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The analysis of survey data is a frequently arising issue in clinical trials, particularly when capturing quantities which are difficult to measure. Typical examples are questionnaires about patient's well‐being, pain, or consent to an intervention. In these, data is captured on a discrete scale containing only a limited number of possible answers, from which the respondent has to pick the answer which fits best his/her personal opinion. This data is generally located on an ordinal scale as answers can usually be arranged in an ascending order, for example, "bad", "neutral", "good" for well‐being. Since responses are usually stored numerically for data processing purposes, analysis of survey data using ordinary linear regression models are commonly applied. However, assumptions of these models are often not met as linear regression requires a constant variability of the response variable and can yield predictions out of the range of response categories. By using linear models, one only gains insights about the mean response which may affect representativeness. In contrast, ordinal regression models can provide probability estimates for all response categories and yield information about the full response scale beyond the mean. In this work, we provide a concise overview of the fundamentals of latent variable based ordinal models, applications to a real data set, and outline the use of state‐of‐the‐art‐software for this purpose. Moreover, we discuss strengths, limitations and typical pitfalls. This is a companion work to a current vignette‐based structured interview study in pediatric anesthesia. [ABSTRACT FROM AUTHOR]

Ordinal data such as Likert items, ratings or generic ordered variables are widespread in psychology. These variables are usually analysed using metric models (e.g., standard linear regression) with important drawbacks in terms of statistical inference (reduced power and increased type‐1 error) and prediction. One possible reason for not using ordinal regression models could be difficulty in understanding parameters or conducting a power analysis. The tutorial aims to present ordinal regression models using a simulation‐based approach. Firstly, we introduced the general model highlighting crucial components and assumptions. Then, we explained how to interpret parameters for a logit and probit model. Then we proposed two ways for simulating data as a function of predictors showing a 2 × 2 interaction with categorical predictors and the interaction between a numeric and categorical predictor. Finally, we showed an example of power analysis using simulations that can be easily extended to complex models with multiple predictors. The tutorial is supported by a collection of custom R functions developed to simulate and understand ordinal regression models. The code to reproduce the proposed simulation, the custom R functions and additional examples of ordinal regression models can be found on the online Open Science Framework repository (https://osf.io/93h5j). [ABSTRACT FROM AUTHOR]

Many organisms leave evidence of their former occurrence, such as scat, abandoned burrows, middens, ancient eDNA or fossils, which indicate areas from which a species has since disappeared. However, combining this evidence with contemporary occurrences within a single modeling framework remains challenging. Traditional binary species‐distribution modeling reduces occurrence to two temporally coarse states (present/absent), so thus cannot leverage the information inherent in temporal sequences of evidence of past occurrence. In contrast, ordinal modeling can use the natural time‐varying order of states (e.g. never occupied versus previously occupied versus currently occupied) to provide greater insights into range shifts. We demonstrate the power of ordinal modeling for identifying the major influences of biogeographic and climatic variables on current and past occupancy of the American pika Ochotona princeps, a climate‐sensitive mammal. Sampling over five years across the species' southernmost, warm‐edge range limit, we tested the effects of these variables at 570 habitat patches where occurrence was classified either as binary or ordinal. The two analyses produced different top models and predictors – ordinal modeling highlighted chronic cold as the most‐important predictor of occurrence, whereas binary modeling indicated primacy of average summer‐long temperatures. Colder wintertime temperatures were associated in ordinal models with higher likelihood of occurrence, which we hypothesize reflect longer retention of insulative and meltwater‐provisioning snowpacks. Our binary results mirrored those of other past pika investigations employing binary analysis, wherein warmer temperatures decrease likelihood of occurrence. Because both ordinal‐ and binary‐analysis top models included climatic and biogeographic factors, results constitute important considerations for climate‐adaptation planning. Cross‐time evidences of species occurrences remain underutilized for assessing responses to climate change. Compared to multi‐state occupancy modeling, which presumes all states occur in the same time period, ordinal models enable use of historical evidence of species' occurrence to identify factors driving species' distributions more finely across time. [ABSTRACT FROM AUTHOR]

There are numerous methods in the literature for Direction-of-Arrival (DOA) estimation, including both classical and machine learning-based approaches that jointly estimate the Number of Sources (NOS) and DOA. However, most of these methods do not fully leverage the potential synergies between these two tasks, which could yield valuable shared information. To address this limitation, in this article, we present a multi-task Convolutional Neural Network (CNN) capable of simultaneously estimating both the NOS and the DOA of the signal. Through experiments on simulated data, we demonstrate that our proposed model surpasses the performance of state-of-the-art methods, especially in challenging environments characterized by high noise levels and dynamic conditions. [ABSTRACT FROM AUTHOR]

This study proposes an interpretable neural network-based nonproportional odds model (N3POM) for ordinal regression. N3POM is different from conventional approaches to ordinal regression with nonproportional models in several ways: (a) N3POM is defined for both continuous and discrete responses, whereas standard methods typically treat the continuous variables as if they were discrete, (b) instead of estimating response-dependent finite-dimensional coefficients of linear models from discrete responses as is done in conventional approaches, we train a nonlinear neural network to serve as a coefficient function. Thanks to the neural network, N3POM offers flexibility while preserving the interpretability of conventional ordinal regression. We establish a sufficient condition under which the predicted conditional cumulative probability locally satisfies the monotonicity constraint over a user-specified region in the covariate space. Additionally, we provide a monotonicity-preserving stochastic (MPS) algorithm for effectively training the neural network. We apply N3POM to several real-world datasets. for this article are available online. [ABSTRACT FROM AUTHOR]

Human dimensions research is valuable to managing human‐wildlife interactions, especially in urban environments where such interactions are common. Survey data, which commonly contain Likert scales and questions, are useful in this field; however, these data can be difficult to analyze with formal modeling approaches. We demonstrate one approach, based on hierarchical Bayesian ordinal regression, to evaluate human‐coyote relationships in Rhode Island, USA. We implemented a survey to collect demographic and sociocultural characteristics of Rhode Island residents and information related to their knowledge of and experiences with coyotes. Our objectives were to assess how these characteristics affected respondents' valuation of and interactions (sightings and incidents) with coyotes. We analyzed 980 surveys from October to December 2020. We found that respondents who had fear of coyotes or experienced an incident between an owned animal and coyote, had the lowest valuation of coyotes. The same demographic of respondents also reported the highest sightings of and incidents with coyote. These results indicate that fearful residents, in addition to pet and livestock owners, are priority targets for disseminating information or programming about coyotes. Our analyses and findings demonstrate how Bayesian ordinal regression can provide clear and appropriate inference from survey data on how groups of people vary in their relationship with wildlife. These results are important in effectively and efficiently allocating resources towards mitigation, education, and management of human‐wildlife interactions. [ABSTRACT FROM AUTHOR]

Background: Anaemia is a serious global public health problem with high prevalence (>40%) in children particularly in low- and middle-income countries including Namibia with a current 46.1% prevalence rate. Aim: This study was aimed at examining the sociodemographic factors influencing the occurrence of childhood anaemia levels in Namibia. Method: A multivariate ordinal regression model was applied to statistically identify potential sociodemographic factors associated with anaemia levels among children under-5 years old using data collected from the 2013 NDHS. Results: The odds of having mild anaemia level was lower for sociodemographic characteristics such as mother's age, total children ever born, health insurance coverage, child's residency, child's age and main language spoken at home. The odds of having moderate anaemia level was higher for characteristics such as mother's age, place of residence, highest education level and child's diarrhoea status, while it was lower for characteristics such as age of head of household, total children ever born, health insurance coverage and sex of child. Similarly, the odds of having severe anaemia level was higher for characteristics such as region, place of residence, highest education level, number of household members, wealth index, health insurance coverage, child's residency and child's diarrhoea status, while it was lower for characteristics such as total children ever born and sex of child. Conclusion: It is therefore recommended that the policies and practices concerning anaemia diagnosis, treatment and prevention in the country be substantially revised by policy-makers, starting with the known prevalent causes and identified sociodemographic factors from this study. [ABSTRACT FROM AUTHOR]

In the field of autofocus for optical systems, although passive focusing methods are widely used due to their cost-effectiveness, fixed focusing windows and evaluation functions in certain scenarios can still lead to focusing failures. Additionally, the lack of datasets limits the extensive research of deep learning methods. In this work, we propose a neural network autofocus method with the capability of dynamically selecting the region of interest (ROI). Our main work is as follows: first, we construct a dataset for automatic focusing of grayscale images; second, we transform the autofocus issue into an ordinal regression problem and propose two focusing strategies: full-stack search and single-frame prediction; and third, we construct a MobileViT network with a linear self-attention mechanism to achieve automatic focusing on dynamic regions of interest. The effectiveness of the proposed focusing method is verified through experiments, and the results show that the focusing MAE of the full-stack search can be as low as 0.094, with a focusing time of 27.8 ms, and the focusing MAE of the single-frame prediction can be as low as 0.142, with a focusing time of 27.5 ms. [ABSTRACT FROM AUTHOR]

This article aims to analyze locals' attitudes toward tax allocation in the tourism sphere in the Republic of Uzbekistan. The analysis is based on the survey data taken from 505 respondents. We explored the impact of the frequency of travelling, age, the importance of travelling, and total family income on people's attitudes toward public financing of touri sm using the ordinal logistic regression. The results show that increasing the frequency of travelling of each respondent increases the odds of people's approval of financing tourism from taxes by two times. At the same time, surprisingly, the increase in family income reduces the probability of approving tourism's budget financing. In brief, the research contributes to the state - of-the-art literature by analyzing factors affecting people's opinions on tourism financing from tax inflow, which might play a crucial role in the development of tourism development strategies. [ABSTRACT FROM AUTHOR]

Vine pair-copula constructions exist for a mix of continuous and ordinal variables. In some steps, this can involve estimating a bivariate copula for a pair of mixed continuous-ordinal variables. To assess the adequacy of copula fits for such a pair, diagnostic and visualization methods based on normal score plots and conditional Q–Q plots are proposed. The former uses a latent continuous variable for the ordinal variable. The methods are applied to data generated from some existing probability models for a mixed continuous-ordinal variable pair, and for such models, Kullback-Leibler divergence is used to assess whether simple parametric copula families can provide adequate fits. The effectiveness of the proposed visualization and diagnostic methods is illustrated on a dataset.

Label noise poses a significant challenge to supervised learning algorithms. Extensive research has been conducted on classification and regression tasks, but label noise filtering methods specifically designed for ordinal regression are lacking. In this paper, we propose a set of ordinal label noise filtering frameworks by theoretically exploring the generalization error bound in noisy environments. Besides, we present a robust label noise estimation method voted by inter-class distance. It takes into account the nonuniformity of ordinal labels and the reliability of the base model. This estimator is integrated into our framework in the proposed Inter-Class Distance-based Filtering (ICDF) algorithm. We empirically demonstrate the effectiveness of ICDF in identifying label noise and achieving improved generalization performance. Our experiments conducted on benchmark and real age estimation datasets show the superiority of ICDF over the existing filters in ordinal label noise cleaning. [ABSTRACT FROM AUTHOR]

Previous work has found mentally simulating events of helping others can enhance prosocial intentions. However, to date, this "prosocial simulation effect" (PSE) has only been demonstrated in North America. We provide the first preregistered replication of PSE outside of North America in a New Zealand sample, following existing protocols (Experiment 1: N = 40) and with modifications to rule out an additional confound (Experiment 2: N = 40). Moreover, given evidence that metric models are problematic for assessing ordinal data, we conducted cumulative link model (CLM)-based analyses. Both experiments provide statistically robust support for the PSE outside of North America, lending greater credence to this effect. We also show that, relative to CLM-based analyses, metric models can underestimate effects in ordinal data, yielding inconsistent results across near-identical experimental designs. We consider this issue against the backdrop of the replication crisis and recommend the use of CLM-based analyses in all research reliant on ordinal scales. General Audience Summary: Can imagination increase intentions to help others? Recent evidence suggests it can act to increase one's willingness to help a stranger in need. Thus, imagination could be a key tool for increasing prosocial intentions in people across the globe. However, existing studies demonstrating this effect are primarily from the United States, so it remains unknown whether imagination has the same effect on prosocial intentions across nations and cultures. This is critical because, even within Western countries, baseline levels of prosociality vary, with the United States being at one extreme. We conducted the first preregistered examination of this effect outside of North America with participants from New Zealand. Furthermore, we used statistical approaches more appropriate for the type of data collected in these studies. Across two experiments, we demonstrate that the enhancing effect of imagination on prosocial intentions is statistically robust and evident cross-nationally. Future work is needed to translate these findings to real-world contexts to increase cooperation as modern society deals with significant global challenges. [ABSTRACT FROM AUTHOR]

Semi-supervised ordinal regression (S2OR) has been recognized as a valuable technique to improve the performance of the ordinal regression (OR) model by leveraging available unlabeled samples. The balancing constraint is a useful approach for semi-supervised algorithms, as it can prevent the trivial solution of classifying a large number of unlabeled examples into a few classes. However, rapid training of the S2OR model with balancing constraints is still an open problem due to the difficulty in formulating and solving the corresponding optimization objective. To tackle this issue, we propose a novel form of balancing constraints and extend the traditional convex–concave procedure (CCCP) approach to solve our objective function. Additionally, we transform the convex inner loop (CIL) problem generated by the CCCP approach into a quadratic problem that resembles support vector machine, where multiple equality constraints are treated as virtual samples. As a result, we can utilize the existing fast solver to efficiently solve the CIL problem. Experimental results conducted on several benchmark and real-world datasets not only validate the effectiveness of our proposed algorithm but also demonstrate its superior performance compared to other supervised and semi-supervised algorithms [ABSTRACT FROM AUTHOR]

Emotional understanding and expression plays a critical role in social interaction. To analyze children's emotional interaction automatically, this study focuses on developing a novel network architecture and a reliable algorithm for expression intensity estimation to measure children's facial expression responses to emotional stimuli. The facial expression intensity variation provides temporal dynamic information of facial behavior, which is critical to interpreting the meaning of expression. In order to avoid laborious manual annotations for expression intensity, existing unsupervised methods attempt to identify relative intensity using ordinal information within a facial expression sequence; however, they fail to estimate absolute intensity accurately. Moreover, appropriate features are needed to represent the continuous appearance changes caused by expression intensity to improve the model's ability to distinguish subtle differences in expression. This study therefore presents a novel semi-supervised method to estimate expression intensity using salient deep learning features. First, the facial expression is represented by the difference response of the convolutional neural network backbone between the target expression and its responding neutral expression, with the goal of suppressing the effects of expression-unrelated features on expression intensity estimation. Then, the pairwise data constructed with ordinal information is input into a Siamese network with a combined hinge loss that guides learning the relative intensity on unlabeled pairwise frames, the absolute intensity of a few labeled key frames, and the intensity range of most unlabeled frames. The average pearson correlation coefficient, intraclass correlation coefficient, and mean absolute error are 0.7683, 0.7405, and 0.1698 on the extended Cohn-Kanade dataset (CK+), and 0.7804, 0.6684, and 0.1864 on the Binghamton University 4D Facial Expression Dataset using the proposed method, results that are superior to the state of the art. The cross-dataset experiment indicates that the proposed method is promising for the analysis of children's emotional interactions. [ABSTRACT FROM AUTHOR]

At present, there are only a very few works done on multi-task ordinal regression(OR). These works assume that different tasks contribute equally to the overall model. However, in practice, different tasks may have distinct contributions to the overall model. This paper proposed a novel multi-task ordinal regression method with task weight discovery method. Firstly, it presented a support-vector-machine-based multi-task OR model. By sharing the classifier parameters, the classification information could be transferred among different tasks. Secondly, considering that different tasks had different contributions to the overall model, it assigned each task a weight, which would be automatically optimized during the learning process. Finally, it adopted a heuristic framework to construct the multi-task OR model and optimized the task weights alternately. The experimental results show that the proposed method achieves 3.8% to 12.3% improvements in terms of MZE and 4.1% to 11% improvements in terms of MAE, compared to the existing multi-task OR methods. Considering the different weights of each task, and by automatically optimizing these weights, the proposed method reduces the classification error of the multi-task ordinal regression model. [ABSTRACT FROM AUTHOR]

Abstract Human dimensions research is valuable to managing human‐wildlife interactions, especially in urban environments where such interactions are common. Survey data, which commonly contain Likert scales and questions, are useful in this field; however, these data can be difficult to analyze with formal modeling approaches. We demonstrate one approach, based on hierarchical Bayesian ordinal regression, to evaluate human‐coyote relationships in Rhode Island, USA. We implemented a survey to collect demographic and sociocultural characteristics of Rhode Island residents and information related to their knowledge of and experiences with coyotes. Our objectives were to assess how these characteristics affected respondents' valuation of and interactions (sightings and incidents) with coyotes. We analyzed 980 surveys from October to December 2020. We found that respondents who had fear of coyotes or experienced an incident between an owned animal and coyote, had the lowest valuation of coyotes. The same demographic of respondents also reported the highest sightings of and incidents with coyote. These results indicate that fearful residents, in addition to pet and livestock owners, are priority targets for disseminating information or programming about coyotes. Our analyses and findings demonstrate how Bayesian ordinal regression can provide clear and appropriate inference from survey data on how groups of people vary in their relationship with wildlife. These results are important in effectively and efficiently allocating resources towards mitigation, education, and management of human‐wildlife interactions.

In the literature several methods have been developed to model ordinal data while considering their natural ordering. However, this study sought to compare two possible link functions for the multilevel ordinal regression using males’ ratings of the police forces in Uganda as an outcome variable. Variables were obtained from the UNGBS database (Uganda National Governance Baseline Survey). The highest proportion of males rated the police as good (40.9%) followed by fair (24.96%), poor (19.1%), and lastly very good (15.1%). The multilevel ordered logistic regression model with both individual and contextual variables had the lowest AIC compared to other models, fitting the data best. All the likelihood ratio test results indicated that there was significant variation in males' ratings of the police forces across districts. Hence, males from the same district were significantly more similar compared to males from another districts. Researchers using data collected by applying multi-stage sampling or any form of nesting should consider multilevel or mixed-effects models.

Building change detection (BCD) holds significant value in the context of monitoring land use, whereas building damage assessment (BDA) plays a crucial role in expediting humanitarian rescue efforts post-disasters. To address these needs, we propose the bitemporal attention module (BAM) as an innovative cross-attention mechanism aimed at effectively capturing spatio-temporal semantic relations between a pair of bitemporal remote sensing images. Within BAM, a shifted windowing scheme has been implemented to confine the scope of the cross-attention mechanism to a specific range, not only excluding remote and irrelevant information but also contributing to computational efficiency. Moreover, existing methods for BDA often overlook the inherent order of ordinal labels, treating the BDA task simplistically as a multiclass semantic segmentation problem. Recognizing the vital significance of ordinal relationships, we approach the BDA task as an ordinal regression problem. To address this, we introduce a rank-consistent ordinal regression loss function to train our proposed change detection network, bitemporal attention transformer. Our method achieves state-of-the-art accuracy on two BCD datasets (LEVIR-CD+ and S2Looking), as well as the largest BDA dataset (xBD).

In spite of the established literature-based evidence regarding the consequences of large truck accidents, limited body of research has been done on the characteristics and risk factors of such road accidents in Iraqi cities and governorates. According to national statistics, there has been a steady increase in the number of trucks and truck-related crashes over the past ten years. This paper aims to investigate the characteristics and risk factors associated with accidents involving large trucks in Al-Najaf governorate. A sample of 400 truck drivers were randomly selected and interviewed to collect the needed accident data. Four generalized linear models have been built; ordinal regression model for total injuries, binary logistic model for fatal accident occurrence, multinomial logit model for accident frequency, and ordinal regression model for accident cost. The analysis results revealed several influential predictors including truck driver age, education level, type of collision, truck speed, truck type, and street lighting condition. The obtained findings should be enlightening and helpful for government organizations looking to promote safety measures for sustainable freight truck transport.

There are numerous methods in the literature for Direction-of-Arrival (DOA) estimation, including both classical and machine learning-based approaches that jointly estimate the Number of Sources (NOS) and DOA. However, most of these methods do not fully leverage the potential synergies between these two tasks, which could yield valuable shared information. To address this limitation, in this article, we present a multi-task Convolutional Neural Network (CNN) capable of simultaneously estimating both the NOS and the DOA of the signal. Through experiments on simulated data, we demonstrate that our proposed model surpasses the performance of state-of-the-art methods, especially in challenging environments characterized by high noise levels and dynamic conditions.

Context. The strategic management of pigs raised in an intensive system has been conducted, in general, according to the average weight of piglets after weaning. Different models using morphometric parameters to predict the probability of any of the three weight classes (light, medium, and heavy) occurring post-weaning present themselves as an alternative to help the producer conduct these strategic managements. Aims. This study aimed to evaluate the development of suckling piglets by using morphometric parameters. Methods. A thousand simple samples were extracted at random from 268 piglets for the training data, which represent 70% of the original data set (384 piglets). The remaining 116 piglets (test data) were excluded from the analysis so as to verify the performance of the prediction (probability of each weight class to occur). Afterwards, the results were compared with the real weight class of the piglet at weaning. The variables in this research were birth weight (PWB), lactation length (Lac), and morphometric parameters of body length (BL), heart girth (HG), body mass index (BMI), ponderal index (PI), surface:mass ratio (SM), and parity order (PO). Different models were developed to predict the probability of any of the three weight classes (light, <3.967 kg; medium, 3.967-5.095 kg; and heavy, >5.095 kg) occurring at piglet weaning on the basis of their parameters 1 day postpartum. An adjustment of the ordinal regression was proposed to predict the weight classifications. Key results. The model with a significant effect of the Lac variables was selected. Conclusions and implications. One day after birth, light-weight and heavy-weight piglets, regardless of their morphometry, have a high likelihood of remaining in the same weight class at weaning. However, this does not apply to medium-sized piglets with diverse morphometry. [ABSTRACT FROM AUTHOR]

Facial expression intensity estimation has promising applications in health care and affective computing, such as monitoring patients’ pain feelings. However, labeling facial expression intensity is a specialized and time-consuming task. Ordinal regression (OR)-based methods address this issue to some extent by estimating the relative intensity but failing to estimate the absolute intensity due to lack of exploring useful information from noisy labels caused by manual and automatic labeling biases. Inspired by label distribution learning (LDL) to resist the noisy labels, this paper introduces the label-distribution-learning-enhanced OR (LDL-EOR) approach for facial expression intensity estimation. This design aims to utilize LDL to improve the accuracy of absolute intensity estimation while keeping the cost of manual labeling low. The label distribution is converted into a continuous intensity value by calculating the mathematical expectation, which makes the prediction results meet both relative and absolute intensity constraints. Ensuring the feasibility of LDL-EOR in different supervised settings, this paper presents a unified label distribution generation framework to automatically relabel training data frame by frame. The generated soft labels are used to supervise the LDL-EOR model and enhance its robustness to the noise existing in the original labels. Numerous experiments were conducted on three public expression datasets (CK+, BU-4DFE, and PAIN) to validate the superiority of LDL-EOR relative to other state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Silent pauses are a natural part of speech production and have consequences for speech perception. However, studies have shown mixed results regarding whether listeners process pauses in native and non-native speech similarly or differently. A possible explanation for these mixed results is that perceptual consequences of pauses differ depending on the type of processing that listeners engage in: a focus on the content/meaning of the speech versus style/form of the speech. Thus, the present study examines the effect of silent pauses of listeners' perception of native and non-native speech in two different tasks: the perceived credibility and the perceived fluency of the speech. Specifically, we ask whether characteristics of silent pauses influence listeners' perception differently for native versus non-native speech, and whether this pattern differs when listeners are rating the credibility versus the fluency of the speech. We find that while native speakers are rated as more fluent than non-native speakers, there is no evidence that native speakers are rated as more credible. Our findings suggest that the way a non-native accent and disfluency together impact speech perception differs depending on the type of processing that listeners are engaged in when listening to the speech. [ABSTRACT FROM AUTHOR]

This paper presents a class of regression models with cumulative logistic functions that are chiefly designed to analyse spatially dependent ordinal data. In contrast to previous works, the proposed model requires neither the sites to be regularly spaced nor the assumption of an underlying continuous variable. It belongs to a more general class of Markov random field models, and can be considered an extension of the ordinal regression model with the proportional odds link function. Our proposed model allows practitioners to interpret the model parameters using odds ratios. Apart from the theoretical developments, this work also highlights the practical aspects of model fitting, including parameterisation, selection of neighbourhood, and calculation of standard errors. Simulation studies with regularly and irregularly spaced sites were conducted. Modelling strategies including pseudo-likelihood methods were found to be useful in both settings. The proposed model and the non-spatial counterpart were applied to the daily air quality index measured in the United Kingdom. The results indicate the presence of spatial effects and the incorporation of spatial effects led to better model performance in terms of various goodness-of-fit measures. [ABSTRACT FROM AUTHOR]

In spite of the established literature-based evidence regarding the consequences of large truck accidents, limited body of research has been done on the characteristics and risk factors of such road accidents in Iraqi cities and governorates. According to national statistics, there has been a steady increase in the number of trucks and truck-related crashes over the past ten years. This paper aims to investigate the characteristics and risk factors associated with accidents involving large trucks in Al-Najaf governorate. A sample of 400 truck drivers were randomly selected and interviewed to collect the needed accident data. Four generalized linear models have been built; ordinal regression model for total injuries, binary logistic model for fatal accident occurrence, multinomial logit model for accident frequency, and ordinal regression model for accident cost. The analysis results revealed several influential predictors including truck driver age, education level, type of collision, truck speed, truck type, and street lighting condition. The obtained findings should be enlightening and helpful for government organizations looking to promote safety measures for sustainable freight truck transport. [ABSTRACT FROM AUTHOR]

Within the framework of the life trajectory paradigm, factors of poor health in older age may include ethnicity, as well as migration history of an individual. Estonia, with a large share of the Russian population, is a good example to analyze the impact of migration and changes in the ethnic environment on health throughout the life course. The purpose of the study is to assess differences in self–rated health of the older population (50+) living in Estonia and Russia, and identify reasons for these differences. The empirical basis of the study was data of the SHARE survey conducted in Estonia in 2010-2011 and the SAGE survey conducted in Russia in 2007-2010. The sample includes urban population aged 50+ in private households: 2,655 Estonians living in Estonia, 1,478 Russians living in Estonia, and 2,446 Russians living in Russia. The tested ordinal regression models show that the native-born in Estonia have a 39% higher chance of rating their health as good compared to Russians in Estonia, which is associated with differences in educational level in the population aged 50-64, while in the population aged 65+ it is associated with differences in living standards between the native-born and immigrants of the first and subsequent generations. At the same time, Russians from Russia are 70% (population aged 50-64) or 60% (population 65+) less likely to rate their health as good, which is related to the lifestyle and loneliness. Russians aged 65+ in Estonia who moved to the country at the age of 25+ have the same chances. The study negatives the healthy migrant effect identified in young immigrants, and also indicates health behavior and poor quality of social connections as possible reasons for poor health of the older Russian residents. [ABSTRACT FROM AUTHOR]

Within the framework of the life trajectory paradigm, factors of poor health in older age may include ethnicity, as well as migration history of an individual. Estonia, with a large share of the Russian population, is a good example to analyze the impact of migration and changes in the ethnic environment on health throughout the life course. The purpose of the study is to assess differences in self–rated health of the older population (50+) living in Estonia and Russia, and identify reasons for these differences. The empirical basis of the study was data of the SHARE survey conducted in Estonia in 2010-2011 and the SAGE survey conducted in Russia in 2007-2010. The sample includes urban population aged 50+ in private households: 2,655 Estonians living in Estonia, 1,478 Russians living in Estonia, and 2,446 Russians living in Russia. The tested ordinal regression models show that the native-born in Estonia have a 39% higher chance of rating their health as good compared to Russians in Estonia, which is associated with differences in educational level in the population aged 50-64, while in the population aged 65+ it is associated with differences in living standards between the native-born and immigrants of the first and subsequent generations. At the same time, Russians from Russia are 70% (population aged 50-64) or 60% (population 65+) less likely to rate their health as good, which is related to the lifestyle and loneliness. Russians aged 65+ in Estonia who moved to the country at the age of 25+ have the same chances. The study negatives the healthy migrant effect identified in young immigrants, and also indicates health behavior and poor quality of social connections as possible reasons for poor health of the older Russian residents. [ABSTRACT FROM AUTHOR]

Background: Addressing the evolving dynamics of family structures, the parent-employment conflict (PEC) emerges as a significant conundrum of the current century. This article seeks to delve into the intricate factors influencing PEC among employed parents in Tehran, Iran. Methods: This study employed a stratified random sampling method across various regions within Tehran province, in 2017. A structured questionnaire, encompassing demographic details, the history of fertility, and attitudes towards childbearing, alongside the delineation of conflicts between professional responsibilities and parental duties used to collect 449 employed parents. Since PEC was an ordinal variable with three categories of low (6-12), middle (12-18), and high (18-30), an ordinal regression method was applied to some selected covariates. Results: The findings suggest that women comparing to men, those with “secondary and high school” and “diploma” comparing to “master degree and PhD” educational levels, governmental employees comparing to free-lance employees, and those employees working 45 hours and more comparing to employees working less than 40 hours in a week had higher PEC. Conclusion: In general, unless socialization norms and policymakers’ views adopt social realities, PEC will not reduce. Policymakers should pay more attention to institutionalize of social supports and implement family supportive policies.

Relatively little is known about the relationship between socio-demographic factors and the chemical exposome in adolescent populations. This knowledge gap hampers global efforts to meet certain UN sustainability goals. The present work addresses this problem in Swedish adolescents by discerning patterns within the chemical exposome and identify demographic groups susceptible to heightened exposures.Enlisting the Riksmaten Adolescents 2016–17 (RMA) study population (N = 1082) in human-biomonitoring, and using proportional odds ordinal logistic regression models, we examined the associations between concentrations of a diverse array of substances (N = 63) with the determinants: gender, age, participant/maternal birth country income per capita level, parental education levels, and geographic place of living (longitude/latitude).Participant/maternal birth country exhibited a significant association with the concentrations of 46 substances, followed by gender (N = 41), and longitude (N = 37). Notably, individuals born in high-income countries by high-income country mothers demonstrated substantially higher estimated adjusted means (EAM) concentrations of polychlorinated biphenyls (PCBs), brominated flame retardants (BFRs) and per- and polyfluoroalkyl substances (PFASs) compared to those born in low-income countries by low-income country mothers. A reverse trend was observed for cobalt (Co), cadmium (Cd), lead (Pb), aluminium (Al), chlorinated pesticides, and phthalate metabolites. Males exhibited higher EAM concentrations of chromium (Cr), mercury (Hg), Pb, PCBs, chlorinated pesticides, BFRs and PFASs than females. In contrast, females displayed higher EAM concentrations of Mn, Co, Cd and metabolites of phthalates and phosphorous flame retardants, and phenolic substances. Geographical disparities, indicative of north-to-south or west-to-east substance concentrations gradients, were identified in Sweden. Only a limited number of lifestyle, physiological and dietary factors were identified as possible drivers of demographic inequalities for specific substances.This research underscores birth country, gender, and geographical disparities as contributors to exposure differences among Swedish adolescents. Identifying underlying drivers is crucial to addressing societal inequalities associated with chemical exposure and aligning with UN sustainability goals.

Abstract Background Oral health knowledge forms part of oral health literacy that enables individuals to inform appropriate oral health decisions and actions. Oral health-related quality of life (OHRQoL) characterizes self-perception of well-being influenced by oral health. This study aimed to examine the relationship between oral health knowledge and OHRQoL. Methods A random sample of 19-to-24-year-old first-year undergraduate students (n = 372) in Minnesota, United States of America was used. Each student was assessed with an online survey using the Comprehensive Measure of Oral Health Knowledge (CMOHK) and the OHRQoL items of the World Health Organization (WHO) Oral Health Questionnaire for Adults. Relationships between OHRQoL parameters and CMOHK together with other covariates were assessed using ordinal regression models. Associations between OHRQoL parameters were examined with the Kendall’s tau-b method. Results Dry mouth (45%) was the most reported OHRQoL issue. The respondents showing good oral health knowledge were less likely to experience speech or pronunciation difficulty (β=-1.12, p = 0.0006), interrupted sleep (β=-1.43, p = 0.0040), taking days off (β=-1.71, p = 0.0054), difficulty doing usual activities (β=-2.37, p = 0.0002), or reduced participation in social activities due to dental or oral issues (β=-1.65, p = 0.0078). Conclusions This study suggested a protective effect of better oral health knowledge on specific OHRQoL issues. In addition to provision of affordable dental services, university-wide oral health education can be implemented to improve OHRQoL in undergraduate students.

Cities are home to a significant proportion of the population in the EU, providing access to job opportunities and public services and, subsequently, driving economic growth. However, cities also face social and environmental challenges such as poverty, prohibitively high housing costs, discrimination, crime, excessive noise and air pollution. This raises the issue of how residents in European cities perceive their lives and assess their overal well-being and satisfaction with the amenities in their city. A U-shaped relationship between life satisfaction and age is tested in a sample of European cities using data from the Quality of Life in European Cities survey, with higher levels of satisfaction expected among younger and older individuals. The results supported the hypothesis and provided evidence for the importance of considering age in the analysis of well-being in urban settings. Subjective well-being is not only influenced by personal factors such as age and individual experiences but also by the quality of the urban environment. The second part employs ordinal logistic regression to analyse individual and contextual factors of well-being in four Czech and Slovak cities, namely Prague, Ostrava, Bratislava, and Košice.

In the field of autofocus for optical systems, although passive focusing methods are widely used due to their cost-effectiveness, fixed focusing windows and evaluation functions in certain scenarios can still lead to focusing failures. Additionally, the lack of datasets limits the extensive research of deep learning methods. In this work, we propose a neural network autofocus method with the capability of dynamically selecting the region of interest (ROI). Our main work is as follows: first, we construct a dataset for automatic focusing of grayscale images; second, we transform the autofocus issue into an ordinal regression problem and propose two focusing strategies: full-stack search and single-frame prediction; and third, we construct a MobileViT network with a linear self-attention mechanism to achieve automatic focusing on dynamic regions of interest. The effectiveness of the proposed focusing method is verified through experiments, and the results show that the focusing MAE of the full-stack search can be as low as 0.094, with a focusing time of 27.8 ms, and the focusing MAE of the single-frame prediction can be as low as 0.142, with a focusing time of 27.5 ms.

Psychological problems are complex in nature and accurate identification of these problems is important. For the identification of psychological problems, one of the preliminary tools is the use of interviews/questionnaires. Questionnaires are preferred over interviews if the group under study is large. A strengths and difficulties questionnaire (SDQ) is one of the most widely used and powerful questionnaires to identify behavioral problems and distresses being faced by the respondents, affecting their day-to-day lives (responsible for social dysfunction). This study was held on college/university students in India, with the objective of examining if the extent of social dysfunction as measured by an impact score can be extracted from behavioral problems which are the components of the difficulty score of SDQ. Two surveys were conducted during the COVID-19 pandemic period, between the months of May–June 2020 and October 2020–February 2021 for the study. Only those responses were considered who felt distressed (“yes” to item 26 of SDQ). The numbers of such responses were 772/1020 and 584/743, respectively, in the two surveys. Distress levels were treated as ordered variables and three categories of distress level, viz., “Normal”, “Borderline”, and “Abnormal” were estimated through behavioral problems using ordinal regression (OR) methods with a negative log-log link function. The fitting of OR models was tested and accepted using Cox and Snell, Nagelkerke, and McFadden test. Hyperactivity-inattention and emotional symptoms were significant contributors to estimating levels of distress among respondents in survey 1 (p < 0.05). In addition to these components, in survey 2, peer problems were also significant. OR models were good at estimating the extreme categories; however, the “Borderline” category was not estimated well. One of the reasons was the use of qualitative and complex data with the least wide “Borderline” category, both for the “Difficulty” and the “Impact” scores.

Estimates of resident satisfaction with public education have great utility in public administration, especially among decision makers in shrinking small communities. But such estimates are typically obtained via surveys, which are costly and often unreliable at high spatial resolutions given low response rates. Our study found that satisfaction with public schools among residents of small communities can be reasonably estimated at the community level using public data. Several models generalized adequately to unseen data—these models typically included the following covariates: state student assessment scores, school reorganizations, net open enrollment, and the cost of educational outcomes relative to neighboring districts. Our findings thus amount to a cost‐effective survey alternative for gauging satisfaction with public schools in small Iowa communities. [ABSTRACT FROM AUTHOR]

Motivated by data measuring progression of leishmaniosis in a cohort of US dogs, we develop a Bayesian longitudinal model with autoregressive errors to jointly analyze ordinal and continuous outcomes. Multivariate methods can borrow strength across responses and may produce improved longitudinal forecasts of disease progression over univariate methods. We explore the performance of our proposed model under simulation, and demonstrate that it has improved prediction accuracy over traditional Bayesian hierarchical models. We further identify an appropriate model selection criterion. We show that our method holds promise for use in the clinical setting, particularly when ordinal outcomes are measured alongside other variables types that may aid clinical decision making. This approach is particularly applicable when multiple, imperfect measures of disease progression are available. [ABSTRACT FROM AUTHOR]