Ordinal Models Research Articles

A Brief Introduction on Latent Variable Based Ordinal Regression Models With an Application to Survey Data.

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

A comparison of joint species distribution models for percent cover data

Abstract Joint species distribution models (JSDMs) have gained considerable traction among ecologists over the past decade, due to their capacity to answer a wide range of questions at both the species‐ and the community‐level. The family of generalised linear latent variable models in particular has proven popular for building JSDMs, being able to handle many response types including presence‐absence data, biomass, overdispersed and/or zero‐inflated counts. We extend latent variable models to handle percent cover response variables, with vegetation, sessile invertebrate and macroalgal cover data representing the prime examples of such data arising in community ecology. Sparsity is a commonly encountered challenge with percent cover data. Responses are typically recorded as percentages covered per plot, though some species may be completely absent or present, that is, have 0% or 100% cover, respectively, rendering the use of beta distribution inadequate. We propose two JSDMs suitable for percent cover data, namely a hurdle beta model and an ordered beta model. We compare the two proposed approaches to a beta distribution for shifted responses, transformed presence‐absence data and an ordinal model for percent cover classes. Results demonstrate the hurdle beta JSDM was generally the most accurate at retrieving the latent variables and predicting ecological percent cover data.

Socioeconomic and Racial Disparities in Primary Open-Angle Glaucoma in the US.

Socioeconomic status is a potentially significant, although difficult to isolate, factor in determining glaucoma severity. To analyze the level of glaucoma severity (mild, moderate, or severe) based on demographic factors, including age, gender, ethnicity, insurance profile, and zip code, and to use this data to extrapolate the effect of socioeconomic status (SES) and race on glaucoma severity at the community level in Memphis, TN. 2913 patients from a tertiary care center with three clinic locations in Memphis, TN who had been given the diagnosis of Primary Open Angle Glaucoma (POAG) through use of the ICD-10 codes for mild (H40.1111, H40.1121, H40.1131), moderate (H40.1112, H40.1122, H40.1132) and severe (H40.1113, H40.1123, H40.1133) POAG between January 2016 and July 2021 were included in this study. Diagnoses were made after a complete glaucoma workup consisting of Snellen visual acuity, applanation IOP measurement, gonioscopy, automated Humphreys Visual Fields (10-2 and 24-2), and optic nerve OCT. Demographic information, including age, gender, ethnicity, insurance profile, and zip code, was also collected with disease severity for each patient. SES was approximated using zip code-level census poverty data and insurance profiles. Statistical analyses were performed, including descriptive, multivariable ordinal logistic modeling and stepwise multivariable linear modeling. Glaucoma severity was shown to increase with poverty rate (OR=1.089, P<0.0071), age (OR=1.030, P<0.0001), male sex (OR=1.374, P<0.0001), and Black race (OR=1.896, P<0.0001). Severity was shown to be decreased in patients with private insurance compared to Medicare (OR=0.895, P<0.093) and those from Shelby County compared to other counties (OR=0.703, P<0.0001). Our findings indicate that worsening glaucoma severity was associated with higher poverty rates in our patient population. However, isolating socioeconomic status (SES) as an independent factor influencing the incidence and severity of glaucoma remains challenging, given the strong correlation between race and SES.

Examining Daily Self-Efficacy, Minority Stressors, and Alcohol and Other Drug Use Among Trauma-Exposed Sexual Minority Women and Transgender and Gender-Diverse People.

This study aimed to develop and test a novel model integrating social-learning and self-medication frameworks by examining the association between self-efficacy to resist alcohol and other drug (AOD) use and daily AOD use and unhealthy drinking risk among trauma-exposed sexual minority women (SMW) and transgender and gender-diverse (TGD) people. We examined whether minority stressors moderated these associations. Data were from 57 trauma-exposed SMW and TGD people who participated in a 14-day daily diary study. Multilevel binary logistic models and ordinal logistic models were employed to examine associations between self-efficacy to resist AOD use and daily AOD use and unhealthy drinking risk at within- and between-person levels. We assessed same- and cross-level interactions between daily self-efficacy to resist AOD use and minority stressors in predicting AOD use and unhealthy drinking risk within the same 24-hour period (i.e., standardized as 6 pm to 6 pm; hereafter referred to as "same-day"). Self-efficacy to resist AOD use was associated with lower AOD use and unhealthy drinking risk. Minority stressors were associated with daily AOD use. Among those who experienced higher (vs. lower) average sexual minority stressors over the 2-week daily diary period, higher-than-usual self-efficacy to resist AOD use was less protective in decreasing risk of same-day unhealthy drinking. Interventions aiming to mitigate AOD use and unhealthy drinking risk by bolstering self-efficacy to resist AOD use should consider the impact of recent cumulative exposure to sexual minority stressors in this population. Further, policy efforts are needed to reduce perpetuation of stigma.

A platinum-coated staggered reactor to intensify lean hydrogen/air combustion: A large eddy simulation study

Catalytic-aided combustion has been proven effective for premixed hydrogen/air mixtures, particularly under lean to ultra-lean conditions. However, minimising the required catalyst sets a significant challenge because noble metals with high catalytic activity are rare and expensive. Therefore, this study aims to intensify the catalytic combustion process by investigating a non-planar reactor comprising an array of platinum-coated half- and full-cylinders through large eddy simulation. A premixed mixture with a fuel-lean equivalence ratio of 0.15 and an incoming Reynolds number of 3500 based on hydraulic diameter is used. For comparison, a planar reactor without cylinders is also studied under the same operating conditions and with the same amount of platinum-coated surface area. The simulation employs the turbulent kinetic energy sub-grid model and the eddy dissipation concept to model the turbulent catalytic reacting flow. The discrete ordinate model is used to account for radiation heat transfer in the catalytic process. Numerical simulations are validated against experimental results prior to analysis. The findings indicate that the placement of cylinders along the reactor length enhances convective mass transfer and intensifies catalytic combustion, resulting in effective combustion over a smaller catalytic surface. Compared to planar models, non-planar reactors demonstrate a much better H2 conversion efficiency throughout the reactor length, saving nearly 62.5 % of the catalyst.

Performance of agricultural extension agents in Indonesia: evidence from a nationally representative survey

ABSTRACT Purpose This study analyses the performance of field extension agents in Indonesia using a multi-dimensional performance indicator. Methodology The study uses data from an online survey of a nationally representative sample of 1,974 field extension agents. Binary and ordinal probit models are utilised to analyse the performance of extension agents and the factors influencing it based on a multi-dimensional measurement of performance. Findings and practical implications Improved performance is associated with increased career progression, availability of external funds, the opportunity to collaborate, involvement in government programs, implementation of disciplinary measures and a diversified use of advisory methods. To promote performance, the government should encourage and improve the institutional arrangements. Theoretical implications This study introduces a multidimensional measurement that covers timeliness, targeting and content dimensions while capturing both the institutional arrangement and demographic characteristics in explaining performance. Originality/value This study introduces an online survey for extension agents that encompasses a nationally representative sample, thereby demonstrating this cost effective approach designed for rapid data collection and facilitating the measurement of performance during unforeseen events, such as the Covid-19 pandemic. Its broad applicability allows for a thorough analysis of agricultural extension agents’ performance in a variety of contexts.

Good Friends and Good Neighbors: Social Capital and Food Insecurity in Families with Newborns

To examine the association between social capital and household food insecurity among US families with newborns. This cross-sectional analysis used enrollment data from 881 newborn-caregiver dyads at six geographically-diverse US academic sites enrolled in the Greenlight Plus Trial, a comparative effectiveness trial to prevent childhood obesity. Ordinal proportional-odds models were used to characterize the associations of two self-reported measures of social capital: 1) caregiver social support and 2) neighborhood social cohesion, with household food insecurity after controlling for sociodemographic characteristics. Among 881 newborn-caregiver dyads (49% Hispanic, 23% non-Hispanic white, 17% non-Hispanic Black; 49% with annual household income <$50,000), food security was high for 75%, marginal for 9%, low for 11% and very low for 4%. In covariate-adjusted analyses, caregivers with a low social support score of 18 had five times the odds (aOR=5.03 95%CI=3.28-7.74) of greater food insecurity compared with caregivers with a high social support score of 30. Caregivers with a low neighborhood social cohesion score of 10 had nearly three times the odds (aOR=2.87 95%CI 1.61-5.11) of greater food insecurity compared with caregivers with a high neighborhood social cohesion score of 20. These associations remained robust when both social capital measures were included in one model. Caregiver social support and neighborhood social cohesion each appear to be inversely associated with food insecurity among US families with newborns. Longitudinal research is needed to determine the directionality of these relationships and whether improving social capital for families with young children reduces household food insecurity.

Trade-off between herbal and allopathic treatments: An ethnopharmacological case study in Rema-kalenga wildlife sanctuary, Bangladesh

The Rema-Kalenga Wildlife Sanctuary (RKWS) is a protected forest in Bangladesh that houses a variety of rare flora and fauna and supports the sustenance of 13 ethnic communities. This forest's indigenous and other inhabitants traditionally have a strong cultural connection to the plants, particularly medicinal plants. Due to modern allopathic medicine's rapid growth and commercial tree species prioritization, many medicinal plants are now on the verge of endangerment. Under such circumstances, it is crucial to explore how the local community perceives the importance of herbal treatments in contrast to allopathy, the underlying reasons for their perceptions, and the specific ailments for which they use the plants. The main objectives are: 1) to evaluate the perceptions of the local community towards allopathy and herbal medicine using Nussbaum's central capabilities approach, 2) to identify medicinal plant diversity, therapeutic usages, and quantitative indices, 3) to determine the factors that influence the use of medicinal plants. Repeated interviews and field surveys were conducted at the RKWS, interviewing 145 people, including the indigenous community (72.42 %) and traditional healers (8.27 %) from the surrounding seven villages. The study identified 51 medicinal plant species belonging to 39 families for their potent medicinal properties in treating various ailments. The predominant parts of the plants used in the treatments were leaves and roots. The uses were classified into 12 categories according to Nussbaum's central capabilities. The findings identified Aloe vera, Phyllanthus emblica, and Azadirachta indica as highly culturally important species. In contrast, Terminalia arjuna, Swertia chirata, and Azadirachta indica were found to have the highest relative importance. The underlying determinants influencing the preferences of individual users towards herbal medicine were income from agroforestry, beliefs, knowledge, and ethnicity, as revealed by the analysis of the ordinal logit model. The respondents viewed their strong inclination toward herbal medicine with many positive attitudes. Herbal medicine users held a negative perception of allopathy except for affiliation and practical reasons being viewed as the positive outcomes. Conversely, allopathic medicine users expressed mixed perceptions towards the treatment, with both positive and negative aspects being identified. Promoting the sustainable use of medicinal plants and their conservation efforts is imperative for the benefit of present and future generations in this region.

Classification-based consensus model considering quantum interference in linguistic distribution environment

Multi-attribute group classification (MAGC) is widely used in dealing with a variety of practical problems. However, cognitive biases, resulting from factors such as irrational behaviors, epistemic uncertainty, and interference phenomena, may critically affect the consensus reaching process (CRP) of MAGC. To eliminate such influences, this study proposes an ordinal classification consensus model in a linguistic environment based on quantum probability theory (QPT), with the aim of classifying the alternatives into several predefined ordinal classifications accepted by a majority of experts. First, the linguistic distribution assessments (LDAs), which can depict the quantitative distribution and qualitative vagueness, are used to express the uncertain preference of experts. Second, the LDAs best-worst method (BWM) and LDAs maximizing deviation model (MDM) are developed to identify the subjective and objective weight of attributes, and then these two types of attributes weight are integrated by quantum-like Bayesian networks (QLBN). Third, a maximum consensus model (MCM), maximizing the consensus level of ordinal classification results of experts, is provided to determine the parameters of QLBN. When the consensus level is lower than the predetermined threshold, a minimum adjustment distance model (MADM) is designed to guide the preference-modification of experts to derive the consensus classification of alternatives. Finally, a risk assessment case study is conducted to verify the feasibility and effectiveness of the proposed method. Sensitivity analysis and comparison analysis are performed to verify the feasibility and effectiveness of the proposed method

Enhancing building energy efficiency: Innovations in glazing systems utilizing solid-solid phase change materials

This study investigates the energy efficiency of a double-glazing window (DGW) integrating a solid–solid phase change material (SSPCM) with limited thickness, applied to the inner glass pane within the air gap. Numerical model, validated against experimental data, is developed using a finite volume method in ANSYS Fluent. In this model, the Discrete Ordinates (DO) model is applied to simulate radiation, while the enthalpy-porosity approach is used to capture the solidification and melting processes in the phase change material. With this model, the energy performance of the system is analyzed under various transient temperature values (10 to 30 °C) and ranges (1 to 5 °C) during the coldest and hottest days of the year, as well as during cloudy and sunny days in Montreal (Dfb), Vancouver (Cfb), and Miami (Aw). According to the obtained results in Montreal, the DGW-SSPCM system consistently saves energy under summer sunny conditions, with optimal performance when the SSPCM remains transparent. However, it incurs energy losses in cloudy days, where the energy lost is 2.3 times greater than the energy saved in sunny days. In Vancouver, the system shows consistent energy savings, particularly at Tc = 30 °C, with average savings of 20.5 kJ (23 %) under summer sunny conditions. The system is most beneficial in Vancouver, where winter energy savings in cloudy days (50.6 kJ) are 7.1 times greater than the losses in sunny days (7.1 kJ). In Miami, the system results in energy losses by 60 % and 5 % (at Tc = 30 °C) under both summer sunny and cloudy conditions, respectively, indicating unsuitability for its climate. During winter sunny conditions, all three cities experience energy losses, with Vancouver showing the lowest of 7.1 kJ (3 %) and Montreal the highest of 64.4 kJ (19 %) at Tc = 30 °C. In winter cloudy conditions, the system saves energy in all cities, with the highest savings in Miami of 54.5 kJ (26 %) at Tc = 30 °C. Overall, the SSPCM-DGW system has proven to be beneficial in Vancouver across various conditions in terms of energy and visual performance. These findings highlight the necessity of considering localized climate factors when designing and implementing energy-efficient glazing systems. Finally, the SSPCM-DGW system has provided complete visual clarity during office hours, making it more suitable for commercial buildings.

Associations of 2 Established Methods of Measuring Gait Speed and Sit-To-Stand Performance With Frailty and Life-Space Mobility in Community-Dwelling Older Adults

ObjectivesThe 4-m gait speed (4mGS) and 10-m gait speed (10mGS) tests and the 30-second sit-to-stand (30sSTS) and 5-times sit-to-stand (5xSTS) tests are commonly used and advocated in consensus recommendations. We compared these tests on their predictive and clinical value concerning the risk of prefrailty/frailty and restricted life-space mobility (RLSM). DesignCross-sectional study. Setting and ParticipantsA sample of 1235 community-dwelling adults (mean ± SD, 68 ± 7 years) participated in this prospective cohort study. MethodsAt baseline assessment, participants completed a survey and functional assessment, from which gait speed, sit-to-stand performance, self-reported mobility limitation, 40-item Frailty Index, and Life Space Assessment were measured. Participants with a 40-item Frailty Index >0.15 and a Life Space Assessment <60 points were classified as having prefrailty/frailty and RLSM, respectively. At 1-year follow-up assessment, prefrailty/frailty and RLSM were evaluated. ResultsCorrelations between gait speed and sit-to-stand measures were high (ρ values >0.80). In multivariable ordinal models, these measures added incremental prognostic value beyond a base model comprising demographics and self-reported mobility limitation variables in predicting baseline and 1-year outcomes. Between 10mGS and 4mGS, models with 10mGS had higher concordance indices (differences, 0.005-0.009), and these differences translated to generally greater net benefit in decision curve analyses. Between 30sSTS and 5xSTS measures, no one measure consistently outperformed the other, with small net benefit differences between measures (<0.2%). Conclusions and ImplicationsIn community-dwelling older adults, gait speed and sit-to-stand measures meaningfully predicted prefrailty/frailty and RLSM. 10mGS provided more robust prognostic information than the 4mGS, whereas 5xSTS and 30sSTS measures showed near equivalence of performance. These findings could guide the choice of functional measures in clinical and research settings.

Developing a Prediction Model for Periodontal Disease Using Ordinal Random Forest

Background: Periodontitis, a common inflammatory disease affecting oral health, is a major public health concern. Early detection of this disease is highly important in order to achieve less invasive and less costly treatment. The ordinal nature of different levels of periodontitis is usually not considered in many studies. It is often recognized and used as a nominal variable, in which case some information related to the ordinal nature is ignored. The aim of this study was to use continuation ratio logistic and ordinal random forest (RF) models to predict the three stages of periodontitis. Methods: Overall, this study evaluated 300 patients referred to the Periodontology Department of Hamadan University, western Iran, between September 2016 and June 2018. The performance of continuous ratio logistic and ordinal forest models was evaluated using the same set of training and test data to predict different types of periodontitis (gingivitis, localized, and generalized periodontitis) based on input variables. Accuracy, kappa, gamma, Somers’d, and precision were utilized for comparison. Results: The results confirmed the higher predictive ability of the ordinal RF model compared to the logistic continuation ratio model for all scoring indices (accuracy of 0.87 vs. 0.80). Alveolar bone loss, attachment loss, probing pocket depth, simplified oral hygiene index, and plaque index were identified as the most important variables. Conclusion: Due to the ordinal nature of different levels of periodontal disease, the use of accurate prediction models such as ordinal RF is suggested since they can take into account the ordinal nature of the response in predicting and evaluating the effect of important variables. Ordinal RF is a well-suited machine learning technique for developing accurate predictive models of periodontal disease risk.

Reliability-based ordinal consensus adjustment model for large scale group decision making

In large scale group decision-making (LSGDM), there are the substantial number of decision makers (DMs) with diverse knowledge, backgrounds, and interests related to the decision-making problem, and it is not possible to assure that all DMs are completely reliable. Thus, in order to enhance the quality of decision-making, it is necessary to analyze the reliabilities of DMs in LSGDM. This paper proposes the method to evaluate the reliabilities of DMs, sorts these DMs according to their degree of reliability, and investigates the consensus reaching process based on categories and an ordinal consensus measure. Considering the DMs' trust network, the uncertainty of a DM's evaluation information represented by a fuzzy preference relation (FPR), the deviation between a DM's FPR and those of the other DMs, and additive consistency of FPRs, the reliability of a DM is assessed using four criteria: PageRank centrality, professional competence, collaborative competence, and additive consistency. Following these reliability assessment criteria, ELECTRE-TRI is employed to sort DMs into three ordered categories according to DMs' different levels of reliability under the four assessment criteria. Furthermore, an improved ordinal consensus measure is designed to consider both the importance weights of positions and the deviation of Borda counts of the same alternative in two rankings. As for the consensus reaching process, due to the varied reliabilities of DMs in different categories, we propose a multiple strategies feedback mechanism for DMs in different categories. Finally, a numerical example is provided to illustrate the rationality and validity of the proposed model.

Statistical Power and Performance of Strategies to Analyze Composites of Survival and Duration of Ventilation in Clinical Trials.

Patients with acute hypoxemic respiratory failure are at high risk of death and prolonged time on the ventilator. Interventions often aim to reduce both mortality and time on the ventilator. Many methods have been proposed for analyzing these endpoints as a single composite outcome (days alive and free of ventilation), but it is unclear which analytical method provides the best performance. Thus, we aimed to determine the analysis method with the highest statistical power for use in clinical trials. Using statistical simulation, we compared multiple methods for analyzing days alive and free of ventilation: the t, Wilcoxon rank-sum, and Kryger Jensen and Lange tests, as well as the proportional odds, hurdle-Poisson, and competing risk models. We compared 14 scenarios relating to: 1) varying baseline distributions of mortality and duration of ventilation, which were based on data from a registry of patients with acute hypoxemic respiratory failure and 2) the varying effects of treatment on mortality and duration of ventilation. All methods have good control of type 1 error rates (i.e., avoid false positive findings). When data are simulated using a proportional odds model, the t test and ordinal models have the highest relative power (92% and 90%, respectively), followed by competing risk models. When the data are simulated using survival models, the competing risk models have the highest power (100% and 92%), followed by the t test and a ten-category ordinal model. All models struggled to detect the effect of the intervention when the treatment only affected one of mortality and duration of ventilation. Overall, the best performing analytical strategy depends on the respective effects of treatment on survival and duration of ventilation and the underlying distribution of the outcomes. The evaluated models each provide a different interpretation for the treatment effect, which must be considered alongside the statistical power when selecting analysis models.

What's the story of the elephant? Evaluation of choose‐your‐own‐adventure activities on public perception of human–elephant conflict

AbstractChoose‐your‐own‐adventure (CYOA) narratives offer immersive experiences that can effectively convey complex conservation concepts, fostering empathy and critical thinking, particularly in addressing issues like human–elephant conflict. Despite their potential, there is limited research on the use of CYOA activities in conservation education. In this study, we created an interactive story centered on elephant conservation, drawing from existing research to distill scientific concepts into engaging narratives and utilizing various modes of delivery (YouTube and live performances) to reach diverse audiences. We conducted postactivity surveys to assess variations in audience perception, learning, and conservation engagement intentions in relation to sociodemographic factors, activity type, and messages encountered. We then modeled the relationships between ordinal responses and explanatory variables using cumulative ordinal mixed models (N = 398). For the YouTube version, we also considered sociodemographic factors (YouTube n = 53, non‐YouTube n = 47). Our findings indicate a preference for live performances over online YouTube activity, with participants gaining knowledge about elephant conservation from both formats. For the YouTube activity, participants strongly agreed that the activity allowed engagement with the character and topic. They also expressed a higher likelihood of participating in several conservation actions, relative to a control group. While CYOA storytelling shows promise for conservation education, challenges remain in simplifying scientific language, assessing its impact on comprehension of complex issues, standardizing outcomes, and effectively communicating knowledge. Further research is recommended to adapt this approach, making it applicable to various audiences and domains beyond conservation.

Factor analysis of the feeding and eating in AutiSm Together Assessment

BackgroundAutistic children experience high rates of feeding challenges, negatively affecting their.health, eating patterns, and social eating experiences. The Feeding and Eating in AutiSm Together; FEAST, a caregiver-report questionnaire, was developed to characterize feeding challenges in autistic children ages 2 to 12 years. The purpose of this study is to determine the construct validity of the FEAST assessment using a large national sample of autistic children. MethodA large national sample (N = 396) of caregivers completed the online survey. An ordinal confirmatory factor model using a robust weighted least squares approach validated the four predicted groupings of feeding challenges (sensory processing; behavior; oral.motor skills; and gastrointestinal) with generally strong loadings onto the four distinct.factors. ResultsModel fit statistics indicated good model fit (Robust Root Mean Square Error of.Approximation=.080, Robust Comparative Fit Index =.909, Robust Tucker-Lewis.Index=.901) Findings provide preliminary confirmation of the construct validity of the.FEAST assessment to characterize feeding difficulties for autistic children ages 2–12. Small to medium correlations between all factors indicate items successfully targeted.four unique areas of feeding challenge. For all factors, the largest correlation was with.behavior. ConclusionAssessments developed to address the complex feeding and mealtime challenges for.autistic children are essential to identify and integrate the most appropriate intervention.approaches. FEAST offers researchers and clinicians with a comprehensive and psychometrically sound assessment tool to address autistic children’s unique feeding.challenges.

Sustainable management of polyethylene terephthalate waste flow using a fuzzy frank weighted assessment model

The consequences for the ecosystem of polyethylene terephthalate (PET) waste are becoming increasingly significant and widespread. Companies managing PET waste strive to enhance sustainability in all areas. The development of systematic decision-making approaches and frameworks for PET waste management is strongly needed. This research aims to present a new methodological framework for the categorization of the most efficient PET waste management solutions. The introduced fuzzy Frank weighted sum product assessment (FWESPA) model enables rational and flexible reasoning by nonlinearly processing uncertain information. A nonlinear aggregation function is proposed for the fusion of fuzzy strategic options. It is advantageous in simulating the impact of strategic options on a final decision. An integral part of the introduced fuzzy FWESPA model is a reverse sorting algorithm. This innovative algorithm can improve the performance of traditional normalization techniques. Also, an improved fuzzy Frank ordinal priority approach linear model is formulated to define the significance of evaluation criteria. The comprehensive real-life study demonstrates the proposed decision-analytics-based approach. The results showed the following rankings of considered alternatives: “recycling” (ℤA2 = 0.8565) > “energy recovery” (ℤA1 = 0.7364) > “remanufacturing” (ℤA4 = 0.690) > “incineration” (ℤA3 = 0.6592). Based on the results presented, alternatives “recycling” (A2) and “energy recovery” (A1) represent dominant alternatives with a slight advantage of recycling. Research findings can be used when deciding the appropriate way to enhance PET waste handling. The findings also describe the benefits and limitations of each treatment option for PET waste, as well as highlight the crucial challenges.

Effects of Growth and Treatment Conditions on the Quality of Norway Spruce (Picea abies L.) Sawn Timber

A study was conducted to improve the effectiveness of silvicultural production of structural sawn timber from softwoods. It intends to explore prediction methods for mechanical timber quality. The study material was obtained from six stands divided into age groups of approximately 40- and 80-year-old trees (examining the influence of age). The stands were differentiated by their applied thinning system of thinning from below or above (examining the influence of the thinning system). Resulting from these different levels of data, i.e., stand parameters, tree anatomy, and visual board properties are examined and analyzed in ordinal logistic models and linear mixed models. Visual board properties were discerned by means of the German standard for visual grading of sawn timber. The mechanical board properties were measured in on-edge bending strength tests and allocated into strength classes, which were modeled in dependence of visual characteristics and forestry conditions. The evaluation of mechanical properties attributed a significant loss of timber quality to short rotation periods, non-ideal water supply, and a single-tree management system. The prediction capabilities of models based on site and tree characteristics were on par with the accuracy of visual grading. Management adaptations by intense thinning from above can lead to a significant decline in Norway spruce (Picea abies L.) timber quality when site factors coincide. Particular care should be taken in the management of locations with high yield potential. Non-destructive evaluation based on site characteristics combined with terrestrial laser scan data of tree characteristics has potential as a pregrading method.

Grossman’s Generalised Health Demand Model: An Application on Türkiye

Health is the main source of economic activity and human capital. Each individual wants to be healthy throughout her life in order to maximize his/her individual benefit and strives for this. In this study, which examined the health demand of individuals, Grossman's health demand (consumption and investment) model, which pioneered research on health demand, was referenced. The demands of 25825 individuals for medical care included in the 2016 and 2019 health research micro datasets in Türkiye were examined using the negative binomial regression model. In addition, the effects of human capital components (education, income, etc.) on the general health status of individuals were analyzed using the ordinal logit model. Health demand is influenced by economic variables as well as social and cultural factors and individuals' life behaviors. Such variables are also included in the analysis. According to the results, it was seen that individuals with chronic diseases are more likely to receive medical care than those who do not have such a disease. Besides, it was concluded that education, income, and sports exercises improve the health status of individuals, whereas aging and an increase in the number of diseases worsen the health status.

The Ties That Bind: The Association Between Social Network Ties, Integration, and Stress Among Incarcerated Women

Social integration is theorized to enhance psychological well-being and reduce stress among incarcerated women. However, little empirical research has examined the association between prison social integration and women’s stress during incarceration. This study expands on previous research by (a) examining the relationship between different types of prison network ties among incarcerated women and self-reported stress and (b) exploring whether women’s perceptions of social integration mediate the association between prison network ties and stress. Using ordinal logistic models that incorporate social network and survey data from two women’s prisons in Pennsylvania ( N = 223) (WO-PINS), we find that pseudo-family–based, friendship-based, and trust-based network ties formed during incarceration are associated with reduced stress among incarcerated women. However, we find limited evidence that perceptions of social integration mediate the relationship between prison network ties and stress, suggesting that prison network ties are associated with stress reduction independent of an individual’s perception of integration.