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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]

Unsupervised domain adaptation (UDA) aims to transfer the knowledge from labeled source domain to unlabeled target domain. The main challenge of UDA stems from the domain shift between the source and target domains. Currently, in the discrete classification problems, most existing UDA methods usually adopt the distribution alignment strategy while enforcing unstable instances to pass through the low-density areas. However, the scenario of ordinal regression (OR) is rarely researched in UDA, and the traditional UDA methods cannot preferably handle OR since they do not preserve the order relationships in data labels, like in human age estimation. To address this issue, we proposed a structure-oriented adaptation strategy, namely, structure preserved ordinal unsupervised domain adaptation (SPODA). More specifically, on one hand, the global structure information was modeled and embedded into an auto-encoder framework via a low-rank transferred structure matrix. On the other hand, the local structure information was preserved through a weighted pair-wise strategy in the latent space. Guided by both the local and global structure information, a well-performance latent space was generated, whose geometric structure was adopted to further obtain a more discriminant ordinal regressor. To further enhance its generalization, a counterpart of SPODA with deep architecture was developed. Finally, extensive experiments indicated that in addressing the OR problem, SPODA was more effective and advanced than existing related domain adaptation methods. [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]

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]

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.

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.

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]

The paper focuses on the issue of motivation for military service in the Czech Armed Forces. It aims to map the Institutional-Occupational (I-O) motivations of the generation of Czech recruits currently starting their military career. The research sample consisted of 1557 recruits. Institutional preference was confirmed. The influence of gender, age, education, partner life and parenthood on these factors were also analyzed. From an institutional point of view, significant influence of age and education was found. From an occupational point of view, results showed, that again age, education and type of partnership cohabitation have a significant influence. [ABSTRACT FROM AUTHOR]

Accurate maturity identification of permanent teeth for children and adolescents using orthopantomogram (OPG) images is crucial in pediatric dentistry.Although recent advancements in deep learning have shown promising outcomes in maturity staging of third molars and mandibular permanent dentition, limited research has focused on assessing the entire dentition. Furthermore, accurate performance is hindered due to variations in tooth appearance and similarities between adjacent stages. This paper develops an automatic methodology to identify the tooth maturity stages of the full permanent dentition. Specifically, a tooth localization model based on YOLOv3 is presented to detect and number each candidate permanent tooth. Subsequently, we propose a novel symmetric and ordinal staging network (SOS-Net) for the precise maturity identification of each tooth. SOS-Net incorporates a symmetric feature combination block (SFC Block) based on bilateral facial symmetry to enhance discriminative feature representation in order to improve performance, while minimizing increases in model parameters. Additionally, we introduce an auxiliary regression branch with a novel loss called adjacent stage aware (ASA) loss to provide information in the order of maturity levels and effectively reduce misclassification between adjacent stages. The proposed methodology is evaluated on a private OPG image dataset collected from individuals aged 3 to 14 years. The ablation studies and visual analysis of SOS-Net verified the effectiveness and interpretability of the SFC block and ASA loss, with a F1-score improvement of 3.77 % . Moreover, comparative experiments demonstrate the good performance of the proposed methodology compared to state-of-the-art methods in maturity staging and age estimation. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy (DR) is a chronic complication of diabetes that damages the retinal blood vessels, leading to impaired vision and even blindness, and is one of the top three eye diseases causing human blindness. An effective DR grading algorithm can help ophthalmologists to diagnose patients and improve efficiency. Therefore, we propose a fusion network based on transformer and convolutional neural network (CNN) to perform DR grading. The proposed approach addresses two critical issues in the task: i) The CNN-based DR classification method has a small receptive field, so the range of available information is limited. The Transformer-based DR classification method has a large receptive field, but it is easy to lose local details; ii) Existing approaches treat DR grading as a traditional multiclass classification task, which ignores ordinal information between DR of different levels. To address i), we fuse the multi-level features of CNN and Transformer at different stages, and realize the enhancement and interaction of local and global information through the fusion module. To address ii), we propose a KC loss by formulating DR grading as a joint ordinal regression and multiclass classification problem, and obtain both category-supervised information and ordinal supervised information. Extensive qualitative and quantitative assessments have shown that our approach achieves superior performance on publicly available DeepDR and IDRiD datasets. [ABSTRACT FROM AUTHOR]

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 overall 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. [ABSTRACT FROM AUTHOR]

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.

The study presents the economic, managerial, and societal perspectives on corporate social responsibility (CSR) as a basis for adding value to enterprises. It investigates the interplay between the digitalisation of activities and the management of Bulgarian financial enterprises, with a focus on HRM and CSR initiatives in a pandemic situation. The study tests the hypothesis that, in pandemic conditions, the CSR of Bulgarian financial enterprises is positively correlated with the digitalisation of general and human resource management. To assess the level of engagement of financial enterprises with CSR causes during the pandemic, the study employs a methodology comprising descriptive statistics and ordinal regression. The main conclusion, based on a nationally representative survey of Bulgarian financial enterprises, is that banks and insurers that heavily invest in digitalisation have demonstrated a higher level of commitment to CSR causes during the COVID-19 pandemic, while conservative and less digitally advanced financial enterprises have had limited CSR activity. By adopting fintech and insurtech solutions directed towards societal needs, market demands, and customer satisfaction, financial enterprises increase their efficiency. Our analysis confirms the interplay between the digitalisation of financial enterprises and support for CSR causes. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The assessment of bone age is important for evaluating child development, optimizing the treatment for endocrine diseases, etc. And the well-known Tanner-Whitehouse (TW) clinical method improves the quantitative description of skeletal development based on setting up a series of distinguishable stages for each bone individually. However, the assessment is affected by rater variability, which makes the assessment result not reliable enough in clinical practice. The main goal of this work is to achieve a reliable and accurate skeletal maturity determination by proposing an automated bone age assessment method called PEARLS, which is based on the TW3-RUS system (analysis of the radius, ulna, phalanges, and metacarpal bones). The proposed method comprises the point estimation of anchor (PEA) module for accurately localizing specific bones, the ranking learning (RL) module for producing a continuous stage representation of each bone by encoding the ordinal relationship between stage labels into the learning process, and the scoring (S) module for outputting the bone age directly based on two standard transform curves. The development of each module in PEARLS is based on different datasets. Finally, corresponding results are presented to evaluate the system performance in localizing specific bones, determining the skeletal maturity stage, and assessing the bone age. The mean average precision of point estimation is 86.29%, the average stage determination precision is 97.33% overall bones, and the average bone age assessment accuracy is 96.8% within 1 year for the female and male cohorts. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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.

Objective: Menopause is associated with multiple health risks. In several studies, a higher incidence or a higher risk for obstructive sleep apnea (OSA) in post-menopausal than pre-menopausal women is reported. This study was designed to verify such a connection between menopause and OSA in a population-based sample. Methods: For a subsample (N = 1209) of the Study of Health in Pomerania (N = 4420), complete polysomnography data was available. Of these, 559 females completed a structured interview about their menstrual cycle. Splines and ordinal regression analysis were used to analyze the resulting data. Results: In the ordinal regression analysis, a significant association between the apnea–hypopnea index (AHI) and menopause indicated that post-menopausal women had a substantially higher risk of OSA. In accordance with previous studies, risk indicators such as body mass index (BMI), age, and the influence of hysterectomies or total oophorectomies were included in the model. Conclusions: Our results clearly confirmed the assumed connection between menopause and OSA. This is important because OSA is most often associated with male patients, and it warrants further research into the underlying mechanisms. [ABSTRACT FROM AUTHOR]

Public expectations of forests as high-quality restorative environments that facilitate subjective well-being and stress relief along with numerous health benefits have been rising sharply during recent decades. In addition, the COVID-19 pandemic and its accompanying restrictive measures also transformed forests into some of the few places to spend time away from home. The presented study drew on the assumption that the pandemic situation and a rise in the number of forest visits would affect the experience, recognition, and appreciation of the well-being aspects related to spending time in forests. The study goal was to elucidate the potential effects of the COVID-19 pandemic on the relationships between forest visits, well-being and stress relief, emotions, perception of nature and forest value and importance, pro-environmental behavior, and societal expectations of the role of forests and forest ecosystem services. A survey using a digital questionnaire was conducted several months after the pandemic outbreak on a representative sample of the Slovak population. The Wilcoxon test and ordinal regression analysis were used to identify significant relationships, e.g., between the recency of anger episodes and the number of forest visits. The results showed that the pandemic strengthened the perception of forests as a high-quality restorative environment and that emotions associated with forest visits played an important role in the perceived importance of forests and their possible overexploitation. The results underscore the urgent need to put demands for forest recreation on par with the forest bioeconomy and to sensitize forest visitors to management and conservation requirements. [ABSTRACT FROM AUTHOR]

Compared to the nominal scale, the ordinal scale for a categorical outcome variable has the property of making a monotonicity assumption for the covariate effects meaningful. This assumption is encoded in the commonly used proportional odds model, but there it is combined with other parametric assumptions such as linearity and additivity. Herein, the considered models are non-parametric and the only condition imposed is that the effects of the covariates on the outcome categories are stochastically monotone according to the ordinal scale. We are not aware of the existence of other comparable multivariable models that would be suitable for inference purposes. We generalize our previously proposed Bayesian monotonic multivariable regression model to ordinal outcomes, and propose an estimation procedure based on reversible jump Markov chain Monte Carlo. The model is based on a marked point process construction, which allows it to approximate arbitrary monotonic regression function shapes, and has a built-in covariate selection property. We study the performance of the proposed approach through extensive simulation studies, and demonstrate its practical application in two real data examples. [ABSTRACT FROM AUTHOR]

Raspberry (Rosales: Rosaceae) production in the U.K. has moved rapidly in the last 10 years to under polythene, combined with a reduced availability of broad-spectrum fungicides. Hence, the incidence of previously less prevalent diseases, such as Cladosporium (Capnodiales: Cladosporiaceae), has largely increased. This study aimed to identify the predominant Cladosporium species on raspberry and to understand the nature of its infection on raspberry fruit. Raspberries were collected from farms across the U.K. and Spain and incubated; fungal isolates were then isolated from typical Cladosporium lesions and identified to the species level based on the sequences of the trans elongation factor α and actin genes. Cladosporium cladosporioides (Fres) de Vries was confirmed as the predominant species responsible for infecting raspberry fruit close to harvest on fruit from the U.K. and Spain, being present on 41.5% of U.K. fruit and 84.6% of Spanish fruit. Raspberries were subsequently inoculated at different developmental stages with C. cladosporioides isolates to determine the susceptibility to Cladosporium skin lesions and stigmata infections in relation to the developmental stage. Only the ripening and ripe raspberries were susceptible to Cladosporium, resulting in skin lesions. Cladosporium can colonise the stigmata of raspberries earlier in fruit development and future research is required to determine if such stigmata infections could cause subsequent skin lesion infections. This study has provided the necessary epidemiological information to develop effective management measures against the Cladosporium species. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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.

We develop a Bayesian nonparametric framework for modeling ordinal regressionrelationships which evolve in discrete time. The motivating applicationinvolves a key problem in fisheries research on estimating dynamically evolvingrelationships between age, length and maturity, the latter recorded on anordinal scale. The methodology builds from nonparametric mixture modeling forthe joint stochastic mechanism of covariates and latent continuous responses.This approach yields highly flexible inference for ordinal regression functionswhile at the same time avoiding the computational challenges of parametricmodels. A novel dependent Dirichlet process prior for time-dependent mixingdistributions extends the model to the dynamic setting. The methodology is usedfor a detailed study of relationships between maturity, age, and length forChilipepper rockfish, using data collected over 15 years along the coast ofCalifornia.

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. [ABSTRACT FROM AUTHOR]

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.