Trajectory Model Research Articles

Peanut Pickup Combine Harvester Seedling Vines Crushing Mechanism Performance Enhancement and Pilot Studies

Aiming at the current peanut pickup combine harvester seedling vines crushing mechanism operation qualified seedling length rate is low, the impurity rate is high, and the seedling vines crushing mechanism is optimized design. On the basis of the material characteristics of peanut seedling vines, taking the crushing mechanism as the research carrier, the interaction mechanism of the seedling vine mechanism was analyzed. Establish the mechanical model and motion trajectory model in the crushing process, simulate the peanut seedling vines crushing process based on the discrete element method, analyze the influence of the cutter shaft rotational speed, movement speed, and bending angle on the crushing of peanut seedling vines, and establish the regression model, and the results of the research show that the influence factors in the main order of priority are: knife shaft speed > movement speed > bending angle The optimal parameter combination is knife shaft speed 2171.94 r/min, movement speed 0.79 m/s, and bending angle 45°. According to the field test conditions, it is determined that the knife shaft rotational speed of 2170 r/min, movement speed of 0.8 m/s, and bending angle of 45° can make the peanut seedling vines crushing mechanism of the work performance to achieve a qualified seedling length rate of 97.292%, the rate of impurity content of 2.746%, and the error with the predicted value is less than 2%, which indicates that this study can provide a reference for the optimal design of the seedling vines crushing mechanism of the peanut pickup combine harvester.

BMI trajectories from infancy to 18 years and mental health in emerging adulthood

BackgroundStudies exploring early life-course BMI trajectories and subsequent mental health outcomes are limited but may provide important insights for early intervention. We investigated associations between BMI trajectories from 0 to 18 years and mental health outcomes in emerging adulthood. MethodsData were obtained from 434 participants in the Melbourne Atopy Cohort Study (MACS). Anthropometric data were collected across 26 timepoints from infancy to age 25 and group-based trajectory modelling was used to develop BMI trajectories from 0.1 to 18 years. Moderate-to-severe psychological distress (MSPD) and likely depression were assessed at age 18 and 25 years. Associations between BMI trajectories and mental health at 25 years and change in mental health between 18 and 25 years were estimated using logistic regression. History of asthma, hay fever or eczema were independently examined as potential effect modifiers. ResultsFive BMI trajectories were identified from 1 month to 18 years. When compared to the stable average BMI trajectory, we found an increased risk of MSPD (OR = 2.97; 95%CI: 1.09,8.06) and likely depression (3.56; 1.39,9.12) at age 25 in the average increasing-to-high trajectory. This group also had a greater likelihood of new-onset depression (4.82; 1.54,15.0) from 18 to 25 years of age. LimitationsMACS participants are not representative of the general population and mental health data was not available before 18 years of age. ConclusionExcessive weight gain across the childhood transition was associated with poorer mental health in emerging adulthood, highlighting the importance of monitoring growth to allow for early identification and stratification of individuals at risk of poor mental health.

Modelling climatic variable impacts on ground-level ozone in Malaysia using backward trajectory and Generative Additive Models

Modelling climatic variable impacts on ground-level ozone in Malaysia using backward trajectory and Generative Additive Models

Heterogeneity in the development of differential reinforcement: examining predictive power of definitions and differential association from a social learning perspective

Social learning theory is a prominent framework for understanding the relevance of socialization processes for predicting behavior. While recent research has focused on placing these processes within developmental contexts, there remain gaps in understanding how differential reinforcement for involvement in antisocial behavior may develop across adolescence and emerging adulthood and how definitions and differential association predict development. All 11 waves of the Pathways to Desistance dataset were analyzed, comprising the responses of 1354 justice-involved youth across seven years post-adjudication. Group-based trajectory modeling was used to identify developmental patterns of differential reinforcement and regression modeling was used to examine predictors of development. Results indicated that a two-group model of development best fit the reinforcement data (High, Low). Greater endorsement of antisocial definitions and greater differential association with deviant peers and a significant and positive interaction between these constructs predicted increased odds of assignment to the High group.

Characteristics of Water Vapor Transport during the “7·20” Extraordinary Heavy Rain Process in Zhengzhou City Simulated by the HYSPLIT Model

Water vapor transport is an important foundation and prerequisite for the occurrence of rainstorms. Consequently, the understanding of water vapor transport as well as the sources of water vapor during rainstorm processes should be considered as essential to study the formation mechanism of rainstorms. In this study, the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model is adopted for backward tracking of water vapor transport trajectories and sources during the “7·20” extraordinary heavy rain process in Zhengzhou City of China that occurred on 20 July 2021. On this basis, the trajectory clustering method is applied to quantitatively analyze the contributions of water vapor sources, aiming to provide a basis for exploring the maintenance mechanism of this extreme rainstorm event. The spatio-temporal characteristics of this rainstorm event show that there are 4 consecutive days with the precipitation reaching or exceeding the rainstorm level across the whole Zhengzhou City, with the daily rainfall amounts at eight national meteorological stations all breaking their respective historical extreme values. The regional-averaged rainfall amount in Zhengzhou City is 527.4 mm, while the maximum accumulated rainfall amount reaches 985.2 mm at Xinmi station and the maximum hourly rainfall amount at Zhengzhou national meteorological station reaches 201.9 mm h−1. The water vapor sources for this rainfall process, ranked in descending order of contribution, are the Western Pacific, inland areas of Northwest China and South China, and South China Sea. The water vapor at lower levels is mainly transported from the Western Pacific and the South China Sea, while those from the inland areas of Northwest China and South China provide a supply of water vapor at upper levels to a certain extent. The water vapor at 950 hPa is mainly sourced from the Western Pacific and South China Sea, accounting for 56% and 44%, respectively. The water vapor at 850 hPa mainly derives from the Western Pacific and the inland areas of South China, contributing 58% and 34% of the total, respectively. The water vapor at 700 hPa mainly comes from the inland areas of Northwest China and South China Sea. Specifically, the water vapor from inland Northwest China contributes 44% of the total, acting as the primary source. The water vapor at 500 hPa is mainly transported from the inland areas of South China and Northwest China, with that from the inland South China (56%) being more prominent. The water vapor at all levels is mainly transported to the rainstorm region through the eastern and southern regions of China from the source areas. Additionally, there are some differences in the water vapor trajectories at a 6 h interval.

Latent Trajectories of Cerebral Perfusion Pressure and Risk Prediction Models Among Patients with Traumatic Brain Injury: Based on an Interpretable Artificial Neural Network

This study aimed to characterize long-term cerebral perfusion pressure (CPP) trajectory in traumatic brain injury (TBI) patients and construct an interpretable prediction model to assess the risk of unfavorable CPP evolution patterns. TBI patients with CPP records were identified from the Medical Information Mart for the Intensive Care (MIMIC)-IV 2.1, eICU Collaborative Research Database (eICU-CRD) 2.0 and HiRID dataset 1.1.1. The research process consisted of two stages. First, group-based trajectory modeling (GBTM) was used to identify different CPP trajectories. Second, different ANN algorithms were employed to predict the trajectories of CPP. A total of 331 eligible patients' records from MIMIC-IV 2.1 and eICU-CRD 2.0 were used for trajectory analysis and model development. Additionally, 310 patients' data from HiRID were used for external validation. The GBTM identified 5 CPP trajectory groups, group 1 and group 5 were merged into class 1 based on unfavorable in-hospital mortality. The best 6 predictors were invasive systolic blood pressure coefficient of variation (ISBPCV), venous blood chloride ion concentration, PaCO2, PT (Prothrombin Time), CPP coefficient of variation (CPPCV), and mean CPP. Compared with other algorithms, Scaled Conjugate Gradient (SCG) performed relatively better in identifying class 1. This study identified 2 CPP trajectory groups associated with elevated risk and 3 with reduced risk. PaCO2 might be a strong predictor for the unfavorable CPP class. The ANN model achieved the primary goal of risk stratification, which is conducive to early intervention and individualized treatment.

Age-related differences in the number of chronic diseases in association with trajectories of depressive symptoms: a population-based cohort study

BackgroundThe number of chronic diseases has been associated with changes in depressive symptoms over time among middle-aged and older adults. This study aimed to explore the association between the number of chronic diseases and trajectories of depressive symptoms and the role of age in this association.MethodsA total of 12,974 middle-aged and older Chinese adults (≥ 45 years) participated in the China Health and Retirement Longitudinal Study (CHARLS) in waves 2011, 2013, 2015, 2018, and 2020. The number of chronic diseases was determined by self-reported hospital diagnosis of hypertension, dyslipidemia, diabetes, cancer, chronic lung diseases, liver disease, heart diseases, stroke, kidney diseases, digestive diseases, emotional, nervous, or psychiatric problems, memory-related disease, arthritis or rheumatism, asthma, and then obtaining the total number of chronic diseases. Depressive symptoms were measured by the 10-item Center for Epidemiologic Studies Depression Scale (CESD-10). Group-based trajectory modeling (GBTM) was adopted to capture the trajectories of depressive symptoms over time. Multinomial logistic regressions were conducted to examine the association between the number of chronic diseases and trajectories of depressive symptoms and the role of age in this association.ResultsFour distinct trajectories of depressive symptoms were observed in 34.68% individuals in mild, 40.76% in moderate, 19.41% in increasing, and 5.15% in severe group. Compared to participants without chronic diseases, those with one, two, three or more chronic diseases had a 1.81, 3, and 7.49-fold higher risk of developing severe depressive symptom trajectory, respectively. Moreover, the association between the number of chronic diseases and severe depressive symptoms trajectory differed by age (45–59 and ≥ 60 years) (P for interaction < 0.05).ConclusionParticipants with middle age may play a promoting role in the association between the number of chronic disease and severe depressive symptoms. The severe depressive symptoms intervention may be more beneficial for middle-aged adults.

Regional patterns of human cortex development correlate with underlying neurobiology

Human brain morphology undergoes complex changes over the lifespan. Despite recent progress in tracking brain development via normative models, current knowledge of underlying biological mechanisms is highly limited. We demonstrate that human cortical thickness development and aging trajectories unfold along patterns of molecular and cellular brain organization, traceable from population-level to individual developmental trajectories. During childhood and adolescence, cortex-wide spatial distributions of dopaminergic receptors, inhibitory neurons, glial cell populations, and brain-metabolic features explain up to 50% of the variance associated with a lifespan model of regional cortical thickness trajectories. In contrast, modeled cortical thickness change patterns during adulthood are best explained by cholinergic and glutamatergic neurotransmitter receptor and transporter distributions. These relationships are supported by developmental gene expression trajectories and translate to individual longitudinal data from over 8000 adolescents, explaining up to 59% of developmental change at cohort- and 18% at single-subject level. Integrating neurobiological brain atlases with normative modeling and population neuroimaging provides a biologically meaningful path to understand brain development and aging in living humans.

Car-following model based on artificial potential field with consideration of horizontal curvature in connected vehicles environment

Connected vehicles (CVs) will gradually replace traditional vehicles to become the main components of traffic flow. Studying the car-following behavior characteristics is crucial for improving traffic flow stability and safety in CVs environment. Additionally, the radius of road curvature significantly impacts vehicle driving behavior, making it necessary to consider it for the car-following models of CVs. The artificial potential field (APF) theory can more accurately and comprehensively depict various microscopic driving behaviors, offering a new approach for modeling vehicle microscopic behavior. Firstly, this paper constructs the attractive and repulsive potential fields considering horizontal curve curvature based on a road coordinate transformation model. Secondly, an Artificial Potential Field-Based Car-Following Model Considering Curvature (APFCCM) in connected vehicles environment is proposed. Finally, the model is calibrated and validated using the Hangzhou - Xifu Freeway dataset from the Tongji Road Trajectory Sharing (TJRD TS) platform, and compared with the full velocity difference model(FVDM), the Intelligent Driver Model (IDM) and the Driving Risk Potential Field Model (DRPFM). The results show that the APFCCM performs well in trajectory simulation, model accuracy, and scenario adaptability, and it has the lowest mean absolute error(MAE) and root mean square error(RMSE) in position, speed, and acceleration metrics.

Group-Based Trajectory Modeling of Serum Sodium and Survival in Sepsis Patients with Lactic Acidosis: Results from MIMIC-IV Database.

Group-Based Trajectory Modeling of Serum Sodium and Survival in Sepsis Patients with Lactic Acidosis: Results from MIMIC-IV Database.

Impacts of moisture transport on extreme precipitation in the Central Plains Urban Agglomeration, China

In recent decades, the Central Plains Urban Agglomeration of China (CPUA) has faced recurring extreme precipitation events (EPEs), leading to severe floods, endangering residents, and causing significant property damage. This study examines the spatiotemporal patterns of summer EPEs in the CPUA from 1961 to 2022. We used the Hybrid Single-Particle Lagrangian Integrated Trajectory model to trace the water vapor trajectories associated with these events, identifying atmospheric circulations linked to various moisture sources. Summer EPEs in the CPUA have become more frequent and intense. Urban regions typically experience stronger EPEs, while mountainous regions encounter more frequent but milder precipitation. The moisture contributing to these events comes from sources including Eurasia (9.94 %), the northern and southern Western North Pacific (48.39 %), and the Bay of Bengal and South China Sea (41.67 %). Notably, contributions from Eurasia and the northern Western North Pacific have increased, whereas those from the Bay of Bengal and the South China Sea have decreased. Events driven by Western North Pacific moisture have stronger impacts on urban areas, influenced by abnormal anticyclonic patterns and the development of the Huang-Huai cyclone, which triggers intense convective activity over the CPUA. The strengthening of the Western North Pacific subtropical high promotes the transport of warm air, which merges with colder inland air, leading to extreme precipitation.

A sacrifice for the greater good? On the main drivers of excessive land take and land use change in Hungary

Despite continuous population decline or stagnation, many Central and Eastern European cities experience urban sprawl and expansion, accompanied by excessive land take and land use change. This paper investigates recent trends in land take and land use change in Hungarian cities, aiming to identify the drivers of these processes. The study utilizes the Urban Atlas (UA) databases from 2006 and 2018 to analyze land use change in selected Hungarian cities, employing QGIS software to compile the database and MS Excel to conduct the statistical analysis. A linear trajectory model is employed to project land use change by 2026, based on 2006–2018 UA data. Additionally, via the in-depth analysis of Debrecen, the study highlights the recent acceleration of land use change to compare the projected trend with the forecasted one. Local climate strategies focus on addressing environmental concerns, including changes in land use at the city level. Therefore, this study reviews the relevant documents associated with each city to assess its commitment to reducing land take. The results indicate that despite its declining population, Hungary has seen a significant expansion in artificial surfaces, primarily due to industrialization and population policies introduced by the central government, which is likely to continue in the coming years. Unfortunately, local climate strategies have been insufficient in preventing cities from experiencing urban sprawl and expansion and preserving local ecosystem services. This underscores the inability of municipalities to defend local interests against central government directives. The findings of this study indicate that the current central policies are the main drivers of land take and urban sprawl and expansion. Prioritizing the compact city model and brownfield developments could effectively mitigate these issues. Finally, the paper advocates that complete jurisdiction over land use decisions be restored to local governments, enabling them to reject investments that may jeopardize local environmental goals.

Early childhood growth trajectories and early adolescent cognitive achievement: the role of catch-up

Human capital deficits in early childhood in terms of health and cognitive developmental issues are conceptualized as a lurking threat to China’s future economic growth by a growing body of scholarly literature; however, it is unknown whether these deficits persist into adolescence. This study draws upon longitudinal, nationally-representative data from the China Family Panel Studies 2010–2020 to investigate the possibility of recovering from early growth retardation and the potential implications for recovery of related cognitive outcomes. In particular, the paper considers whether there is evidence of cognitive catch-up from early cognitive delays. Based on group-based trajectory models with inverse probability of treatment weighting, this study finds that mid-childhood catch-up growth may mitigate the negative effects of early childhood stunting on early adolescent academic performance. Moreover, children who recover from stunting demonstrate cognitive catch-up in Chinese but not in math. This evidence not only reaffirms the significance of early childhood development but also suggests an opportunity for interventions to avert undernutrition and cognitive deficits after early childhood.

High-density lipoprotein cholesterol trajectory and new-onset metabolic dysfunction-associated fatty liver disease incidence: a longitudinal study

BackgroundAlthough high-density lipoprotein cholesterol (HDL-C) exerts a significant influence on the development of metabolic dysfunction-associated fatty liver disease (MAFLD), the association of dynamic changes in HDL-C levels with the risk of MAFLD remains unclear. Thus, the aim of the current study was to explore the association between the changing trajectories of HDL-C and new-onset MAFLD. The findings of this study may provide a theoretical basis for future personalized intervention and prevention targeting MAFLD.MethodsA total of 1507 participants who met the inclusion criteria were recruited from a community-based physical examination population in Nanjing, China from 2017 to 2021. Group-based trajectory models were constructed to determine the heterogeneous HDL-C trajectories. The incidence of MAFLD in each group in 2022 was followed up, and the Cox proportional hazards regression model was applied to investigate the associations between different HDL-C trajectories and the risk of new-onset MAFLD.ResultsThe incidences of MAFLD in the low-stable, moderate-stable, moderate-high-stable, and high-stable groups of HDL-C trajectory were 26.5%, 13.8%, 7.2% and 2.6%, respectively. The incidence rate of MAFLD in the order of the above trajectory groups exhibited a decreasing trend (χ2 = 72.55, Ptrend<0.001). After adjusting for confounders, the risk of MAFLD onset in HDL-C low-stable group was still 5.421 times (95%CI: 1.303–22.554, P = 0.020) higher than that in the high-stable group. Subgroup analyses of the combined (moderate high-stable and high-stable groups combined), moderate-stable and low-stable groups showed that sex, age, and overweight/obesity did not affect the association between HDL-C trajectory and MAFLD risk.ConclusionsPersistently low HDL-C level is a risk factor for the onset of MAFLD. Long-term monitoring of HDL-C levels and timely intervention for those experiencing persistent declines are crucial for early prevention of MAFLD.

A Droplet Generator Using Piezoelectric Ceramics to Impact Metallic Pellets.

Metal micro-droplet ejection technology has attracted attention for its potential applications in the rapid prototyping of micro-metal parts and microelectronic packaging. The current micro-droplet ejection device developed based on this technology faces challenges such as the requirement of a micro-oxygen ejection environment, a complex feeding structure, and high costs. Therefore, a drop-on-demand droplet generator for metallic pellets with impact feed ejection is designed in this paper. This device has a simple and compact structure, does not require a high-cost heat source, and can perform drop-on-demand ejection of metallic pellets in an atmospheric environment. A micro-channel feeding method based on piezoelectric ceramic actuator drives is proposed. A rigid dynamics metallic pellet flight trajectory model is established to analyze the relationships between the driving voltage and the flight trajectory of the pellets. With the help of Fluent to simulate and analyze the melting and ejection processes of the pellets inside the nozzle, the changes in the variable parameters of the flow field in the process of the melting and flight of a single molten drop are studied. The droplet generator produces stable droplets with a 500 µs pulse width and 1100 mm/s initial velocity of the projectile. The simulation results show that a single projectile has to go through three stages including feeding, melting, and ejecting, which take 39.5 ms, 7.85 ms, and 17.65 ms. The total simulation time is 65.0 ms. It is expected that the injection frequency of the metal projectile droplet-generating device will reach 15 Hz.

Longitudinal Trajectories of Physical Teen Dating Perpetration and Trait Anger Expression: A Group-Based Trajectory Approach.

Dating violence is often perpetrated among adolescents, which increases the efforts to better understand individual risk factors for preventive efforts. Although multiple forms (i.e., physical and psychological) often coexist, few studies have studied the chronicity and developmental classes of physical teen dating violence and examined whether subtypes of trait anger predict teen dating perpetration over time. Thus, the purpose of the current study is to investigate: (a) the developmental group-based patterns of physical teen dating violence; and (b) whether subtypes of trait anger expression predict association in these classes using the Bullying, Sexual, and Dating Violence Trajectories from Early to Late Adolescence in the Midwestern United States, 2007 to 2013 dataset. Group-based trajectory modeling results identified four classes of teen dating violence perpetration (e.g., Accelerating, Abstaining, Desisting, and High Chronic). Multinomial logistic regression results indicated that expressing anger outwardly was positively associated with Desisting and High Chronic patterns, but not Accelerating patterns. These results suggest that there are four classes of teen dating violence perpetration and underscore the effects of expression of trait anger as a risk factor of teen dating violence perpetration. Practical and theoretical implications are discussed.

Decoding heterogeneity in data-driven self-monitoring adherence trajectories in digital lifestyle interventions for weight loss: a qualitative study

BackgroundData-driven trajectory modeling approaches have been used to identify participant subgroups with differing responses to digital lifestyle interventions. Identifying contributing factors to different participant subgroups can inform tailored strategies to early “rescue” intervention non-responders. Self-monitoring (SM) is a central mechanism in lifestyle interventions for driving behavior change and can serve as an early indicator for later intervention response. This qualitative study aimed to compare SM experiences between intervention response subgroups and to identify contributing factors to intervention response subgroups in a 6-month digital lifestyle intervention for adults with overweight or obesity.ResultsParticipants were middle-aged (52.9 ± 10.2 years), mostly female (65%), and of Hispanic ethnicity (55%). Four major themes with emerged from the thematic analysis: Acceptance towards SM Technologies, Perceived SM Benefits, Perceived SM Barriers, and Responses When Facing SM Barriers. Participants across both subgroups perceived SM as positive feedback, aiding in diet and physical activity behavior changes. Both groups cited individual and technical barriers to SM, including forgetfulness, the burdensome SM process, and inaccuracy. The Responder Group displayed positive problem-solving skills that helped them overcome the SM barriers. In contrast, some in the Non-responder Group felt discouraged from SM. Both subgroups found diet SM particularly challenging, especially due to technical issues such as the inaccurate food database, the time-consuming food entry process in the Fitbit app.ConclusionsOur study indicates that qualitative analysis is valuable for translating data-driven findings to actionable intervention improvement strategies. Our findings may inform the development of practical SM improvement strategies in future digital lifestyle interventions for weight loss. Notably, building problem solving skills emerge as a key approach to prevent potential non-responders from intervention disengagement.

Early detection of students' mental health issues from a traditional daily health observation scheme in Japanese schools and its digitalization.

The implementation of school-based mental health screening offers promise for early detection of mental health issues in children; however, various barriers hinder its widespread adoption. This study aimed to investigate the predictive value of digital data obtained from an established daily health observation scheme in Japanese schools to identify later mental health issues in children. Data for the analysis were obtained from 2,433 students enrolled in five public schools. The data acquisition period spanned 76 school days, from September 1, 2022, to December 23, 2022, and student absences were recorded during this period. Depressive and anxiety symptoms were assessed in January 2023. The students' daily physical and emotional health status was recorded as "daily health issue" scores and group-based trajectory modeling was employed to classify the long-term trends in these scores. Additionally, rolling z-scores were utilized to capture variability in daily health issue scores, with z-scores above +1 considered unusual responses. After 4 months of daily health observations, students' response trends were classified into five trajectory groups. The group experiencing the highest number of daily health issues (Group 5; 5.4% of the sample) exhibited more subsequent depressive and anxiety symptoms compared to the group with fewer issues (Group 1; 47.5%) (incident rate ratio [IRR] = 5.17; 95% confidence interval [CI]: 3.82, 6.99). Group 5 also demonstrated significantly more days of absence than Group 1 (IRR = 2.14, 95% CI: 1.19, 3.85). The average daily health issue scores for the entire period were associated with both depressive/anxiety symptoms and the number of days absent from school (IRR = 1.59, 95% CI: 1.45, 1.73; IRR = 1.18, 95% CI: 1.04, 1.35, respectively). Furthermore, a higher number of unusual responses during the entire period was also associated with more depressive/anxiety symptoms (IRR = 1.10, 95% CI: 1.07, 1.12). The current study is the first to demonstrate the predictive capability of a traditional daily health observation scheme to identify mental health issues in children. This study highlights the scheme's potential to screen and safeguard children's mental health, emphasizing the importance of digitalization and collaboration with various stakeholders.

Comparison of rotational stability of the Implantable Collamer Lens after using a vertical or horizontal implanting orientation.

To compare rotational stability of the Implantable Collamer Lens (ICL) between horizontal and vertical implantation. Zhongshan Ophthalmic Center, China. Prospective 1:1 matched design. 94 cases (185 eyes with a vertical elliptical ciliary sulcus) were included with a 1:1 matched design based on ciliary sulcus morphology, preset deviation angle, and vault. Follow-ups at 4 days, 1 month, 3 months, and 6 months post-surgery measured rotational angles using slit-lamp photography. Latent class trajectory modeling was employed to investigate the postoperative rotational angle trajectories. Six months after surgery, both groups exhibited similar visual acuity and refractive outcomes. The horizontal group had a significantly greater rotation angle than the vertical group (F group = 13.638, P < 0.001). Additionally, a statistically significant difference (P = 0.004) in the average trajectories of rotational angles was observed. The vertical group displayed a greater presence in the low-stable trajectory subgroup while demonstrating a reduced presence in the moderate-increase and high-fluctuation trajectory subgroups compared to the horizontal group. The horizontal group had a 3.750 times higher risk of rotation angle ≥3° compared to the vertical group, which represented a statistically significant difference (95% CI: 1.346∼10.446). In both groups, a positive correlation between the preset deviation angle and the rotation angle was observed, with correlation coefficients of 0.320 (P = 0.030) and 0.371 (P = 0.011), respectively. Vertical ICL implantation showed better rotational stability than horizontal implantation in eyes with a vertical elliptical ciliary sulcus, offering guidance for ICL surgery.

Relation-aware interaction spatio-temporal network for 3D human pose estimation

3D human pose estimation is a fundamental task in analyzing human behavior, which has many practical applications. However, existing methods suffer from high time complexity and weak capability to acquire the relations at the human joint level and the spatio-temporal level. To this end, the Relation-aware Interaction Spatio-temporal Network (RISNet) is presented to achieve a better speed-accuracy trade-off in a parallel interactive architecture. Firstly, the Spatial Kinematics Modeling Block (SKMB) is proposed to encode spatially positional correlations among human joints, thereby capturing cross-joint kinematic dependencies in each frame. Secondly, the Temporal Trajectory Modeling Block (TTMB) is employed to further process the temporal motion trajectory of individual joints at several various frame scales. Besides, the bi-directional interaction modules across branches are presented to enhance modeling abilities at the spatio-temporal level. Experiments on Human 3.6M, HumanEva-I and MPI-INF-3DHP benchmarks indicate that the RISNet gains significant improvement compared to several state-of-the-art techniques. In conclusion, the proposed approach elegantly extracts critical features of body joints in the spatio-temporal domain with fewer model parameters and lower time complexity.