the pace, scope and content of change have transformed society, we have 'greater indeterminacy to decide our movements and identity' (p. 67). Personal identity has been released from many of its traditional moorings. The pervasive influence of contemporary 'psy-sciences' and exponential growth of therapeutic experts is one aspect of these changes. Seemingly, we live with more flux and flow than ever before. Have our theories kept up with theses fundamental changes? This paper offers a critical, post-Foulksean, post-psychoanalytic perspective on the normative world and our practices within it. It begins with history and the importance of locating who we are and what we do within the mutations of long-term change. [ABSTRACT FROM AUTHOR]

The objective of this paper is to assess the distribution of the Partial Autocorrelation Function (PACF), both theoretically and empirically, emphasizing its crucial role in modeling and forecasting time series data. Additionally, it evaluates the deviation of the sum of sample PACF from normality: identifying the lag at which departure occurs. Our investigation reveals that the sum of the sample PACF, and consequently its components, diverges from the expected normal distribution beyond a certain lag. This observation challenges conventional assumptions in time series modeling and forecasting, indicating a necessity for reassessment of existing methodologies. Through our analysis, we illustrate the practical implications of our findings using real-world scenarios, highlighting their significance in unraveling complex data patterns. This study delves into 185 years of monthly Bank of England Rate data, utilizing this extensive dataset to conduct an empirical analysis. Furthermore, our research paves the way for future exploration, offering insights into the complexities and potential revisions in time series analysis, modeling, and forecasting. [ABSTRACT FROM AUTHOR]

Local polynomial fitting exhibits numerous compelling statistical properties, particularly within the intricate realm of multivariate analysis. However, as functional data analysis gains prominence as a dynamic and pertinent field in data science, the exigency arises for the formulation of a specialized theory tailored to local polynomial fitting. We explored the intricate task of estimating the regression function operator and its partial derivatives for stationary mixing random processes, denoted as $ (Y_i, X_i) $, using local higher-order polynomial fitting. Our key contributions include establishing the joint asymptotic normality of the estimates for both the regression function and its partial derivatives, specifically in the context of strongly mixing processes. Additionally, we provide explicit expressions for the bias and the variance-covariance matrix of the asymptotic distribution. Demonstrating uniform strong consistency over compact subsets, along with delineating the rates of convergence, we substantiated these results for both the regression function and its partial derivatives. Importantly, these findings rooted in reasonably broad conditions that underpinned the underlying models. To demonstrate practical applicability, we leveraged our results to compute pointwise confidence regions. Finally, we extended our ideas to the nonparametric conditional distribution, and obtained its limiting distribution.

The aim of this paper is to present the notions of actions and coverings of cat¹-groupoids and to prove the natural equivalence between their categories. Moreover, in this context, we characterize the quotient concept of cat¹-groupoids. Finally we extend these notions to catn-groupoids which are groupoid version of catn-groups. [ABSTRACT FROM AUTHOR]

Novel necessary and sufficient conditions for the identifiability of the linear mixed model are derived. These conditions either relax or generalize previously reported conditions. The novel conditions are translated to criteria that can be checked for most commonly employed parametrizations of the random effect's covariance matrix of linear mixed model. [ABSTRACT FROM AUTHOR]

Abstract In various practical situations, the information about the process distribution is sometimes partially or completely unavailable. In these instances, practitioners prefer to use nonparametric charts as they don’t restrict the assumption of normality or specific distribution. In this current article, a nonparametric double homogeneously weighted moving average control chart based on the Wilcoxon signed-rank statistic is developed for monitoring the location parameter of the process. The run-length profiles of the newly developed chart are obtained by using Monte Carlo simulations. Comparisons are made based on various performance metrics of run-length distribution among proposed and existing nonparametric counterparts charts. The extra quadratic loss is used to evaluate the overall performance of the proposed and existing charts. The newly developed scheme showed comparatively better results than its existing counterparts. For practical implementation of the suggested scheme, the real-world dataset related to the inside diameter of the automobile piston rings is also used.

This article presents the authors’ approaches to understanding the concept of corporeality in its normative dimension. The purpose of the study is to conceptualize the images of human corporeality that exist in the system of legal regulation. Based on the idea that the research category is a representation of certain characteristics of the human body, the authors substantiate the possibility of using institutional and functional-activity approaches to analyzing human corporeality. Both of these approaches are based on distinct foundations, which include social institutions, fields of activity and functional purposes of the human body. The common basis for the two approaches lies in the biosocial component, which is considered one of the defining characteristics of an image of corporeality. Depending on the approach used, the authors propose three classifications of images of corporeality: private and public, collective and individual and normal and abnormal. Regulatory practices that are aimed at consolidating these images of corporeality are analyzed within the framework of the current legal regulations in the BRICS countries. The authors conclude by noting that corporeality is a biosocial category that serves as the basis for legal subjectivity, while gaps in existing images of corporeality are the basis for its normalization.

Polytomous categorical data are frequent in studies, that can be obtained with an individual or grouped structure. In both structures, the generalized logit model is commonly used to relate the covariates on the response variable. After fitting a model, one of the challenges is the definition of an appropriate residual and choosing diagnostic techniques. Since the polytomous variable is multivariate, raw, Pearson, or deviance residuals are vectors and their asymptotic distribution is generally unknown, which leads to difficulties in graphical visualization and interpretation. Therefore, the definition of appropriate residuals and the choice of the correct analysis in diagnostic tools is important, especially for nominal data, where a restriction of methods is observed. This paper proposes the use of randomized quantile residuals associated with individual and grouped nominal data, as well as Euclidean and Mahalanobis distance measures, as an alternative to reduce the dimension of the residuals. We developed simulation studies with both data structures associated. The half-normal plots with simulation envelopes were used to assess model performance. These studies demonstrated a good performance of the quantile residuals, and the distance measurements allowed a better interpretation of the graphical techniques. We illustrate the proposed procedures with two applications to real data. [ABSTRACT FROM AUTHOR]

The World Health Organization (WHO) published a report in 1996 about the definition of a normal birth and recommended certain methods to support the physiological processes of labour. The WHO definition further stated that the aim of care in normal birth is to achieve a healthy mother and child with the least possible level of interventions, and in every case, there should be a valid reason to interfere with the natural process. Since this initial report, childbirthing around the world has become heavily medicalised with high rates of interventions and increasingly high rates of operative births. Over the past few years, there has been a renewed interest worldwide to support normality at childbirth and the term "natural birth" has re-appeared in the literature and is used interchangeably with the term "normal birth". At present, there is a consensus in the literature and the majority of professional societies refer to "natural birth" or "normal birth" as the birth achieved with no medical interventions and with no pharmacological means of pain management. In order to promote "normality" and "naturalness" at childbirth with minimal or no interventions and to enhance the birthing experience of women, it is now accepted that we need to revisit our intrapartum care practices, embrace a respectful and woman-centred birthing philosophy, utilise the appropriate clinical skills and tools, and adopt a new organizational model of midwife led care. [ABSTRACT FROM AUTHOR]

In this paper, we give a deterministic one-to-one pushdown transducer and a normal sequence of digits compressed by it. This solves positively a question left open in a previous paper by V. Becher, P. A. Heiber and the first author. [ABSTRACT FROM AUTHOR]

Copyright of Strategic Universe Journal / Univers Strategic is the property of Dimitrie Cantemir Christian University, Institute for Security Studies 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 production of reference materials necessitates, among other factors, evaluations of homogeneity and stability. The aim of the homogeneity study is to ensure uniformity in property values across all samples. On the other hand, stability pertains to a sample's ability to retain its property value over time. ISO Guide 35 dictates that homogeneity, between and within samples, is appraised using a one-way random effects analysis of variance, while stability is gauged by a univariate linear regression model. Both these statistical techniques presuppose the normality and homoscedasticity of the residuals. Notably, the ISO Guide 35 standard omits any reference to the assumption of homoscedasticity in its prescribed statistical methods. This communication introduces the Shapiro–Wilk and Koenker–Bassett tests as tools to verify the assumptions of normality and homoscedasticity, respectively. Moreover, it advances the feasible generalized least squares method as a remedy for instances where these assumptions are breached. Initial findings suggest that, in certain scenarios, breaches in statistical model assumptions might influence evaluations of homogeneity and/or stability. [ABSTRACT FROM AUTHOR]

A very well-covered graph is a well-covered graph without isolated vertices such that the size of its minimal vertex covers is half of the number of vertices. If G is a Cohen–Macaulay very well-covered graph, we deeply investigate some algebraic properties of the cover ideal of G via the Rees algebra associated to the ideal, and especially when G is a whisker graph. [ABSTRACT FROM AUTHOR]

Copyright of Österreichische Zeitschrift für Soziologie is the property of Springer Nature 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.)

We present a comprehensive generalization of the Zassenhaus Lemma, the Scherier Refinement Theorem, and the Jordan-Hölder Theorem, extending their applicability to both crossed modules and 2-crossed modules. It is discovered that the previously established normality conditions are insufficient for forming quotient objects of 2-subcrossed modules, as demonstrated through an illustrative example, and these conditions are rigorously revised to allow these generalizations. These new conditions now yield results that are categorically accurate. Additionally, the study has led to the derivation of several supplementary results, including isomorphism theorems for 2-crossed modules. [ABSTRACT FROM AUTHOR]

At the time of writing, in the summer of 2024, we are confronted with a ‘polycrisis’ (e.g., Tooze 2022). This term is used to describe a situation in which multiple crises do not simply add up to a somewhat bigger crisis, but rather create a significantly different, amplified crisis in which the sub-crises influence each other in interdependent ways. As numerous studies have demonstrated (e.g., Heitmeyer 2024; Roberts 2022; Nowotny 2016), crises engender feelings of uncertainty, insecurity, and subsequently fear (Bauman 2006). The aim of this paper is to pose the overarching question: How do governments and citizens cope with such uncertainties? Les crises provoquent la peur, la panique, l’incertitude et l’impuissance. L’incertitude et l’insécurité mettent à l’épreuve tous les acteurs concernés ; chacun attend des instructions, une planification, des explications et la sécurité. Cependant, nous affrontons des alarmismes, des simplifications, une série de stratégies de légitimation et d’erreurs. Plus précisément, les erreurs sont souvent placées avant les intérêts communautaires, nationaux ou même locaux. Ces évolutions sont illustrées par une analyse qualitative et quantitative détaillée du discours des débats en Autriche, à l’été 2023. Je soutiens que les appels fallacieux au bon sens et à la normalité dépendent de leur contexte, avec des contenus, des fonctions et des effets différents observables. De tels appels instrumentalisent une « politique des émotions » de différentes manières. Ainsi, une nouvelle logique politique est normalisée, remplaçant le discours rationnel, la délibération et la formulation de politiques dirigées par des experts.

IntroductionAttempts to explain the leader emergence process have resulted in several seemingly contradictory assertions about who attains power and influence in groups. As such, this study pioneers a novel integration of the two dominant explanations of leader emergence: superiority (trait-based perspective) and similarity (social identity perspective). We posit that the two primary aspects of non-normality—uniqueness and strangeness—have opposing relationships with leader emergence in groups.MethodologyThrough multiple regression analyses, we utilized peer and self-evaluations in a sample of 308 participants in seven social organizations to assess whether and how non-normality evaluations predict formal and informal leadership emergence.ResultsThis study provides general support for a relationship between the non-normality dimensions and leader emergence, particularly when evaluations are made by peers. Additionally, we demonstrate that non-normality evaluations predict leader emergence above and beyond evaluations of the Big Five.DiscussionThis research lays the groundwork for a new era in leadership research that embraces the duality of individual traits and social perceptions, paving the way for a more nuanced and effective approach to leader emergence within dynamic group settings.

In recent years the social sciences have been paying closer attention to failure, to its manifestations in the contemporary world and to the modalities of dealing with it both in theory and in practice. An emergent and interdisciplinary field of analysis has been consolidating under the label of failure studies reflecting a number of social trends. These include the instability of winner-take-all systems, the ubiquity of the new spirit of capitalism, metric-based forms of governmentality, platformization, and changes in cultural attitudes to failure. We argue that the normality of failure calls for a better conceptualization of it. What is needed is a clearer thinking about what failure really means, a better understanding of the mechanisms that generate, reproduce, and terminate it as a normal part of life. The essays collected for this symposium offer fresh insights on the analysis of failure. Taking different areas of social life as a focus, they critically examine the failures of large complex socio-technical systems; the purposefully agency of players in systems failure; the failures of governance and metagovernance; new meanings of policy failures; kaleidoscopic failure; network failure and the moral economy of failure. In doing so they we suggest that a sociology of failure needs to be built on socio-historical understandings of failure in different contexts, cultures, and environments.

Background: Facial surgeries require meticulous planning and outcome assessments, where facial analysis plays a critical role. This study introduces a new approach by utilizing three-dimensional (3D) imaging techniques, which are known for their ability to measure facial areas and volumes accurately. The purpose of this study is to introduce and evaluate a free web-based software application designed to take area and volume measurements on 3D models of patient faces. Methods: This study employed the online facial analysis software to conduct ten measurements on 3D models of subjects, including five measurements of area and five measurements of volume. These measurements were then compared with those obtained from the established 3D modeling software called Blender (version 3.2) using the Bland–Altman plot. To ensure accuracy, the intra-rater and inter-rater reliabilities of the web-based software were evaluated using the Intraclass Correlation Coefficient (ICC) method. Additionally, statistical assumptions such as normality and homoscedasticity were rigorously verified before analysis. Results: This study found that the web-based facial analysis software showed high agreement with the 3D software Blender within 95% confidence limits. Moreover, the online application demonstrated excellent intra-rater and inter-rater reliability in most analyses, as indicated by the ICC test. Conclusion: The findings suggest that the free online 3D software is reliable for facial analysis, particularly in measuring areas and volumes. This indicates its potential utility in enhancing surgical planning and evaluation in facial surgeries. This study underscores the software's capability to improve surgical outcomes by integrating precise area and volume measurements into facial surgery planning and assessment processes. [ABSTRACT FROM AUTHOR]

Normality, in the colloquial sense, has historically been considered an aspirational trait, synonymous with ideality. The arithmetic average and, by extension, statistics including linear regression coefficients, have often been used to characterize normality, and are often used as a way to summarize samples and identify outliers. We provide intuition behind the behavior of such statistics in high dimensions, and demonstrate that even for datasets with a relatively low number of dimensions, data start to exhibit a number of peculiarities which become severe as the number of dimensions increases. Whilst our main goal is to familiarize researchers with these peculiarities, we also show that normality can be better characterized with 'typicality', an information theoretic concept relating to entropy. An application of typicality to both synthetic and real-world data concerning political values reveals that in multi-dimensional space, to be 'normal' is actually to be atypical. We briefly explore the ramifications for outlier detection, demonstrating how typicality, in contrast with the popular Mahalanobis distance, represents a viable method for outlier detection. [ABSTRACT FROM AUTHOR]

The overlapping disasters of the Australian 2019–2020 bushfire season and the COVID-19 pandemic, figured alongside the imaginary of projected future disasters, have provided a space of legitimation to experiment with controversial facial recognition technologies (FRTs). Drawing upon interviews conducted with senior Australian government administrators and researchers, I argue that FRTs are being used to respond to the trauma of disaster through its novel mediation and refiguration, tied to discourses of resilience which have been used to justify the expansion of FRT as a means for relief and the provision of aid. This legitimation, however, is challenged by the difficulty FRT encounters in capturing the face in its vital and its mortal malleability. What I term 'the problem of the corpse' serves to bring to light the 'paranoid' gaze of the biometric apparatus, disrupting the aim of using biometric infrastructure to produce a 'new normal' in the ongoing aftermath of disaster. [ABSTRACT FROM AUTHOR]

Currently, there is no solid criterion for judging the quality of the estimators in factor analysis. This paper presents a new evaluation method for exploratory factor analysis that pinpoints an appropriate number of factors along with the best method for factor extraction. The proposed technique consists of two steps: testing the normality of the residuals from the fitted model via the Shapiro-Wilk test and using an empirical quantified index to judge the quality of the factor model. Examples are presented to demonstrate how the method is implemented and to verify its effectiveness. [ABSTRACT FROM AUTHOR]

In this paper, my aim is to develop a phenomenological understanding of discrimination from the perspective of the discriminator. Since early existential phenomenology, the phenomenon of discrimination has received a great deal of attention. While much of this work has focused on the experience of the discriminatee, recent scholarship has begun to reflect on the intentional structures on the side of the discriminator. In a contribution to this trend, I argue that our sense of what is (ab)normal plays a constitutively significant role in the reiteration and reinforcement of harmful discriminatory practices. More specifically, I argue that Husserl's distinction between two forms of normality, namely, concordance-normality [Einstimmigkeit] and optimal-normality [Optimalität], is an important tool for illuminating otherwise overlooked aspects of the discriminator's experience. I achieve this by demonstrating how these two notions of normality play distinct constitutive roles when comparing deliberate acts of discrimination committed with malintent, compared with more habitual and prereflective expectations which are already discriminatory in nucleo. I argue that at the heart of discriminatory practices there is a naïve, normalizing attempt to stabilize concordance at the expense of critical self-reflection, normative revisions, and enriched horizons of expectation. In doing so, this paper provides a novel and important contribution to philosophical discussions surrounding discrimination. [ABSTRACT FROM AUTHOR]

Purpose: One of the most pertinent questions in total knee arthroplasty (TKA) is: what could be considered normal coronal alignment? This study aims to define normal, neutral, deviant and aberrant coronal alignment using large data from a computed tomography (CT)‐scan database and previously published phenotypes. Methods: Coronal alignment parameters from 11,191 knee osteoarthritis (OA) patients were measured based on three dimensional reconstructed CT data using a validated planning software. Based on these measurements, patients' coronal alignment was phenotyped according to the functional knee phenotype concept. These phenotypes represent an alignment variation of the overall hip knee ankle angle (HKA), femoral mechanical angle (FMA) and tibial mechanical angle (TMA). Each phenotype is defined by a specific mean and covers a range of ±1.5° from this mean. Coronal alignment is classified as normal, neutral, deviant and aberrant based on distribution frequency. Mean values and distribution among the phenotypes are presented and compared between two populations (OA patients in this study and non‐OA patients from a previously published study). Results: The arithmetic HKA (aHKA), combined normalised data of FMA and TMA, showed that 36.0% of knees were neutral within ±1 SD from the mean in both angles, 44.3% had either a TMA or a FMA within ±1–2 SD (normally aligned), 15.3% of the patients were deviant within ±2–3 SD and only 4.4% of them had an aberrant alignment (±3–4 SD in 3.4% and >4 SD in 1.0% of the patients respectively). However, combining the normalised data of HKA, FMA and TMA, 15.4% of patients were neutral in all three angles, 39.7% were at least normal, 27.7% had at least one deviant angle and 17.2% had at least one aberrant angle. For HKA, the males exhibited 1° varus and females were neutral. For FMA, the females exhibited 0.7° more valgus in mean than males and grew 1.8° per category (males grew 2.1° per category). For TMA, the males exhibited 1.3° more varus than females and both grew 2.3° and 2.4° (females) per category. Normal coronal alignment was 179.2° ± 2.8–5.6° (males) and 180.5 > ± 2.8–5.6° (females) for HKA, 93.1 > ± 2.1–4.2° (males) and 93.8 > ± 1.8–3.6° (females) for FMA and 86.7 > ± 2.3–4.6° (males) and 88 > ± 2.4–4.8° (females) for TMA. This means HKA 6.4 varus or 4.8° valgus (males) or 5.1° varus to 6.1° valgus was considered normal. For FMA HKA 1.1 varus or 7.3° valgus (males) or 0.2° valgus to 7.4° valgus was considered normal. For TMA HKA 7.9 varus or 1.3° valgus (males) or 6.8° varus to 2.8° valgus was considered normal. Aberrant coronal alignment started from 179.2° ± 8.4° (males) and 180.5 > ± 8.4° (females) for HKA, 93.1 > ± 6.3° (males) 93.8 > ± 5.4° (females) for FMA and 86.7 > ± 6.9° (males) and 88 > ± 7.2° (females) for TMA. This means HKA > 9.2° varus or 7.6° valgus (males) or 7.9° varus to 8.9° valgus was considered aberrant. Conclusion: Definitions of neutrality, normality, deviance as well as aberrance for coronal alignment in TKA were proposed in this study according to their distribution frequencies. This can be seen as an important first step towards a safe transition from the conventional one‐size‐fits‐all to a more personalised coronal alignment target. There should be further definitions combining bony alignment, joint surfaces' morphology, soft tissue laxities and joint kinematics. Level of Evidence: III. [ABSTRACT FROM AUTHOR]

The epistemicist theory aims to explain ignorance due to vagueness by semantic plasticity: the shiftiness of intensions across close possible worlds resulting from shiftiness in usage. This explanation is challenged by the Omniscient Speaker Puzzle (Sennet in Philos Stud 161(2):273–285, 2012). Suppose that an omniscient speaker, Barney, who knows all the facts about usage and how these facts determine the intensions of expressions, cooks up a scheme to stabilise the intension of a normally semantically plastic term like ‘rich’. It seems that ‘rich’ would display all the phenomena associated with vagueness without being semantically plastic, thus making the epistemicist explanation of ignorance due to vagueness insufficient. In this paper, I present a few choice points for epistemicism that arise as a result of the puzzle. [ABSTRACT FROM AUTHOR]

Primarily groundwater is consumed for the drinking and irrigation purpose. However, due to increasing anthropogenic activities, its quality and quantity have substantially declined over time. The focus of this study is to evaluate the pre-monsoonal groundwater quality and its spatial variability for drinking purposes in the Gurugram, Haryana, India. Ground Water quality index (GWQI) developed on the basis of the Geographical Information System is effective in the assessment of groundwater quality and its spatial variability, but it is unable to account for uncertainties related to environmental problems. Thus, a Hybrid Fuzzy-GIS-based Water Quality Index (FGQI) has been proposed for the assessment of groundwater quality. The study conducted factor analysis to identify the prime factors responsible for groundwater contamination and collected pre-monsoonal groundwater samples through primary sampling. The groundwater quality was assessed based on eight hydro geochemical parameters (pH, TDS, Calcium, Chloride, Sulfate, Fluoride, Potassium, and Sodium). The spatial interpolation of each parameter was performed using appropriate techniques, selected based on a normality test. The guidelines of the World Health Organization and Bureau of Indian Standard were incorporated in the development of GWQI and FGQI, respectively. Correlation analysis was performed to determine the best fuzzy overlay technique for FGQI, and the Fuzzy GAMMA technique with gamma equal to 0.9 was selected. Finally, the GWQI and FGQI were classified into three classes: unsuitable, moderate suitable, and suitable using “natural break”. A higher index indicates a higher water quality. The results show that the groundwater in the central part of Gurugram is suitable for drinking, while it is not suitable in the extreme north, south-east, and western regions. In conclusion, this study finds that FGQI effectively assesses the groundwater quality in the region better than GWQI. [ABSTRACT FROM AUTHOR]

This paper studies the problem of simultaneously testing that each of k independent samples come from a normal population. The means and variances of those populations may differ. The proposed procedures are based on the BHEP test and they allow k to increase, which can be even larger than the sample sizes. [ABSTRACT FROM AUTHOR]

Academic textbooks, statistical literature, and publication guidelines provide conflicting, ambiguous and often incomplete answers to the question of how researchers should handle the normality assumption for classical general linear model tests when conducting their analyses. Previous studies have shown that normality violations can impact on type I errors, power, parameter estimates and standard error estimates of classical tests. This paper reviews the arguments in favour and against normality testing, the role of the central limit theorem, types of violations that tests within the general linear model are susceptible to, methods for evaluating the normality assumption, and the paradox that normality tests have low power in small sample sizes where the influence of assumption violations are likely to be most profound. A Monte Carlo simulation study was used to evaluate the power of 18 normality tests across 18 alternative distributions, and the effect of normality deviations on estimates of centrality, scatter and regression coefficients. The results demonstrate that the type of normality test and distribution matters, and that a conditional testing procedure utilising normality tests to select between classic, non-parametric and robust tests should not be used. Instead, an alternative procedure for managing the normality assumption is advised, and demonstrated in the supplementary materials using R code and data that are provided.

Estimating the fruit size is an important factor because it directly influences size-specific yield estimation, which would be useful for pricing in the market. In this paper, it was considered a method of constructing models for estimating the proportion of fruit size grades of citrus using polynomial regression. In order to construct models, curvilinear regressions were performed, utilizing the fruit diameters of a kind of citrus (Citrus junos Sieb. ex Tanaka) in the harvest. The constructed models were validated by comparison with another model, which was constructed using a combination of four datasets obtained from three orchards differing in the number of fruit sets. The estimation model's accuracy (EMA, defined as the sum of the absolute difference between the actual and estimated proportions of each grade) was used for the evaluation of constructed models. The EMAs of 14 models applied to 28 validation data were ranging from 2.0% to 6.1%. In all validations, the proportions of fruit size grade were insignificant at a 5% level by Pearson's chi-square test. Additionally, a comparison of EMAs differing in the number of trees by the constructed models showed that most were within EMA ≤ 10.0% in the case calculated by 10 trees. Validation of five farmers' orchards indicated that the EMA of two was within 10.0%, and the EMA of three was at 11.3 to 12.5%. These results revealed that the constructed models could be applied to orchards for differing numbers of fruit sets. The acceptable accuracy was derived by at least over 10 trees investigated at one time. [ABSTRACT FROM AUTHOR]

What Works Clearinghouse (WWC, 2022) recommends a design-comparable effect size (D-CES; i.e., gAB) to gauge an intervention in single-case experimental design (SCED) studies, or to synthesize findings in meta-analysis. So far, no research has examined gAB's performance under non-normal distributions. This study expanded Pustejovsky et al. (2014) to investigate the impact of data distributions, number of cases (m), number of measurements (N), within-case reliability or intra-class correlation (ρ), ratio of variance components (λ), and autocorrelation (ϕ) on gAB in multiple-baseline (MB) design. The performance of gAB was assessed by relative bias (RB), relative bias of variance (RBV), MSE, and coverage rate of 95% CIs (CR). Findings revealed that gAB was unbiased even under non-normal distributions. gAB's variance was generally overestimated, and its 95% CI was over-covered, especially when distributions were normal or nearly normal combined with small m and N. Large imprecision of gAB occurred when m was small and ρ was large. According to the ANOVA results, data distributions contributed to approximately 49% of variance in RB and 25% of variance in both RBV and CR. m and ρ each contributed to 34% of variance in MSE. We recommend gAB for MB studies and meta-analysis with N ≥ 16 and when either (1) data distributions are normal or nearly normal, m = 6, and ρ = 0.6 or 0.8, or (2) data distributions are mildly or moderately non-normal, m ≥ 4, and ρ = 0.2, 0.4, or 0.6. The paper concludes with a discussion of gAB's applicability and design-comparability, and sound reporting practices of ES indices. [ABSTRACT FROM AUTHOR]

This paper presents the findings of research aimed at verifying Peter H. Kahn Jr.'s thesis on environmental generational amnesia. It was conducted in 2022 in a local community affected by environmental contamination. The settlement in Szopienice contained a non-ferrous metals plant, which operated for several decades and had negative impacts on the surrounding area. The consequences were particularly severe for local children, who suffered from lead poisoning. To verify Kahn's thesis, the researchers conducted 17 in-depth interviews with the inhabitants of Szopienice.During analysis of the interviews, the authors were mainly interested in the strategies the interviewees used to reduce the cognitive dissonance between the state of environmental contamination in Szopienice, as they remember it, and today's knowledge of the magnitude of environmental contamination. The theoretical framework for this analysis was the study of collective memory, with particular emphasis on exploring the concept of normality and with reference to the concepts of Alfred Schütz. [ABSTRACT FROM AUTHOR]

Previous studies show that processes related to traditional pretests to prove the perfect fulfillment of assumptions in comparison means tests lead to severe alterations in the overall Type I error probability and power. These problems seem to be overcome when pretests based on an equivalence approach are used. The paper proposes a lack of fit tests based on equivalence to pretest normality on homoscedastic samples with measurable departures from normality. The Type I error probability and power produced by this equivalence pretest are compared with two traditional goodness of fit pretests and with the direct use of the t-Student and Wilcoxon test of means comparison. Furthermore, since the irrelevance limit for the lack of fit test is an arbitrary value, we propose a non-subjective methodology to find it. Results show that this proposed equivalence test controls the overall Type I Error Probability and produces adequate power; therefore, its use is recommended. [ABSTRACT FROM AUTHOR]

Copyright of Revista ADM is the property of Asociacion Dental Mexicana 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.)

Let K be a field and K[x1, x2] the polynomial ring in two variables over K with each xi of degree 1. Let L be the generalized mixed product ideal induced by a monomial ideal I ⊂ K[x1, x2], where the ideals substituting the monomials in I are squarefree Veronese ideals. In this paper, we study the integral closure of L, and the normality of R(L), the Rees algebra of L. Furthermore, we give a geometric description of the integral closure of R(L). [ABSTRACT FROM AUTHOR]

In this article, I argue that psychopathology ubiquitously pervades individual and social life. As Freud wrote, each of us finds some way of distorting reality, and as Laing contended, human beings have an almost unlimited capacity for self-deception. History is a chronicle of fantasies, mirages, distortions, and metaphysical consolations believed as apodictic reality, and the bizarre magico-salvific stratagems people adopted to ward off disease, catastrophe, and death. And yet many (even psychoanalysts) maintain the notion (or fantasy) that we perceive reality clearly and sanely. I contend, on the contrary, that we have no epistemologically foolproof way of discerning our own deceptions and defects, and that we find all manner of ingenious excuses not to see ourselves. [ABSTRACT FROM AUTHOR]

Copyright of Zeitschrift für Internationale Beziehungen (ZIB) is the property of Nomos Verlagsgesellschaft mbH & Co. KG 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 objective of this paper is to assess the distribution of the Partial Autocorrelation Function (PACF), both theoretically and empirically, emphasizing its crucial role in modeling and forecasting time series data. Additionally, it evaluates the deviation of the sum of sample PACF from normality: identifying the lag at which departure occurs. Our investigation reveals that the sum of the sample PACF, and consequently its components, diverges from the expected normal distribution beyond a certain lag. This observation challenges conventional assumptions in time series modeling and forecasting, indicating a necessity for reassessment of existing methodologies. Through our analysis, we illustrate the practical implications of our findings using real-world scenarios, highlighting their significance in unraveling complex data patterns. This study delves into 185 years of monthly Bank of England Rate data, utilizing this extensive dataset to conduct an empirical analysis. Furthermore, our research paves the way for future exploration, offering insights into the complexities and potential revisions in time series analysis, modeling, and forecasting.