Hierarchical Model Research Articles

Factors of individual differences between the speeds of the hand and ball during baseball pitching: Assessment of middle finger function based on the hierarchical linear model

ABSTRACT This study aimed to reveal individual differences between the speeds of the hand and ball and clarify the factors affecting those differences. Motion data from 20 highly skilled baseball pitchers, captured using a 16-camera motion analysis system with a sampling frequency of 1,000 hz, were used for correlation and hierarchical linear model analyses. For the hierarchical linear models, the speed of the metacarpophalangeal joint was the independent variable in the 1st level. The independent variables chosen in the 2nd level were the variables regarding the finger length, mean peak angular velocities, and mean peak accelerations of the middle finger joint. Intra-subject correlation analysis showed high positive correlation coefficients between the metacarpophalangeal joint and ball speeds, ranging from 0.803 to 0.992. The difference between the hand and ball speeds ranged from 8.4 m∙s−1 to 12.0 m∙s−1. The hierarchical linear model analyses indicated that the individual differences between the speeds of the hand and ball were partly accounted for by the mean peak accelerations of the proximal and distal interphalangeal joints, suggesting that the force exerted by proximal interphalangeal joint flexion plays an important role in ball speed increment.

Dangerous depths of bifurcation: the rise of “inter-national security narcissists” and undersea cable (dis) connections

ABSTRACT This article examines the rising bifurcation in global undersea cable networks amid growing U.S.-China tensions, building on the “weaponized interdependence” (WI) framework. While WI sheds light on how states leverage network centrality for strategic benefits, it falls short of capturing the proactive reshaping of network topologies for national security. We argue this bifurcation fosters parallel, redundant systems that, despite increased costs, enhance control and security. A sequential game model illustrates how U.S. and Chinese security-driven decisions contribute to network divergence, while a hierarchical Q-learning model explores how path dependency and lock-in effects sustain this split. Empirical cases, like the Cap-1 project, demonstrate the practical impact, emphasizing security’s role in reconfiguring global communications infrastructure.

Symptom trajectories over time by cannabis use status among patients undergoing cancer treatment.

Adults with cancer may perceive cannabis as beneficial for managing their cancer-related symptoms, but the evidence supporting its medical use is varied and inconclusive. This study characterized associations of cannabis use with cancer-related symptom trajectories. Participants were adults undergoing cancer treatment at the Stephenson Cancer Center (SCC; n = 218) in Oklahoma; they were 71% female, 10% minoritized race, and 45% had stage III or IV cancer. Participants completed surveys at baseline and 2-, 4-, and 6-months post-baseline. Assessments queried about cannabis use behavior, physical and psychological distress via the Rotterdam Symptoms Checklist (RSCL), respiratory symptoms via the American Thoracic Society Questionnaire (ATSQ), and quality of life indices (physical and social functioning, pain interference, sleep quality, fatigue) via the Patient Reported Outcomes Measurement Information System (PROMIS-29). Cannabis use status was categorized based on self-reported past 30-day cannabis use at each assessment as non-use [no use at any assessment], occasional-use [use at 1-2 assessments], or consistent-use [use at 3-4 assessments]. Longitudinal hierarchical linear modeling evaluated associations of cannabis use status with average symptoms and symptom trajectories across all four assessments. One-third (33%) of participants reported past 30-day cannabis use at ≥ 1 assessment. Participants who reported cannabis use (occasional-use and consistent-use) had more severe symptoms overall across assessments. While most cancer symptom trajectories did not differ by cannabis use status, participants who reported consistent cannabis use uniquely showed worsening physical function over time. Cannabis use was associated with greater cancer-related symptom severity over time.

Three-year survival follow-up of patients with gastrointestinal cancer treated during the COVID-19 pandemic in Spain: data from the PANDORA-TTD20 study.

The initial SARS-CoV-2 pandemic wave in Spain in 2020 precipitated significant paradigm shifts in gastrointestinal oncology patient management. This study captures the "Zeitgeist" of this period by analyzing adaptive strategies, treatment modifications, and survival outcomes, leveraging a 3-year follow-up perspective to extract insights from this unprecedented experience. We conducted a multicenter, retrospective cohort study utilizing the RETUD-TTD registry, encompassing 703 patients across 19 Spanish centers in April 2020. We evaluated alterations in clinical practice, therapeutic approaches, coronavirus disease 2019 (COVID-19)-related impacts, and patient survival. A Bayesian hierarchical model was employed to identify potential regional-specific frailties. The peak of the pandemic in April 2020 catalyzed substantial shifts in oncological care delivery. Outpatient consultations decreased by 13%, with a notable selection bias toward cases with more favorable prognostic indicators. Multidisciplinary tumor board discussions were significantly curtailed (eg, mean monthly colorectal cancer cases discussed was reduced from 40 to 23), compromising qualitative care measures. This occurred concurrently with an average of over 3 oncologists per center on medical leave. Contrary to initial concerns, the healthcare system demonstrated remarkable resilience. The majority of patients received standard-of-care therapies with regulatory approval, albeit with regimen modifications in 15% of cases. These adaptations included extended dosing intervals, dose intensity modulations, and transitions to oral formulations while maintaining unexpectedly stable long-term survival outcomes. The Bayesian frailty model detected minimal unmeasured prognostic factors related to geographic location, and the type of pandemic-induced adaptation did not significantly impact survival. The model revealed that coronavirus disease 2019's impact was less pronounced than other core prognostic variables. The decentralized Spanish healthcare system exhibited substantial robustness in managing pre-pandemic diagnosed gastrointestinal malignancies, despite asymmetrical, and occasionally severe organizational disruptions. The insights gleaned from this experience could inform future crisis preparedness strategies and optimize care provision during subsequent public health emergencies.

Application of Hierarchical Bayesian Models for Modeling Economic Costs in the Implementation of New Diagnostic Tests

The COVID-19 pandemic has highlighted the need for reliable and rapid diagnostic tests to control the spread of infection. The introduction of new rapid antigen tests often goes in tandem with the limited data availability, making it challenging to assess their performance at the initial phase of the pandemic. Sensitivity and specificity, the key performance characteristics provided by manufacturers, are typically derived under laboratory conditions and may not accurately reflect the tests' performance in field settings. We use the hierarchical Bayesian model to obtain their realistic estimates in real world conditions and show how it may be used in situations in which new tests with limited history are presented on the market. Proposed methodology allows for the efficient information pooling, thereby improving on the accuracy of parameter estimates for new tests. The results suggest that the application of hierarchical model on the Czech data led to a considerabile reduction in uncertainty associated with the parameter estimates as well as with potential economic cost implied by false positive test results. The model can thus assist in better informed decision-making and financial planning of both the government and corporations.

A Dynamical Interpretation of the Intensification of the Winter North Atlantic Jet Stream in Reanalysis

Abstract Jet streams play an important role in determining weather variability and extremes. A better understanding of the mechanisms driving long-term changes in the jet is essential to successfully anticipate extreme meteorological events. This study analyzes the intensification trend of the North Atlantic jet using the ERA5 reanalysis and investigates the dynamical mechanisms involved. The results highlight the importance of an increase in diabatic heating in the free troposphere below the jet entrance over the Gulf Stream sector. This change in diabatic heating modifies the jet directly and produces a local intensification and a slight poleward shift. A two-dimensional frontal-geostrophic model illustrates this mechanism by considering the enhanced diabatic heating associated with the baroclinic growth of extratropical cyclones. The change in diabatic heating also affects the jet indirectly by increasing the mean baroclinicity and subsequent eddy momentum flux convergence. This indirect mechanism has also an effect downstream, where there is an acceleration of the jet core and reduced westerlies along the flanks, reducing the width of the jet. An idealized warming experiment confirms this mechanism by determining the jet response downstream of an idealized land–sea contrast. Finally, using a single-model ensemble of fully coupled climate simulations, we show that the differences in the evolution of the North Atlantic jet are related to the latitude of the increase in baroclinicity, which has a large spread. What emerges from the model hierarchy is a consistent dynamical chain of mechanisms associated with the intensification trend of the North Atlantic jet stream.

Integrating green image with relationship marketing: A moderating role of green image

This study integrates social exchange theory, the biophilia hypothesis, the hierarchy of effects model, and self-congruency theory to examine how relational benefits and green image influence positive emotion, customer loyalty, and customer green behavior (CGB). Data were collected from 314 respondents via an online marketing research firm and analyzed using SmartPLS 4.0. The results show that customers’ perceptions of relational benefits have a direct impact on both customer loyalty and positive emotions. While green image significantly affects customer loyalty, it does not have a direct impact on positive emotions or CGB. However, green image enhances the positive association between customer emotions and CGB, suggesting that customers are more likely to engage in green behavior when they feel a positive emotional connection with a company they perceive as environmentally responsible. These findings suggest that restaurant firms should implement marketing strategies to improve their green image by emphasizing the value of relational benefits. Both theoretical and managerial implications are discussed.

The association between maternal stress and human milk concentrations of cortisol and prolactin.

Psychosocial stress affects the relationship between prolactin (PRL) and cortisol (CORT). The dynamics of PRL and CORT changes under stress in human milk (HM) are largely unknown. We investigated how maternal stress related to recent life changes affects milk CORT and PRL concentrations. The study involved 116 mothers exclusively breastfeeding 5-month-old infants. Maternal psychological stress was evaluated using the Recent Life Changes Questionnaire (RLCQ). Stress response was determined by administering the cold pressor test and measuring CORT in saliva taken during and in milk collected after the test. Hormones concentrations were assayed using the ELISA method. The hierarchical regression models were run to test the association between maternal RLCQ, salivary CORT, and PRL, and CORT in milk. Maternal RLCQ correlated positively with the CORT in saliva, however, no direct association was found between RLCQ and PRL. After controlling for covariates, a positive association was found between salivary and milk CORT. A negative relationship was observed between salivary CORT and milk PRL. The results of the present study indicate that maternal psychological stress may affect the relationship between CORT and PRL in HM. In response to psychological stress, both hormones transported via milk can program infant development in the early postnatal period.

A hierarchical active inference model of spatial alternation tasks and the hippocampal-prefrontal circuit.

Cognitive problem-solving benefits from cognitive maps aiding navigation and planning. Physical space navigation involves hippocampal (HC) allocentric codes, while abstract task space engages medial prefrontal cortex (mPFC) task-specific codes. Previous studies show that challenging tasks, like spatial alternation, require integrating these two types of maps. The disruption of the HC-mPFC circuit impairs performance. We propose a hierarchical active inference model clarifying how this circuit solves spatial interaction tasks by bridging physical and task-space maps. Simulations demonstrate that the model's dual layers develop effective cognitive maps for physical and task space. The model solves spatial alternation tasks through reciprocal interactions between the two layers. Disrupting its communication impairs decision-making, which is consistent with empirical evidence. Additionally, the model adapts to switching between multiple alternation rules, providing a mechanistic explanation of how the HC-mPFC circuit supports spatial alternation tasks and the effects of disruption.

Within-person, longitudinal associations between neighborhood cohesion and adult mental health: A test of bidirectional relations.

There is theory and evidence supporting a relationship between neighborhood cohesion and mental health among adult people. However, most studies have used a cross-sectional design, and longitudinal studies have provided mixed support for this hypothesis. Moreover, while neighborhood cohesion is assumed to be a consistent predictor of mental health, the possibility of a reciprocal relation has been overlooked. The aim of the current study was to investigate the within-person reciprocal associations between neighborhood cohesion and adult mental health. This study used data from Understanding Society: the UK Household Longitudinal Study including waves 1, 3, 6, 9, and 12 (n = 81,895). A short version of Buckner's Neighborhood Cohesion Instrument was used, along with two well-established measures of mental health: the 12-item General Health Questionnaire and the mental component summary score of the 12-item Short-Form Health Survey. Hierarchical Bayesian continuous time dynamic modeling revealed significant reciprocal within-person cross-effects (i.e. deterministic effects) between neighborhood cohesion and adult mental health, a larger standardized effect of mental health on neighborhood cohesion than than vice versa. Moreover, peak standardized cross-lagged effects were found for a time interval of approximately 1 year. Finally, the combined stochastic and deterministic interpretation revealed effects of neighborhood cohesion on mental health that were opposite to what was expected, suggesting a faster dissipation of some initially correlated change. Although neighborhood cohesion has traditionally been conceptualized in terms of its contribution to mental health, there is greater support for the view that mental health precedes neighborhood cohesion rather than the reverse.

The Effects of Lucid Dreaming and Nightmares on Sleep Quality and Mental Health Outcomes

ABSTRACT Objectives This study aimed to investigate relationships between lucid dreaming and sleep and mental health outcomes within a representative sample of the general population. We also sought to examine how nightmares interact with the relationship between lucid dreaming, sleep, and mental health outcomes. Methods Participants (N = 1332) completed measures of lucid dream frequency, nightmare frequency, anxiety and depressive symptoms, stress, and sleep quality. Hierarchical regression models were conducted, where step-1 examined the direct effect of lucid dreaming on all outcome variables, and step-2 added nightmares into the equation (to examine direct effects of lucid dreaming frequency and nightmares, and the indirect effect of nightmares). Results Step-1 results demonstrated that lucid dreaming positively predicted poor sleep quality, stress, anxiety, and depressive symptoms. Step-2 results revealed that nightmares were the only significant predictor of poor sleep quality, stress, and anxiety symptoms, accounting for all of the variance of lucid dreaming. Nightmares and the interaction of nightmares and lucid dreaming positively predicted depressive symptoms. Conclusions Our results suggest that nightmares alone explain associations between lucid dreaming and poor sleep quality, anxiety symptoms, and stress. However, both nightmares and the combination of nightmares and lucid dreaming are associated with increased depressive symptoms.

Assessing Barriers and Difficulties to Healthcare Access Among Syrian Refugees in Jordan: An Observational Study

(1) Background: Worldwide, refugees may have some difficulties in accessing healthcare services. However, little is known about the factors that may predict the level of accessibility to the public healthcare system in the host countries. (2) Aim: To examine the level of accessibility of Syrian refugees to the public healthcare system in Jordan and further to identify the prediction of socioeconomic factors and barriers to healthcare access. (3) Methods: A cross-sectional study was conducted with a convenient sample of 356 Syrian refugees residing outside camps (Irbid, Ajloun, and Jarash). Data were collected using the Access to Healthcare Services Scale instrument (adopted from the Canadian Community Health Survey), which is composed of 2 sections: the general access scale (8 items) and the difficulties scale (20 items). One-way ANOVA test and independent t-test were used to examine epidemiological correlations among variables, whereas a hierarchical linear regression model was used to examine the predictability of socioeconomic factors and barriers to overall access to the public healthcare system by exploring the incremental impact of additional predictors. (4) Results: the mean age of the 356 participants was 35.22 years old, 56.5% were female, 67.4% were married, most of them 46.1% have secondary education, and non-employed 69.9%. Significant associations were observed among participants with different marital status (p < 0.001), educational level (mean 11.85 vs. 19.85, p < 0.001), working status (15.47 vs. 17.93, p < 0.001), family household number (16.42 vs. 17.0, p < 0.001), and health insurance (none: 15.50 vs. governmental 24.50, p < 0.001). Multivariate analysis revealed that the most important factors that may predict the overall access to healthcare services were: family monthly income (beta −0.19, p < 0.001), household family number (beta 0.17, <0.001), health insurance (beta −0.09, p = 0.047), and barriers (beta −0.43, <0.001), even after adjusting for potential confounding effects: sex, age, educational level, and place of residence. (5) Conclusions: Our findings indicate that socioeconomic factors and barriers may considerably predict overall access to public healthcare in Jordan. It is crucially important, therefore, for the Jordanian government and international organizations to create and develop strategic plans and programs that enhance refugees’ access to health services, positively impacting their health and wellness.

Hierarchical modelling of epibenthic communities on the Scotian Shelf and Gulf of Maine (Atlantic Canada) in support of conservation planning

Identification of ecologically significant units at different spatial scales is essential for management of biodiversity attributes. This case study illustrates a coupled methodological approach to delineate benthic habitats and associated assemblages at different spatial scales. Two complementary analyses were employed based on the occurrences of 99 epibenthic invertebrate species in Atlantic Canada, sampled during depth-stratified random trawl sets. To identify epibenthic assemblage types, isometric feature mapping and partitioning around medoids (ISOPAM) was used in combination with a joint species distribution model (JSDM), which also produced continuous distribution surfaces across the spatial domain. ISOPAM identified nine significantly spatially coherent assemblages (biotopes) at spatial scales of hundreds of meters to hundreds of kilometers, with associated diagnostic species. Those assemblages were closely matched by eight regions of common profile using the JSDM, indicating strong environmental influences on their distributions. These smaller-scale assemblages were grouped into three biophysical units. The results from the JSDM were validated with independent data showing good congruence. This indicates that the spatial units ascribed to identified assemblages are robust and suitable for marine spatial planning and monitoring.

Increasing Phylogenetic Clustering of Arbuscular Mycorrhizal Fungal Communities in Roots Explains Enhanced Plant Growth and Phosphorus Uptake.

Temporal variation during the assembly of arbuscular mycorrhizal (AM) fungal communities within plant roots have been posited as critical drivers of the plant-fungal symbiotic outcomes. However, functional implications of these dynamics for the host plant remain poorly understood. We conducted a controlled pot experiment with Sorghum bicolor to investigate how temporal shifts in AM fungal community composition and phylogenetic diversity influence plant growth and phosphorus responses to the symbiosis. We characterised the root-colonising AM fungal communities across three time points and explored their community assembly processes by analysing their phylogenetic diversity and employing joint species distribution modelling with the Hierarchical Modelling of Species Communities (HMSC) framework. We found strong AM fungal turnover through time with a high phylogenetic signal, indicating recruitment of phylogenetically clustered AM fungal species in the host. This temporal phylogenetic clustering of communities coincided with marked increases in plant biomass and phosphorus responses to the AM fungal symbiosis, suggesting that host selection for specific fungi may be a key determinant of these benefits.

A Bayesian Approach to Modeling Variance of Intensive Longitudinal Biomarker Data as a Predictor of Health Outcomes.

Intensive longitudinal biomarker data are increasingly common in scientific studies that seek temporally granular understanding of the role of behavioral and physiological factors in relation to outcomes of interest. Intensive longitudinal biomarker data, such as those obtained from wearable devices, are often obtained at a high frequency typically resulting in several hundred to thousand observations per individual measured over minutes, hours, or days. Often in longitudinal studies, the primary focus is on relating the means of biomarker trajectories to an outcome, and the variances are treated as nuisance parameters, although they may also be informative for the outcomes. In this paper, we propose a Bayesian hierarchical model to jointly model a cross-sectional outcome and the intensive longitudinal biomarkers. To model the variability of biomarkers and deal with the high intensity of data, we develop subject-level cubic B-splines and allow the sharing of information across individuals for both the residual variability and the random effects variability. Then different levels of variability are extracted and incorporated into an outcome submodel for inferential and predictive purposes. We demonstrate the utility of the proposed model via an application involving bio-monitoring of hertz-level heart rate information from a study on social stress.

Approximate Cross-Validated Mean Estimates for Bayesian Hierarchical Regression Models

We introduce a novel procedure for obtaining cross-validated predictive estimates for Bayesian hierarchical regression models (BHRMs). BHRMs are popular for modeling complex dependence structures (e.g., Gaussian processes and Gaussian Markov random fields) but can be computationally expensive to run. Cross-validation (CV) is, therefore, not a common practice to evaluate the predictive performance of BHRMs. Our method circumvents the need to rerun computationally costly estimation methods for each cross-validation fold and makes CV more feasible for large BHRMs. We shift the CV problem from probability-based sampling to a familiar and straightforward optimization problem by conditioning on the variance-covariance parameters. Our approximation applies to leave-one-out CV and leave-one-cluster-out CV, the latter of which is more appropriate for models with complex dependencies. In many cases, this produces estimates equivalent to full CV. We provide theoretical results, demonstrate the efficacy of our method on publicly available data and in simulations, and compare the model performance with several competing methods for CV approximation. Code and other supplementary materials available online.

Why there are more species in several small patches versus few large patches: A multispecies modelling approach

Abstract As global biodiversity is rapidly declining due to habitat loss, it is important to determine how to protect it. The policies of many conservation agencies are still prioritizing the preservation of a single large habitat area (SL) versus several small areas of the same size (SS), despite empirical support favouring SS. However, to date, while many studies have explored this issue using model communities, the results are mixed. In competitive community models, fragmentation has a negative effect at low habitat amounts, whereas neutral community models suggest positive effects. This work aims to explore spatial multispecies models to verify the SS > SL pattern and determine its causes. We used three types of models: a classical neutral, neutral with habitat selection and a competitive hierarchical model. All models have three parameters, namely extinction, reproduction and dispersal distance. In the classical neutral model, species are equivalent and have the same parameters; when there is a reproduction event, they send the propagule to a surrounding patch blindly, and if the patch is already occupied, the propagule dies. In the neutral with habitat selection model, the propagules select an empty patch, so their survival is ensured. In the hierarchical model, species have competitive dominance and more dominant ones can replace the less dominant with a replacement rate parameter. We use 100 species and start simulating the colonization of an empty area; then, we destroy a fixed amount of habitat using different regular patch sizes. The results for all models are almost always that SS retains more species than SL. The extinction of species in patches depends on the quotient between reproduction and extinction rates in relation to patch size. The only case when SL > SS is when dispersal is insufficient to connect patches, and patch size is smaller than the minimum to prevent extinction. If patch size is above the critical size to maintain local populations, the SS > SL pattern is due to the sampling effect of aggregated species distribution combined with the reduction of dispersal ability of species. High‐dispersal rates produce the homogenization of species distribution and a reduction of species richness and this is why habitat fragmentation by habitat loss retains more species when patches are smaller. The model predicts that if a set of species have more dispersal capabilities, the SS effect would be lower than for species with less dispersal. Read the free Plain Language Summary for this article on the Journal blog.

The Moderating Effect of Self-control on the Relationship between Financial Knowledge and Retirement Planning among Commercial Bank Employees in Eldoret Town, Kenya

Retirement planning is a goal-oriented behavior where individuals devote effort to prepare for their retirement life effectively, thus reducing retirement worry, keeping stress under wraps, and enhancing retirement preparedness and confidence. However, there is little literature about retirement planning among employees working in Kenyan commercial banks. To fill this gap, this study aimed to establish the moderating effect of self-control on financial knowledge and retirement planning among commercial bank employees in Kenya. This study was guided by goal setting, social cognitive, and behavioral life cycle theories. The study adopted an explanatory research design, with data being collected from a target population of 1058 employees of 32 commercial banks in Eldoret town using a closed-ended questionnaire. A sample size of 290 respondents was obtained using Yamane’s formula. The study used simple random sampling techniques to sample the commercial banks, and thereafter the researcher used systematic sampling techniques to select employees as respondents. The study hypotheses were tested using a hierarchical regression model. Results reveal that financial knowledge had a significant positive impact on retirement planning (β =.626, P =.000), confirming that employees with better financial knowledge are more likely to plan effectively for retirement. Similarly, self-control not only directly influences retirement planning (β = .258, P =.000) but also moderates the relationship between financial knowledge and retirement planning (β =.137, P =.000). Results further indicate that the moderation model is a superior model than the direct effect model as it accounts for more variance (60.5%) in retirement planning as shown by R² =.605 compared to 44.9% (R² =.449). This moderation process contributes to new knowledge in literature and theory. These study findings will be helpful to organizations and governments in developing policies and strategies related to retirement systems in the workplace. Limitations and recommendations have also been discussed.

Burden of Parkinson's disease in Central Asia from 1990 to 2021: findings from the Global Burden of Disease study.

Central Asia is known to face various ecological challenges that constitutes major risk factors for Parkinson's disease (PD). This study examines the burden of PD in Central Asia, a region where data on neurological disorders is notably sparse. Building on the latest Global Burden of Disease Study (GBD 2021), this study investigates the Years of Life Lost (YLLs), Years Lived with Disability (YLDs), and Disability-Adjusted Life Years (DALYs) associated with PD in Central Asia and its countries from 1990 to 2021. The authors calculated average annual percent change (AAPC) to analyze trends, and compared individual country estimates to global figures. Additionally, incorporating data from the World Bank, both Bayesian hierarchical and non-hierarchical frequentist regression models were employed to assess their impact on DALYs. The DALYs varied across the study period, primarily driven by YLLs. While YLLs showed a uniform trend, YLDs were mostly incremental. Kazakhstan had the highest estimates across all metrics and was the only country aligned with global patterns. Age- and sex-specific estimates revealed substantial variations, with notably high figures found in male subjects from Tajikistan. The YLLs, YLDs, and DALYs for Kazakhstan, Uzbekistan, and Turkmenistan saw a significant increase in AAPCs. In contrast, Kyrgyzstan and Tajikistan saw declines, likely attributable to civic conflict and inter-country differences in population structure. Further comparison of DALY trends revealed significant deviations for all countries from the global pattern. This study showed an overall increase in PD burden from 1990 to 2021. These findings underscore the need for targeted strategies to reduce PD burden, with a particular focus on Kazakhstan. Integrating historical information is crucial for discussing the plausible mechanisms in studies sourced from the GBD.

Hierarchical multi-label classification model for science and technology news based on heterogeneous graph semantic enhancement

In the context of high-quality economic development, technological innovation has emerged as a fundamental driver of socio-economic progress. The consequent proliferation of science and technology news, which acts as a vital medium for disseminating technological advancements and policy changes, has attracted considerable attention from technology management agencies and innovation organizations. Nevertheless, online science and technology news has historically exhibited characteristics such as limited scale, disorderliness, and multi-dimensionality, which is extremely inconvenient for users of deep application. While single-label classification techniques can effectively categorize textual information, they face challenges in leading science and technology news classification due to a lack of a hierarchical knowledge framework and insufficient capacity to reveal knowledge integration features. This study proposes a hierarchical multi-label classification model for science and technology news, enhanced by heterogeneous graph semantics. The model captures multi-dimensional themes and hierarchical structural features within science and technology news through a hierarchical transmission module. It integrates graph convolutional networks to extract node information and hierarchical relationships from heterogeneous graphs, while also incorporating prior knowledge from domain knowledge graphs to address data scarcity. This approach enhances the understanding and classification capabilities of the semantics of science and technology news. Experimental results demonstrate that the model achieves precision, recall, and F1 scores of 84.21%, 88.89%, and 86.49%, respectively, significantly surpassing baseline models. This research presents an innovative solution for hierarchical multi-label classification tasks, demonstrating significant application potential in addressing data scarcity and complex thematic classification challenges.