Variable Trajectories Research Articles

Neural Reinforcement Learning Signals Predict Recovery From Impulse Control Disorder Symptoms in Parkinson’s Disease

BackgroundImpulse control disorders (ICDs) in Parkinson’s disease are associated with a heavy burden on patients and caretakers. While recovery can occur, ICDs persist in many patients despite optimal management. The basis for this interindividual variability in recovery is unclear and poses a major challenge to personalized health care. MethodsWe adopted a computational psychiatry approach and leveraged the longitudinal, prospective Personalized Parkinson Project (136 people with Parkinson’s disease, within 5 years of diagnosis) to combine dopaminergic learning theory–informed functional magnetic resonance imaging with machine learning (at baseline) to predict ICD symptom recovery after 2 years of follow-up. We focused on change in Questionnaire for Impulsive-Compulsive Disorders in Parkinson’s Disease Rating Scale scores in the entire sample regardless of an ICD diagnosis. ResultsGreater reinforcement learning signals during gain trials but not loss trials at baseline, including those in the ventral striatum and medial prefrontal cortex, and the behavioral accuracy score measured while on medication were associated with greater recovery from impulse control symptoms 2 years later. These signals accounted for a unique proportion of the relevant variability over and above that explained by other known factors, such as decreases in dopamine agonist use. ConclusionsOur results provide a proof of principle for combining generative model–based inference of latent learning processes with machine learning–based predictive modeling of variability in clinical symptom recovery trajectories. We showed that reinforcement learning modeling parameters predicted recovery from ICD symptoms in Parkinson’s disease.

Group Cohesion in Experiential Growth Groups: A Multilevel Growth Analysis

ABSTRACT Group cohesion is considered a key group therapeutic factor; however, limited knowledge exists on the session-to-session development of group cohesion in the context of training groups or on the factors that may predict such developments. We utilized multilevel growth modeling to examine the longitudinal changes in 44 counselor trainees’ ratings of group cohesion in seven experiential growth groups across four semesters. We found significant between-person variability in the growth trajectory of group cohesion and identified trainee self-disclosure and gender as significant predictors of group cohesion. The results indicated the complexity of within- and between-person changes in group cohesion in training groups and the need for further research to identify potential predictors of these changes. We also discussed ways for group facilitators to attend to self-disclosure as an important marker of group cohesion.

Resolution Enhancement by Variable Zoom Trajectory in X-Ray Computed Tomography

Flat objects like electronic boards are challenging samples for high-resolution X-ray computed tomography scanning because their largest dimension significantly limits the magnification using circular trajectory scans. One way to improve spatial resolution for such samples is to utilize variable zoom trajectory. During variable zoom trajectory scanning, the source-to-object distance changes during the 360° rotation to maximize the magnification in the projections. Here, we propose an automatic variable zoom trajectory generation algorithm for arbitrary object and region of interest (ROI). We analyze how such a trajectory can enhance resolution in different cases and how isotropic is the resolution in the reconstructed volume. We demonstrate that the resolution can be improved without destroying the sample. However, the improvement is manifested mainly in directions in which we achieved the highest magnification in the projection.

Oracle-efficient estimation and global inferences for variance function of functional data

A new two-step reconstruction-based moment estimator and an asymptotically correct smooth simultaneous confidence band as a global inference tool are proposed for the heteroscedastic variance function of dense functional data. Step one involves spline smoothing for individual trajectory reconstructions and step two employs kernel regression on the individual squared residuals to estimate each trajectory variability. Then by the method of moment an estimator for the variance function of functional data is constructed. The estimation procedure is innovative by synthesizing spline smoothing and kernel regression together, which allows one not only to apply the fast computing speed of spline regression but also to employ the flexible local estimation and the extreme value theory of kernel smoothing. The resulting estimator for the variance function is shown to be oracle-efficient in the sense that it is uniformly as efficient as the ideal estimator when all trajectories were known by “oracle”. As a result, an asymptotically correct simultaneous confidence band for the variance function is established. Simulation results support our asymptotic theory with fast computation. As an illustration, the proposed method is applied to the analyses of two real data sets leading to a number of discoveries.

Autonomous Underwater Vehicle Trajectory Prediction with the Nonlinear Kepler Optimization Algorithm–Bidirectional Long Short-Term Memory–Time-Variable Attention Model

Autonomous underwater vehicles (AUVs) have been widely used in ocean missions. When they fail in the ocean, it is important to predict their trajectory. Existing methods rely heavily on historical trajectory data while overlooking the influence of the ocean environment on an AUV’s trajectory. At the same time, these methods fail to use the dependency between variables in the trajectory. To address these challenges, this paper proposes an AUV trajectory prediction model known as the nonlinear Kepler optimization algorithm–bidirectional long short-term memory–time-variable attention (NKOA-BiLSTM-TVA) model. This paper introduces opposition-based learning during the initialization process of the KOA and improves the algorithm by incorporating a nonlinear factor into the planet position update process. We designed an attention mechanism layer that spans both time and variable dimensions, called TVA. TVA can extract features from both the time and variable dimensions of the trajectory and use the dependency between trajectory variables to predict the trajectory. First, the model uses a convolutional neural network (CNN) to extract spatial features from the trajectory. Next, it combines a BiLSTM network with TVA to predict the AUV’s trajectory. Finally, the improved NKOA is used to optimize the model’s hyperparameters. Experimental results show that the NKOA-BiLSTM-TVA model has an excellent parameter optimization effect and higher prediction accuracy in AUV trajectory prediction tasks. It also achieves excellent results in ship trajectory prediction.

Computer assisted surgical anatomy mapping (CASAM) of the distal Posterior Interosseous Nerve (PIN) and its relation to the wrist arthroscopy portals: A cadaver study

IntroductionThe distal Posterior Interosseous Nerve (PIN) plays an important part in the sensory innervation of the wrist joint. Introduction of the arthroscopy portals during wrist arthroscopy might injure the PIN. The anatomic variation in the trajectory of the PIN and the proximity to the dorsal arthroscopy portals have not yet been fully explored. Materials and methodsComputer assisted surgical anatomy mapping (CASAM) is a technique to digitally compute and merge photographic images using anatomic landmarks and visualize variation in anatomy. A standard wrist arthroscopy procedure was carried out on eight cadaver forearms. CASAM was used to map the trajectory of the distal PIN and measure the distance to bony landmarks and the nearest wrist arthroscopy portals. Descriptive statistics were provided for anatomical measurements. ResultsCASAM illustrated great variation in the PIN trajectories between the specimens. The mean distance from the PIN to Lister's tubercle was 9 ​mm (range 3–14, SD 3.9), the distance to the ulnar styloid was 27 ​mm (range 23–32, SD 3.3). None of the nerves showed signs of iatrogenic injury from placement of the arthroscopy portals. The 3–4 portal and the 6R portal were closest to the PIN with a respective mean distance of 9 ​mm (range 4–15, SD 3.8) and 19 ​mm (range 13–22, SD 3.2) ConclusionCASAM demonstrated the importance of understanding nerve anatomy variations and offered insight into which arthroscopy portals are most likely to damage the distal PIN. However, we conclude that the overall risk of PIN injury from wrist arthroscopy is low due to the proximity to the portals.

Colony context and size-dependent compensation mechanisms give rise to variations in nuclear growth trajectories.

To investigate the fundamental question of how cellular variations arise across spatiotemporal scales in a population of identical healthy cells, we focused on nuclear growth in hiPS cell colonies as a model system. We generated a 3D timelapse dataset of thousands of nuclei over multiple days, and developed open-source tools for image and data analysis and an interactive timelapse viewer for exploring quantitative features of nuclear size and shape. We performed a data-driven analysis of nuclear growth variations across timescales. We found that individual nuclear volume growth trajectories arise from short timescale variations attributable to their spatiotemporal context within the colony. We identified a strikingly time-invariant volume compensation relationship between nuclear growth duration and starting volume across the population. Notably, we discovered that inheritance plays a crucial role in determining these two key nuclear growth features while other growth features are determined by their spatiotemporal context and are not inherited.

Genome-wide analysis of the family 10 plant pathogenesis-related proteins in Pyrus bretschneideri and functional analysis of PbrMLP for Colletotrichum fructicola resistance

Pathogenesis-related (PR) genes are key regulators of plant adaptation responses to biotic and abiotic stresses. Family 10 PRs (PR10s, also known as major latex proteins) are usually induced by pathogens and environmental stresses. However, the evolutionary trajectory and functional divergence of the PR10 gene family in Chinese white pear (Pyrus bretschneideri ‘Dangshan Suli’) remain uncharacterized. The presence of 61 PR10s was detected across six Rosaceae species. The PR10 gene family was classified into two distinct groups by employing phylogenetic analysis and the taxonomic criteria of model plants. Interspecies synchrony revealed an ancient origin of the PR10 family in the six Rosaceae species, with 18 synchronic gene pairs. The expansion and evolution of the PR10 family were driven by various types of gene duplication events, with whole-genome duplication (WGD) being the primary mechanism. A candidate Colletotrichum fructicola (C. fructicola) resistance gene Pyrusbretschneiderimajor latex-like proteins (PbrMLP) belonging to the PR10 family was screened through transcriptomics and qRT-PCR. In addition, PbrMLP-silenced pear seedlings were more sensitive to C. fructicola than the controls. These results showed that PbrMLP is a candidate gene vital for anthracnose resistance in pears. These findings offer novel insights into the molecular mechanisms underlying the resistance to pear anthracnose infection in Rosaceae species and identify potential target genes for developing disease-resistant cultivars through genetic engineering.

Film cooling performances of short fan-shaped-holes under oscillating freestream

Freestream oscillation presents a significant challenge in optimized design of short fan-shaped-hole, a good choice for more promising double-wall cooled blades. An experimental investigation was conducted to understand detailed effects of key geometrical parameters of the fan-shaped hole, freestream oscillating frequency and cooling air-to-mainstream blowing ratio on the unstable film cooling performances. The selected geometrical parameters included the length-to-diameter ratio, the lateral diffusion angle, and the length ratio of cylindrical section-to-diffusion section. Time-resolved planar quantitative light sheet technique was employed to visualize the temporal variations of jet trajectory and transported scalar concentrations. The experimental results indicated that freestream oscillation causes variations in jet mechanisms, changing the trend in film cooling with blowing ratio and reversing the universally-acknowledged harmful influence of non-fully development of cooling air in short tube. The optimized short-hole can achieve an increment up to 40% in film effectiveness under oscillating freestream, in comparison with the long-hole-jet. The primary principle of the optimized design of short shaped-hole is properly enlarging the lateral expansion angle, aiming at the higher steady film effectiveness while the lower unsteadiness due to the transient fluctuations. Further enlarging the length ratio can improve the stability of film cooling in an oscillating cycle.

Coot optimization algorithm-tuned neural network-enhanced PID controllers for robust trajectory tracking of three-link rigid robot manipulator

Robotic manipulators are nonlinear systems, multi-input multi-output, highly coupled and complicated whose performance is negatively impacted by external disturbances and parameter un-certainties. Therefore, the controllers designed for such systems must be capable of managing their complexity. The main aim of this study is to tackle the trajectory tracking issue of the three-Link Rigid Robot Manipulator (3-LRRM) based on designing three control structures using a combi-nation Neural Network (NN) with Proportional, Integral and Derivative (PID) actions named Neural Controller Like PIPD (NN-PIPD) controller, Neural Network plus PID (NN + PID) controller NN + PID controller and Elman Neural Network Like PID (ELNN-PID) controller. The parameters of the proposed controllers are adjusted utilizing the Coot Optimization Algorithm (COOA) in order to reduce the Integral Time Square Error (ITSE). A novel objective function for tuning process to produce a controller with minimum value of the chattering in the control signal is proposed. The performance of the proposed controllers is evaluated in terms of disturbance rejection, model uncertainty, fluctuating initial conditions and reference trajectory tracking. According to the simulation results proved that the suggested NN-PIPD controller outperforms all other proposed controller structures for tracking performance, stability, and robustness. As a result of the com-parison analysis the optimal controller was considered to be an NN-PIPD controller for tracking trajectory, rejecting disturbances, and parameter variation with minimizing ITSE of 0.001777.

Policy-making optimization based on generative adversarial networks: A case study of mapping energy transition pathways to China's carbon neutrality

Mapping energy transition pathways is pivotal for achieving carbon neutrality. However, potential pathways delineated by rule-based models differ significantly due to model characteristics, posing a grand challenge in subsequent policy-making. Inspired by the ability of deep convolutional generative adversarial networks (DCGAN) to extract features and generate images, we integrate model outputs concerning 16 energies’ transition pathways to carbon neutrality through DCGAN, assimilating the uncertainties among these outputs. Since DCGAN absorbs the patterns of published data, it offers new insights into policy-making of energy transitions. DCGAN indicates that natural gas and its application with carbon capture and storage play more crucial roles than these currently suggested levels. Additionally, hydrogen and nuclear energies require further development over 2020−2060, serving as a cushion during the substantial energy restructuring. Our study not only provides novel insights into methods mapping the trajectories of critical variables to carbon neutrality but extends DCGAN's application into policy-making optimization.

Earnings trajectories of individuals in same-sex and different-sex couples: Evidence from administrative data

We unite two interrelated bodies of work – a growing literature on sexual orientation earnings gaps and a rich tradition of research on intragenerational career trajectories – to examine how labor markets and life courses interact to produce gender and sexual orientation inequalities over time. We use the 1982–2019 Canadian Longitudinal Administrative Databank, a unique longitudinal database constructed from tax records, to answer core questions about the mechanisms that underlie sexual orientation earnings inequality. Growth curve models reveal how sexual orientation earnings gaps evolve over time spent in the workforce, and how they relate to differences in demographic and work characteristics for those in same- and different-sex couples at various points in the life course. We find that sexual orientation earnings gaps converge and diverge at unique career stages for men and women, and at each stage relate to unique mechanisms, especially work characteristics and family status. We find little significant variation in average earnings trajectories by sexual orientation across cohorts who were subject to differing legal and social environments surrounding sexual orientation.

Letter to the Editor: Variations in 1-year Trajectories of Levels of Pain and Capability After Shoulder Arthroplasty Are Associated With Baseline Mental Health.

Letter to the Editor: Variations in 1-year Trajectories of Levels of Pain and Capability After Shoulder Arthroplasty Are Associated With Baseline Mental Health.

Development in context: Comparing short-term trajectories of expectancy, task values, and costs in four university STEM courses

University students studying science, technology, engineering, and mathematics (STEM) tend to lose motivation, on average, over time. However, detailed understanding of when and how students are most likely to lose motivation, as well as whether motivational trajectories differ across contexts, is lacking. Accordingly, we investigated trajectories of expectancy, task values, and perceived costs in four introductory STEM courses (N = 2,104). Latent change score models revealed nuanced patterns of change, indicating average motivational declines in expectancy and task values and average increases in cost perceptions observed in prior research tend to occur primarily at the beginning of the semester. Motivational trajectories also differed in the shape and timing of mean changes across the four STEM courses, suggesting motivational loss trends may not be universal. Variation in trajectories also related to course performance and major persistence, with different motivational beliefs playing different roles at various times in the semester. Findings highlighted the importance of early course performance in predicting motivational change, as well as the importance of motivational changes for short- and long-term outcomes. Findings underscore the contextualized nature of motivational constructs, as both change patterns and relations to correlates differed across settings, indicating a need for researchers to carefully consider how samples are aggregated and further uncover contextual features that shape motivation. Overall, our study findings add essential understanding of motivational strengths and needs in real-world classroom contexts, with implications for the design and timing of intervention for instructors by examining what developmental patterns are common across settings and which are more particular to specific settings.

Effects of rotor–rotor interaction for a small tandem rotor operating in a crosswind

This study investigates the effects of rotor–rotor interaction on the wake and thrust characteristics of a small tandem rotor operating in a crosswind. Flow velocity and force measurements were conducted in a wind tunnel with two rotors arranged parallel to a crosswind. The results show that the rotor–rotor interaction significantly influences the wake characteristics and thrust generations of the tandem rotor and its effects vary depending on the crosswind speed and distance between rotor tips. In the tandem rotor configuration, the front rotor wake prevents the crosswind flow from reaching the rear rotor wake, thereby reducing the crosswind influence on it. However, under the strong rotor–rotor interaction, such as that caused by high crosswind speeds and short distances between rotor tips, the wakes of both rotors collide with each other and rapidly break down as they proceed downward. Tip-vortex characteristics are also affected by rotor–rotor interaction, which is investigated in terms of variations in the time-averaged tip-vortex trajectory and dissipation ratio with the strength of rotor–rotor interaction. These wake variations by rotor–rotor interaction lead to a decrease in thrust coefficients of the front and rear rotors, with a more significant reduction observed for the rear rotor. The thrust of the rear rotor is more significantly reduced as the crosswind speed increases and the distance between rotor tips narrows. This is mainly attributed to the increased axially induced velocity near the leading tips on the advancing side, retreating side, and centerline.

Variations of flow patterns and pressure pulsations in variable speed pump-turbine during start-up process

Abstract In recent years, variable speed pumped-storage (VSPS) technology has become a hotspot of research and application due to its excellent power grid regulation capabilities. Since the speed of a VSPS unit is changeable, when switching operating conditions, more stable operating trajectories can be chosen to reduce vibration and damage. The simulations of transient processes of VSPS units normally use 1D method, which cannot judge the quality of operating conditions because of no flow pattern and pressure pulsation information. Using 3D CFD simulation based on OpenFOAM, we analyse the flow patterns and pressure pulsations when a VSPS unit with a full size frequency converter (FSFC) is in the turbine start-up process. The typical operating points in the variable speed transient trajectories obtained from 1D simulation are studied. It is found that in the initial stage of start-up, high-speed circulation flow arises in the vaneless space, leading to flow separation and large pressure pulsation. The results can provide a reference for the operation of VSPS power stations, so as to avoid operating conditions with adverse flow patterns and pressure pulsations.

Associations of prenatal blood pressure trajectory and variability with child neurodevelopment at 2 years old

BackgroundThe patterns of blood pressure (BP) change throughout the pregnancy were related to adverse birth outcomes. However, little is known about the long-term effect of BP change patterns on child neurodevelopment. This study aimed to explore the relationship between the BP trajectory and BP variability during pregnancy and early childhood neurodevelopment.MethodA total of 2797 mother-newborn pairs were derived from the Wuhan Healthy Baby Cohort Study. BP was measured during each antenatal visit, and Mental and Psychomotor Development Indexes (MDI and PDI) were assessed using the Bayley Scales of Infant Development (BSID) when the children were 2 years old. Delayed neurodevelopment was defined as scores of PDI or MDI less than − 1SD relative to the mean score of the study population. A group-based multi-trajectory model was adopted to identify multi-trajectories of systolic blood pressure (SBP) and diastolic blood pressure (DBP). Visit-to-visit BP variability was assessed by the coefficient of variation (CV), standard deviation (SD), and average real variability (ARV). Generalized linear models and multivariate logistic regressions were used to assess the associations of BP trajectories and variability with BSID scores and delayed neurodevelopment, respectively.ResultsFive distinct trajectories for SBP and DBP were identified, namely, “Low-increasing,” “Low-stable,” “Moderate-decreasing,” “Moderate-increasing,” and “High-stable” groups. Compared with the “Low-stable” group, the children whose mothers’ BP fell into the other four groups had lower PDI scores, and mothers in the “Low-increasing,” “Moderate-increasing,” and “Moderate-decreasing” groups had 43% (OR: 1.43, 95% CI: 1.01, 2.03), 48% (OR: 1.48, 95% CI: 1.05, 2.08) and 45% (OR:1.45, 95% CI: 1.03, 2.04) higher risk of having offspring with delayed psychomotor neurodevelopment, respectively. High DBP variability was associated with lower BSID scores, and delayed psychomotor neurodevelopment (OR = 1.46, 95% CI: 1.10, 1.92 for DBP-SD; OR = 1.53, 95% CI: 1.16, 2.02 for DBP-CV).ConclusionOur findings suggest that BP change patterns assessed by multi-trajectory and visit-to-visit variability were associated with lower BSID scores and delayed neurodevelopment. Health professionals should be aware of the influence of BP level and its oscillations during pregnancy on the risk of delayed neurodevelopment.

Longitudinal Profiles of Cultural Identity Processes and Associations with Psychosocial Outcomes Among Adolescents Participating in the Identity Project in Italy

Cultural identity formation is a complex developmental task that influences adolescents’ adjustment. However, less is known about individual variations in trajectories of cultural identity processes and how they relate to youth psychosocial outcomes. Using a person-centered approach, this study investigated patterns of change over a year in cultural identity exploration and resolution, respectively, among ethnically diverse adolescents in Italy. The sample included 173 high school students (Mage = 15 yrs, SD = 0.62, range = 14–17; 58.4% female; 26% immigrant background) who had participated in the Identity Project, a school-based intervention targeting ethnic-racial identity development. Longitudinal latent profile analysis revealed only one profile of change for exploration, whereas four unique profiles for resolution emerged (“stable low,” “stable average,” “increase low-to-average,” “increase high-to-higher”). Overall, youth in the resolution-increase profiles reported the best outcomes. The findings highlight the heterogeneity of adolescents’ resolution trajectories and the benefits of an increased sense of clarity concerning one’s cultural identity for positive psychosocial functioning.

Numerical Optimization of Variable Blank Holder Force Trajectories in Stamping Process for Multi-Defect Reduction.

An intelligent optimization technology was proposed to mitigate prevalent multi-defects, particularly failure, wrinkling, and springback in sheet metal forming. This method combined deep neural networks (DNNs), genetic algorithms (GAs), and Monte Carlo simulation (MCS), collectively as DNN-GA-MCS. Our primary aim was to determine intricate process parameters while elucidating the intricate relationship between processing methodologies and material properties. To achieve this goal, variable blank holder force (VBHF) trajectories were implemented into five sub-stroke steps, facilitating adjustments to the blank holder force via numerical simulations with an oil pan model. The Forming Limit Diagram (FLD) predicted by machine learning algorithms based on the Generalized Incremental Stress State Dependent Damage (GISSMO) model provided a robust framework for evaluating sheet failure dynamics during the stamping process. Numerical results confirmed significant improvements in formed quality: compared with the average value of training sets, the improvements of 18.89%, 13.59%, and 14.26% are achieved in failure, wrinkling, and springback; in the purposed two-segmented mode VBHF case application, the average value of three defects is improved by 12.62%, and the total summation of VBHF is reduced by 14.07%. Statistical methodologies grounded in material flow analysis were applied, accompanied by the proposal of distinctive optimization strategies for the die structure aimed at enhancing material flow efficiency. In conclusion, our advanced methodology exhibits considerable potential to improve sheet metal forming processes, highlighting its significant effect on defect reduction.

Inertia, Progress, or Regress? Observing and Explaining Heterogenous Tech Firm Demographic Diversity Trajectories

Tech firms are under strong pressure to increase their demographic diversity. While activists and scholars have tended to treat the sector as homogenously hostile to women and racialized minorities, recent theory on organizational inequalities stresses heterogeneity in firm-level inequality regimes. Beginning with an inductive exploration of variation in executive, managerial, and professional workforce trajectories, we find that between 2008 and 2016 most Tech firms show little change, but that there are also significant clusters of firms that were becoming either much more or much less diverse for all three occupational levels. We model these trajectories as a function of firm visibility, the regulation of federal contractors by the U.S. Department of Labor, and leadership composition. Multinomial logistic regression models show that firms with an increasing (decreasing) diversity pattern in managerial and executive positions are also more likely to become more (less) diverse in their much more numerous professional jobs. Managers are more influential than executive in this regard. Regulatory pressure is associated with increased executive diversity trajectories, but not with managerial or professional trajectories. We conclude that increased Tech diversity is possible but requires leadership, particularly at the middle manager level. In addition, regulatory and visibility pressures primarily produce symbolic shuffles in top jobs.