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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.

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.

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.

The COVID-19 pandemic raised the need for an increase in public services to counter the negative economic ramifications of the pandemic. However, this meteoric emergence of coronavirus inadvertently gave birth to inefficiencies entrenched in the delivery of government interventions to contain the disease and its economic effects. The purpose of this paper is to investigate the efficiency in delivery of government services and interventions aimed at ameliorating the COVID-19 pandemic. This study uses descriptive statistics and ordinal regression analysis to identify the drivers of inefficiency perceptions among recipients of social security interventions during the pandemic. Survey data for a sample of 855 participants was drawn from King Cetshwayo District municipality in KwaZulu Natal, South Africa. The key findings revealed that the delivery of most government interventions in South Africa was perceived as not well coordinated and poorly communicated, hence inefficient. Drivers of these perceptions included age, income level, race, and employment status. In addition, whether or not an individual had received some form of social security assistance during the pandemic also influenced their perceptions about government efficiency in providing social security support. We recommend strengthening monitoring and evaluation mechanisms across government service delivery initiatives and improving communication of government programs to improve user experience and access.

BackgroundMultimorbidity is becoming more prevalent in low-and middle-income countries (LMICs). However, the evidence base on the burden and its longitudinal outcomes are limited. This study aimed to determine the longitudinal outcomes of patients with multimorbidity among a sample of individuals attending chronic outpatient non communicable diseases (NCDs) care in Bahir Dar, northwest Ethiopia.MethodsA facility-based longitudinal study was conducted among 1,123 participants aged 40+ attending care for single NCD (n = 491) or multimorbidity (n = 633). Data were collected both at baseline and after 1 year through standardized interviews and record reviews. Data were analyzed using Stata V.16. Descriptive statistics and longitudinal panel data analyzes were run to describe independent variables and identify factors predicting outcomes. Statistical significance was considered at p-value

Introduction: Cancer is a serious and common disease, which had a substantial problem in the social status of patients. There was no empirical evidence on the effect of cancer on social support. Objective: This study aimed to determine the level of social support among cancer patients in a comprehensive cancer center in Ethiopia. Methods: An institution-based cross-sectional study was done. About 386 study participants who were selected through systematic random sampling involved in the study. Training and close supervision and monitoring were done. The collected data were analyzed using SPSS-25. Descriptive statistics and Chi-square test were done. Ordinal bivariate and multivariate logistic regression were done to show the net effect of independent variables on the dependent variable. Model fitting information, the goodness of test, and the test of parallel line assumption test of the ordinal logistic regression model were carried out. Results: A total of 386 study subjected were included in the final analysis. The poor, moderate, and strong levels of social support among cancer patients were found to be 45.3%, 34.2%, and 20.5% respectively. The mean score of social support among cancer patients was 10.4 ± 2.6SD. Age, Marital status, residence, educational status, stage III were found to be significant factors for the level of social support. Conclusion: and recommendation: The level of poor, moderate, and strong social support was found to be 45.3%, 34.2 and 20.5 respectively. Emphasis should be given to those cancer patients who had poor social support, and frequent social status assessment should be done.

In this study, a new method is proposed to assess heart and lung signal quality objectively and automatically on a 5-level scale in real-time, and to assess the effect of signal quality on vital sign estimation. A total of 207 10 s long chest sounds were taken from 119 preterm and full-term babies. Thirty of the recordings from ten subjects were obtained with synchronous vital signs from the Neonatal Intensive Care Unit (NICU). As a reference, seven annotators independently assessed the signal quality. For automatic quality classification, 400 features were extracted from the chest sounds. After feature ranking and selection, class balancing, and hyperparameter optimization, a variety of multi-class and ordinal classification and regression algorithms were trained. Then, heart rate and breathing rate were automatically estimated from the chest sounds. For the deep learning model, YAMNet, a deep convolutional neural network pre-trained on the AudioSet-Youtube corpus for sound classification was used. After modification of the final output layers of the neural network and class balancing, transfer learning was applied to YAMNet for heart and lung signal quality classification. The results of subject-wise leave-one-out cross-validation show that the best-performing models had a balanced accuracy of 56.8% and 51.2% for heart and lung qualities, respectively. The best-performing models for real-time analysis (< 200 ms) had a balanced accuracy of 56.7% and 46.3%, respectively. Our experimental results underscore that increasing the signal quality leads to a reduction in vital sign error.

People derive considerable social benefits from interacting with others that they encounter across a host of environmental domains: their city, region, country, continent, and even the world at large. We explore the extent to which perceived happiness hinges on one's sense of connection within each domain, drawing upon a large international sample of 120k respondents from 74 nations in the World Value Survey (2017–2020). This large battery of social and political attitudes includes items that ask: “tell me how close you feel to…” followed by each of city, region, country, continent, and the world. Options ranged from ‘very close’ to ‘not very close at all.’ Perceived happiness was scored on a 4-option item asking: “taking all things together, would you say you are…” with options ranging from ‘very happy’ to ‘not at all happy.’ After accounting for various demographic variables like age, sex, education, urban/rural environment, and income, results from a stepwise ordinal logistic regression analysis showed that one's perceived happiness was predicted by feeling connected to almost all domains; and the more connected one felt, the happier they were. One's connection however to region was not significant, wherein we suspect the item was too ambiguous for a clear response. Implications for overall wellbeing are discussed, as are directions for future research.

Abstract Background Protein protein interactions (PPIs) are essential to most of the biological processes. The prediction of PPIs is beneficial to the understanding of protein functions and thus is helpful to pathological analysis, disease diagnosis and drug design etc. As the amount of protein data is growing fast in the post genomic era, high-throughput experimental methods are expensive and time-consuming for the prediction of PPIs. Thus, computational methods have attracted researcher’s attention in recent years. A large number of computational methods have been proposed based on different protein sequence encoders. Results Notably, the confidence score of a protein sequence pair could be regarded as a kind of measurement to PPIs. The higher the confidence score for one protein pair is, the more likely the protein pair interacts. Thus in this paper, a deep learning framework, called ordinal regression and recurrent convolutional neural network (OR-RCNN) method, is introduced to predict PPIs from the perspective of confidence score. It mainly contains two parts: the encoder part of protein sequence pair and the prediction part of PPIs by confidence score. In the first part, two recurrent convolutional neural networks (RCNNs) with shared parameters are applied to construct two protein sequence embedding vectors, which can automatically extract robust local features and sequential information from the protein pairs. Based on it, the two embedding vectors are encoded into one novel embedding vector by element-wise multiplication. By taking the ordinal information behind confidence score into consideration, ordinal regression is used to construct multiple sub-classifiers in the second part. The results of multiple sub-classifiers are aggregated to obtain the final confidence score. Following that, the existence of PPIs is determined by the confidence score. We set a threshold $$\theta$$ θ , and say the interaction exists between the protein pair if its confidence score is bigger than $$\theta$$ θ . Conclusions We applied our method to predict PPIs on data sets S. cerevisiae and Homo sapiens. Through experimental verification, our method outperforms state-of-the-art PPI prediction models.

Agritourism has become a popular pursuit for farms and ranches in the United States, aiming to diversify revenue sources and meet agricultural edu­cation and community-building goals. How­ever, there has been limited research around the challenges experienced by operators and limited access to resources that can help address these chal­lenges. This article fills that gap in knowledge by examining the challenges agritourism opera­tions currently face in the Northeast, Midwest, South, and West regions of the U.S. In this study, we use a mixed-methods approach to the Five Dimen­sions of Access framework developed by Penchansky and Thomas (1981). We opera­tionalize their model in an ordinal probit regression to analyze data from a national survey of agritourism operators, analyzed by region. Results from the quantitative analysis are sub­stantiated using qualitative, open-ended comments from the same survey. The analyses show that agritourism operators encounter different challenges according to their region. We find that operators in most regions of the United States are concerned about agritourism liability. However, states in the West region experience more chal­lenges with regulations, zoning, and permitting, while operators in the South have more problems with e-connectivity. These results can be applied in three ways: support services for agritourism, policy and regulations, and future research.

Generally, modeling poverty aims to obtain the best criteria for assessing poverty status. There are two approaches to model the factors that affect poverty, namely consumption approach and discrete choice model. The advantage of the discrete choice model compared to the consumption approach is that the discrete choice model provides a probabilistic estimate for classifying samples into different poverty categories. This study aims to examined how the factors that affect poverty in Yogyakarta through Regularized Ordinal Regression with elastic net approach both for parallel, non-parallel, and semi-parallel models. The data used in this study is Susenas March 2018 for Yogyakarta provinces. The result of this study shows that the best discrete choice model for Yogyakarta’s modelling is the parallel model. Households that live in villages, have a large number of household members, are headed by women, have elderly household heads, have low education, and work in the primary sector tend to be more vulnerable to poverty. Therefore, a simultaneous policy with inclusive economic development is needed to reduce cross-border, cross-gender, and cross-sector inequality

Social media are deemed influential in making decisions and seeking advice. Due to their explosive growth as critical channels for information, their content can trigger a place visit, a change of transport mode or destination, or plans’ cancellation. The main objective of this paper is to investigate the influence of social media on users’ activity and mobility planning. Responses of 738 participants in a digital survey were used to formulate ordinal regression models. The developed models determine the contribution of users’ demographic characteristics, travel characteristics and social media usage to mobility decisions after using social media as a source of information. These decisions were expressed in two dependent variables; (i) the impact of social media use in activity and mobility planning; (ii) the impact of the proposed transport mode by social media information, on mode choice. Analysis of the results indicated that the models, which considered all the characteristics together, could better predict the two variables.

Yilin Ning,1,2 Peh Joo Ho,3,4 Nathalie C Støer,5,6 Ka Keat Lim,7,8 Hwee-Lin Wee,3,9 Mikael Hartman,1– 3,10 Marie Reilly,11 Chuen Seng Tan3 1NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore, Singapore; 2Yong Loo Lin School of Medicine, Department of Surgery, National University of Singapore and National University Health System, Singapore, Singapore; 3Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore; 4Genome Institute of Singapore, Singapore, Singapore; 5Norwegian National Advisory Unit on Women’s Health, Oslo University Hospital, Oslo, Norway; 6Department of Research, Cancer Registry of Norway, Oslo, Norway; 7Department of Population Health Sciences, School of Population Health & Environmental Sciences (SPHES), Faculty of Life Sciences & Medicine, King’s College London, London, UK; 8Programme in Health Services & Systems Research, Duke-NUS Medical School, Singapore, Singapore; 9Department of Pharmacy, Faculty of Science, National University of Singapore, Singapore, Singapore; 10Department of Surgery, National University Hospital, Singapore, Singapore; 11Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, SwedenCorrespondence: Chuen Seng TanSaw Swee Hock School of Public Health, National University of Singapore and National University Health System, Tahir Foundation Building, 12 Science Drive 2, #10-01, Singapore, 117549, SingaporeTel +65-66013206Email ephtcs@nus.edu.sgPurpose: Assessing the clinical importance of an exposure effect on a quality of life (QoL) score often requires quantifying the effect in terms of a difference in scores. Using the linear regression model (LRM) for this purpose assumes the ordinal score is a proxy for an underlying continuous variable, but the analysis offers no assessment for the validity of the assumption. We propose an approach that assesses the proxy assumption and estimates the exposure effect by using the cumulative link model (CLM).Patients and methods: CLM is a well-established regression model that assumes an ordinal score is an ordered category generated from applying thresholds to a latent continuous variable. Our approach assesses the proxy assumption by testing whether these thresholds are equidistant. We compared the performance of CLM and LRM using simulated ordinal data and illustrated their application to the effect of time since diagnosis on five subscales of fatigue among breast cancer survivors measured using the Multidimensional Fatigue Inventory.Results: CLM had good performance in estimating the difference in means with simulated ordinal data satisfying the proxy assumption, even when the outcome had only a few categories. When the proxy assumption was inadequate, both the CLM and LRM had biased estimates with poor coverage. The proxy assumption was appropriate for four of the five subscales in our real data application to fatigue scores, which highlighted the importance of assessing the proxy assumption to avoid reporting invalid estimates in terms of the difference in scores.Conclusion: The proxy assumption is critical to the interpretation of the exposure effect on the difference in mean QoL scores. CLM offers a valid test for the presence of an association, a method for assessing the proxy assumption, and when the assumption is adequate, an assessment for clinical significance using the difference in means.Keywords: cumulative link model, ordered probit model, ordinal outcome, ordinal regression, probit link, quality of life

Many real-world data are labeled with natural orders, i.e., ordinal labels. Examples can be found in a wide variety of fields. Ordinal regression is a problem to predict ordinal labels for given patterns. There are specially developed ordinal regression methods to tackle this type of problems, but they are usually centralized. However, in some scenarios, data are collected distributedly by nodes of a network. For the purpose of privacy protection or due to some practical constraints, it is difficult or impossible to transmit the data to a fusion center for processing. Thus the centralized ordinal regression methods are inapplicable. In this paper, we formulate a distributed generalized ordered logit model for distributed ordinal regression. To estimate parameters in the model, a distributed constrained optimization formulation based on maximum likelihood methods is established. Then, we propose a projected gradient based algorithm to solve the optimization problem. We prove the consensus and the convergence of the proposed distributed algorithm. We also conduct numerical simulations on synthetic and real-world datasets. Simulation results show that the proposed distributed algorithm is comparable to the corresponding centralized algorithm. Even when the data label distribution among nodes is unbalanced, the proposed algorithm still has competitive performance.

Ordinal regression (OR), also called ordinal classification, is a special multi-classification designed for problems with ordered classes. Imbalanced data hinders the performance of classification algorithms, especially for OR algorithms, as imbalanced class distributions often arise in OR problems. In this article, we address an active learning based solution for imbalanced OR problem. We propose an active learning algorithm for OR (AL-OR) to select the most informative samples from unlabeled samples, mark them and add them to the training set. Based on AL-OR, we put forward an improved active learning for imbalanced OR (IAL-IOR), which further adjust the sampling strategy of AL-OR dynamically to make the training data as valuable and balanced as possible. Recall rate for multi-classification and new mean absolute error are designed to measure the performance of the algorithms. To the best of our knowledge, our algorithm is the first algorithm for imbalanced OR in algorithm level. The experimental results show that the proposed algorithms have faster convergence and much better generalization ability than the classical methods and the state-of-the-art methods under the evaluation measurements for imbalance problems. In addition, we also proved the effectiveness of our algorithms by statistical analysis.

Most genomic prediction models are linear regression models that assume continuous and normally distributed phenotypes, but responses to diseases such as stripe rust (caused by Puccinia striiformis f. sp. tritici) are commonly recorded in ordinal scales and percentages. Disease severity (SEV) and infection type (IT) data in germplasm screening nurseries generally do not follow these assumptions. On this regard, researchers may ignore the lack of normality, transform the phenotypes, use generalized linear models, or use supervised learning algorithms and classification models with no restriction on the distribution of response variables, which are less sensitive when modeling ordinal scores. The goal of this research was to compare classification and regression genomic selection models for skewed phenotypes using stripe rust SEV and IT in winter wheat. We extensively compared both regression and classification prediction models using two training populations composed of breeding lines phenotyped in 4 years (2016–2018 and 2020) and a diversity panel phenotyped in 4 years (2013–2016). The prediction models used 19,861 genotyping-by-sequencing single-nucleotide polymorphism markers. Overall, square root transformed phenotypes using ridge regression best linear unbiased prediction and support vector machine regression models displayed the highest combination of accuracy and relative efficiency across the regression and classification models. Furthermore, a classification system based on support vector machine and ordinal Bayesian models with a 2-Class scale for SEV reached the highest class accuracy of 0.99. This study showed that breeders can use linear and non-parametric regression models within their own breeding lines over combined years to accurately predict skewed phenotypes.

Within the highly complex ecosystem of industry-university collaboration in open innovation, three specific antecedents typically characterize the patterns of their interaction, i.e., motivations, barriers, and channels of knowledge transfer. However, an investigation of the extent to which these antecedents of opening up innovation impact the perceived importance of universities as an external knowledge source to the industry is still missing in the literature. Based on a research framework developed from a review of the literature, a two-stage ordinal regression, and neural network approach was performed to investigate this impact. In the first stage, the hypotheses of the proposed research framework were tested based on an ordinal regression, and those antecedents that significantly impacted the importance perception were revealed. In the second stage, an artificial neural network analysis was carried out to capture the complex relationships among the significant antecedents and the important perception of universities as an external knowledge source to the industry. On the whole, the findings of our study expand the existing open innovation literature and contribute to a more articulate view of the collaboration between industry and university in this field by providing a first perspective on which of the three antecedents has a significant impact on this perception and how such an impact can be predicted.

Background and Purpose: Prediction models for functional outcomes after ischemic stroke are useful for statistical analyses in clinical trials and guiding patient expectations. While there are models predicting dichotomous functional outcomes after ischemic stroke, there are no models that predict ordinal mRS outcomes. We aimed to create a model that predicts, at the time of hospital discharge, a patient's modified Rankin Scale (mRS) score on day 90 after ischemic stroke.Methods: We used data from three multi-center prospective studies: CRISP, DEFUSE 2, and DEFUSE 3 to derive and validate an ordinal logistic regression model that predicts the 90-day mRS score based on variables available during the stroke hospitalization. Forward selection was used to retain independent significant variables in the multivariable model.Results: The prediction model was derived using data on 297 stroke patients from the CRISP and DEFUSE 2 studies. National Institutes of Health Stroke Scale (NIHSS) at discharge and age were retained as significant (p < 0.001) independent predictors of the 90-day mRS score. When applied to the external validation set (DEFUSE 3, n = 160), the model accurately predicted the 90-day mRS score within one point for 78% of the patients in the validation cohort.Conclusions: A simple model using age and NIHSS score at time of discharge can predict 90-day mRS scores in patients with ischemic stroke. This model can be useful for prognostication in routine clinical care and to impute missing data in clinical trials.

Ordinal regression methods are widely used to predict the ordered labels of data, among which support vector ordinal regression (SVOR) methods are popular because of their good generalization. In many realistic circumstances, data are collected by a distributed network. In order to protect privacy or due to some practical constraints, data cannot be transmitted to a center for processing. However, as far as we know, existing SVOR methods are all centralized. In the above situations, centralized methods are inapplicable, and distributed methods are more suitable choices. In this paper, we propose a distributed SVOR (dSVOR) algorithm. First, we formulate a constrained optimization problem for SVOR in distributed circumstances. Since there are some difficulties in solving the problem with classical methods, we used the random approximation method and the hinge loss function to transform the problem into a convex optimization problem with constraints. Then, we propose subgradient-based algorithm dSVOR to solve it. To illustrate the effectiveness, we theoretically analyze the consensus and convergence of the proposed method, and conduct experiments on both synthetic data and a real-world example. The experimental results show that the proposed dSVOR could achieve close performance to that of the corresponding centralized method, which needs all the data to be collected together.

Regularization techniques such as the lasso (Tibshirani 1996) and elastic net (Zou and Hastie 2005) can be used to improve regression model coefficient estimation and prediction accuracy, as well as to perform variable selection. Ordinal regression models are widely used in applications where the use of regularization could be beneficial; however, these models are not included in many popular software packages for regularized regression. We propose a coordinate descent algorithm to fit a broad class of ordinal regression models with an elastic net penalty. Furthermore, we demonstrate that each model in this class generalizes to a more flexible form, that can be used to model either ordered or unordered categorical response data. We call this the elementwise link multinomial-ordinal class, and it includes widely used models such as multinomial logistic regression (which also has an ordinal form) and ordinal logistic regression (which also has an unordered multinomial form). We introduce an elastic net penalty class that applies to either model form, and additionally, this penalty can be used to shrink a non-ordinal model toward its ordinal counterpart. Finally, we introduce the R package ordinalNet, which implements the algorithm for this model class.

This paper seeks to examine the role of factors originated from outside (economic, political, competitive environment and relationships) and within (entrepreneur’s attitude) the organization on the business risk perceived by entrepreneurs. To test the hypothetical relationships, an ordinal regression with two link functions was applied on an original dataset of 641 small and medium-sized enterprises (SMEs) operating in Slovakia and Czech Republic. The analysis revealed that not only economic factors can predict business risk, but along with them are political and competitive environments, relationship with supply chain actors and entrepreneur’s attitude. Consistent with prior research, it is found that an unstable economic environment leads the business to expose themselves to business risk. Also, a friendly regulation framework and quality education contribute significantly to reducing the level of risk. The research triggers the interest of policymakers who design policies aimed at improving the business environment by reducing the level of risk that firms face in doing business. Also, this paper is useful for managerial perspective, since entrepreneur attitude was found to be a predictor of business risk.

In recent years, research on Twitter sentiment analysis, which analyzes Twitter data (tweets) to extract user sentiments about a topic, has grown rapidly. Many researchers prefer the use of machine learning algorithms for such analysis. This study aims to perform a detailed sentiment analysis of tweets based on ordinal regression using machine learning techniques. The proposed approach consists of first pre-processing tweets and using a feature extraction method that creates an efficient feature. Then, under several classes, these features scoring and balancing. Multinomial logistic regression (SoftMax), Support Vector Regression (SVR), Decision Trees (DTs), and Random Forest (RF) algorithms are used for sentiment analysis classification in the proposed framework. For the actual implementation of this system, a twitter dataset publicly made available by the NLTK corpora resources is used. Experimental findings reveal that the proposed approach can detect ordinal regression using machine learning methods with good accuracy. Moreover, results indicate that Decision Trees obtains the best results outperforming all the other algorithms.

This paper proposes a novel end-to-end pipeline that uses the ordinal information and relative relation of images for visibility estimation (VISOR-NET). By encoding ordinal information into a set of relatively ordered image pairs, VISOR-NET can learn a global ranking function effectively. Due to the lack of real scenes or continuous labels in public foggy datasets, we collect a large-scale dataset that we term Foggy Highway Visibility Images (FHVI), which are taken from real surveillance scenes, and synthesize an INDoor Foggy images dataset (INDF) with continuous annotation. This work measures the estimation effectiveness on two public datasets and our FHVI dataset as a classification task and then on the INDF dataset as a regression task. Comprehensive experiments with existing deep-learning methods demonstrate the performance of the proposed method in terms of estimation accuracy, the convergence rate, model stability, and data requirements. Moreover, this method can extend inter-level visibility estimation to intra-level visibility estimation and can realize approximate regression estimation under discrete-level labels.

This study tries to prove whether there is an influence between the level of financial literacy, gender and age of a person on his ability to do retirement planning. This study uses a survey method by distributing questionnaires online. The sample of this research is workers who have not retired or workers who are still working even though they have retired. The sampling technique was purposive sampling with judgment, where the criteria used were workers with an age range of 23-65 years. Data analysis used in this study is ordinal regression. The results of this study found that gender and age had an influence on the ability to plan for retirement, but the level of financial literacy was not found to have an influence on the ability to do retirement planning.

Abstract Background Nowadays, the youth are more engaging with their more advanced smartphones having high-quality graphics and gaming features. However, existing literature depicts that adolescents suffer from several forms of psychological problems including mental health, depression, loneliness, insomnia and low self-control due to mobile game addiction. Therefore, this study aims to find the prevalence and motivating factors for mobile game addiction among university students of Bangladesh. Methods A cross-sectional survey was carried out to collect the required information from 1125 students of three universities in Bangladesh. Descriptive statistics, χ2 test and ordinal regression model are employed to meet the objective of this study. Results The findings reveal that male students are more likely to show addictive behaviours than their counterparts in the context of mobile game addiction. The results depict that loneliness, duration of using smartphones and playing mobile games, and source of entertainment are the main cause of mobile addiction. Also, more than half of the respondents (54.3%) are severely addicted to mobile games who were influenced by friends and YouTube gamers to play games. Moreover, students are suffering from several physical problems such as headaches, eye discomfort, blurry vision and ear discomfort. Conclusion Considering the findings of this paper, the authors suggest that the authorities should consider this immediately and arrange a positive entertainment environment to prevent students from mobile games. Furthermore, it is necessary to encourage students to participate in sports or other extracurricular activities that may be helpful to lessen mobile game addiction among students in Bangladesh.

When humans touch an object with their fingertips, they can immediately describe its tactile properties using haptic adjectives, such as hardness and roughness; however, human perception is subjective and noisy, with significant variation across individuals and interactions. Recent research has worked to provide robots with similar haptic intelligence but was focused on identifying binary haptic adjectives, ignoring both attribute intensity and perceptual variability. Combining ordinal haptic adjective labels gathered from human subjects for a set of 60 objects with features automatically extracted from raw multi-modal tactile data collected by a robot repeatedly touching the same objects, we designed a machine-learning method that incorporates partial knowledge of the distribution of object labels into training; then, from a single interaction, it predicts a probability distribution over the set of ordinal labels. In addition to analyzing the collected labels (10 basic haptic adjectives) and demonstrating the quality of our method's predictions, we hold out specific features to determine the influence of individual sensor modalities on the predictive performance for each adjective. Our results demonstrate the feasibility of modeling both the intensity and the variation of haptic perception, two crucial yet previously neglected components of human haptic perception.

The study examines the level of satisfaction with 21 different areas of life among 300 respondents from Wielkopolska voivodship at the age of 30 and over. What distinguishes this analysis from other such studies is the fact that when examining partial satisfaction, it was weighed by, specified subjectively by respondents, the importance of the areas for which this satisfaction was measured. In the course of the analysis, it turned out that the respondents were the most satisfied with their private life, life as parent and independence. The smallest satisfaction of the respondents derives from social activity, but it is above all the effect of the lack of meaning of this sphere of life in the hierarchy of respected life values. In addition, it has been noticed that within individual fields there are certain factors that differentiate probability of being more satisfied for some categories of people in comparison with others. Especially personality traits strongly affect the diversity of life satisfaction. Being a social person, trusting other people, being conscientious, peaceful and open to new experiences increase the probability of being a more satisfied person (especially with social areas) and having traits from the second pole of the personality scale reduces this probability.

The Can de Palleiro (CP) is an autochthonous canine breed from Galicia (NW Spain). Interestingly, no previous research has been published about the behaviour of this breed. Thus, the aim of the present study was to obtain a deeper understanding of CP behavioural and temperamental traits and detect any potentially problematic behaviour by using the Canine Behavioural Assessment and Research Questionnaire (C-BARQ) and the Socially Acceptable Behaviour (SAB) test. Behavioural information was obtained from 377 dogs—177 CPs and 200 general population (GP) dogs—using the C-BARQ. Additionally, 32 dogs were enrolled to perform the SAB test (19 CPs and 13 GP dogs) in order to directly evaluate their temperament. Our results indicated that CP dogs had a lower tendency to show aggressiveness towards their owners (0.18 times lower, p = 0.033) and less fear of other dogs (by 0.43 times, p = 0.001), as well as higher trainability levels (2.56 times higher, p < 0.001) when compared to GP dogs. CP dogs also had increased odds of showing chasing behaviour (3.81 times higher, p < 0.001). Conversely, CPs had reduced odds of non-social fear, separation-related problems and excitability (by 0.42, 0.35 and 0.48 times, respectively; p < 0.001, p < 0.001 and p = 0.002). The current research represents a starting point for the study of the behaviour of CPs, which appear to be a working breed, with guarding and, especially, herding characteristics.

This paper aims to investigate the changes in the virtual perception on the built heritage at the traditional core settlement of Kumbakonam Town at Tamilnadu and to analyze their implications. Specifically, the major objectives of the study are (1) to identify the architectural elements that manifest the built heritage of Kumbakonam Town and (2) to assess the contributions of these elements to the changes in the visual perception of the town. To achieve these objectives, this study adopts an empirical model that analyzes the architectural elements of the buildings in the study area. Direct observations and documentations of 373 buildings are collected to analyze those elements that contribute to the changes in the visual perception on the built heritage of Kumbakonam Town. An ordinary regression model is used to examine the characteristics of the built heritage across the chariot processional route of the town. Several architectural elements, including pilasters, horizontal cornices, arched windows, and ornamental parapets, improve the image of the town. These empirical findings support the policy framework that enhances the visual perception of Kumbakonam Town.

rognostics or Remaining Useful Life (RUL) Estimation from multi-sensor time series data is useful to enable condition-based maintenance and ensure high operational availability of equipment. We propose a novel deep learning based approach for Prognostics with Uncertainty Quantification that is useful in scenarios where: (i) access to labeled failure data is scarce due to rarity of failures (ii) inherent noise ispresent in the sensor readings. The two scenarios mentioned are unavoidable sources of uncertainty in the RUL estimation process, often resulting in unreliable RUL estimates. To address (i), we formulate RUL estimation as an Ordinal Regression (OR) problem and propose LSTM-OR: deep Long Short Term Memory (LSTM) network-based approach to learn the OR function. We show that LSTM-OR naturally allows for the incorporation of censored operational instances in training along with the failed instances, leading to more robust learning. To address (ii), we propose a simple yet effective approach to quantify predictive uncertainty in the RUL estimation models by training an ensemble of LSTM-OR models. Through empirical evaluation on the publicly available turbofan engine benchmark datasets, we demonstrate that LSTMOR is at par with commonly used deep metric regressionbased approaches for RUL estimation when sufficient failed instances are available for training. Importantly, LSTM-OR outperforms these metric regression-based approaches in the practical scenario where failed training instances are scarce, but sufficient operational (censored) instances are additionally available. Furthermore, our uncertainty quantification approach yields high-quality predictive uncertainty estimates while also leading to improved RUL estimates compared to single best LSTM-OR models.

The increase in relative level of education and the growing middle-class income earners in Nigeria have made packaged food items attractive to consumers. These foods provide handy, nutritious and convenient food for consumers; however, they also come with public health concerns. From a policy perspective, food labelling can gauge consumers’ response to the safety conditions of packaged food. However, understanding consumers’ awareness and the use of food label information has attracted little research attention in many developing countries in sub-Saharan Africa. In this study, we investigate consumers’ awareness of the use of food safety labels in Lagos state, Nigeria. We used primary data collected using pretested structured questionnaire from 220 food shoppers who bought pre-packaged foods. A two-stage sampling technique was used to select the respondents. Consumers’ awareness and use of food safety information was revealed using descriptive statistics; Ordinal Regression Analysis was used to examine the socioeconomic factors determining the frequency of reading food safety labels. The study therefore recommends that there should be continuous awareness campaigns on the importance of food labels in ensuring safety and food producers should make sure their food labels are legible.

Public transport use has drastically declined during the COVID-19 pandemic because the virus spreads through close contact with infected people and contaminated surfaces, especially in closed-environments. Evidence suggests that following the necessary safety guidelines can limit the spread of the virus, however, non-compliance to precautionary measures has been reported widely. Ignoring the precautionary measures may lead to quick transmission of the virus since public transport vehicles generally resemble closed-environments. This study explores people’s intentions to use public transport during the COVID-19 pandemic while adhering to precautionary measures. For this purpose, a total of 1516 useable responses were collected through a questionnaire survey conducted in Lahore, Pakistan. Regression models were developed to model the intentions to use public transport during the pandemic, to adhere to the precautionary measures while using public transport during the pandemic, and to use public transport while suffering from COVID-19 disease. The results of this study will help in understanding the intentions of the public transport users during the COVID-19 pandemic. It will also provide insights for policymakers and public transport operators about further necessary actions to promote safe public transport use during the current and any possible future pandemics. As public transport use declined during the pandemic, policy implications for alternative mobility options such as demand-responsive-transport (DRT) are also presented. Considering the study results, a policy interventions framework is proposed to promote the safe use of various public transport modes, particularly in developing regions.

While the problem of vaccine hesitancy is not new, it has become more pronounced with the new COVID-19 vaccines and represents an obstacle to resolving the crisis. Even people who would usually trust vaccines and experts now prefer to wait for more information. A cross-sectional online survey was conducted in Slovenia in December 2020 to find out the attitudes of the population regarding COVID-19 vaccination and the factors that affect these attitudes. Based on 12,042 fully completed questionnaires, we find that higher intention to get vaccinated is associated with men, older respondents, physicians and medical students, respondents who got the influenza vaccination, those who knew someone who had gotten hospitalised or died from COVID-19 and those who have more trust in experts, institutions and vaccines. Nurses and technicians were less likely to get vaccinated. In answers to an open question, sceptics were split into those doubting the quality due to the rapid development of the vaccine and those that reported personal experiences with side effects of prior vaccinations. Although the Slovenian population is diverse in its attitudes towards vaccination, the results are comparable to those found in other countries. However, there are potential limitations to the generalizability of the findings that should be addressed in future studies.

App-based demand-responsive transit (DRT) services are emerging where conventional public transport is unable to meet the demand. SWVL (choice A) and Airlift (choice B) are two such DRT bus services operating in Lahore, Pakistan. It is important for the policy makers and operators to evaluate the satisfaction levels of the passengers using these services. This study evaluated passengers’ satisfaction regarding choice A and choice B through a questionnaire survey. A total of 440 responses were collected from the users of the DRT services through personal interviews and a web-based approach. Factor analysis on the collected data produced two underlying factors, namely service-attributes and bus ambiance. Ordinal regression showed that the service-attributes and ambiance were significant predictors of overall satisfaction levels about choice A. Service-attributes indicated a larger positive impact on overall satisfaction with choice A as compared to ambiance. Although the ordinal model for choice B fitted the data well, the predictors were found to be insignificant. The results offer an insight into which predictors affect the overall satisfaction and how it can be improved.

Abstract Background As primary health care (PHC) centers in Saudi Arabia provide standardized preventive and curative oral health care (OHC) services, challenges remain. In addition, evidence of determinants of OHC seeking behavior is unknown. The aim of this study was to identify common challenges faced by patients seeking OHC in PHC centers and assess determinants of demand for OHC in Riyadh. Methods After institutional approval and piloting, 320 adult patients were sampled at two large PHC centers in October 2015. Using a modified version of General Practice Assessment (GAPQ) and New York State Department of Health (NYSDOH) Questionnaires, information about cumulative number of visits to a dentist, patient profiles, provider characteristics, and challenges were collected. We used descriptive statistics to summarize data and employed ordinal regression for analyzing extent of effects of challenges and determinants of demand for OHC. Results Oral health condition was reported to be good in 31 % of the patients, very good in 25 % of the patients, and fair in 20 % of the patients. More than half (53 %) of patients visited a dentist in the past 12 months once, 20 % twice, and 25 % at least three times. High cost of private clinic and unavailability of dentists were reported as the most common difficulties in seeing a dentist. Patients who were very satisfied with dental care and treatment plan, those with less than excellent oral health conditions and male patients had less number of cumulative visits to a dentist compared with patients with less satisfaction, patients with perceived excellent oral health, and female patients respectively. Conclusions Our findings provide a strong evidence of challenges faced by patients and determinants of demand for OHC seeking behavior. The findings can inform policy maker not only in patient satisfaction of OHC, but also implications on facilities and health care systems. We conclude with recommendations for future research, especially on oral health preventive measures in PHC centers that correct inherent dental problems and other underlying challenges.

The quality of work life reveals how an employee perceives different aspects of his/ her workplace. Such perceptions with respect to the workplace have effects on job satisfaction, the desire to work in the current organization and the desire to work in the relevant line of work for years to come. The objective of the present study is to determine which determinants of the quality of work life have significant effects on job satisfaction, commitment to the organization and line of work. Data derived from employees of travel agencies by using the questionnaire technique was analyzed by using ordinal regression technique. The factors that affect job satisfaction, commitment to organization and commitment to line of work were detected.

This paper shows how job search success, measured as search duration and entry salary, is affected directly or indirectly by personal and process as well as structural characteristics. A specific focus is on the relevance of practical experience which is claimed to be a key feature of employability. While self-assessed practical and namely international experience is positively related to salary, but not to search duration, the number and duration of internships does not affect job search success. Results are relevant for higher education institutions to develop their curricula, for students to prepare for job search, and for employers to understand the genesis of employability and their potential means to impact it.

Introduction: the prevention of reinfection of sexually transmitted infections (STIs) is highly dependent on the level of risk perception and the subsequent adoption of preventive behaviors. While perceived risk is assumed to be key to adoption of preventive measures, the evidence regarding the predictors of perceived risk to STI reinfection are limited. Methods: this paper is based on a cross sectional facility based survey conducted in North Ethiopia from January to June; 2015. Patients attending public health facilities for STI care responded to a structured questionnaire at clinic exist. Ordinal logistic regression was employed to identify factors associated with risk perception. Results: of the 1082 STI patients who participated in the study, 843(77.91%) indicated a high perceived risk of STI reinfection. The major factor associated with high perceived risk of reinfection was willingness to notify partner; the odds of being willing to notify partner was greater among those with perceived high risk (AOR=3.01, 95% CI: 2.13-4.25). In addition, high perceived risk was associated with female index cases (AOR=1.49, 95% CI: 1.07-2.08), those who had high school education and above (AOR=1.68, 95% CI: 1.07-2.65), those aged 25 years and above (AOR=1.52, 95% CI: 1.09-2.12), those who had a single partner (AOR=1.82, 95% CI: 1.20-2.74), and those who had low perceived stigma (AOR=1.42, 95% CI: 1.04-1.95).

This comment reanalyses the data presented in Rydell (Rydell 2017 R. Soc. open. sci. 4, 161077. (doi:10.1098/rsos.161077)) which were analysed using only very basic statistics like Fisher’s exact test and McNemar’s test. We demonstrate how the use of more advanced statistical methods can make better use of the available data, quantify the observed effects and strengthen the conclusions in Rydell (Rydell 2017 R. Soc. open. sci. 4, 161077. (doi:10.1098/rsos.161077)). We have no intention to discredit the original authors. Their analyses were basic but correct.

Depth estimation is a crucial and fundamental problem in the computer vision field. Conventional methods re-construct scenes using feature points extracted from multiple images; however, these approaches require multiple images and thus are not easily implemented in various real-time applications. Moreover, the special equipment required by hardware-based approaches using 3D sensors is expensive. Therefore, software-based methods for estimating depth from a single image using machine learning or deep learning are emerging as new alternatives. In this paper, we propose an algorithm that generates a depth map in real time using a single image and an optimized lightweight efficient neural network (L-ENet) algorithm instead of physical equipment, such as an infrared sensor or multi-view camera. Because depth values have a continuous nature and can produce locally ambiguous results, pixel-wise prediction with ordinal depth range classification was applied in this study. In addition, in our method various convolution techniques are applied to extract a dense feature map, and the number of parameters is greatly reduced by reducing the network layer. By using the proposed L-ENet algorithm, an accurate depth map can be generated from a single image quickly and, in a comparison with the ground truth, we can produce depth values closer to those of the ground truth with small errors. Experiments confirmed that the proposed L-ENet can achieve a significantly improved estimation performance over the state-of-the-art algorithms in depth estimation based on a single image.

Commuting refers to the act of daily traveling, between two locations, for employment purposes and suggests a multidimensional phenomenon in Regional Science, which is determined by social, economic, geographic and political factors. This paper conducts initially a literal approach of the phenomenon and further proceeds to an empirical ordinal regression analysis for the spatial network consisting of two Greece regions (Thessaly and Sterea Hellas) that verifies the theoretical framework. The theoretical review contributes to the formulation of the independent (predictor) variables and generally to the construction of the ordinal regression model and the corresponding empirical approach, in order to specialize the theoretical framework to the Greek case and to recognize the spatial patterns that rule the commuting phenomenon. Some conclusions that are made, come to an agreement with the well established theories of gravity modeling, expanding city and binomial law and others elect an amount of interesting contradictions to the theoretical framework.