Towards accurate transcutaneous CO2 sensing: A behavioral model using time-correlated single photon counting.

Travel booking behaviour under prolonged disruption: A behavioural orientation-adaptation trajectories model

Applying hidden markov models for user and entity behavioural analytics model

The use of pesticides in agriculture boosts food production yields. However, pesticides are directly linked to declining pollinator populations, consequently impacting crop productivity. Assessing the effects of pesticides on pollinating bees is crucial for informing crop management strategies and public policies. This manuscript provides a dataset from the study that evaluated the toxicity of the active ingredients λ-cyhalothrin (insecticide) and fenpyroximate (acaricide) on the stingless bee species Nannotrigona testaceicornis (Apidae: Meliponini). The results include measurements of bee's weight, survival, ingestion rates, behavioural responses, and the bee behavioural stress index (BSI). This dataset can be further used for ecological risk assessments, determination of risk factors and quotients, and developing behavioural indices, models, and clustering analyses. Policymakers can use this dataset to support their decision-making processes.

Behavioral logic and model analysis of smart phone user interface interaction design

Latent behavioural modelling of multi-dimensional vehicle parking patterns for urban EV charging planning

Cyclists navigating roundabout crosswalks face heightened crash risk due to ambiguous right-of-way rules and the absence of signal control. Understanding their operational behaviour at these conflict points is essential for designing safer facilities. Existing behavioural models often overlook the way cyclists perceive vehicle motion when deciding to cross and spatial dependence among feasible trajectories. This study develops a Generalized Spatially Correlated Nested Logit (GSCNL) model to capture cyclists' decision processes when crossing in the presence of motorized vehicles. The model extends the spatial Generalized Extreme Value (GEV) framework to represent correlation among spatially adjacent movement alternatives. Each alternative corresponds to a potential future position that a cyclist may occupy at the next time step. Spatial grouping is used to model dependencies among neighbouring choices. Two perception assumptions were investigated: (1) cyclists perceive interacting vehicles as moving in constant velocity, and (2) cyclists perceive vehicles as following curved paths. Results show that the constant-velocity formulation provides a superior fit, suggesting that cyclists may not exhibit strong perceptual sensitivity to vehicle acceleration. The proposed GSCNL and the conventional Spatially Correlated Nested Logit (SCNL) model achieved a similar overall fit. However, the GSCNL offered enhanced interpretability of non-proportional substitution patterns through its spatial correlation structure. The findings suggest potential advancements in spatial choice modelling of vulnerable road users. They may also inform the development of safer roundabout designs that better reflect the behavioural mechanisms underlying cyclists’ crossing decisions. • Introduces the GSCNL model to analyse cyclists' crossing behaviour at roundabouts. • Uses spatio-temporal composite proximity variables to capture cyclists' perception of approaching vehicles. • Shows cyclists mainly perceive vehicles as moving straight, with limited response to acceleration cues. • Offers an interpretable framework for microsimulation and cyclist-aware roundabout crosswalk design.

A theoretical perspective is necessary for developing a research model to explore the phenomenon of seeking professional psychological help. Previous studies on seeking professional psychological help still show inconsistent results from a theoretical perspective. This scoping review aims to conduct a scoping literature review of the theoretical perspectives employed in studies of adults seeking professional psychological help. The research method employs the population, concept, and context (PCC) framework from five databases: Scopus, Web of Science, PubMed, ScienceDirect, and ProQuest (academic journals, dissertations, and theses). The databases were accessed in March 2025, yielding 10 articles that met the inclusion criteria (empirical research articles published in English; context: focus on mental health; concept: theoretical perspective/theoretical framework/conceptual theory of seeking help from professionals/formal settings; population: age >18 years; research methods: quantitative and qualitative). The research findings indicate that seven theoretical perspectives are frequently employed in studies of professional psychological help-seeking behavior: Andersen’s Behavioral Model of Health Services Use, the Theory of Planned Behavior, Bronfenbrenner’s Bioecological Theory, the Commission on Social Determinants of Health, Social Identity Theory, the Person-Centered Approach, and the Health Belief Model. The results of this study provide a foundation for future investigations into the behavior of adults in seeking professional psychological assistance.

Artificial neural networks (ANNs) are increasingly applied in health care outcome prediction, yet their relative benefits compared with traditional methods in health services research remain unclear. To examine health care utilization and costs among community-dwelling older adults using the Andersen Behavioral Model, and to compare the performance of logistic regression and ANN models. Cross-sectional study utilizing linked data from CMS Medicare fee-for-service (FFS) claims and Consumer Assessment of Healthcare Providers and Systems (CAHPS) surveys (2018-2022). The sample included 254,748 Medicare beneficiaries aged 65 and older. Outcomes were high Medicare costs (top 25%), 30-day readmissions, and preventable hospitalizations (PQIs). Predictors included socioeconomic factors, chronic conditions, and patient-reported measures. Model performance was assessed using the area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and Brier scores. Chronic conditions, including heart disease and depression, significantly predicted higher Medicare costs. Poor self-rated health, functional limitations, dual eligibility, and lower educational attainment correlated strongly with readmissions and preventable hospitalizations. ANN and logistic regression models demonstrated comparable performance across outcomes, with similar AUC, sensitivity, specificity, PPV, NPV, and Brier scores. Both logistic regression and ANN models effectively predict health care utilization and high-risk outcomes among older adults using structured Medicare data. Logistic regression offers interpretability and robust predictive power, whereas ANN models may provide additional value as healthcare datasets grow increasingly complex and comprehensive.

Psychological impact and coping responses of non-offending caregivers following child sexual abuse disclosure: A qualitative systematic review.

Low-component design and behavioral modeling of a 343-level inverter system

Healthcare access is essential for fostering resilient communities and improving health outcomes. Limited access to healthcare services can negatively affect healthcare utilization and exacerbate existing health disparities. Traditionally, healthcare access has been conceptualized in terms of potential access, often measured by the distance to the nearest facility. However, this perspective overlooks the behavioral dimension of access, which captures how people actually utilize existing facilities. To address this research gap, this paper examines realized access to advance our understanding of healthcare access by integrating both spatial and social dimensions into human mobility modeling. Specifically, we develop a mobility behavioral model based on discrete choice theory to uncover people's decision-making process in healthcare access. Model parameters are then estimated using maximum likelihood method. We further conduct a case study to examine human mobility patterns in pharmacy access in Los Angeles County, California. Our results highlight that both spatial proximity and social similarity are critical determinants in modeling people's access behaviors. Particularly, social similarity indicates that people are more likely to access pharmacies located in communities with sociodemographic characteristics similar to their own. Our mobility model also achieves higher predictive accuracy than the random forest model. Overall, this paper offers insights to support decision-making in healthcare access planning, emphasizing that services should be both geographically proximate and socially accessible to the communities theyserve.

Abstract Learning from experience is theorised to be driven by reward prediction error (RPE) signals that reflect updates to our expectations of reward. Despite numerous studies on the neural correlates of RPEs, the question of how large-scale networks (LSNs) in the brain reconfigure in response to an RPE learning signal remains open. Here we examine how functional networks change in response to RPEs depending on the context. In our study participants performed a probabilistic reversal learning task whilst we acquired fMRI data in two experimental settings: reward-seeking and punishment-avoiding. Participants' behavior was best explained by models with different learning rates for positive and negative RPEs. Furthermore, no evidence was found for context-dependent learning rates. Using behaviorally fitted RPE models, we performed a whole-brain network analysis. This analysis revealed classical reward structures, where striatal reward networks emerge as modules when the community structure is examined at a finer resolution, and a ventromedial prefrontal network emerges at a coarser resolution. Using the same behavioral model, we found that, compared to negative RPEs, positive RPEs increased within-network integration and decreased between-network integration. This indicates that there are distinctly different neural processes for positive and negative RPEs.

Counterfactual learning, the ability to learn from what could have happened under different circumstances, is a key cognitive mechanism supporting behavioral adaptation. While its neural and computational underpinnings are increasingly understood, the temporal dynamics of attention toward factual and counterfactual outcomes remain poorly characterized. Here, we investigate the biological mechanisms underlying this process using a reinforcement learning task combined with eye-tracking and pupillometry in 36 human participants. Participants completed a two-armed bandit task with full outcome feedback and exhibited a robust confirmation bias, learning more from outcomes that supported their previous choices. Gaze patterns revealed a consistent temporal sequence of fixations from factual to counterfactual outcomes, modulated in opposite directions by the valence of each outcome. Pupil dilation, a proxy for noradrenergic arousal, was influenced by the factual outcome and by the similarity between factual and counterfactual feedback, consistent with increased surprise during disconfirmatory events. These results provide a mechanistic account of how attentional and arousal systems jointly shape outcome evaluation. By integrating behavioral modeling with physiological markers, this work contributes to a broader understanding of the adaptive constraints on decision-making and offers new insight into how organisms evaluate hypothetical alternatives in learning contexts.

Enhancing inpatient services for children and adolescent mental illness is a primary goal of medical systems, yet knowledge of child and adolescent psychiatric hospitalization utilization is limited. This study aimed to characterize hospitalizations and rehospitalizations over a decade and to identify factors associated with rehospitalizations. In this retrospective analysis, we obtained data from the Beijing Hospital Electronic Record Database from January 1, 2013, to December 31, 2022. We analyzed an admission-level dataset (n = 20215 admissions) to describe overall trends and established a patient-level cohort (n = 5048 unique patients) to evaluate rehospitalization risk. Modified Poisson regression models, with variables selected using the Andersen Behavioral Model, were used to identify factors associated with psychiatric rehospitalizations at 30-day, 180-day, and 365-day intervals. From 2013 to 2022, 20215 hospitalizations (involving 17175 unique patients) were analyzed. Mood disorders (50.6%), behavioral and emotional disorders (27.8%), and psychotic disorders (13.3%) were the most prevalent diagnoses. Specialized hospitals served a higher proportion of adolescents (≥ 14 years: 78.6%) and females (60.5%) than general hospitals (P < 0.05). In the readmission cohort(n = 5048), cumulative rehospitalization rates were 9.7% at 30 days, 16.8% at 180 days, and 20.3% at 365 days. Multivariable analysis showed that the psychotic disorders (30-day RR:1.57; 365-day RR: 1.27) and eating disorders (30-day RR:1.91; 365-day RR: 1.56) were associated with significantly elevated risks compared to depressive disorders. Older age (12-17 years) was consistently associated with a higher rehospitalization risk of rehospitalization (P < 0.05). Female patients faced a significantly higher risk of 365-day rehospitalization (RR: 1.15, P = 0.03), sex showed no significant association with shorter-term readmission. Residence status and insurance type did not have significant independent effects in the adjusted models. Psychiatric hospitalizations for children and adolescents in Beijing increased substantially from 2013 to 2022. Rehospitalization risks were primarily driven by diagnostic categories and age. These findings highlight the need for targeted post-discharge monitoring for high-risk diagnostic groups and provide a basis for optimizing pediatric mental health resource allocation in urban settings.

Maintaining physical function, and thus independence, is essential for people ageing with diabetes. For older adults with long-term insulin-treated diabetes, managing frailty, preserving body mass index (BMI) and muscle strength and mitigating hypoglycaemia risk are key challenges. Resistance training (RT) offers benefits, including reduced hypoglycaemia risk, increased bone density and improved muscle strength, yet its uptake remains low. This study explores behavioural influences and barriers to RT participation among older adults living with insulin-treated diabetes and prefrailty. A qualitative approach was employed, involving semi-structured interviews with individuals living with prefrailty aged 60 years or older with insulin-treated diabetes (type 1 diabetes (n = 12); type 2 diabetes with BMI < =30 kg/m2 (n = 4)). Frailty was assessed using the Rockwood Clinical Frailty Scale (3-4). Data were analysed using framework analysis and aligned to the COM-B model of behaviour change to deductively identify barriers and facilitators. Barriers to RT were identified across psychological capability, physical capability and social opportunity COM-B domains. Key barriers included fears of fatigue, hypoglycaemia and injury, diabetes-related complications and difficulties using RT equipment. Outdated advice about exercise safety and lack of awareness of RT's benefits further hindered participation. Facilitators included tailored education on diabetes-specific RT benefits, a supportive, flexible training environment and the presence of an exercise-competent partner. This study highlights perceptual and practical barriers that discourage older adults with diabetes and prefrailty from engaging in resistance exercise. Addressing these barriers through educational initiatives and creating adaptable exercise programmes could enhance exercise participation rates in this population.

Public distrust in physicians constitutes a significant barrier to effective healthcare utilization, particularly in China, where physician-patient tensions are well-documented. This study analyzed the association between public distrust in physicians and healthcare utilization in China. Utilizing a longitudinal nationally representative dataset from the 2012-2022 China Family Panel Studies, the final sample consisted of 168,174 observations. This study employed a two-way fixed-effects logistic regression analysis within the framework of Andersen's Behavioral Model to examine the association between distrust and healthcare utilization, while controlling for predisposing characteristics, enabling resources, and need factors. Rural areas exhibited a higher outpatient service utilization rate than urban areas. Urban inpatient service utilization rates rose annually from 9.43% in 2012 to 12.36% in 2018, surpassing rural rates, before falling to 9.71% in 2022, below rural levels. Rural residents who reported higher levels of public distrust in physicians showed a lower odds of accessing outpatient care (OR: 0.98, 95% CI: 0.96-0.99). Urban residents who reported higher levels of public distrust in physicians showed a lower odds of accessing inpatient care (OR: 0.97, 95% CI: 0.95-0.99). Higher public distrust in physicians was associated with reduced outpatient care access among rural residents and diminished inpatient care access among urban residents. Improving physician-patient trust is a critical policy priority in China. Targeted policy interventions, including strengthening physician-patient trust in rural grassroots clinics, improving the quality of urban inpatient services, and promoting the balanced allocation of medical resources, are needed so as to narrow the urban-rural trust gap and promote the equitable and rational use of medical services.

Humans are remarkable in their ability to quickly learn to perform complex tasks. Reinforcement learning (RL) has long been proposed as a model of human learning, and while leading machine RL models have surpassed human expertise at many classic board games and video games, they require vast experience to learn successfully-none of today's algorithms accounts for humans' ability to learn so many different tasks so quickly. We study human learning on 90 simple yet challenging video games, showing how people learn most games within a few minutes. To explain this behaviour, we propose a strong form of model-based RL, which we call theory-based RL, because it uses cognitively grounded intuitive theories-rich, abstract, causal representations of objects, agents and their interactions-to explore and model an environment and plan effectively to achieve goals. We instantiate the approach in an agent called EMPA (the exploring, modelling and planning agent). EMPA matches human learning efficiency, generalizing robustly to new game situations and levels, as humans do, and exhibiting similar exploration and learning dynamics. Our work points the way for future efforts to build more detailed behavioural models as well as more human-like learning of complex tasks in artificial intelligence systems. This article is part of the theme issue 'World models in natural and artificial intelligence'.

Tissue clearing techniques, combined with immunolabeling and three-dimensional (3-D) high-resolution imaging, have emerged as powerful tools for mapping the architecture of nerves that supply a specific tissue. However, despite significant advances in these techniques, visualizing nerve fibers within joint structures remains a technically challenging task. Moreover, most current protocols are optimized for use in mice, limiting their application in other animal species with greater tissue size, such as rats, which offer advantages for anatomical studies combined with behavioral models of sensory innervation and pain. Therefore, continued refinement of tissue clearing and imaging methods in rat tissue is essential for improving the resolution and translational relevance of joint innervation studies. In this work, we assessed and compared two tissue clearing protocols, a modified polyethylene glycol-associated solvent system (PEGASOS) and a newly developed hybrid method that combines CUBIC/3DISCO for tissue clearing (c-Clear), to visualize neurofilament-positive (NF+ ) nerve fibers in rat knees using 3-D fluorescence imaging. In summary, c-Clear resulted in a better option to detect NF+ fibers in the rat knee, whereas PEGASOS cleared tissues revealed greater autofluorescence within the muscle and bone marrow, compromising neurofilament visualization. Additionally, we highlight the importance of multi-angle imaging approaches when using cutting-edge light-sheet microscopy to capture the spatial context of neural innervation patterns within the complex knee microenvironment.

Assessing vision and autism spectrum disorder-relevant social interaction phenotypes in Dscam mice.