Model-building Approach Research Articles

Role of policyholders’ beliefs in life insurance lapses: a model building approach

Purpose This study aims to understand the lapse behavior of life insurance policyholders. Despite being accessible for nearly two centuries, only a small fraction of individuals purchase such policies and many of those who do let them lapse. The belief hypothesis model (BHM) is introduced to elucidate the correlation between policyholders' beliefs and their decisions regarding life insurance lapses. Design/methodology/approach BHM establishes a comprehensive linkage between core beliefs, external data and the lapse behavior exhibited by policyholders. To derive policyholders’ core beliefs about life insurance lapses, the authors conducted a semistructured, in-depth interview with 42 policyholders and 11 insurance advisors, using a grounded theory approach with zero-order, first-order and second-order coding. Findings The study's findings reveal that policy lapsation is influenced by various factors such as policyholders' beliefs about life insurance, process-related attitudes, trust in insurers and advisors and personal financial viewpoints. Policyholders who consider life insurance unnecessary or misunderstand its purpose are likelier to lapse their policies. Cumbersome documentation processes and technical issues also contribute to policy lapsation, emphasizing the significance of simplified procedures. Trust in insurers and advisors, personal financial literacy and payment preferences influence policy lapsation. Practical implications The findings of this research can be practically applied by companies to improve customer retention and by regulatory bodies to encourage policyholders to honor their insurance commitments. Originality/value Distinguishing itself from conventional hypotheses-driven and factor-centric models, BHM integrates consumer beliefs, thus enriching comprehension and decision-making insights.

ACL Injury Etiology in Its Context: A Systems Thinking, Group Model Building Approach.

Background/Objectives: Given the complex nature of Anterior Cruciate Ligament (ACL) injury, it is important to analyze its etiology with suitable approaches in order to formulate intervention strategies for effective prevention. The present study employs system thinking techniques to develop a Causal Loop Diagram (CLD) Model for investigating the risk factors for ACL Injury (CLD-ACLI), through a Group Model Building approach. Methods: A two-stage procedure was applied involving a comprehensive literature review followed by several systems thinking group-modeling co-creation workshops with stakeholders. Results: Based on input from experts and stakeholders, combined with the latest scientific findings, the derived CLD-ACLI model revealed a series of interesting complex nonlinear interrelationships causal loops between the likelihood of ACL injury and the number of risk factors. Particularly, the interaction among institutional, psychological, neurocognitive, neuromuscular, malalignment factors, and trauma history seem to affect neuromuscular control, which subsequently may alter the biomechanics of landing, predisposing the ACL to injury. Further, according to the proposed CLD-ACLI model, the risk for injury may increase further if specific environmental and anatomical factors affect the shear forces imposed on the ACL. Conclusions: The proposed CLD-ACLI model constitutes a rigorous useful conceptual presentation agreed upon among experts on the dynamic interactions among potential intrinsic and extrinsic risk factors for ACL injury. The presented causal loop model constitutes a vital step for developing a validated quantitative system dynamics simulation model for evaluating ACL injury-prevention strategies prior to implementation.

The Complex Interrelationships of the Risk Factors Leading to Hamstring Injury and Implications for Injury Prevention: A Group Model Building Approach

There is a gap in the literature regarding the complex interrelationships among hamstring injury (HI) risk factors. System dynamics (SD) modeling is considered an appropriate approach for understanding the complex etiology of HI for effective injury prevention. This study adopted the SD method and developed a causal loop model (CLD) to elucidate the intricate relationships among HI risk factors. This is performed by combining literature evidence and insights from expert stakeholders through a group model building (GMB) approach. The GMB methodology facilitated the identification of sixty-five critical factors influencing the HI risk, revealing the dynamic interplay between factors. Stakeholder engagement underscored the importance of previous injury characteristics (level of influence of previous injury, severity of previous injury, quality and size of scar tissue) and the quality of rehabilitation. HI-CLD revealed that many factors had indirect effects on HI risk. The HI-causal loop model establishes a foundation for a future stock and flow quantitative SD model aiming to advance HI prevention strategies through an interdisciplinary collaborative effort. These findings underscore the complexity of HI prevention, necessitating a holistic approach that integrates the views of diverse professional expertise. Appropriate inter-professional collaboration and continuous athlete screening are important for effective injury prevention strategies.

A novel data-driven approach for coal-fired boiler under deep peak shaving to predict and optimize NOx emission and heat exchange performance

Coal-fired boilers considering deep peak shaving operate under wide and variable load requirements with complex and varying working conditions, making the prediction and optimization of combustion target variables challenging. This study proposed a data-driven approach for the prediction model building, tuning and combustion optimization. In this approach, a neural network for prediction based on Gaussian kernel and long short-term memory (LSTM), which captured and fused the working condition and temporal information, was first proposed. To address incomplete historical data coverage of working conditions, a fine-tuning framework with the idea of transfer and few-shot tuning was then introduced. Finally, an improved fitness function considering both optimization objectives and combustion adjustment limitations was designed to mitigate the risks associated with large-scale adjustments of operable variables. By conducting experiments on a 1000 MW boiler, targeting NOx concentration and the outlet temperature of the air pre-heater, the proposed method demonstrated high prediction accuracy with R2 values of 0.979 and 0.989, and significantly reduced mean squared error (MSE) by 98.05% and 97.37% on new conditions compared to untuned models. It achieved the reductions in NOx and temperature of 24.70 mgm−3 and 2.013 ∘C, and decreased the average changes of operable variables by 9.66% and 11.24% compared to using only rigid constraints.

Binding Activity Classification of Anti-SARS-CoV-2 Molecules using Deep Learning Across Multiple Assays

The coronavirus disease-2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2), has urgently necessitated effective therapeutic solutions, with a focus on rapidly identifying and classifying potential small-molecule drugs. Given traditional methods’ labor-intensive and time-consuming nature, deep learning has emerged as an essential tool for efficiently processing and extracting insights from complex biological data. To utilize deep learning techniques, particularly deep neural networks (DNN) enhanced with the synthetic minority oversampling technique (SMOTE), to enhance the classification of binding activities in anti-SARS-CoV-2 molecules across various bioassays. We used 11 bioassay datasets covering various SARS-CoV-2 interactions and inhibitory mechanisms. These assays ranged from spike-ACE2 protein-protein interaction to ACE2 enzymatic activity and 3CL enzymatic activity. To address the prevalent class imbalance in these datasets, the SMOTE technique was employed to generate new samples for the minority class. In our model-building approach, we divided the dataset into 80% training and 20% test sets, reserving 10% of the training set for validation. Our approach involved employing a DNN that integrates ReLU and sigmoid activation functions, incorporates batch normalization, and uses Adam optimization. The hyperparameters and architecture of the DNN were optimized through various tests on layers, minibatch sizes, epoch sizes, and learning rates. A 40% dropout rate was incorporated to mitigate overfitting. For model evaluation, we computed performance metrics, such as balanced accuracy (BACC), precision, recall, F1 score, Matthews’ correlation coefficient (MCC), and area under the curve (AUC). The performance of the DNN across 11 bioassay test sets revealed varying outcomes, significantly influenced by the ratios of active-to-inactive compounds. Assays, such as AlphaLISA and CoV-PPE, demonstrated robust performance across various metrics, including BACC, precision, recall, and AUC, when configured with more balanced ratios (1:3 and 1:1, respectively). This suggests the effective identification of active compounds in both cases. In contrast, assays with higher imbalance ratios, such as 3CL (1:38) and cytopathic effect (1:15), demonstrated higher recall but lower precision, highlighting challenges in accurately identifying active compounds among numerous inactive compounds. However, even in these challenging settings, the model achieved favorable BACC and recall scores. Overall, the DNN model generally performed well, as indicated by the BACC, MCC, and AUC values, especially when considering the degree of dataset imbalance in each assay. This study demonstrates the significant impact of deep learning, particularly DNN models enhanced with SMOTE, in improving the identification of active compounds in bioassay datasets for COVID-19 drug discovery, outperforming traditional machine learning models. Furthermore, this study highlights the efficacy of advanced computational techniques in addressing high-throughput screening data imbalances.

Sex, gender diversity, and brain structure in early adolescence.

There remains little consensus about the relationship between sex and brain structure, particularly in early adolescence. Moreover, few pediatric neuroimaging studies have analyzed both sex and gender as variables of interest-many of which included small sample sizes and relied on binary definitions of gender. The current study examined gender diversity with a continuous felt-gender score and categorized sex based on X and Y allele frequency in a large sample of children ages 9-11 years old (N = 7195). Then, a statistical model-building approach was employed to determine whether gender diversity and sex independently or jointly relate to brain morphology, including subcortical volume, cortical thickness, gyrification, and white matter microstructure. Additional sensitivity analyses found that male versus female differences in gyrification and white matter were largely accounted for by total brain volume, rather than sex per se. The model with sex, but not gender diversity, was the best-fitting model in 60.1% of gray matter regions and 61.9% of white matter regions after adjusting for brain volume. The proportion of variance accounted for by sex was negligible to small in all cases. While models including felt-gender explained a greater amount of variance in a few regions, the felt-gender score alone was not a significant predictor on its own for any white or gray matter regions examined. Overall, these findings demonstrate that at ages 9-11 years old, sex accounts for a small proportion of variance in brain structure, while gender diversity is not directly associated with neurostructural diversity.

Heightened response to positive facial cues as a potential marker of resilience following childhood adversity

ABSTRACT Background: Childhood maltreatment profoundly influences social and emotional development, increasing psychiatric risk. Alterations in the implicit processing of threat-related cues following early abuse and neglect represent a marker of mental health vulnerability. Less is known about how early adversity influences the perception of positive social cues, despite their central role in establishing and maintaining social interactions and their association with better mental health outcomes. Methods: The sample consisted of 42 children and adolescents with substantiated childhood maltreatment experiences and 32 peers (mean age 13.3), matched on age, pubertal status, gender, socioeconomic status, ethnicity, and cognitive ability. A computerised experimental task assessed the perceived emotional intensity of positive (happy) and negative (fearful) facial expressions. Mental health symptoms were measured via self- and parental reports, and perceived social support was self-reported. Results: The experience of abuse and neglect was associated with heightened perceived intensity of positive facial cues. Cross-sectional post-hoc moderation and mediation analyses, employing a model-building approach, revealed that in maltreatment-exposed participants: (i) their increased response to positive facial cues was associated with lower symptoms; (ii) the presence of social support accounted for their heightened perceived intensity of positive facial cues; (iii) the presence of social support putatively contributed to lower symptoms by increasing the perceived intensity of positive facial cues. No group differences in perceived intensity of negative expressions were observed. Conclusions: These findings provide fresh insight into how positive faces are processed following maltreatment experience in childhood. Maltreatment experience was found to be associated with heightened perceived intensity of happy faces, which in turn was associated with better mental health and greater levels of social support. This suggests that heightened saliency of positive emotions acts protectively in children with maltreatment experience.

Developing a Model for Quantifying QTc-Prolongation Risk to Enhance Medication Safety Assessment: A Retrospective Analysis.

There are currently no established methods to predict quantitatively whether the start of a drug with the potential to prolong the QTc interval poses patients at risk for relevant QTc prolongation. Therefore, this retrospective study aimed to pave the way for the development of models for estimating QTc prolongation in patients newly exposed to medications with QTc-prolonging potential. Data of patients with a documented QTc prolongation after initiation of a QTc-prolonging drug were extracted from hospital charts. Using a standard model-building approach, general linear mixed models were identified as the best models for predicting both the extent of QTc prolongation and its absolute value after the start of a QTc-time-prolonging drug. The cohort consisted of 107 adults with a mean age of 64.2 years. Patients were taking an average of 2.4 drugs associated with QTc prolongation, with amiodarone, propofol, pipamperone, ondansetron, and mirtazapine being the most frequently involved. There was a significant but weak correlation between measured and predicted absolute QTc values under medication (r2 = 0.262, p < 0.05), as well as for QTc prolongation (r2 = 0.238, p < 0.05). As the developed models are based on a relatively small number of subjects, further research is necessary to ensure their applicability and reliability in real-world scenarios. Overall, this research contributes to the understanding of QTc prolongation and its association with medications, providing insight into the development of predictive models. With improvements, these models could potentially aid healthcare professionals in assessing the risk of QTc prolongation before adding a new drug and in making informed decisions in clinical settings.

Uncertainty quantification in neural-network based pain intensity estimation.

Improper pain management leads to severe physical or mental consequences, including suffering, a negative impact on quality of life, and an increased risk of opioid dependency. Assessing the presence and severity of pain is imperative to prevent such outcomes and determine the appropriate intervention. However, the evaluation of pain intensity is a challenging task because different individuals experience pain differently. To overcome this, many researchers in the field have employed machine learning models to evaluate pain intensity objectively using physiological signals. However, these efforts have primarily focused on pain point estimation, disregarding inherent uncertainty and variability in the data and model. A point estimate, which provides only partial information, is not sufficient for sound clinical decision-making. This study proposes a neural network-based method for objective pain interval estimation, and quantification of uncertainty. Our approach, which enables objective pain intensity estimation with desired confidence probabilities, affords clinicians a better understanding of a person's pain intensity. We explored three distinct algorithms: the bootstrap method, lower and upper bound estimation (LossL) optimized by genetic algorithm, and modified lower and upper bound estimation (LossS) optimized by gradient descent algorithm. Our empirical results demonstrate that LossS outperforms the other two by providing narrower prediction intervals. For 50%, 75%, 85%, and 95% prediction interval coverage probability, LossS provides average interval widths that are 22.4%, 7.9%, 16.7%, and 9.1% narrower than those of LossL, and 19.3%, 21.1%, 23.6%, and 26.9% narrower than those of bootstrap. As LossS outperforms, we assessed its performance in three different model-building approaches: (1) a generalized approach using a single model for the entire population, (2) a personalized approach with separate models for each individual, and (3) a hybrid approach with models for clusters of individuals. Results demonstrate that the hybrid model-building approach provides the best performance.

Early nap cessation in young children as a correlate of language and psychosocial outcomes: Evidence from a large Canadian sample

ObjectivesMost children stop napping between 2 and 5years old. We tested the association of early nap cessation (ie, children who stopped before their third birthday) and language, cognition functioning and psychosocial outcomes. MethodsData were from a national, longitudinal sample of Canadian children, with three timepoints. Children were 0-to-1 year old at T1, 2-to-3 years old at T2, and 4-to-5 years old at T3. Early nap cessation was tested as a correlate of children’s psychosocial functioning (cross-sectionally and longitudinally), cognitive function (longitudinally), and language skills (longitudinally). There were 4923 children (50.9% male; 90.0% White) and their parents in this study who were included in the main analyses. Parents reported on demographics, perinatal and developmental variables, child functioning, and child sleep. Children completed direct assessments of receptive language and cognitive ability. Nap cessation, demographic, and developmental-control variables were tested as correlates of cross-sectional and longitudinal outcomes using linear regression (with a model-building approach). ResultsEarly nap cessation correlated with higher receptive language ability (β = 0.059 ± 0.028) and lower anxiety (β = −0.039 ± 0.028) at T3, after controlling for known correlates of nap cessation, nighttime sleep, and other sociodemographic correlates of the outcomes. Cognitive ability, hyperactivity-inattention, and aggression were not correlated with nap cessation. ConclusionsEarly nap cessation is related to specific benefits (ie, better receptive language and lower anxiety symptoms). These findings align with previous research. Future research should investigate differences associated with late nap cessation and in nap-encouraging cultures, and by ethnicity.

Examining Associations Between Cannabis Use Disorder and Measures of Weekly and Within-Day Cannabis Frequency, Quantity, and Potency in College Students.

Background: College student cannabis use has increased significantly in recent years, and individuals aged 18-25 are at elevated risk for development of cannabis use disorder (CUD). While weekly cannabis use frequency is a commonly used measure of cannabis consumption, there is increasing scientific interest in exploring more nuanced measures of cannabis use. Currently, limited research exists examining the clinical utility of cannabis quantity, within-day frequency, and potency variables. Methods: We used cross-sectional survey data from a sample of 617 undergraduate students in the state of Colorado. A two-part model-building approach was leveraged to examine whether within-session cannabis quantity and within-day cannabis use frequency were associated with odds of experiencing any CUD symptoms and total number of CUD symptoms endorsed. We also examined whether cannabis flower potency was associated with odds of experiencing any CUD symptoms and total number of CUD symptoms endorsed among a subset (N=288) of the sample who reported knowledge of the cannabinoid content of their most frequently used products. Results: Weekly flower use frequency (odds ratio [OR]=1.27, p<0.001) and weekly concentrate use frequency (OR=1.10, p=0.044) were positively associated with increased odds of experiencing any CUD symptoms, but cannabis quantity and within-day frequency variables were not. In addition, no association was found between flower potency and odds of endorsing any CUD symptoms. Among individuals endorsing at least one symptom, weekly flower use frequency (incident rate ratio [IRR]=1.06, p<0.001) was positively associated with total symptom count, but weekly concentrate use frequency, cannabis quantity variables, and within-day frequency variables were not. Among individuals endorsing symptoms, a positive association was found between flower potency and total symptom count (IRR=1.01, p=0.008). Conclusion: Current methods of assessing within-session cannabis quantity and within-day cannabis use frequency may lack clinical utility in examining college student CUD symptoms over and above weekly cannabis use frequency. Cannabis flower potency may prove useful in assessment of CUD symptom severity, but further research is warranted.

Interface State Density Prediction between an Insulator and a Semiconductor by Gaussian Process Regression Models for a Modified Process.

During data-driven process condition optimization on a laboratory scale, only a small-size data set is accessible and should be effectively utilized. On the other hand, during process development, new operations are frequently inserted or current operations are modified. These accessible data sets are somewhat related but not exactly the same type. In this study, we focus on the prediction of the quality of the interface between an insulator and GaN as a semiconductor for the potential application of GaN power semiconductor devices. The quality of the interface was represented as the interface state density, Dit, and the inserted operation to the process was the ultraviolet (UV)/O3-gas treatment. Our retrospective evaluation of model-building approaches for Dit prediction from a process condition revealed that for the UV/O3-treated interfaces, data of interfaces without the treatment contributed to performance improvement. Such performance improvement was not observed when using a data set of Si as the semiconductor. As a modeling method, the automatic relevance vector-based Gaussian process regression with the prior distribution of the length-scale parameters exhibited a relatively high predictive performance and represented a reasonable uncertainty of prediction as reflected by the distance to the training data set. This feature is a prerequisite for a potential application of Bayesian optimization. Furthermore, hyperparameters in the prior distribution of the length-scales could be optimized by leave-one-out cross-validation.

Systems modelling as an approach for eliciting the mechanisms for hip fracture recovery among older adults in a participatory stakeholder engagement setting.

Due to an aging population, the rising prevalence and incidence of hip fractures and the associated health and economic burden present a challenge to healthcare systems worldwide. Studies have shown that a complex interplay of physiological, psychological, and social factors often affects the recovery trajectories of older adults with hip fractures, often complicating the recovery process. This research aims to actively engage stakeholders (including doctors, physiotherapists, hip fracture patients, and caregivers) using the systems modeling methodology of Group Model Building (GMB) to elicit the factors that promote or inhibit hip fracture recovery, incorporating a feedback perspective to inform system-wide interventions. Hip fracture stakeholder engagement was facilitated through the Group Model Building approach in a two-half-day workshop of 25 stakeholders. This approach combined different techniques to develop a comprehensive qualitative whole-system view model of the factors that promote or inhibit hip fracture recovery. A conceptual, qualitative model of the dynamics of hip fracture recovery was developed that draws on stakeholders' personal experiences through a moderated interaction. Stakeholders identified four domains (i.e., expectation formation, rehabilitation, affordability/availability, and resilience building) that play a significant role in the hip fracture recovery journey.. The insight that recovery of loss of function due to hip fracture is attributed to (a) the recognition of a gap between pre-fracture physical function and current physical function; and (b) the marshaling of psychological resilience to respond promptly to a physical functional loss via uptake of rehabilitation services is supported by findings and has several policy implications.

System Dynamics Modelling: Integrating Empty Fruit Bunch Biomass Logistics to Reduce GHG Emissions

The world is shifting toward renewable energy sources due to global warming and rising GHG emissions. Malaysia has joined other nations in the conference of parties to develop policies for the reduction of GHG and carbon emissions. Malaysia is switching towards sustainable, eco-friendly and renewable energy sources. EFB biomass, one of the by-products of palm oil, has enormous potential as a sustainable energy source. Malaysia, one of the top exporters of palm oil, is unable to employ EFB-biomass-based power generation due to storage, logistics and supply-chain-related constraints. Therefore, this study integrates EFB biomass supply-chain logistics to overcome the reported challenges. The current study employs the system dynamics (SD) approach to achieve the objectives as it explains the dynamics of interaction and behaviour among the sub-systems. A document-based model-building approach is employed to collect data to develop the base model. The document-based model-building approach and system dynamics modelling facilitates the achievement of two outcomes: integrated EFB biomass logistics and GHG reduction using EFB. These outcomes are crucial to enhancing the base model and realizing the zero-carbon emission goal to contribute to sustainable development goals.

Linear Quadratic Control Using Model-Free Reinforcement Learning

In this article, we consider linear quadratic (LQ) control problem with process and measurement noises. We analyze the LQ problem in terms of the average cost and the structure of the value function. We assume that the dynamics of the linear system is unknown and only noisy measurements of the state variable are available. Using noisy measurements of the state variable, we propose two model-free iterative algorithms to solve the LQ problem. The proposed algorithms are variants of policy iteration routine where the policy is greedy with respect to the average of all previous iterations. We rigorously analyze the properties of the proposed algorithms, including stability of the generated controllers and convergence. We analyze the effect of measurement noise on the performance of the proposed algorithms, the classical off-policy, and the classical <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Q$</tex-math></inline-formula> -learning routines. We also investigate a model-building approach, inspired by adaptive control, where a model of the dynamical system is estimated and the optimal control problem is solved assuming that the estimated model is the true model. We use a benchmark to evaluate and compare our proposed algorithms with the classical off-policy, the classical <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Q$</tex-math></inline-formula> -learning, and the policy gradient. We show that our model-building approach performs nearly identical to the analytical solution and our proposed policy iteration-based algorithms outperform the classical off-policy and the classical <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Q$</tex-math></inline-formula> -learning algorithms on this benchmark but do not outperform the model-building approach.

Elucidating the relationship between red fluorescence and structural dynamics of carbon dots dispersed in different solvents.

The mechanism of intrinsic fluorescence of carbon dots (CDs), the latest nanomaterial from the carbon family, was supposedly deciphered through multiple theories. However, the much sought-after persistent red emission of CDs as a foreseeable consequence of experiments remains elusive prompting the question of whether tuning of the red emission of CDs is a predictable outcome or a serendipitous coincidence. Herein, we tried to decode the same by exploring Alizarin Red S (ARS)-based red emitting CDs in different solvents with wisely chosen analytical tools. The findings are aptly supported by molecular dynamics studies through an experimental intuition-driven model-building approach. Parallel interception of the CDs with powder X-ray diffraction (pXRD) and photophysical spectroscopic studies revealed an important relationship between the solvent and CDs. Tautomerism, a well-known phenomenon with chemical entities, was found to be operative for CDs that greatly influence the Stokes shift and ultimately the fluorescence outcome. Most importantly, pXRD studies established the turbostratism of the CDs where the well-ordered graphitic structure of CDs gets disrupted with solvent molecules. The extent of such disruption is a function of solvent and CD composition that plays a formidable role in obtaining red fluorescence. Thus, for the first time, we demonstrate that the red emission of CDs is related to its structural integrity and if taken care of could be sustained, a tremendously desirable outcome for relevant applications.

A new approach to event- and model-based feature-driven software testing and comparison with similar approaches

A software can be thought as a composition of features. Feature-oriented software development (FOSD) builds the development process on features. Part of the FOSD process is testing, and accordingly, it should be feature-driven. In model-based testing, test cases are systematically generated using the model. This research concentrates on event-based graphical models and utilizes event sequence graphs (ESGs). We develop a new test sequence generation algorithm for ESGs and named it short and frequent test sequences (SFT). Then we compare it with the existing test sequence generation algorithm called TSD. Moreover, we introduce two model-building approaches, namely daisy and swim lane, for ESGs and analyze their effects on feature-driven testing. For the evaluation, we use five different feature-driven software models. The evaluation results shows that both modeling approaches are advantageous in certain test objectives. For testing the software product as a whole, test sequence(s) should be generated by TSD from daisy modeled ESG. If a certain feature within the software product or its interaction with another feature is to be tested, then test sequence(s) should be generated by SFT from swim lane modeled ESG.

Predictors of Early Nap Cessation: Longitudinal Findings from a Large Study of Young Children

Most children cease napping between 2 and 5-years-old, with considerable inter-child variability. We tested the predictors of early nap cessation (i.e., children who cease napping before three years old) using longitudinal data from 5504 Canadian children (51.1% male; 89.8% White) in three cohorts with two timepoints each. Children were 0–1-years-old at baseline (M = 10.19 months SD = 3.95 months) and 2–3-years-old at follow-up (M = 30.83 months, SD = 4.60 months). Parents reported on demographic, perinatal, growth, developmental, child and parent functioning, and child sleep variables. At follow-up, 10.9% ± 0.8% had ceased napping. Multigroup multivariate logistic regression was conducted using a model building approach to identify predictors of early nap cessation. Early nap cessation was predicted by older child age (ORs range from 1.15 to 1.24, moderated by cohort), female sex (OR = 1.29; 95% CI: 1.07–1.55), having an older sibling (OR = 1.33; 95% CI: 1.10–1.62), achieving more developmental milestones (OR = 1.08; 95% CI: 1.03–1.13), and longer nighttime sleep duration (OR = 1.06; 95% CI: 1.01–1.11). Non-White ethnicity (OR = 0.41; 95% CI: 0.28–0.60), birthweight < 2500 gs (OR = 0.60; 95% CI: 0.37–0.96), parent working/in school (ORs range from 0.50 to 0.58, moderated by cohort), and the birth mother consuming alcohol during pregnancy (OR = 0.56; 95% CI: 0.40–0.79) were related to a lower likelihood of nap cessation. Findings suggest nap cessation is influenced by developmental and socio-environmental factors.

Understanding EU Fisheries Management Dynamics by Engaging Stakeholders through Online Group Model-Building

The Common Fisheries Policy (CFP) has a challenging mandate to find the right policy mix to simultaneously achieve all three aspects of sustainability: economic, social, and environmental. But development and implementation of an effective and sustainable fisheries management policy has been a challenge all over the world. The evidence of this failure is found in the continuous decline in fish stocks. Faced with the difficulty in fulfilling this mandate, the European Commission has increasingly been embracing fisheries stakeholders’ involvement. Stakeholder involvement in policy development and implementation is important because it tries to bring the relevant interested parties together, understanding and paying attention to what is important to each and every stakeholder, identifying the individual and common issues. This process in turn can foster connections, trust, confidence, and buy-in, and commitment for the implementation of the policy. This research describes a group model-building (GMB) approach using system dynamic methodology as a participatory model building tool, enabling stakeholders to become deeply involved in the identification and modelling of the complex issues faced by the EU fisheries. Given the geographical diversity of the stakeholders, GMB was applied online, both synchronously and asynchronously, providing participants time to carefully reflect on key variables, their relationships, and the behaviour of the overall system. The study demonstrated the need and relevance of an adequate engagement of the stakeholders, with online stakeholder consultation proving an effective method of engagement. Hence, the study is very relevant for both scientists and managers. The GMB process meant the final model evolved significantly from the initial one offered, which pointed to active involvement in and progressive learning from the modelling process itself, as the methodology argues. Two quantitative stock-flow models using actual numbers were built not only to aid the GMB process but to depict how all three aspects of sustainability could actually be met with the right set of policies that consider feedback loops and inherent trade-offs.

Associations between sleep and overweight/obesity in adolescents vary by race/ethnicity and socioeconomic status.

To examine the interaction between sleep and social determinants of health (SDOH) [race/ethnicity and socioeconomic status (SES)] on overweight/obesity in adolescents. Cross-sectional. We conducted a secondary analysis using the 2017-2018 National Survey of Children's Health data. We included adolescents (10-17 years old) who had sleep and body mass index (BMI) data available (n=24,337) in analyses (samples with BMI <5th percentile excluded). Parents reported children's sleep duration and regularity. High BMI (≥85th percentile) for age defines overweight/obesity. We selected SDOH (race/ethnicity, family income, primary caregiver education and neighbourhood condition) and covariates (age, sex, smoking, exercise and depression) using a hierarchical model-building approach. Accounting for complex survey design, logistic regression estimated the interaction between sleep and SDOH. There were significant interactions between sleep duration and SDOH. The association between increasing sleep and decreasing odds of overweight/obesity only showed in the following subgroups: White, family income ≥400% federal poverty level (FPL) or primary caregiver' education ≥ high school. Compared with these subgroups, Hispanic adolescents and adolescents whose family income was below 100% FPL and whose caregiver education was below high school had weakened and reversed associations. Sleep regularity was not associated with overweight/obesity. Increasing sleep duration was associated with a decreased risk of overweight/obesity, but the association was not present in adolescents from racial/ethnic minority groups (i.e. Hispanic) and those with low SES. The study findings suggest that associations between sleep and overweight/obesity vary by race and SES. Identification of additional mechanisms for obesity is needed for racial/ethnic minority groups and those from families with low SES. Also, the complexity of these relationships underscores the importance of community-based needs assessment in the design of targeted and meaningful interventions to address complex health conditions such as poor sleep and obesity.