Covariance Control Research Articles

Mild behavioral impairment in people with mild cognitive impairment: Are the two conditions related?

Mild cognitive impairment (MCI) and mild behavioral impairment (MBI) are both considered potential prodromal stages of dementia, especially Alzheimer's disease. Previous literature has lacked specific information about MBI in individuals with MCI and associations of several aspects of both, MBI and MCI. Our aim was to investigate whether associations exist between aspects of MBI and aspects of cognitive performance in certain dimensions of the Montreal Cognitive Assessment (MoCA). We used baseline data from the double-blind randomized controlled intervention MCI-CCT-study. Current cognitive performance of individuals with MCI was measured with the MoCA. MBI was assessed with the MBI Shortscale (MBI short), which was administered through a self-report interview. Associations were assessed with Pearson correlations. Sensitivity analyses were conducted for gender and cognition. Group differences were examined with independent samples t-tests or Welch test. Significant correlations were considered in binary logistic regressions under control of covariates. There was no significant correlation between the current MoCA and MBI short scores in the total sample or in the gender-related analysis. Using dichotomized cognitive performance, significant correlations between MCI and MBI were revealed for individuals with lower MoCA scores. On the task level, several significant associations were identified between MoCA dimensions and MBI dimensions in the total sample and in the sensitivity analyses, also under control of covariates. Our findings support the hypothesis that with increasing cognitive decline, the association between MCI and MBI becomes stronger. Furthermore, a certain cut-off on the MoCA must be reached to identify a correlation.

Lifestyle-specific association between adversity dimensions with group-based mental health trajectories.

Childhood adversity (CA) has a substantial correlation with mental health problems. Keeping a healthy lifestyle is essential for mental health interventions; it is unclear, however, how healthy lifestyle affect the relationship between CA and persistent mental health problems. This longitudinal study (n=1112, 54.5% male) collected the data on CA (measured through three dimensions: threat, deprivation and unpredictability), mental health problems, and lifestyle factors. Group-based multi-trajectory modeling (GBMTM) was utilized to estimate trajectories for three mental health problems (i.e., depression, ADHD and overanxiety). Close friendships, regular physical activity, appropriate sleep duration, shorter screen time, and healthy eating were combined to establish a healthy lifestyle score (which ranges from 0 to 5). Higher scores indicated a healthier lifestyle. Three trajectories of mental health problems were identified: persistently low risk (24.9%), persistently medium-high risk (50.0%), and persistently high risk (25.1%). Multinomial logistic regression showed that high adversity (high-threat: β=2.01, P<0.001; high-deprivation: β=1.03, P<0.001; high-unpredictability: β=0.83, P=0.001; high-overall adversity: β=1.64, P<0.001) resulted in a persistently high risk of mental health problems; these outcomes were maintained after robust control for covariates. Further lifestyle stratification, null associations were observed among children with a healthy lifestyle, irrespective of their gender; however, after controlling for covariates, the above associations remained relatively stable only among boys. The generalizability of our findings is restricted by 1) limited racial diversity and 2) missing data. This finding underscores the benefits of promoting a healthy lifestyle in children to prevent persistent mental health problems caused by CA.

Covariance Control for Uncrewed Aircraft Systems Under Correlated Uncertainty

Uncrewed aircraft systems and advanced air mobility are associated with uncertain conditions, such as wind prediction errors, and they may deviate from dedicated airspaces referred to as corridors. However, further studies on these aspects are required. This study focuses on the covariance control for a stochastic discrete-time linear system under correlated uncertainties. A covariance control problem with chance constraints can be formulated as a convex-programming problem. Numerical simulations of uncrewed aircraft systems path planning can be used to compare two control laws: covariance control laws that consider correlated and noncorrelated noise. Numerical simulations were conducted to verify that the covariance control law considering correlated noise generates a trajectory that appropriately satisfies the constraints. Consequently, the effect of considering correlated noise in the covariance control law was clarified. In addition, covariance control has been demonstrated to generate appropriate trajectories for various state constraints. Moreover, numerical simulations indicated that covariance control facilitated the control of uncertainty, and the effectiveness of the covariance control was evaluated. The insights gained from the results of this study can inform enhanced uncertainty management for improving reliability and safety in real-world applications of uncrewed aircraft systems and advanced air mobility.

Analysis of a Dry Friction Force Law for the Covariant Optimal Control of Mechanical Systems with Revolute Joints

This contribution shows a geometric optimal control procedure to solve the trajectory generation problem for the navigation (generic motion) of mechanical systems with revolute joints. The mechanical system is analyzed as a nonlinear Lagrangian system affected by dry friction at the joint level. Rayleigh’s dissipation function is used to model this dissipative effect of joint-level friction, and regarded as a potential. Rayleigh’s potential is an invariant scalar quantity from which friction forces derive and are represented by a smooth model that approaches the traditional Coulomb’s law in our proposal. For the optimal control procedure, an invariant cost function is formed with the motion equations and a Riemannian metric. The goal is to minimize the consumed energy per unit time of the system. Covariant control equations are obtained by applying Pontryagin’s principle, and time-integrated using a Finite Elements Method-based solver. The obtained solution is an optimal trajectory that is then applied to a mechanical system using a proportional–derivative plus feedforward controller to guarantee the trajectory tracking control problem. Simulations and experiments confirm that including joint-level friction forces at the modeling stage of the optimal control procedure increases performance, compared with scenarios where the friction is not taken into account, or when friction compensation is performed at the feedback level during motion control.

Lasting Legacy: The Enduring Relationship Between Racially Restrictive Housing Covenants and Health and Wellbeing.

Racially restrictive covenants in housing deeds, commonplace in Minnesotafor houses built from the 1910s to the 1950s, provided a foundation for the myriad of policies that made it difficult for people of color to obtain housing. Though covenants were ruled illegal in 1968, their legacy continues to shape neighborhoods. The Mapping Prejudice Project's efforts in Hennepin County, Minnesota, produced the first systematic documentation of racially restrictive covenants. We use this novel data set to explore the relationship between historic covenants and current health and wellbeing outcomes. Using regression analysis to control for neighborhood level covariates, we compare previously covenanted neighborhoods to neighborhoods without covenants. Today, previously covenanted neighborhoods have higher life expectancy and lower rates of obesity, diabetes, coronary heart disease, and asthma than neighborhoods without racially restrictive covenants. Additionally, previously covenanted neighborhoods have less upward mobility for children from poorer households, and there are larger gaps in upward mobility between white and Black children. These findings contribute to a growing literature that shows racist policies, even decades after they are legally enforceable, leave an imprint on neighborhoods. Using the novel data from the MappingPrejudiceProject, we provide statistical analysis that confirms qualitative and anecdotal evidence on the role of racial covenants in shaping neighborhoods.

Higher-order correction of persistent batch effects in correlation networks.

Systems biology analyses often use correlations in gene expression profiles to infer co-expression networks that are then used as input for gene regulatory network inference or to identify functional modules of co-expressed or putatively co-regulated genes. While systematic biases, including batch effects, are known to induce spurious associations and confound differential gene expression analyses (DE), the impact of batch effects on gene co-expression has not been fully explored. Methods have been developed to adjust expression values, ensuring conditional independence of mean and variance from batch or other covariates for each gene, resulting in improved fidelity of DE analysis. However, such adjustments do not address the potential for spurious differential co-expression (DC) between groups. Consequently, uncorrected, artifactual DC can skew the correlation structure, leading to the identification of false, non-biological associations, even when the input data are corrected using standard batch correction. In this work, we demonstrate the persistence of confounders in covariance after standard batch correction using synthetic and real-world gene expression data examples. We then introduce Co-expression Batch Reduction Adjustment (COBRA), a method for computing a batch-corrected gene co-expression matrix based on estimating a conditional covariance matrix. COBRA estimates a reduced set of parameters expressing the co-expression matrix as a function of the sample covariates, allowing control for continuous and categorical covariates. COBRA is computationally efficient, leveraging the inherently modular structure of genomic data to estimate accurate gene regulatory associations and facilitate functional analysis for high-dimensional genomic data. COBRA is available under the GLP3 open source license in R and Python in netZoo (https://netzoo.github.io).

The association between sleep quantity, insomnia and lung cancer risk - A systematic review and meta-analysis.

The effect of various sleep traits on the risk of lung cancer differs among pre-existing studies. This study aims to systematically review and synthesise the association between sleep duration and insomnia with the incidence of lung cancer. PubMed, Embase and The Cochrane Library were searched from inception to 23 April 2023 for observational studies examining the effect of sleep quantity or insomnia on lung cancer incidence. We pooled maximally-adjusted hazard ratios and odds ratios separately using random effects inverse variance weighted models. The risk of bias of observational studies was assessed using the Newcastle-Ottawa Scale. We included 11 observational studies with a pooled cohort of 5,049,141 patients. The mean age of the patients was 49.5 ± 17.7years, and 51.4% were males. The risk of bias ranged from low-moderate. Individuals who slept for a shorter or longer duration than the reference range of sleep per night showed an increased risk of lung cancer by 11% (HR:1.11; 95%CI:1.00-1.23) and 16% (HR:1.16; 95%CI:1.06-1.27) respectively. Furthermore, individuals with insomnia symptoms had a 9% greater risk of lung cancer than those without symptoms (HR:1.09; 95%CI:1.05-1.13). This study suggests that insufficient sleep, excessive sleep and insomnia may be associated with an increased risk of lung cancer. Physicians should be mindful of this association and encourage healthy sleep practises among patients. Given the observed heterogeneity among some pre-existing studies, future research with longer periods of follow-up, greater control for covariates and objective testing of sleep parameters may add value to this topic.

Co-optimization of sensor fusion and control strategies in electrical and electronic systems

In multi-sensor fusion, resource scheduling and control is responsible for rationally assigning multiple sensors in a networked system to the tasks faced by the system, and is the key to improving the quality of information fusion, increasing the overall system effectiveness, and obtaining the best observation results. Resource scheduling control is mainly to determine which sensors are selected to monitor and track the target according to certain optimization criteria, as well as the working mode, parameters, and measurement process of the sensors, and also to determine how the sensors can cooperate with each other and how the sensors can be adapted to the environment, and so on. This paper is to study the resource scheduling and control algorithm based on covariance control, so as to optimize the sensor resource scheduling and control.

Difference-in-differences Estimator of Quantile Treatment Effect on the Treated

We propose a new difference-in-differences (DID) estimator of the quantile treatment effect on the treated (QTT). The model assumes a common time effect on the cumulative distribution functions of untreated potential outcomes, allowing for covariates. This condition holds if and only if the net change in the untreated outcome densities is common across treated and control groups. Unlike the Changes-in-Changes model proposed by Athey and Imbens (2006), our model is compatible with the usual DID assumption for means, and it provides a computationally simple and straightforward way to control for covariates. We establish uniform consistency and weak convergence of the proposed estimator of QTT and the related functions. The estimators and the simultaneous confidence bands remain valid even for discrete outcome variables. As an empirical application, the distributional impact of the earned income tax credit on birth weight is investigated. We provide a STATA ado file package.

Stochastic Trajectory Planning for Autonomous Aerobraking Using Convex Optimization and Covariance Control

Trajectory planning for autonomous aerobraking poses significant technical challenges, primarily due to the complexities associated with ensuring reliable onboard computation, mitigating flying uncertainty, enhancing aerodeceleration efficacy, and guaranteeing sufficient precision. To address this issue, this paper utilizes convex optimization for the first time to synchronously generate an anti-uncertainty trajectory and corresponding closed-loop controller with efficient, robust, and high-precision computational capability. Specifically, a double-loop planning framework is developed. In the inner loop, techniques of covariance control and linear covariance analysis are employed to give rise to a stochastic trajectory planning problem that optimizes the orbiter’s capacity to withstand and overcome deviations during flight. Furthermore, for onboard planning considerations, the problem’s nonlinearity is equivalently reduced by projecting the time-independent variable into the altitude variable. On this basis, a novel state-complete linearization method is introduced to enable the utilization of sequential convex programming with improved convergence. For the outer loop, the orbiter’s motion is decomposed into low-accuracy longitudinal and high-precision dynamics. A differential-correction-based technique is specifically designed to remedy the dynamics in turn. Numerical simulations demonstrate that the proposed method can reduce mission risk by at least 70% while only experiencing an additional cost of velocity impulse amounting to 20%.

Pathologic Complete Response, Total Neoadjuvant Therapy and the Survival Paradox in Locally Advanced Rectal Cancer.

Pathologic complete response (pCR) after preoperative chemoradiation (nCRT) correlates with improved overall survival for patients with locally advanced rectal cancers (LARCs). Escalation protocols including total neoadjuvant therapy (TNT), which delivers multi-agent chemotherapy and chemoradiation before surgery, are associated with increased complete response rates. However, TNT is not associated with improved overall survival. The authors hypothesized that the route to pCR may be an important predictor of oncologic outcome. Adults with LARC between 2006 and 2017 were identified in the National Cancer Database. The cohort was limited to those who received neoadjuvant radiation (45-70 Gy) and underwent proctectomy. Of 25,880 patients, 16 % received TNT and 84 % had nCRT followed by either multi-agent (27 %), single-agent (14 %), or no adjuvant chemotherapy (44 %). Overall, 18 % achieved pCR, with higher rates in the TNT cohort than in the nCRT (18 %) or multi-agent (14 %) chemotherapy cohorts. With control for covariates, the OS in the pCR cohort was similar for the patients that received single-agent therapy and those that received multi-agent adjuvant therapy, and superior to the TNT and no adjuvant therapy cohorts. Conversely, among the patients who did not achieve pCR, those who received single-agent chemotherapy had OS comparable with those who had multi-agent adjuvant therapy and TNT, which was better than no adjuvant therapy. Patients achieving pCR after TNT had worse OS than those who had CRT alone, suggesting that the neoadjuvant route by which pCR is achieved is prognostically relevant. Therefore, in the era of neoadjuvant therapy escalation, pCR does not necessarily portend a uniformly favorable prognosis.

Cumulative lifetime violence, social determinants of health, and cannabis use disorder post-cannabis legalization in a community sample of men: An intersectional perspective.

Despite Canada having the highest disease burden globally for cannabis use disorder (CUD) and violence being ubiquitous in men's lives, little is known about how intersections among social determinants of health (SDOH) and cumulative lifetime violence severity (CLVS) influence CUD in men post-cannabis legalization. Using data collected in a survey with a national community sample of 597 men who self-identified as having experienced violence, we conducted a latent profile analysis using 11 subscales of the CLVS-44 scale and explored differential associations between CLVS profiles and CUD considering SDOH covariates. Four profiles were distinguished by intersections among CLVS-44 subscale severity and roles as target and perpetrator. CLVS profiles were significantly associated with CUD in the unadjusted model and in the adjusted model where age, adverse housing, and education were significant covariate controls. In the adjusted model, CUD was differentially associated with CLVS profiles and significantly higher in Profile 4 (highest severity target and perpetrator) than in Profile 1 (lowest severity target, no perpetration). Chi-square tests showed significant intersection between adverse housing, younger age, Profile 4 CLVS, and moderate to severe CUD among cannabis users. These results reveal the importance of understanding simultaneous intersections among indicators of CLVS in determining profiles of lifetime violence. Also critical are intersections among CLVS profiles and significant covariates as a basis for trauma- and violence-informed care for CUD that prioritizes men most disadvantaged by this convergence and attends to individual and structural health disparities at practice and policy levels.

Examining short-term and long-term effects of self-esteem on relationship satisfaction using a dyadic response surface analysis

The present study investigates the relationship between self-esteem, its interactive patterns within couples, and relationship satisfaction. We employed dyadic response surface analysis (DRSA), an advanced method for examining the links between dyadic interaction patterns and outcome variables. Analyzing data from 731 Dutch heterosexual couples from the Longitudinal internet Studies for the Social Sciences data set across short-term (two-month) and long-term (two-year) timeframes, our findings align with previous research. Both actor and partner self-esteem had significant positive effects on relationship satisfaction, indicating that one’s self-esteem is positively associated with outcomes for both oneself and one’s partner. Additionally, we observed an additive effect where the combined self-esteem of a couple was positively associated with satisfaction. However, an actor superiority effect was noted, where individuals were most satisfied when self-reporting higher self-esteem than their partners, suggesting that one’s own self-esteem plays a more crucial role in their relationship perceptions than their partner’s and that self-esteem may not act as a shared resource. We did not find a significant self-esteem similarity effect on relationship satisfaction. These results were consistent in both timeframe analyses, with and without covariate controls. This study contributes to bridging the gap between existing theoretical models by providing novel insights into how self-esteem patterns within couples relates to relationship wellbeing.

Testosterone does not mediate the correlation between dietary inflammation and serum klotho levels among males: insights from NHANES database.

Serum Klotho (S-Klotho) is a transmembrane protein holds pivotal roles in anti-aging. The Dietary Inflammation Index (DII), a meticulously dietary tool, quantifies the inflammatory potential of an individual's diet. The existing research strongly suggests that a low DII diet plays a significant role in delaying aging and reducing aging-related symptoms in males. Testosterone could potentially act as a mediating intermediary between DII and S-Klotho. However, this aspect remains unexplored. This study aims to investigate the potential causal link of testosterone between DII and S-Klotho in males. We utilized data from National Health and Nutrition Examination Survey (NHANES) which focused on male participants from 2013-2016. Mediation analyses were used to investigate the effects of testosterone (TT), free testosterone (FT), and free androgen index (FAI) on the DII-S-Klotho relationship, using three modes adjusting for covariates. Mediation analysis unveiled a significant inverse correlation between DII and S-Klotho levels (model 1: c = -14.78, p = 0.046). The interaction between DII and S-Klotho was modulated by TT in model 1 (ab = -1.36; 95% CI: -5.59, -0.55; p = 0.008), but lost significance after adjustments (model 2: ab = -0.39; 95% CI: -4.15, 1.66; p = 0.378; model 3: ab = -0.59; 95% CI: -4.08, 2.15; p = 0.442). For FT, the mediating impact was not statistically significant (model 1: ab = 0.43; 95% CI: -0.51, 5.44; p = 0.188; model 2: ab = 0.72; 95% CI: -0.26, 5.91; p = 0.136; model 3: ab = 0.84; 95% CI: -0.02, 8.06; p = 0.056). Conversely, FAI consistently influenced the DII-S-Klotho relationship (model 1: ab = 2.39; 95% CI: 0.69, 9.42; p = 0.002), maintaining significance after adjustments (model 2: ab = 3.2; 95% CI: 0.98, 11.72; p = 0.004; model 3: ab = 3.15; 95% CI: 0.89, 14.51; p = 0.026). This study observed no mediating influence of TT or FT on the correlation between DII and S-Klotho after covariate control. Remarkably, FAIcontinued to significantly mediate the DII-S-Klotho connection even followingcovariate adjustment, although its significance in males warrants careful consideration.

Neighborhood socioeconomic disadvantages influence outcomes following rotator cuff repair in the non-Medicaid population

BackgroundPrior investigations have utilized various surrogate markers of socioeconomic status to assess how healthcare disparities impact outcomes after rotator cuff repair (RCR). When taken as individual markers, these factors have inconsistent associations. Medicaid insurance status is an accessible marker that has recently been correlated with less optimal outcomes after RCR. Socioeconomic disparities exist within the non-Medicaid population as well and are arguably more difficult to characterize. The Area Deprivation Index (ADI) uses seventeen socioeconomic variables to establish a spectrum of neighborhood healthcare disparity. The purpose of this study was to determine the influence of neighborhood socioeconomic disadvantages, quantified by ADI, on 2-year patient reported outcome scores following RCR in the non-Medicaid population. MethodsA retrospective review of patients who underwent RCR from 2015-2020 was performed. All procedures were performed by a group of seven surgeons at a large academic center. Patient demographics and comorbidities were collected from charts. Rotator cuff tear size was assessed from arthroscopic pictures. ADI scores were calculated based on patients’ home addresses using the Neighborhood Atlas tool. The primary outcome measure was American Shoulder and Elbow Surgeons (ASES) score with minimum follow-up of two years. A linear regression analysis with covariate control for age and patient comorbidities was performed. ResultsThere were 287 patients with a mean age of 60.11 years. The linear regression model between ADI and two-year ASES score was significant (p = .02). When controlling for both age and patient comorbidities, every 0.9-point reduction in ADI resulted in a 1-point increase in the ASES score (P = 0.03). Patients with an ADI of 8, 9, or 10 had lower mean two-year ASES scores than those with an ADI of 1 (87.08 vs 93.19, p = .04), but both groups had similar change from preoperative ASES score (40.17 vs 32.88, p =.12). The change in ASES score at two-years in our study surpassed all established MCID values irrespective of ADI. ConclusionPatients with greater levels of disparity in their home neighborhoods have worse final ASES scores at two years, but patients significantly improve from their preoperative state regardless of social disadvantages. This is the first study to the authors’ knowledge that examines ADI and outcomes following RCR. Providers should be aware that patients with higher ADI scores may have inferior preoperative shoulder function. The results of this study support the utilization of primary rotator cuff repair in applicable tears regardless of socioeconomic status.

Increasing mental health issues in college students from 2016-2019: Assessing the intersections of race/ethnicity, gender, and sexual orientation

BackgroundThis study aimed to evaluate how trends in mental health (e.g., diagnosis/treatment of depression, anxiety, suicidal ideation) varied across intersections of gender, race/ethnicity, and sexual orientation in a large, national sample of undergraduate students. MethodsData from the American College Health Association, and National College Health Assessment II: 2016–2019 were analyzed (N = 228,640 undergraduate students from 442 campuses, ages 18–24; 67.8 % female, 40.4 % BIPOC, 3.0 % non-binary (trans/non-conforming), 19.4 % LGBQ+). We used logistic regression to predict each mental health indicator; covariates included year, gender, BIPOC, LGBQ+ status, and their interactions, as well as other covariate controls (e.g., region, year in school). ResultsThere were significant and steady increases in the odds of each mental health indicator by year (ORs = 1.12–1.13), which were significantly greater in magnitude for LGBQ+ students (ORs = 1.20–1.23). Increases did not vary by gender, race/ethnicity, or intersections between these groups and LGBQ+ status. There were significant interactions between identity groups that aligned with intersectional and minority stress theories (which did not vary by year). Non-Hispanic White students had significantly greater odds of past-year treatment/diagnosis of depression and anxiety compared to BIPOC students; however, BIPOC students had significantly greater odds of past-year suicidal ideation and this was pronounced for BIPOC women. Being non-binary x LGBQ+ was associated with significantly greater odds of each indicator. DiscussionResults affirm the importance of promoting mental health among college students, with a particular focus on how to better serve and support BIPOC, non-binary, and LGBQ+ students.

Is reproductive development adaptively calibrated to early experience? Evidence from a national sample of females.

Many developmental theories have not been sufficiently evaluated using designs that control for unobserved familial confounds. Our long-term goal is to determine the causal structure underlying associations between early environmental conditions and later psychosocial and health outcomes. Our overall objective in this study was to further evaluate predictions derived from applications of life history theory to female reproductive development, key among them that reproductive milestones translate early environmental risk into fertility, health, and behavioral outcomes. To this end, we used female data from the National Longitudinal Survey of Youth 1979 and structural equation modeling to conduct increasingly severe tests, beginning with covariate control and then progressing to sibling control and behavioral genetic designs. After adjusting for confounds varying between sets of siblings, we did not find evidence that age at menarche reflected components of early environment or that any focal outcomes reflected early fragmented family structure (birth to age nine). Although we detected no links between measured environment and individual differences in age at sexual debut, we did find that it reflected both shared and nonshared influences in our behavior genetic models. Interestingly, delayed sexual debut (into young adulthood) reflected identification of parents as the greatest influences and forecasted an array of fertility-related outcomes. Taken together, these findings challenge theories suggesting menarche timing is adaptively calibrated to early environment. They also highlight the need for more research using sibling control and related designs to examine the roles of environments in development. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Health literacy and patient rights awareness in ethnic Hungarian mothers in Eastern Europe.

The paper proposes to identify the determinants of patients' rights awareness in mothers and to examine the relationship of health literacy with awareness of those rights. Our results are based on data from a convenience sample of 894 non-health professional ethnic Hungarian mothers from Hungary, Romania and Slovakia. Health literacy is measured with the HLS-EU-16 questionnaire. Analysis of variance reveals a significant association of health literacy with patient rights awareness. Our results show that health literacy is the highest among patients who filed a complaint through formal channels and/or took legal measures to restore their rights upon violation. A logistic regression model is built to identify the likelihood of having high patient rights awareness, that is, acting formally for the restoration of rights upon infringement. The model controls for covariates. When controlled for covariates, the likelihood of having high patient rights awareness increases with age, and is higher for mothers with highest education, for inhabitants of larger towns, as well as for those with adequate health literacy. The findings of our study have implications for health policy, as they reveal significant inequalities in patient rights culture.

Stochastic linear quadratic optimal control problem with terminal state constraints

AbstractThis paper addresses the stochastic linear quadratic (LQ) control problem with first‐ and second‐order moment constraints on the terminal state. The problem is a modified version of the optimal covariance control problem, where the terminal state is steered to a given probability distribution. Studying a multiplicative‐noise stochastic system rather than an additive‐noise system is a salient feature. Unlike the existing ideas in the optimal steering, by using the Lagrange multipliers method and establishing the stochastic maximum principle, our problem is converted into solving forward–backward stochastic difference equations (FBSDEs), which is a special stochastic two‐point boundary‐value problem (TPBVP). We provide the optimal closed‐loop controller and necessary and sufficient solvability conditions by developing a nonhomogeneous relationship between the optimal state and costate in FBSDEs. Finally, numerical examples are given to demonstrate our results.

Detection of mild cognitive impairment using digital assessments of cognition

AbstractBackgroundConventional cognitive assessment typically provides a single measure of performance at a single point in time in a laboratory setting. Smartphone‐based ecological momentary assessments (EMA) facilitate short‐term intensive assessment of cognitive performance in people’s natural environments. Though heterogeneous variance in digital cognitive assessments has been observed between older adults with mild cognitive impairment (MCI) and cognitively unimpaired (CU), the independent contribution of intra‐individual variability in performance to detection of MCI is poorly characterized in diverse populations of older adults. We examine the ability of digital cognitive assessments of processing speed in discriminating MCI from CU in older adults from a racially/ethnically diverse community sample.MethodEinstein Aging Study (EAS), smartphone‐based digital assessments of cognition, included the response time‐based symbol match test of processing speed. This was assessed 6 times per day over two weeks and was repeated annually. MCI was defined based on in‐house neuropsychological evaluations using Jak‐Bondi criteria. Statistical methods for modeling MCI using digital cognitive assessments include pattern‐mixture discriminant analysis based on heterogeneous linear mixed effects model for digital cognitive measures given MCI, joint modeling using shared random effects with random location scale for digital cognitive measures, and quadratic logistic regression models. All methods take into account the unbalanced adherence rate among individuals and control for covariates including age, sex, years of education, and race/ethnicity. Stratified analyses by race/ethnicity among non‐Hispanic (NH) Whites and Blacks were performed.ResultAmong 311 systematically recruited community‐dwelling non‐demented older adults from the EAS (Mean age = 77.5 years, SD = 4.8; 67.5% Female; 45.3% non‐Hispanic (NH) White, 40.8% NH‐Black), 97 (31.2%) individuals had MCI. Adherence to the EMA protocol was slightly better in individuals who were CU compared to those with MCI. Results from the quadratic logistic model (the easiest to apply) showed evidence of contribution from the quadratic terms that reflect heterogeneous intra‐individual variability in all samples and particularly among NH‐Blacks (p = 0.008).ConclusionWe observe evidence that intra‐individual variability in processing speed improves the ability to discriminate cognitive impairment, particularly among NH‐Black older adults. Leveraging digital technology and performance variability across frequent assessments in the smartphone‐based EMA offers novel opportunities to identify cognitive impairment.