State Mindfulness During Meditation Predicts Enhanced Cognitive Reappraisal

Science.gov (United States)

Hanley, Adam; Farb, Norman A.; Froeliger, Brett E.

2013-01-01

Putatively, mindfulness meditation involves generation of a state of “nonappraisal”, yet, little is known about how mindfulness may influence appraisal processes. We investigated whether the state and practice of mindfulness could enhance cognitive reappraisal. Participants (N = 44; M age = 24.44, SD = 4.00, range 19 – 38, 82.2% female) were randomized to either 1) mindfulness, 2) suppression, or 3) mind-wandering induction training conditions. Cognitive reappraisal was assessed with the Emotion Regulation Questionnaire (ERQ) prior to experimental induction, and state mindfulness was assessed immediately following induction using the Toronto Mindfulness Scale (TMS). Participants practiced their assigned strategy for one week and then were reassessed with the ERQ reappraisal subscale. Participants receiving mindfulness training reported significantly higher levels of state mindfulness than participants in the thought suppression and mind wandering conditions. Although brief mindfulness training did not lead to significantly greater increases in reappraisal than the other two conditions, state mindfulness during mindfulness meditation was prospectively associated with increases in reappraisal. Path analysis revealed that the indirect effect between mindfulness training and reappraisal was significant through state mindfulness. Degree of state mindfulness achieved during the act of mindfulness meditation significantly predicted increases in reappraisal over time, suggesting that mindfulness may promote emotion regulation by enhancing cognitive reappraisal. PMID:26085851

Resting-state slow wave power, healthy aging and cognitive performance

OpenAIRE

Eleni L. Vlahou; Franka Thurm; Iris-Tatjana Kolassa; Winfried Schlee

2014-01-01

Cognitive functions and spontaneous neural activity show significant changes over the life-span, but the interrelations between age, cognition and resting-state brain oscillations are not well understood. Here, we assessed performance on the Trail Making Test and resting-state magnetoencephalographic (MEG) recordings from 53 healthy adults (18–89 years old) to investigate associations between age-dependent changes in spontaneous oscillatory activity and cognitive performance. Results show tha...

Using Emotion as Information in Future-Oriented Cognition: Individual Differences in the Context of State Negative Affect

Science.gov (United States)

Marroquín, Brett; Boyle, Chloe C.; Nolen-Hoeksema, Susan; Stanton, Annette L.

2016-01-01

Predictions about the future are susceptible to mood-congruent influences of emotional state. However, recent work suggests individuals also differ in the degree to which they incorporate emotion into cognition. This study examined the role of such individual differences in the context of state negative emotion. We examined whether trait tendencies to use negative or positive emotion as information affect individuals' predictions of what will happen in the future (likelihood estimation) and how events will feel (affective forecasting), and whether trait influences depend on emotional state. Participants (N=119) reported on tendencies to use emotion as information (“following feelings”), underwent an emotion induction (negative versus neutral), and made likelihood estimates and affective forecasts for future events. Views of the future were predicted by both emotional state and individual differences in following feelings. Whereas following negative feelings affected most future-oriented cognition across emotional states, following positive feelings specifically buffered individuals' views of the future in the negative emotion condition, and specifically for positive future events, a category of future-event prediction especially important in psychological health. Individual differences may confer predisposition toward optimistic or pessimistic expectations of the future in the context of acute negative emotion, with implications for adaptive and maladaptive functioning. PMID:27041783

Information matrix estimation procedures for cognitive diagnostic models.

Science.gov (United States)

Liu, Yanlou; Xin, Tao; Andersson, Björn; Tian, Wei

2018-03-06

Two new methods to estimate the asymptotic covariance matrix for marginal maximum likelihood estimation of cognitive diagnosis models (CDMs), the inverse of the observed information matrix and the sandwich-type estimator, are introduced. Unlike several previous covariance matrix estimators, the new methods take into account both the item and structural parameters. The relationships between the observed information matrix, the empirical cross-product information matrix, the sandwich-type covariance matrix and the two approaches proposed by de la Torre (2009, J. Educ. Behav. Stat., 34, 115) are discussed. Simulation results show that, for a correctly specified CDM and Q-matrix or with a slightly misspecified probability model, the observed information matrix and the sandwich-type covariance matrix exhibit good performance with respect to providing consistent standard errors of item parameter estimates. However, with substantial model misspecification only the sandwich-type covariance matrix exhibits robust performance. © 2018 The British Psychological Society.

Advanced Physiological Estimation of Cognitive Status. Part 2

Science.gov (United States)

2011-05-24

fatigue, overload) Technology Transfer Opportunity Technology from PDT – Methods to acquire various physiological signals (EEG, EOG , EMG, ECG, etc...Integrated Hardware for Experiments EEG Sensor Array EOG Sensor Array Eye Tracker Amplifiers and Signal Conditioners Laptop Computer...REPORT Advanced Physiological Estimation of Cognitive Status - Part II 14. ABSTRACT 16. SECURITY CLASSIFICATION OF: This report describes ongoing work

The Influence of Affective States Varying in Motivational Intensity on Cognitive Scope

Directory of Open Access Journals (Sweden)

Eddie eHarmon-Jones

2012-09-01

Full Text Available We review a program of research that has suggested that affective states high in motivationally intensity (e.g., enthusiasm, disgust narrow cognitive scope, whereas affective states low in motivationally intensity (e.g., joy, sadness broaden cognitive scope. Further supporting this interpretation, indices of brain activations, derived from human electroencephalography, suggest that the motivational intensity of the affective state predicts the narrowing of cognitive scope. Finally, research suggests that the relationship between emotive intensity and cognitive scope is bi-directional, such that manipulated changes in cognitive scope influence early brain activations associated with emotive intensity. In the end, the review highlights how emotion can impair and improve certain cognitive processes.

EEG Estimates of Cognitive Workload and Engagement Predict Math Problem Solving Outcomes

Science.gov (United States)

Beal, Carole R.; Galan, Federico Cirett

2012-01-01

In the present study, the authors focused on the use of electroencephalography (EEG) data about cognitive workload and sustained attention to predict math problem solving outcomes. EEG data were recorded as students solved a series of easy and difficult math problems. Sequences of attention and cognitive workload estimates derived from the EEG…

Lithium-Ion Battery Capacity Estimation: A Method Based on Visual Cognition

Directory of Open Access Journals (Sweden)

Yujie Cheng

2017-01-01

Full Text Available This study introduces visual cognition into Lithium-ion battery capacity estimation. The proposed method consists of four steps. First, the acquired charging current or discharge voltage data in each cycle are arranged to form a two-dimensional image. Second, the generated image is decomposed into multiple spatial-frequency channels with a set of orientation subbands by using non-subsampled contourlet transform (NSCT. NSCT imitates the multichannel characteristic of the human visual system (HVS that provides multiresolution, localization, directionality, and shift invariance. Third, several time-domain indicators of the NSCT coefficients are extracted to form an initial high-dimensional feature vector. Similarly, inspired by the HVS manifold sensing characteristic, the Laplacian eigenmap manifold learning method, which is considered to reveal the evolutionary law of battery performance degradation within a low-dimensional intrinsic manifold, is used to further obtain a low-dimensional feature vector. Finally, battery capacity degradation is estimated using the geodesic distance on the manifold between the initial and the most recent features. Verification experiments were conducted using data obtained under different operating and aging conditions. Results suggest that the proposed visual cognition approach provides a highly accurate means of estimating battery capacity and thus offers a promising method derived from the emerging field of cognitive computing.

Crowdsourced estimation of cognitive decline and resilience in Alzheimer's disease.

Science.gov (United States)

Allen, Genevera I; Amoroso, Nicola; Anghel, Catalina; Balagurusamy, Venkat; Bare, Christopher J; Beaton, Derek; Bellotti, Roberto; Bennett, David A; Boehme, Kevin L; Boutros, Paul C; Caberlotto, Laura; Caloian, Cristian; Campbell, Frederick; Chaibub Neto, Elias; Chang, Yu-Chuan; Chen, Beibei; Chen, Chien-Yu; Chien, Ting-Ying; Clark, Tim; Das, Sudeshna; Davatzikos, Christos; Deng, Jieyao; Dillenberger, Donna; Dobson, Richard J B; Dong, Qilin; Doshi, Jimit; Duma, Denise; Errico, Rosangela; Erus, Guray; Everett, Evan; Fardo, David W; Friend, Stephen H; Fröhlich, Holger; Gan, Jessica; St George-Hyslop, Peter; Ghosh, Satrajit S; Glaab, Enrico; Green, Robert C; Guan, Yuanfang; Hong, Ming-Yi; Huang, Chao; Hwang, Jinseub; Ibrahim, Joseph; Inglese, Paolo; Iyappan, Anandhi; Jiang, Qijia; Katsumata, Yuriko; Kauwe, John S K; Klein, Arno; Kong, Dehan; Krause, Roland; Lalonde, Emilie; Lauria, Mario; Lee, Eunjee; Lin, Xihui; Liu, Zhandong; Livingstone, Julie; Logsdon, Benjamin A; Lovestone, Simon; Ma, Tsung-Wei; Malhotra, Ashutosh; Mangravite, Lara M; Maxwell, Taylor J; Merrill, Emily; Nagorski, John; Namasivayam, Aishwarya; Narayan, Manjari; Naz, Mufassra; Newhouse, Stephen J; Norman, Thea C; Nurtdinov, Ramil N; Oyang, Yen-Jen; Pawitan, Yudi; Peng, Shengwen; Peters, Mette A; Piccolo, Stephen R; Praveen, Paurush; Priami, Corrado; Sabelnykova, Veronica Y; Senger, Philipp; Shen, Xia; Simmons, Andrew; Sotiras, Aristeidis; Stolovitzky, Gustavo; Tangaro, Sabina; Tateo, Andrea; Tung, Yi-An; Tustison, Nicholas J; Varol, Erdem; Vradenburg, George; Weiner, Michael W; Xiao, Guanghua; Xie, Lei; Xie, Yang; Xu, Jia; Yang, Hojin; Zhan, Xiaowei; Zhou, Yunyun; Zhu, Fan; Zhu, Hongtu; Zhu, Shanfeng

2016-06-01

Identifying accurate biomarkers of cognitive decline is essential for advancing early diagnosis and prevention therapies in Alzheimer's disease. The Alzheimer's disease DREAM Challenge was designed as a computational crowdsourced project to benchmark the current state-of-the-art in predicting cognitive outcomes in Alzheimer's disease based on high dimensional, publicly available genetic and structural imaging data. This meta-analysis failed to identify a meaningful predictor developed from either data modality, suggesting that alternate approaches should be considered for prediction of cognitive performance. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Experimental study on the operators' cognitive behavior analysis for the plant anomaly diagnosis

International Nuclear Information System (INIS)

Takahashi, Makoto; Kubo, Osamu; Yasuta, Akira

1996-01-01

In this paper, a method of human cognitive state estimation based on physiological measures has been applied to the analysis of cognitive behavior during anomaly diagnosis observed with nuclear power plant simulator. This method has also been combined with the conventional experimental protocol such as operational sequence and questionnaire results. The simulator experiments have been performed using plant experts and the results demonstrate that the cognitive state estimation method can be an effective way for understanding cognitive behavior during the anomaly diagnosis of the nuclear power plant. It has also been shown from the results that the combined use of the human cognitive state estimation and the conventional experimental protocol can provide effective information in decreasing the ambiguity of the analysis results. (author)

The Compression Flow as a Measure to Estimate the Cognitive Impairment Severity in Resting State fMRI and 18FDG-PET Alzheimer's Disease Connectomes

Directory of Open Access Journals (Sweden)

Antonio Giuliano Zippo

2015-12-01

Full Text Available The human brain appears organized in compartments characterized by seemingly specific functional purposes on many spatial scales. A complementary functional state binds information from specialized districts to return what is called integrated information. This fundamental network dynamics undergoes to severe disarrays in diverse degenerative conditions such as Alzheimer's Diseases (AD. The AD represents a multifarious syndrome characterized by structural, functional and metabolic landmarks. In particular, in the early stages of AD, adaptive functional modifications of the brain networks mislead initial diagnoses because cognitive abilities may result indiscernible from normal subjects. As a matter of facts, current measures of functional integration fail to catch significant differences among normal, mild cognitive impairment (MCI and even AD subjects. The aim of this work is to introduce a new topological feature called Compression Flow (CF to finely estimate the extent of the functional integration in the brain networks. The method uses a Monte Carlo-like estimation of the information integration flows returning the compression ratio between the size of the injected information and the size of the condensed information within the network. We analyzed the resting state connectomes of 75 subjects of the Alzheimer's Disease Neuroimaging Initiative 2 (ADNI repository. Our analyses are focused on the 18FGD-PET and functional MRI (fMRI acquisitions in several clinical screening conditions. Results indicated that CF effectively discriminate MCI, AD and normal subjects by showing a significant decrease of the functional integration in the AD and MCI brain connectomes. This result did not emerge by using a set of common complex network statistics. Furthermore, CF was best correlated with individual clinical scoring scales. In conclusion, we presented a novel measure to quantify the functional integration that resulted efficient to discriminate

State-dependent cognition and its relevance to cultural evolution.

Science.gov (United States)

Nettle, Daniel

2018-02-05

Individuals cope with their worlds by using information. In humans in particular, an important potential source of information is cultural tradition. Evolutionary models have examined when it is advantageous to use cultural information, and psychological studies have examined the cognitive biases and priorities that may transform cultural traditions over time. However, these studies have not generally incorporated the idea that individuals vary in state. I argue that variation in state is likely to influence the relative payoffs of using cultural information versus gathering personal information; and also that people in different states will have different cognitive biases and priorities, leading them to transform cultural information in different ways. I explore hunger as one example of state variable likely to have consequences for cultural evolution. Variation in state has the potential to explain why cultural traditions and dynamics are so variable between individuals and populations. It offers evolutionarily-grounded links between the ecology in which individuals live, individual-level cognitive processes, and patterns of culture. However, incorporating heterogeneity of state also makes the modelling of cultural evolution more complex, particularly if the distribution of states is itself influenced by the distribution of cultural beliefs and practices. Copyright © 2018 Elsevier B.V. All rights reserved.

Montreal Cognitive Assessment and Mini-Mental State Examination reliable change indices in healthy older adults.

Science.gov (United States)

Kopecek, Miloslav; Bezdicek, Ondrej; Sulc, Zdenek; Lukavsky, Jiri; Stepankova, Hana

2017-08-01

Cognitive tests are used repeatedly to assess the treatment response or progression of cognitive disorders. The Montreal Cognitive Assessment (MoCA) is a valid screening test for mild cognitive impairment. The aim of our study was to establish 90% reliable change indices (RCI) for the MoCA together with the Mini-Mental State Examination (MMSE) in cognitively healthy older adults. We analyzed 197 cognitively healthy and functional independent volunteers aged 60-94 years, who met strict inclusion criteria for four consecutive years. The RCI methods by Chelune and Hsu were used. For 1, 2, and 3 years, the 90% RCI for MoCA using Chelune's formula were -4 ≤, ≥4; -4 ≤, ≥4 and -5 ≤, ≥4 points, respectively, and -3 ≤, ≥3 for the MMSE each year. Ninety percent RCI for MoCA using Hsu's formula ranged from -6 to 0, respectively, and +3 to +8 dependent on the baseline MoCA. Our study demonstrated RCI for the MoCA and MMSE in a 3-year time period that can be used for the estimation of cognitive decline or improvement in clinical settings. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

The Cognitive Estimation Task Is Nonunitary: Evidence for Multiple Magnitude Representation Mechanisms Among Normative and ADHD College Students

Directory of Open Access Journals (Sweden)

Sarit Ashkenazi

2017-02-01

Full Text Available There is a current debate on whether the cognitive system has a shared representation for all magnitudes or whether there are unique representations. To investigate this question, we used the Biber cognitive estimation task. In this task, participants were asked to provide estimates for questions such as, “How many sticks of spaghetti are in a package?” The task uses different estimation categories (e.g., time, numerical quantity, distance, and weight to look at real-life magnitude representations. Experiment 1 assessed (N = 95 a Hebrew version of the Biber Cognitive Estimation Task and found that different estimation categories had different relations, for example, weight, time, and distance shared variance, but numerical estimation did not. We suggest that numerical estimation does not require the use of measurement in units, hence, it represents a more “pure” numerical estimation. Experiment 2 found that different factors explain individual abilities in different estimation categories. For example, numerical estimation was predicted by preverbal innate quantity understanding (approximate number sense and working memory, whereas time estimations were supported by IQ. These results demonstrate that cognitive estimation is not a unified construct.

Reexamination of optimal quantum state estimation of pure states

International Nuclear Information System (INIS)

Hayashi, A.; Hashimoto, T.; Horibe, M.

2005-01-01

A direct derivation is given for the optimal mean fidelity of quantum state estimation of a d-dimensional unknown pure state with its N copies given as input, which was first obtained by Hayashi in terms of an infinite set of covariant positive operator valued measures (POVM's) and by Bruss and Macchiavello establishing a connection to optimal quantum cloning. An explicit condition for POVM measurement operators for optimal estimators is obtained, by which we construct optimal estimators with finite POVMs using exact quadratures on a hypersphere. These finite optimal estimators are not generally universal, where universality means the fidelity is independent of input states. However, any optimal estimator with finite POVM for M(>N) copies is universal if it is used for N copies as input

Resting-state slow wave power, healthy aging and cognitive performance.

Science.gov (United States)

Vlahou, Eleni L; Thurm, Franka; Kolassa, Iris-Tatjana; Schlee, Winfried

2014-05-29

Cognitive functions and spontaneous neural activity show significant changes over the life-span, but the interrelations between age, cognition and resting-state brain oscillations are not well understood. Here, we assessed performance on the Trail Making Test and resting-state magnetoencephalographic (MEG) recordings from 53 healthy adults (18-89 years old) to investigate associations between age-dependent changes in spontaneous oscillatory activity and cognitive performance. Results show that healthy aging is accompanied by a marked and linear decrease of resting-state activity in the slow frequency range (0.5-6.5 Hz). The effects of slow wave power on cognitive performance were expressed as interactions with age: For older (>54 years), but not younger participants, enhanced delta and theta power in temporal and central regions was positively associated with perceptual speed and executive functioning. Consistent with previous work, these findings substantiate further the important role of slow wave oscillations in neurocognitive function during healthy aging.

Introduction to quantum-state estimation

CERN Document Server

Teo, Yong Siah

2016-01-01

Quantum-state estimation is an important field in quantum information theory that deals with the characterization of states of affairs for quantum sources. This book begins with background formalism in estimation theory to establish the necessary prerequisites. This basic understanding allows us to explore popular likelihood- and entropy-related estimation schemes that are suitable for an introductory survey on the subject. Discussions on practical aspects of quantum-state estimation ensue, with emphasis on the evaluation of tomographic performances for estimation schemes, experimental realizations of quantum measurements and detection of single-mode multi-photon sources. Finally, the concepts of phase-space distribution functions, which compatibly describe these multi-photon sources, are introduced to bridge the gap between discrete and continuous quantum degrees of freedom. This book is intended to serve as an instructive and self-contained medium for advanced undergraduate and postgraduate students to gra...

State Estimation for Tensegrity Robots

Science.gov (United States)

Caluwaerts, Ken; Bruce, Jonathan; Friesen, Jeffrey M.; Sunspiral, Vytas

2016-01-01

Tensegrity robots are a class of compliant robots that have many desirable traits when designing mass efficient systems that must interact with uncertain environments. Various promising control approaches have been proposed for tensegrity systems in simulation. Unfortunately, state estimation methods for tensegrity robots have not yet been thoroughly studied. In this paper, we present the design and evaluation of a state estimator for tensegrity robots. This state estimator will enable existing and future control algorithms to transfer from simulation to hardware. Our approach is based on the unscented Kalman filter (UKF) and combines inertial measurements, ultra wideband time-of-flight ranging measurements, and actuator state information. We evaluate the effectiveness of our method on the SUPERball, a tensegrity based planetary exploration robotic prototype. In particular, we conduct tests for evaluating both the robot's success in estimating global position in relation to fixed ranging base stations during rolling maneuvers as well as local behavior due to small-amplitude deformations induced by cable actuation.

Sex Differences in Fluid Reasoning: Manifest and Latent Estimates from the Cognitive Abilities Test

Directory of Open Access Journals (Sweden)

Joni M. Lakin

2014-06-01

Full Text Available The size and nature of sex differences in cognitive ability continues to be a source of controversy. Conflicting findings result from the selection of measures, samples, and methods used to estimate sex differences. Existing sex differences work on the Cognitive Abilities Test (CogAT has analyzed manifest variables, leaving open questions about sex differences in latent narrow cognitive abilities and the underlying broad ability of fluid reasoning (Gf. This study attempted to address these questions. A confirmatory bifactor model was used to estimate Gf and three residual narrow ability factors (verbal, quantitative, and figural. We found that latent mean differences were larger than manifest estimates for all three narrow abilities. However, mean differences in Gf were trivial, consistent with previous research. In estimating group variances, the Gf factor showed substantially greater male variability (around 20% greater. The narrow abilities varied: verbal reasoning showed small variability differences while quantitative and figural showed substantial differences in variance (up to 60% greater. These results add precision and nuance to the study of the variability and masking hypothesis.

Estimating state-contingent production functions

DEFF Research Database (Denmark)

Rasmussen, Svend; Karantininis, Kostas

The paper reviews the empirical problem of estimating state-contingent production functions. The major problem is that states of nature may not be registered and/or that the number of observation per state is low. Monte Carlo simulation is used to generate an artificial, uncertain production...... environment based on Cobb Douglas production functions with state-contingent parameters. The pa-rameters are subsequently estimated based on different sizes of samples using Generalized Least Squares and Generalized Maximum Entropy and the results are compared. It is concluded that Maximum Entropy may...

On the Estimation of Standard Errors in Cognitive Diagnosis Models

Science.gov (United States)

Philipp, Michel; Strobl, Carolin; de la Torre, Jimmy; Zeileis, Achim

2018-01-01

Cognitive diagnosis models (CDMs) are an increasingly popular method to assess mastery or nonmastery of a set of fine-grained abilities in educational or psychological assessments. Several inference techniques are available to quantify the uncertainty of model parameter estimates, to compare different versions of CDMs, or to check model…

Crowdsourced estimation of cognitive decline and resilience in Alzheimer’s disease

Science.gov (United States)

Allen, Genevera I; Amoroso, Nicola; Anghel, Catalina; Balagurusamy, Venkat; Bare, Christopher J; Beaton, Derek; Bellotti, Roberto; Bennett, David A; Boehme, Kevin; Boutros, Paul C; Caberlotto, Laura; Caloian, Cristian; Campbell, Frederick; Neto, Elias Chaibub; Chang, Yu-Chuan; Chen, Beibei; Chen, Chien-Yu; Chien, Ting-Ying; Clark, Tim; Das, Sudeshna; Davatzikos, Christos; Deng, Jieyao; Dillenberger, Donna; Dobson, Richard JB; Dong, Qilin; Doshi, Jimit; Duma, Denise; Errico, Rosangela; Erus, Guray; Everett, Evan; Fardo, David W; Friend, Stephen H; Fröhlich, Holger; Gan, Jessica; St George-Hyslop, Peter; Ghosh, Satrajit S; Glaab, Enrico; Green, Robert C; Guan, Yuanfang; Hong, Ming-Yi; Huang, Chao; Hwang, Jinseub; Ibrahim, Joseph; Inglese, Paolo; Jiang, Qijia; Katsumata, Yuriko; Kong, Dehan; Krause, Roland; Lalonde, Emilie; Lauria, Mario; Lee, Eunjee; Lin, Xihui; Liu, Zhandong; Livingstone, Julie; Logsdon, Benjamin A; Lovestone, Simon; Lyappan, Anandhi; Ma, Michelle; Malhotra, Ashutosh; Maxwell, Taylor J; Merrill, Emily; Nagorski, John; Namasivayam, Aishwarya; Narayan, Manjari; Naz, Mufassra; Newhouse, Stephen J; Norman, Thea C; Nurtdinov, Ramil N; Oyang, Yen-Jen; Pawitan, Yudi; Peng, Shengwen; Piccolo, Stephen R; Praveen, Paurush; Priami, Corrado; Sabelnykova, Veronica Y; Senger, Philipp; Shen, Xia; Simmons, Andrew; Sotiras, Aristeidis; Stolovitzky, Gustavo; Tangaro, Sabina; Tateo, Andrea; Tung, Yi-An; Tustison, Nicholas J; Varol, Erdem; Vradenburg, George; Weiner, Michael W; Xiao, Guanghua; Xie, Lei; Xie, Yang; Xu, Jia; Yang, Hojin; Zhan, Xiaowei; Zhou, Yunyun; Zhu, Fan; Zhu, Hongtu; Zhu, Shanfeng

2017-01-01

Identifying accurate biomarkers of cognitive decline is essential for advancing early diagnosis and prevention therapies in Alzheimer’s Disease. The Alzheimer’s Disease DREAM Challenge was designed as a computational crowdsourced project to benchmark the current state-of-the-art in predicting cognitive outcomes in Alzheimer’s Disease based on high-dimensional, publicly available genetic and structural imaging data. This meta-analysis failed to identify a meaningful predictor developed from either data modality, suggesting that alternate approaches should be considered for to prediction of cognitive performance. PMID:27079753

Mini‑Mental State Exam versus Montreal Cognitive Assessment in ...

African Journals Online (AJOL)

Background: Mini‑mental state exam (MMSE) was used several times but no study has examined cognition on the Montreal Cognitive Assessment (MoCA) in diabetes and diabetic retinopathy (DR). In this study, we compared MMSE with MoCA in patients with DR and searched for an association between the severity of DR ...

The Problem State : A Cognitive Bottleneck in Multitasking

NARCIS (Netherlands)

Borst, Jelmer P.; Taatgen, Niels A.; van Rijn, Hedderik

The main challenge for theories of multitasking is to predict when and how tasks interfere. He re, we focus on interference related to the problem state. a directly accessible intermediate representation of the current state of a task. On the basis of Salvucci and Taatgen's (2008) threaded cognition

Large-scale resting state network correlates of cognitive impairment in Parkinson’s disease and related dopaminergic deficits

Directory of Open Access Journals (Sweden)

Alexander V Lebedev

2014-04-01

Full Text Available Cognitive impairment is a common non-motor feature of Parkinson’s disease (PD. The current study aimed to investigate resting state fMRI correlates of cognitive impairment in PD from a large-scale network perspective, and to assess the impact of dopamine deficiency on these networks. Thirty PD patients with resting state fMRI were included from the Parkinson’s Progression Marker Initiative (PPMI database. Eighteen patients from this sample were also scanned with 123I-FP-CIT SPECT. A standardized neuropsychological battery was administered, evaluating verbal memory, visuospatial, and executive cognitive domains. Image preprocessing was performed using an SPM8-based workflow, obtaining time-series from 90 regions-of-interest (ROIs defined from the AAL brain atlas. The Brain Connectivity Toolbox was used to extract nodal strength from all ROIs and modularity of the cognitive circuitry determined using the meta-analytical software Neurosynth. Brain-behavior covariance patterns between cognitive functions and nodal strength were estimated using Partial Least Squares. Extracted latent variable scores were correlated with performances in the three cognitive domains and striatal dopamine transporter binding ratios (SBR using linear modeling. Finally, influence of nigrostriatal dopaminergic deficiency on modularity of the cognitive network was analyzed. Less severe executive impairment was associated with increased dorsal fronto-parietal cortical processing and inhibited subcortical and primary sensory involvement. This pattern was positively influenced by the relative preservation of nigrostriatal dopaminergic function. The pattern associated with better memory performance favored prefronto-limbic processing, and did not reveal associations with presynaptic striatal dopamine uptake. SBR ratios were negatively associated with modularity of the cognitive network, suggesting integrative effects of the preserved nigrostriatal dopamine system on this

An Empirical Method to Fuse Partially Overlapping State Vectors for Distributed State Estimation

NARCIS (Netherlands)

Sijs, J.; Hanebeck, U.; Noack, B.

2013-01-01

State fusion is a method for merging multiple estimates of the same state into a single fused estimate. Dealing with multiple estimates is one of the main concerns in distributed state estimation, where an estimated value of the desired state vector is computed in each node of a networked system.

fMRI resting state networks and their association with cognitive fluctuations in dementia with Lewy bodies

Directory of Open Access Journals (Sweden)

Luis R. Peraza

2014-01-01

Full Text Available Cognitive fluctuations are a core symptom in dementia with Lewy bodies (DLB and may relate to pathological alterations in distributed brain networks. To test this we analysed resting state fMRI changes in a cohort of fluctuating DLB patients (n = 16 compared with age matched controls (n = 17 with the aim of finding functional connectivity (FC differences between these two groups and whether these associate with cognitive fluctuations in DLB. Resting state networks (RSNs were estimated using independent component analysis and FC between the RSN maps and the entirety of the brain was assessed using dual regression. The default mode network (DMN appeared unaffected in DLB compared to controls but significant cluster differences between DLB and controls were found for the left fronto-parietal, temporal, and sensory–motor networks. Desynchronization of a number of cortical and subcortical areas related to the left fronto-parietal network was associated with the severity and frequency of cognitive fluctuations. Our findings provide empirical evidence for the potential role of attention–executive networks in the aetiology of this core symptom in DLB.

Cognitive impairment in the remitted state of unipolar depressive disorder: A systematic review

DEFF Research Database (Denmark)

Hasselbalch, Bo Jacob; Knorr, Ulla; Kessing, Lars Vedel

2010-01-01

BACKGROUND: It is unclear whether cognitive impairment is prevalent in the remitted state of unipolar disorder. AIM: To evaluate whether cognitive function is impaired in the remitted state in patients with unipolar depression compared with healthy control individuals, and to investigate...... were prevalent including non-stringent definition of remission and non-correction for multiple testing. Only few studies investigated the association between cognition and prior course of illness and the results were divergent. LIMITATIONS: Stringent criteria were used in the assessment of eligibility...... of studies. The studies were first and foremost selected according to the criteria for remission used. CONCLUSION: Cognitive dysfunction seems to be present in individuals suffering from unipolar disorder in the remitted state. We recommend that future studies should focus on disentangling the state...

State Alcohol-Impaired-Driving Estimates

Science.gov (United States)

... 2012 Data DOT HS 812 017 May 2014 State Alcohol-Impaired-Driving Estimates This fact sheet contains ... alcohol involvement in fatal crashes for the United States and individually for the 50 States, the District ...

Normalized Mini-Mental State Examination for assessing cognitive change in population-based brain aging studies.

Science.gov (United States)

Philipps, Viviane; Amieva, Hélène; Andrieu, Sandrine; Dufouil, Carole; Berr, Claudine; Dartigues, Jean-François; Jacqmin-Gadda, Hélène; Proust-Lima, Cécile

2014-01-01

The Mini-Mental State Examination (MMSE) is widely used in population-based longitudinal studies to quantify cognitive change. However, its poor metrological properties, mainly ceiling/floor effects and varying sensitivity to change, have largely restricted its usefulness. We propose a normalizing transformation that corrects these properties, and makes possible the use of standard statistical methods to analyze change in MMSE scores. The normalizing transformation designed to correct at best the metrological properties of MMSE was estimated and validated on two population-based studies (n = 4,889, 20-year follow-up) by cross-validation. The transformation was also validated on two external studies with heterogeneous samples mixing normal and pathological aging, and samples including only demented subjects. The normalizing transformation provided correct inference in contrast with models analyzing the change in crude MMSE that most often lead to biased estimates of risk factors and incorrect conclusions. Cognitive change can be easily and properly assessed with the normalized MMSE using standard statistical methods such as linear (mixed) models. © 2014 S. Karger AG, Basel.

Bad Data Detection and Identification for State Estimation

DEFF Research Database (Denmark)

Khazraj, Hesam; Silva, Filipe Miguel Faria da; Bak, Claus Leth

2017-01-01

state estimations. To achieve this object largest normalized residual test (rNmax) is applied to detect and analysis bad data in phasor measurements, power flow and power injections of buses used for the novel PMU-based state estimation. The main advantage of new PMU-based static state estimation......Bad data analysis is an important part of both dynamic and static state estimations. This paper present novel algorithm of phase measurement unit (PMU)-based static state estimation to detect and identify multiple bad data in critical measurements, which is not possible with traditional static...... is that phasor measurements can be added separately into the proposed state estimation. This paper proposes an ideal method to combine the phasor measurements into the conventional state estimator in a systematic way, so that no significant modification is necessary to the existing algorithm. The main advantage...

Resting-state abnormalities in amnestic mild cognitive impairment: a meta-analysis.

Science.gov (United States)

Lau, W K W; Leung, M-K; Lee, T M C; Law, A C K

2016-04-26

Amnestic mild cognitive impairment (aMCI) is a prodromal stage of Alzheimer's disease (AD). As no effective drug can cure AD, early diagnosis and intervention for aMCI are urgently needed. The standard diagnostic procedure for aMCI primarily relies on subjective neuropsychological examinations that require the judgment of experienced clinicians. The development of other objective and reliable aMCI markers, such as neural markers, is therefore required. Previous neuroimaging findings revealed various abnormalities in resting-state activity in MCI patients, but the findings have been inconsistent. The current study provides an updated activation likelihood estimation meta-analysis of resting-state functional magnetic resonance imaging (fMRI) data on aMCI. The authors searched on the MEDLINE/PubMed databases for whole-brain resting-state fMRI studies on aMCI published until March 2015. We included 21 whole-brain resting-state fMRI studies that reported a total of 156 distinct foci. Significant regional resting-state differences were consistently found in aMCI patients relative to controls, including the posterior cingulate cortex, right angular gyrus, right parahippocampal gyrus, left fusiform gyrus, left supramarginal gyrus and bilateral middle temporal gyri. Our findings support that abnormalities in resting-state activities of these regions may serve as neuroimaging markers for aMCI.

Use of Addenbrooke’s cognitive examination-revised to evaluate the patients’ state in general medical practice

Directory of Open Access Journals (Sweden)

Nikolai Nikolayevich Ivanets

2012-01-01

Full Text Available The differential diagnosis of cognitive impairments is of great importance in mental disorders detectable in general medical practice. Objective: to study whether Addenbrooke's Cognitive Examination — Revised (ACE-R may be used in these patients. Patients and methods. The study was conducted in two steps at somatic hospitals and city polyclinics. It enrolled 130patients (36 men and 94 women with anxiety-depression spectrum disorders (ADSD, mild cognitive disorders (MCD and a concurrence of these conditions. The authors used the following psychometric scales: the hospital anxiety and depression scale; the mini-mental state examination; the frontal assessment battery; ACE-R; ten words learning test. The psychometric characteristics of ACE-R and the possibilities of its use were estimated to detect MCD. The differences in the spectrum of cognitive impairments were analyzed in patients with different types of ADSD. Results. ACE-R is shown to be an effective neuropsychological tool for the primary diagnosis, detection, and evaluation of MCD in the general medical network. The results of ACE-R use indicate that the spectrum of cognitive impairments has substantial differences in patients with different types of non-psychotic disorders.

The Impact of Age and Cognitive Reserve on Resting-State Brain Connectivity

Directory of Open Access Journals (Sweden)

Jessica I. Fleck

2017-12-01

Full Text Available Cognitive reserve (CR is a protective mechanism that supports sustained cognitive function following damage to the physical brain associated with age, injury, or disease. The goal of the research was to identify relationships between age, CR, and brain connectivity. A sample of 90 cognitively normal adults, ages 45–64 years, had their resting-state brain activity recorded with electroencephalography (EEG and completed a series of memory and executive function assessments. CR was estimated using years of education and verbal IQ scores. Participants were divided into younger and older age groups and low- and high-CR groups. We observed greater left- than right-hemisphere coherence in younger participants, and greater right- than left-hemisphere coherence in older participants. In addition, greater coherence was observed under eyes-closed than eyes-open recording conditions for both low-CR and high-CR participants, with a more substantial difference between recording conditions in individuals high in CR regardless of age. Finally, younger participants low in CR exhibited greater mean coherence than younger participants high in CR, whereas the opposite pattern was observed in older participants, with greater coherence in older participants high in CR. Together, these findings suggest the possibility of a shift in the relationship between CR and brain connectivity during aging.

State estimation for a hexapod robot

CSIR Research Space (South Africa)

Lubbe, Estelle

2015-09-01

Full Text Available This paper introduces a state estimation methodology for a hexapod robot that makes use of proprioceptive sensors and a kinematic model of the robot. The methodology focuses on providing reliable full pose state estimation for a commercially...

State energy data report 1994: Consumption estimates

Energy Technology Data Exchange (ETDEWEB)

NONE

1996-10-01

This document provides annual time series estimates of State-level energy consumption by major economic sector. The estimates are developed in the State Energy Data System (SEDS), operated by EIA. SEDS provides State energy consumption estimates to members of Congress, Federal and State agencies, and the general public, and provides the historical series needed for EIA`s energy models. Division is made for each energy type and end use sector. Nuclear electric power is included.

State energy data report 1994: Consumption estimates

International Nuclear Information System (INIS)

1996-10-01

This document provides annual time series estimates of State-level energy consumption by major economic sector. The estimates are developed in the State Energy Data System (SEDS), operated by EIA. SEDS provides State energy consumption estimates to members of Congress, Federal and State agencies, and the general public, and provides the historical series needed for EIA's energy models. Division is made for each energy type and end use sector. Nuclear electric power is included

State energy data report 1995 - consumption estimates

Energy Technology Data Exchange (ETDEWEB)

NONE

1997-12-01

The State Energy Data Report (SEDR) provides annual time series estimates of State-level energy consumption by major economic sectors. The estimates are developed in the State Energy Data System (SEDS), which is maintained and operated by the Energy Information Administration (EIA). The goal in maintaining SEDS exists for two principal reasons: (1) to provide State energy consumption estimates to Members of Congress, Federal and State agencies, and the general public, and (2) to provide the historical series necessary for EIA`s energy models.

The effect of adult children living in the United States on the likelihood of cognitive impairment for older parents living in Mexico.

Science.gov (United States)

Downer, Brian; González-González, Cesar; Goldman, Noreen; Pebley, Anne R; Wong, Rebeca

2018-01-01

The increased risk for poor physical and mental health outcomes for older parents in Mexico who have an adult child living in the United States may contribute to an increased risk for cognitive impairment in this population. The objective of this study was to examine if older adults in Mexico who have one or more adult children living in the United States are more or less likely to develop cognitive impairment over an 11-year period compared to older adults who do not have any adult children living in the United States. Data for this study came from Wave I (2001) and Wave III (2012) of the Mexican Health and Aging Study. The final sample included 2609 participants aged 60 and over who were not cognitively impaired in 2001 and had one or more adult children (age ≥15). Participants were matched using a propensity score that was estimated with a multivariable logistic regression model that included sociodemographic characteristics and migration history of the older parents. Having one or more adult children living in the United States is associated with lower socioeconomic status and higher number of depressive symptoms, but greater social engagement for older parents living in Mexico. No significant differences in the odds for developing cognitive impairment according to having one or more adult children living in the United States were detected. In summary, having one or more adult children living in the United States was associated with characteristics that may increase and decrease the risk for cognitive impairment. This may contribute to the non-significant relationship between migration status of adult children and likelihood for cognitive impairment for older parents living in Mexico.

UAV State Estimation Modeling Techniques in AHRS

Science.gov (United States)

Razali, Shikin; Zhahir, Amzari

2017-11-01

Autonomous unmanned aerial vehicle (UAV) system is depending on state estimation feedback to control flight operation. Estimation on the correct state improves navigation accuracy and achieves flight mission safely. One of the sensors configuration used in UAV state is Attitude Heading and Reference System (AHRS) with application of Extended Kalman Filter (EKF) or feedback controller. The results of these two different techniques in estimating UAV states in AHRS configuration are displayed through position and attitude graphs.

Location-based resource allocation for OFDMA cognitive radio systems

KAUST Repository

Nam, Haewoon

2010-01-01

In cognitive radio systems, in order for the secondary users to opportunistically share the spectrum without interfering the primary users, an accurate spectrum measurement and a precise estimation of the interference at the primary users are necessary but are challenging tasks. Since it is impractical in cognitive radio systems to assume that the channel state information of the interference link is available at the cognitive transmitter, the interference at the primary users is hard to be estimated accurately. This paper introduces a resource allocation algorithm for OFDMA-based cognitive radio systems, which utilizes location information of the primary and secondary users instead of the channel state information of the interference link. Simulation results show that it is indeed effective to incorporate location information into resource allocation so that a near-optimal capacity is achieved.

Structure-function relationships in elderly resting-state-networks : influence of age and cognitive performance

OpenAIRE

Jockwitz, Christiane

2016-01-01

The aim of this work was to investigate the structure-function relationship in cognitive resting state networks in a large population-based elderly sample. The first study characterized the functional connectivity in four cognitive resting state networks with respect to age, gender and cognitive performance: Default Mode Network (DMN), executive, and left and right frontoparietal resting state networks. The second study assessed the structural correlates of the functional reorganization of th...

State energy data report 1993: Consumption estimates

Energy Technology Data Exchange (ETDEWEB)

NONE

1995-07-01

The State Energy Data Report (SEDR) provides annual time series estimates of State-level energy consumption by major economic sector. The estimates are developed in the State Energy Data System (SEDS), which is maintained and operated by the Energy Information Administration (EIA). The goal in maintaining SEDS is to create historical time series of energy consumption by State that are defined as consistently as possible over time and across sectors. SEDS exists for two principal reasons: (1) to provide State energy consumption estimates to Members of Congress, Federal and State agencies, and the general public; and (2) to provide the historical series necessary for EIA`s energy models.

Effect of Integrated Cognitive Therapy on Hippocampal Functional Connectivity Patterns in Stroke Patients with Cognitive Dysfunction: A Resting-State fMRI Study

Directory of Open Access Journals (Sweden)

Shanli Yang

2014-01-01

Full Text Available Objective. This study aimed to identify abnormal hippocampal functional connectivity (FC following ischemic stroke using resting-state fMRI. We also explored whether abnormal hippocampal FC could be modulated by integrated cognitive therapy and tested whether these alterations were associated with cognitive performance. Methods. 18 right-handed cognitively impaired ischemic stroke patients and 18 healty control (HC subjects were included in this study. Stroke subjects were scanned at baseline and after integrated cognitive therapy, while HCs were only scanned at baseline, to identify regions that show significant correlations with the seed region. Behavioral and cognitive assessments were obtained before each scan. Results. During the resting state, we found abnormal hippocampal FC associated with temporal regions, insular cortex, cerebellum, and prefrontal cortex in stroke patients compared to HCs. After integrated cognitive therapy, however, the stroke group showed increased hippocampal FC mainly located in the prefrontal gyrus and the default mode network (DMN. Altered hippocampal FC was associated with cognitive improvement. Conclusion. Resting-state fMRI may provide novel insight into the study of functional networks in the brain after stroke. Furthermore, altered hippocampal FC may be a compensatory mechanism for cognitive recovery after ischemic stroke.

Self-learning estimation of quantum states

International Nuclear Information System (INIS)

Hannemann, Th.; Reiss, D.; Balzer, Ch.; Neuhauser, W.; Toschek, P.E.; Wunderlich, Ch.

2002-01-01

We report the experimental estimation of arbitrary qubit states using a succession of N measurements on individual qubits, where the measurement basis is changed during the estimation procedure conditioned on the outcome of previous measurements (self-learning estimation). Two hyperfine states of a single trapped 171 Yb + ion serve as a qubit. It is demonstrated that the difference in fidelity between this adaptive strategy and passive strategies increases in the presence of decoherence

State Energy Data Report, 1991: Consumption estimates

International Nuclear Information System (INIS)

1993-05-01

The State Energy Data Report (SEDR) provides annual time series estimates of State-level energy consumption by major economic sector. The estimates are developed in the State Energy Data System (SEDS), which is maintained and operated by the Energy Information Administration (EIA). The goal in maintaining SEDS is to create historical time series of energy consumption by State that are defined as consistently as possible over time and across sectors. SEDS exists for two principal reasons: (1) to provide State energy consumption estimates to the Government, policy makers, and the public; and (2) to provide the historical series necessary for EIA's energy models

A comparison of the mini mental state exam to the Montreal cognitive assessment in identifying cognitive deficits in Parkinson's disease

NARCIS (Netherlands)

Zadikoff, Cindy; Fox, Susan H.; Tang-Wai, David F.; Thomsen, Teri; de Bie, Rob M. A.; Wadia, Pettarusup; Miyasaki, Janis; Duff-Canning, Sarah; Lang, Anthony E.; Marras, Connie

2008-01-01

Dementia is an important and increasingly recognized problem in Parkinson's disease (PD). The mini-mental state examination (MMSE) often fails to detect early cognitive decline. The Montreal cognitive assessment (MoCA) is a brief tool developed to detect mild cognitive impairment that assesses a

Resting-State Functional Connectivity and Cognitive Impairment in Children with Perinatal Stroke

Directory of Open Access Journals (Sweden)

Nigul Ilves

2016-01-01

Full Text Available Perinatal stroke is a leading cause of congenital hemiparesis and neurocognitive deficits in children. Dysfunctions in the large-scale resting-state functional networks may underlie cognitive and behavioral disability in these children. We studied resting-state functional connectivity in patients with perinatal stroke collected from the Estonian Pediatric Stroke Database. Neurodevelopment of children was assessed by the Pediatric Stroke Outcome Measurement and the Kaufman Assessment Battery. The study included 36 children (age range 7.6–17.9 years: 10 with periventricular venous infarction (PVI, 7 with arterial ischemic stroke (AIS, and 19 controls. There were no differences in severity of hemiparesis between the PVI and AIS groups. A significant increase in default mode network connectivity (FDR 0.1 and lower cognitive functions (p<0.05 were found in children with AIS compared to the controls and the PVI group. The children with PVI had no significant differences in the resting-state networks compared to the controls and their cognitive functions were normal. Our findings demonstrate impairment in cognitive functions and neural network profile in hemiparetic children with AIS compared to children with PVI and controls. Changes in the resting-state networks found in children with AIS could possibly serve as the underlying derangements of cognitive brain functions in these children.

Cognitive impairment in the remitted state of unipolar depressive disorder: A systematic review

DEFF Research Database (Denmark)

Hasselbalch, Bo Jacob; Knorr, Ulla; Kessing, Lars Vedel

2010-01-01

BACKGROUND: It is unclear whether cognitive impairment is prevalent in the remitted state of unipolar disorder. AIM: To evaluate whether cognitive function is impaired in the remitted state in patients with unipolar depression compared with healthy control individuals, and to investigate the asso...

The reliability of assigning individuals to cognitive states using the Mini Mental-State Examination: a population-based prospective cohort study.

Science.gov (United States)

Marioni, Riccardo E; Chatfield, Mark; Brayne, Carol; Matthews, Fiona E

2011-09-06

Previous investigations of test re-test reliability of the Mini-Mental State Examination (MMSE) have used correlations and statistics such as Cronbach's α to assess consistency. In practice, the MMSE is usually used to group individuals into cognitive states. The reliability of this grouping (state based approach) has not been fully explored. MMSE data were collected on a subset of 2,275 older participants (≥ 65 years) from the population-based Medical Research Council Cognitive Function and Ageing Study. Two measurements taken approximately two months apart were used to investigate three state-based categorisations. Descriptive statistics were used to determine how many people remained in the same cognitive group or went up or down groups. Weighted logistic regression was used to identify predictive characteristics of those who moved group. The proportion of people who remained in the same MMSE group at screen and follow-up assessment ranged from 58% to 78%. The proportion of individuals who went up one or more groups was roughly equal to the proportion that went down one or more groups; most of the change occurred when measurements were close to the cut-points. There was no consistently significant predictor for changing cognitive group. A state-based approach to analysing the reliability of the MMSE provided similar results to correlation analyses. State-based models of cognitive change or individual trajectory models using raw scores need multiple waves to help overcome natural variation in MMSE scores and to help identify true cognitive change.

Cognitive Distortions in Depressed Women: Trait, or State Dependent?

Directory of Open Access Journals (Sweden)

Sedat BATMAZ

2015-12-01

Conclusion: The results have revealed that self-criticism, helplessness, hopelessness and preoccupation with danger related distortions had trait-like features, whereas self-blame related distortions were state dependent. This has clinical implications for the psychotherapeutic treatment of cognitive distortions in depression. Specifically, self-criticism related distortions should be managed during cognitive therapy for depression since the other subscales seem rather problematic. [JCBPR 2015; 4(3.000: 147-152

Cognitive screening in substance users: Diagnostic accuracies of the Mini-Mental State Examination, Addenbrooke's Cognitive Examination-Revised, and Montreal Cognitive Assessment.

Science.gov (United States)

Ridley, Nicole; Batchelor, Jennifer; Draper, Brian; Demirkol, Apo; Lintzeris, Nicholas; Withall, Adrienne

2018-03-01

Despite the considerable prevalence of cognitive impairment in substance-using populations, there has been little investigation of the utility of cognitive screening measures within this context. In the present study the accuracy of three cognitive screening measures in this population was examined-the Mini-Mental State Examination (MMSE), the Addenbrooke's Cognitive Examination-Revised (ACE-R), and the Montreal Cognitive Assessment (MoCA). A sample of 30 treatment-seeking substance users and 20 healthy individuals living in the community were administered the screening measures and a neuropsychological battery (NPB). Agreement of classification of cognitive impairment by the screening measures and NPB was examined. Results indicated that the ACE-R and MoCA had good discriminative ability in detection of cognitive impairment, with areas under the receiver-operating characteristic (ROC) curve of .85 (95% confidence interval, CI [.75. .94] and .84 (95% CI [.71, .93]) respectively. The MMSE had fair discriminative ability (.78, 95% CI [.65, .93]). The optimal cut-score for the ACE-R was 93 (impairment = score of 92 or less), at which it correctly classified 89% of individuals as cognitively impaired or intact, while the optimal cut-score for the MoCA was cognitive impairment in the context of substance use.

Quantitative EEG and its Correlation with Cardiovascular, Cognition and mood State: an Integrated Study in Simulated Microgravity

Science.gov (United States)

Zhang, Jianyuan; Hu, Bin; Chen, Wenjuan; Moore, Philip; Xu, Tingting; Dong, Qunxi; Liu, Zhenyu; Luo, Yuejia; Chen, Shanguang

2014-12-01

The focus of the study is the estimation of the effects of microgravity on the central nervous activity and its underlying influencing mechanisms. To validate the microgravity-induced physiological and psychological effects on EEG, quantitative EEG features, cardiovascular indicators, mood state, and cognitive performances data collection was achieved during a 45 day period using a -6°head-down bed rest (HDBR) integrated approach. The results demonstrated significant differences in EEG data, as an increased Theta wave, a decreased Beta wave and a reduced complexity of brain, accompanied with an increased heart rate and pulse rate, decreased positive emotion, and degraded emotion conflict monitoring performance. The canonical correlation analysis (CCA) based cardiovascular and cognitive related EEG model showed the cardiovascular effect on EEG mainly affected bilateral temporal region and the cognitive effect impacted parietal-occipital and frontal regions. The results obtained in the study support the use of an approach which combines a multi-factor influential mechanism hypothesis. The changes in the EEG data may be influenced by both cardiovascular and cognitive effects.

Estimating the Technology of Cognitive and Noncognitive Skill Formation. NBER Working Paper No. 15664

Science.gov (United States)

Cunha, Flavio; Heckman, James; Schennach, Susanne

2010-01-01

This paper formulates and estimates multistage production functions for childrens' cognitive and noncognitive skills. Skills are determined by parental environments and investments at different stages of childhood. We estimate the elasticity of substitution between investments in one period and stocks of skills in that period to assess the…

Do enhanced states exist? Boosting cognitive capacities through an action video-game.

Science.gov (United States)

Kozhevnikov, Maria; Li, Yahui; Wong, Sabrina; Obana, Takashi; Amihai, Ido

2018-04-01

This research reports the existence of enhanced cognitive states in which dramatic temporary improvements in temporal and spatial aspects of attention were exhibited by participants who played (but not by those who merely observed) action video-games meeting certain criteria. Specifically, Experiments 1 and 2 demonstrate that the attentional improvements were exhibited only by participants whose skills matched the difficulty level of the video game. Experiment 2 showed that arousal (as reflected by the reduction in parasympathetic activity and increase in sympathetic activity) is a critical physiological condition for enhanced cognitive states and corresponding attentional enhancements. Experiment 3 showed that the cognitive enhancements were transient, and were no longer observed after 30 min of rest following video-gaming. Moreover, the results suggest that the enhancements were specific to tasks requiring visual-spatial focused attention, but not distribution of spatial attention as has been reported to improve significantly and durably as a result of long-term video-game playing. Overall, the results suggest that the observed enhancements cannot be simply due to the activity of video-gaming per se, but might rather represent an enhanced cognitive state resulting from specific conditions (heightened arousal in combination with active engagement and optimal challenge), resonant with what has been described in previous phenomenological literature as "flow" (Csikszentmihalyi, 1975) or "peak experiences" (Maslov, 1962). The findings provide empirical evidence for the existence of the enhanced cognitive states and suggest possibilities for consciously accessing latent resources of our brain to temporarily boost our cognitive capacities upon demand. Copyright © 2018 Elsevier B.V. All rights reserved.

The reliability of assigning individuals to cognitive states using the Mini Mental-State Examination: a population-based prospective cohort study

Directory of Open Access Journals (Sweden)

Brayne Carol

2011-09-01

Full Text Available Abstract Background Previous investigations of test re-test reliability of the Mini-Mental State Examination (MMSE have used correlations and statistics such as Cronbach's α to assess consistency. In practice, the MMSE is usually used to group individuals into cognitive states. The reliability of this grouping (state based approach has not been fully explored. Methods MMSE data were collected on a subset of 2,275 older participants (≥ 65 years from the population-based Medical Research Council Cognitive Function and Ageing Study. Two measurements taken approximately two months apart were used to investigate three state-based categorisations. Descriptive statistics were used to determine how many people remained in the same cognitive group or went up or down groups. Weighted logistic regression was used to identify predictive characteristics of those who moved group. Results The proportion of people who remained in the same MMSE group at screen and follow-up assessment ranged from 58% to 78%. The proportion of individuals who went up one or more groups was roughly equal to the proportion that went down one or more groups; most of the change occurred when measurements were close to the cut-points. There was no consistently significant predictor for changing cognitive group. Conclusion A state-based approach to analysing the reliability of the MMSE provided similar results to correlation analyses. State-based models of cognitive change or individual trajectory models using raw scores need multiple waves to help overcome natural variation in MMSE scores and to help identify true cognitive change.

An Empirical State Error Covariance Matrix for Batch State Estimation

Science.gov (United States)

Frisbee, Joseph H., Jr.

2011-01-01

State estimation techniques serve effectively to provide mean state estimates. However, the state error covariance matrices provided as part of these techniques suffer from some degree of lack of confidence in their ability to adequately describe the uncertainty in the estimated states. A specific problem with the traditional form of state error covariance matrices is that they represent only a mapping of the assumed observation error characteristics into the state space. Any errors that arise from other sources (environment modeling, precision, etc.) are not directly represented in a traditional, theoretical state error covariance matrix. Consider that an actual observation contains only measurement error and that an estimated observation contains all other errors, known and unknown. It then follows that a measurement residual (the difference between expected and observed measurements) contains all errors for that measurement. Therefore, a direct and appropriate inclusion of the actual measurement residuals in the state error covariance matrix will result in an empirical state error covariance matrix. This empirical state error covariance matrix will fully account for the error in the state estimate. By way of a literal reinterpretation of the equations involved in the weighted least squares estimation algorithm, it is possible to arrive at an appropriate, and formally correct, empirical state error covariance matrix. The first specific step of the method is to use the average form of the weighted measurement residual variance performance index rather than its usual total weighted residual form. Next it is helpful to interpret the solution to the normal equations as the average of a collection of sample vectors drawn from a hypothetical parent population. From here, using a standard statistical analysis approach, it directly follows as to how to determine the standard empirical state error covariance matrix. This matrix will contain the total uncertainty in the

Processing deficits in monitoring analog and digital displays: Implications for attentional theory and mental-state estimation research

Science.gov (United States)

Payne, David G.; Gunther, Virginia A. L.

1988-01-01

Subjects performed short term memory tasks, involving both spatial and verbal components, and a visual monitoring task involving either analog or digital display formats. These two tasks (memory vs. monitoring) were performed both singly and in conjunction. Contrary to expectations derived from multiple resource theories of attentional processes, there was no evidence that when the two tasks involved the same cognitive codes (i.e., either both spatial or both verbal/linguistics) there was more of a dual task performance decrement than when the two tasks employed different cognitive codes/processes. These results are discussed in terms of their implications for theories of attentional processes and also for research in mental state estimation.

State estimation for large-scale wastewater treatment plants.

Science.gov (United States)

Busch, Jan; Elixmann, David; Kühl, Peter; Gerkens, Carine; Schlöder, Johannes P; Bock, Hans G; Marquardt, Wolfgang

2013-09-01

Many relevant process states in wastewater treatment are not measurable, or their measurements are subject to considerable uncertainty. This poses a serious problem for process monitoring and control. Model-based state estimation can provide estimates of the unknown states and increase the reliability of measurements. In this paper, an integrated approach is presented for the optimization-based sensor network design and the estimation problem. Using the ASM1 model in the reference scenario BSM1, a cost-optimal sensor network is designed and the prominent estimators EKF and MHE are evaluated. Very good estimation results for the system comprising 78 states are found requiring sensor networks of only moderate complexity. Copyright © 2013 Elsevier Ltd. All rights reserved.

Estimation of Branch Topology Errors in Power Networks by WLAN State Estimation

Energy Technology Data Exchange (ETDEWEB)

Kim, Hong Rae [Soonchunhyang University(Korea); Song, Kyung Bin [Kei Myoung University(Korea)

2000-06-01

The purpose of this paper is to detect and identify topological errors in order to maintain a reliable database for the state estimator. In this paper, a two stage estimation procedure is used to identify the topology errors. At the first stage, the WLAV state estimator which has characteristics to remove bad data during the estimation procedure is run for finding out the suspected branches at which topology errors take place. The resulting residuals are normalized and the measurements with significant normalized residuals are selected. A set of suspected branches is formed based on these selected measurements; if the selected measurement if a line flow, the corresponding branch is suspected; if it is an injection, then all the branches connecting the injection bus to its immediate neighbors are suspected. A new WLAV state estimator adding the branch flow errors in the state vector is developed to identify the branch topology errors. Sample cases of single topology error and topology error with a measurement error are applied to IEEE 14 bus test system. (author). 24 refs., 1 fig., 9 tabs.

Limitations of a Short Demographic Questionnaire for Bedside Estimation of Patients’ Global Cognitive Functioning in Epilepsy Patients

Directory of Open Access Journals (Sweden)

Iris Gorny

2018-03-01

Full Text Available ObjectivesThe German socio-demographic estimation scale was developed by Jahn et al. (1 to quickly predict premorbid global cognitive functioning in patients. So far, it has been validated in healthy adults and has shown a good correlation with the full and verbal IQ of the Wechsler Adult Intelligence Scale (WAIS in this group. However, there are no data regarding its use as a bedside test in epilepsy patients.MethodsForty native German speaking adult patients with refractory epilepsy were included. They completed a neuropsychological assessment, including a nine scale short form of the German version of the WAIS-III and the German socio-demographic estimation scale by Jahn et al. (1 during their presurgical diagnostic stay in our center. We calculated means, correlations, and the rate of concordance (range ±5 and ±7.5 IQ score points between these two measures for the whole group, and a subsample of 19 patients with a global cognitive functioning level within 1 SD of the mean (IQ score range 85–115 and who had completed their formal education before epilepsy onset.ResultsThe German demographic estimation scale by Jahn et al. (1 showed a significant mean overestimation of the global cognitive functioning level of eight points in the epilepsy patient sample compared with the short form WAIS-III score. The accuracy within a range of ±5 or ±7.5 IQ score points for each patient was similar to that of the healthy controls reported by Jahn et al. (1 in our subsample, but not in our whole sample.ConclusionOur results show that the socio-demographic scale by Jahn et al. (1 is not sufficiently reliable as an estimation tool of global cognitive functioning in epilepsy patients. It can be used to estimate global cognitive functioning in a subset of patients with a normal global cognitive functioning level who have completed their formal education before epilepsy onset, but it does not reliably predict global cognitive functioning in epilepsy patients

Structurally-constrained relationships between cognitive states in the human brain.

Directory of Open Access Journals (Sweden)

Ann M Hermundstad

2014-05-01

Full Text Available The anatomical connectivity of the human brain supports diverse patterns of correlated neural activity that are thought to underlie cognitive function. In a manner sensitive to underlying structural brain architecture, we examine the extent to which such patterns of correlated activity systematically vary across cognitive states. Anatomical white matter connectivity is compared with functional correlations in neural activity measured via blood oxygen level dependent (BOLD signals. Functional connectivity is separately measured at rest, during an attention task, and during a memory task. We assess these structural and functional measures within previously-identified resting-state functional networks, denoted task-positive and task-negative networks, that have been independently shown to be strongly anticorrelated at rest but also involve regions of the brain that routinely increase and decrease in activity during task-driven processes. We find that the density of anatomical connections within and between task-positive and task-negative networks is differentially related to strong, task-dependent correlations in neural activity. The space mapped out by the observed structure-function relationships is used to define a quantitative measure of separation between resting, attention, and memory states. We find that the degree of separation between states is related to both general measures of behavioral performance and relative differences in task-specific measures of attention versus memory performance. These findings suggest that the observed separation between cognitive states reflects underlying organizational principles of human brain structure and function.

Cognitive two-way relay beamforming: Design with resilience to channel state uncertainties

KAUST Repository

Ubaidulla, P.; Alouini, Mohamed-Slim; Aissa, Sonia

2016-01-01

In this paper, we propose a robust distributed relay beamformer design for cognitive radio network operating under uncertainties in the available channel state information. The cognitive network consists of a pair of transceivers and a set of non

Visual steady state in relation to age and cognitive function

DEFF Research Database (Denmark)

Horwitz, Anna; Dyhr Thomsen, Mia; Wiegand, Iris

2017-01-01

Neocortical gamma activity is crucial for sensory perception and cognition. This study examines the value of using non-task stimulation-induced EEG oscillations to predict cognitive status in a birth cohort of healthy Danish males (Metropolit) with varying cognitive ability. In particular, we...... examine the steady-state VEP power response (SSVEP-PR) in the alpha (8Hz) and gamma (36Hz) bands in 54 males (avg. age: 62.0 years) and compare these with 10 young healthy participants (avg. age 27.6 years). Furthermore, we correlate the individual alpha-to-gamma difference in relative visual-area power...

Resting-state fMRI and social cognition: An opportunity to connect.

Science.gov (United States)

Doruyter, Alex; Groenewold, Nynke A; Dupont, Patrick; Stein, Dan J; Warwick, James M

2017-09-01

Many psychiatric disorders are characterized by altered social cognition. The importance of social cognition has previously been recognized by the National Institute of Mental Health Research Domain Criteria project, in which it features as a core domain. Social task-based functional magnetic resonance imaging (fMRI) currently offers the most direct insight into how the brain processes social information; however, resting-state fMRI may be just as important in understanding the biology and network nature of social processing. Resting-state fMRI allows researchers to investigate the functional relationships between brain regions in a neutral state: so-called resting functional connectivity (RFC). There is evidence that RFC is predictive of how the brain processes information during social tasks. This is important because it shifts the focus from possibly context-dependent aberrations to context-independent aberrations in functional network architecture. Rather than being analysed in isolation, the study of resting-state brain networks shows promise in linking results of task-based fMRI results, structural connectivity, molecular imaging findings, and performance measures of social cognition-which may prove crucial in furthering our understanding of the social brain. Copyright © 2017 John Wiley & Sons, Ltd.

Mathematical model of transmission network static state estimation

Directory of Open Access Journals (Sweden)

Ivanov Aleksandar

2012-01-01

Full Text Available In this paper the characteristics and capabilities of the power transmission network static state estimator are presented. The solving process of the mathematical model containing the measurement errors and their processing is developed. To evaluate difference between the general model of state estimation and the fast decoupled state estimation model, the both models are applied to an example, and so derived results are compared.

Clinical Trials of Blood Pressure Lowering and Antihypertensive Medication: is Cognitive Measurement State-Of-The-Art?

Science.gov (United States)

Elias, Merrill F; Torres, Rachael V; Davey, Adam

2018-02-22

Randomized controlled trials of blood pressure (BP) lowering and antihypertensive medication use on cognitive outcomes have often been disappointing, reporting mixed findings and small effect sizes. We evaluate the extent to which cognitive assessment protocols used in these trials approach state-of-the-art. Overall, we find that a primary focus on cognition and the systematic selection of cognitive outcomes across trials take a backseat to other trial goals. Twelve trials investigating change in cognitive functioning were examined and none met criteria for state-of-the-art assessment, including use of at least 4 tests indexing 2 cognitive domains. Four trials investigating incident dementia were also examined. Each trial used state-of-the-art diagnostic criteria to assess dementia, although follow-up periods were relatively short, with only 2 trials lasting for at least 3 years. Weaknesses in each trial may act to obscure or weaken the positive effects of BP lowering on cognitive functioning. Improving trial designs in terms of cognitive outcomes selected and length of follow-up periods employed could lead to more promising findings. We offer logical steps to achieve state-of-the-art assessment protocols, with examples, in hopes of improving future trials.

Linear Covariance Analysis and Epoch State Estimators

Science.gov (United States)

Markley, F. Landis; Carpenter, J. Russell

2014-01-01

This paper extends in two directions the results of prior work on generalized linear covariance analysis of both batch least-squares and sequential estimators. The first is an improved treatment of process noise in the batch, or epoch state, estimator with an epoch time that may be later than some or all of the measurements in the batch. The second is to account for process noise in specifying the gains in the epoch state estimator. We establish the conditions under which the latter estimator is equivalent to the Kalman filter.

The functional implications of motor, cognitive, psychiatric, and social problem-solving states in Huntington's disease.

Science.gov (United States)

Van Liew, Charles; Gluhm, Shea; Goldstein, Jody; Cronan, Terry A; Corey-Bloom, Jody

2013-01-01

Huntington's disease (HD) is a genetic, neurodegenerative disorder characterized by motor, cognitive, and psychiatric dysfunction. In HD, the inability to solve problems successfully affects not only disease coping, but also interpersonal relationships, judgment, and independent living. The aim of the present study was to examine social problem-solving (SPS) in well-characterized HD and at-risk (AR) individuals and to examine its unique and conjoint effects with motor, cognitive, and psychiatric states on functional ratings. Sixty-three participants, 31 HD and 32 gene-positive AR, were included in the study. Participants completed the Social Problem-Solving Inventory-Revised: Long (SPSI-R:L), a 52-item, reliable, standardized measure of SPS. Items are aggregated under five scales (Positive, Negative, and Rational Problem-Solving; Impulsivity/Carelessness and Avoidance Styles). Participants also completed the Unified Huntington's Disease Rating Scale functional, behavioral, and cognitive assessments, as well as additional neuropsychological examinations and the Symptom Checklist-90-Revised (SCL-90R). A structural equation model was used to examine the effects of motor, cognitive, psychiatric, and SPS states on functionality. The multifactor structural model fit well descriptively. Cognitive and motor states uniquely and significantly predicted function in HD; however, neither psychiatric nor SPS states did. SPS was, however, significantly related to motor, cognitive, and psychiatric states, suggesting that it may bridge the correlative gap between psychiatric and cognitive states in HD. SPS may be worth assessing in conjunction with the standard gamut of clinical assessments in HD. Suggestions for future research and implications for patients, families, caregivers, and clinicians are discussed.

State energy data report 1996: Consumption estimates

Energy Technology Data Exchange (ETDEWEB)

NONE

1999-02-01

The State Energy Data Report (SEDR) provides annual time series estimates of State-level energy consumption by major economic sectors. The estimates are developed in the Combined State Energy Data System (CSEDS), which is maintained and operated by the Energy Information Administration (EIA). The goal in maintaining CSEDS is to create historical time series of energy consumption by State that are defined as consistently as possible over time and across sectors. CSEDS exists for two principal reasons: (1) to provide State energy consumption estimates to Members of Congress, Federal and State agencies, and the general public and (2) to provide the historical series necessary for EIA`s energy models. To the degree possible, energy consumption has been assigned to five sectors: residential, commercial, industrial, transportation, and electric utility sectors. Fuels covered are coal, natural gas, petroleum, nuclear electric power, hydroelectric power, biomass, and other, defined as electric power generated from geothermal, wind, photovoltaic, and solar thermal energy. 322 tabs.

State energy data report 1996: Consumption estimates

International Nuclear Information System (INIS)

1999-02-01

The State Energy Data Report (SEDR) provides annual time series estimates of State-level energy consumption by major economic sectors. The estimates are developed in the Combined State Energy Data System (CSEDS), which is maintained and operated by the Energy Information Administration (EIA). The goal in maintaining CSEDS is to create historical time series of energy consumption by State that are defined as consistently as possible over time and across sectors. CSEDS exists for two principal reasons: (1) to provide State energy consumption estimates to Members of Congress, Federal and State agencies, and the general public and (2) to provide the historical series necessary for EIA's energy models. To the degree possible, energy consumption has been assigned to five sectors: residential, commercial, industrial, transportation, and electric utility sectors. Fuels covered are coal, natural gas, petroleum, nuclear electric power, hydroelectric power, biomass, and other, defined as electric power generated from geothermal, wind, photovoltaic, and solar thermal energy. 322 tabs

Fluctuations of Attentional Networks and Default Mode Network during the Resting State Reflect Variations in Cognitive States: Evidence from a Novel Resting-state Experience Sampling Method.

Science.gov (United States)

Van Calster, Laurens; D'Argembeau, Arnaud; Salmon, Eric; Peters, Frédéric; Majerus, Steve

2017-01-01

Neuroimaging studies have revealed the recruitment of a range of neural networks during the resting state, which might reflect a variety of cognitive experiences and processes occurring in an individual's mind. In this study, we focused on the default mode network (DMN) and attentional networks and investigated their association with distinct mental states when participants are not performing an explicit task. To investigate the range of possible cognitive experiences more directly, this study proposes a novel method of resting-state fMRI experience sampling, informed by a phenomenological investigation of the fluctuation of mental states during the resting state. We hypothesized that DMN activity would increase as a function of internal mentation and that the activity of dorsal and ventral networks would indicate states of top-down versus bottom-up attention at rest. Results showed that dorsal attention network activity fluctuated as a function of subjective reports of attentional control, providing evidence that activity of this network reflects the perceived recruitment of controlled attentional processes during spontaneous cognition. Activity of the DMN increased when participants reported to be in a subjective state of internal mentation, but not when they reported to be in a state of perception. This study provides direct evidence for a link between fluctuations of resting-state neural activity and fluctuations in specific cognitive processes.

New developments in state estimation for Nonlinear Systems

DEFF Research Database (Denmark)

Nørgård, Peter Magnus; Poulsen, Niels Kjølstad; Ravn, Ole

2000-01-01

Based on an interpolation formula, accurate state estimators for nonlinear systems can be derived. The estimators do not require derivative information which makes them simple to implement.; State estimators for nonlinear systems are derived based on polynomial approximations obtained with a mult......-known estimators, such as the extended Kalman filter (EKF) and its higher-order relatives, in most practical applications....

On state estimation in electric drives

International Nuclear Information System (INIS)

Leon, A.E.; Solsona, J.A.

2010-01-01

This paper deals with state estimation in electric drives. On one hand a nonlinear observer is designed, whereas on the other hand the speed state is estimated by using the dirty derivative from the position measured. The dirty derivative is an approximate version of the perfect derivative which introduces an estimation error few times analyzed in drive applications. For this reason, our proposal in this work consists in illustrating several aspects on the performance of the dirty derivator in presence of both model uncertainties and noisy measurements. To this end, a case study is introduced. The case study considers rotor speed estimation in a permanent magnet stepper motor, by assuming that rotor position and electrical variables are measured. In addition, this paper presents comments about the connection between dirty derivators and observers, and advantages and disadvantages of both techniques are also remarked.

State-Level Estimates of Cancer-Related Absenteeism Costs

Science.gov (United States)

Tangka, Florence K.; Trogdon, Justin G.; Nwaise, Isaac; Ekwueme, Donatus U.; Guy, Gery P.; Orenstein, Diane

2016-01-01

Background Cancer is one of the top five most costly diseases in the United States and leads to substantial work loss. Nevertheless, limited state-level estimates of cancer absenteeism costs have been published. Methods In analyses of data from the 2004–2008 Medical Expenditure Panel Survey, the 2004 National Nursing Home Survey, the U.S. Census Bureau for 2008, and the 2009 Current Population Survey, we used regression modeling to estimate annual state-level absenteeism costs attributable to cancer from 2004 to 2008. Results We estimated that the state-level median number of days of absenteeism per year among employed cancer patients was 6.1 days and that annual state-level cancer absenteeism costs ranged from $14.9 million to $915.9 million (median = $115.9 million) across states in 2010 dollars. Absenteeism costs are approximately 6.5% of the costs of premature cancer mortality. Conclusions The results from this study suggest that lost productivity attributable to cancer is a substantial cost to employees and employers and contributes to estimates of the overall impact of cancer in a state population. PMID:23969498

SCREENING FOR POSTSTROKE COGNITIVE IMPAIRMENT VIA MINI MENTAL STATE EXAMINATION AND MONTREAL COGNITIVE ASSESSMENT SCALE

Directory of Open Access Journals (Sweden)

Mirena Valkova

2012-10-01

Full Text Available Objective: The aim of our study is to examine cognitive performance after mild stroke via Mini Mental State Examination (MMSE and Montreal cognitive assessment scale (MoCA and to compare the results.Material and methods: We examined 54 patients with mild stroke (aged 52 to 72 (mean 63.17, SD 5.96; 34 males and 20 females and 54 controls, adjusted by age, sex and education level. All subjects were tested via MMSE (Bulgarian version and MoCa (Bulgarian version. Data was collected in the single step model at the 90th day after stroke incident for patients and at the day of obtaining informed consent for controls. Results: Patients have poorer performance on both MMSE and MoCa than controls. MoCa has comparatively good discriminative validity and sensitivity.Conclusions: Although MMSE is one of the classical screening tools for cognitive impairment widely used in Bulgaria, other screening tools should not be ignored. On the basis of our results, MoCa is also a good screening instrument, especially for poststroke cognitive impairment.

Algorithm of the managing systems state estimation

Directory of Open Access Journals (Sweden)

Skubilin M. D.

2010-02-01

Full Text Available The possibility of an electronic estimation of automatic and automated managing systems state is analyzed. An estimation of a current state (functional readiness of technical equipment and person-operator as integrated system allows to take operatively adequate measures on an exception and-or minimisation of consequences of system’s transition in a supernumerary state. The offered method is universal enough and can be recommended for normalisation of situations on transport, mainly in aircraft.

The state of the "state" debate in hypnosis: a view from the cognitive-behavioral perspective.

Science.gov (United States)

Chaves, J F

1997-07-01

For most of the past 50 years, hypnosis research has been driven by a debate about whether hypnotic phenomena can be best described and understood as the product of an altered state of consciousness. The meanings of some of the pivotal concepts in this debate and the nature of the phenomena that gave rise to them were ambiguous at the outset and led to misconceptions and surplus meanings that have obscured the debate through most of its history. The nature of the posited hypnotic state and its assumed consequences have changed during this period, reflecting the abandonment of untenable versions of hypnotic state theory. Carefully conducted studies in laboratories around the world have refined our understanding of hypnotic phenomena and helped identify the critical variables that interact to elicit them. With the maturation of the cognitive-behavioral perspective and the growing refinement of state conceptions of hypnosis, questions arise whether the state debate is still the axis about which hypnosis research and theory pivots. Although heuristic value of this debate has been enormous, we must guard against the cognitive constraints of our own metaphors and conceptual frameworks.

Approximation to estimation of critical state

International Nuclear Information System (INIS)

Orso, Jose A.; Rosario, Universidad Nacional

2011-01-01

The position of the control rod for the critical state of the nuclear reactor depends on several factors; including, but not limited to the temperature and configuration of the fuel elements inside the core. Therefore, the position can not be known in advance. In this paper theoretical estimations are developed to obtain an equation that allows calculating the position of the control rod for the critical state (approximation to critical) of the nuclear reactor RA-4; and will be used to create a software performing the estimation by entering the count rate of the reactor pulse channel and the length obtained from the control rod (in cm). For the final estimation of the approximation to critical state, a function obtained experimentally indicating control rods reactivity according to the function of their position is used, work is done mathematically to obtain a linear function, which gets the length of the control rod, which has to be removed to get the reactor in critical position. (author) [es

Memory, Cognition and the Endogenous Evoked Potentials of the Brain: the Estimation of the Disturbance of Cognitive Functions and Capacity of Working Memory Without the Psychological Testing.

Science.gov (United States)

Gnezditskiy, V V; Korepina, O S; Chatskaya, A V; Klochkova, O I

2017-01-01

Cognition, cognitive and memory impairments is widely discussed in the literature, especially in the psycho physiological and the neurologic. In essence, this literature is dedicated to the psycho physiological tests, different scales. However, instrument neurophysiologic methods not so widely are used for these purposes. This review is dedicated to the instrument methods of neurophysiology, in particular to the endogenous evoked potentials method Р 300 (by characteristic latency 300 ms), in the estimation of cognitive functions and memory, to their special features dependent on age and to special features to their changes with the pathology. Method cognitive EP - Р 300 is the response of the brain, recorded under the conditions of the identification of the significant distinguishing stimulus, it facilitates the inspection of cognitive functions and memory in the healthy persons and patients with different manifestation of cognitive impairments. In the review it is shown on the basis of literature and our own data, that working (operative) memory and the capacity of the working memory it can be evaluated with the aid of the indices Р 300 within the normal subject and with the pathology. Testing with the estimation of working memory according to latent period of the peak Р 300 can be carried out and when conducting psychological testing is not possible for any reasons. Together with these cognitive EP are used for evidence pharmacotherapy of many neurotropic drugs.

Cognitive constraints increase estimation biases: Cognitive load and delay in judgments

OpenAIRE

Allred, Sarah; Crawford, L. Elizabeth; Duffy, Sean; Smith, John

2014-01-01

Previous work has demonstrated that memory for simple stimuli can be biased by information about the category of which the stimulus is a member. These biases have been interpreted as optimally integrating noisy sensory information with category information. A separate literature has demonstrated that cognitive load can lead to biases in social cognition. Here we link the two, asking whether delay (Experiment 1) and cognitive load (Experiment 2) affect the extent to which observers' memories f...

Cognitive and affective components of challenge and threat states.

Science.gov (United States)

Meijen, Carla; Jones, Marc V; McCarthy, Paul J; Sheffield, David; Allen, Mark S

2013-01-01

We explored the cognitive and affective components of the Theory of Challenge and Threat States in Athletes (TCTSA) using a cross-sectional design. One hundred and seventy-seven collegiate athletes indicated how they typically approached an important competition on measures of self-efficacy, perceived control, achievement goals, emotional states and interpretation of emotional states. Participants also indicated to what extent they typically perceived the important competition as a challenge and/or a threat. The results suggest that a perception of challenge was not predicted by any of the cognitive components. A perception of threat was positively predicted by avoidance goals and negatively predicted by self-efficacy and approach goals. Both challenge and threat had a positive relationship with anxiety. Practical implications of this study are that an avoidance orientation appeared to be related to potentially negative constructs such as anxiety, threat and dejection. The findings may suggest that practitioners and researchers should focus on reducing an avoidance orientation, however the results should be treated with caution in applied settings, as this study did not examine how the combination of constructs exactly influences sport performance. The results provided partial support for the TCTSA with stronger support for proposed relationships with threat rather than challenge states.

Cognition Is Related to Resting-State Small-World Network Topology: An Magnetoencephalographic Study

NARCIS (Netherlands)

Douw, L.; Schoonheim, M.M.; Landi, D.; van der Meer, M.L.; Geurts, J.J.G.; Reijneveld, J.C.; Klein, M.; Stam, C.J.

2011-01-01

Brain networks and cognition have recently begun to attract attention: studies suggest that more efficiently wired resting-state brain networks are indeed correlated with better cognitive performance. "Small-world" brain networks combine local segregation with global integration, hereby subserving

Distributed Dynamic State Estimation with Extended Kalman Filter

Energy Technology Data Exchange (ETDEWEB)

Du, Pengwei; Huang, Zhenyu; Sun, Yannan; Diao, Ruisheng; Kalsi, Karanjit; Anderson, Kevin K.; Li, Yulan; Lee, Barry

2011-08-04

Increasing complexity associated with large-scale renewable resources and novel smart-grid technologies necessitates real-time monitoring and control. Our previous work applied the extended Kalman filter (EKF) with the use of phasor measurement data (PMU) for dynamic state estimation. However, high computation complexity creates significant challenges for real-time applications. In this paper, the problem of distributed dynamic state estimation is investigated. One domain decomposition method is proposed to utilize decentralized computing resources. The performance of distributed dynamic state estimation is tested on a 16-machine, 68-bus test system.

Role of state-dependent learning in the cognitive effects of caffeine in mice

OpenAIRE

Sanday, Leandro [UNIFESP; Zanin, Karina Agustini [UNIFESP; Patti, Camilla de Lima [UNIFESP; Fernandes-Santos, Luciano [UNIFESP; Oliveira, Larissa C. [UNIFESP; Longo, Beatriz Monteiro [UNIFESP; Andersen, Monica Levy [UNIFESP; Tufik, Sergio [UNIFESP; Frussa-Filho, Roberto [UNIFESP

2013-01-01

Caffeine is the most widely used psychoactive substance in the world and it is generally believed that it promotes beneficial effects on cognitive performance. However, there is also evidence suggesting that caffeine has inhibitory effects on learning and memory. Considering that caffeine may have anxiogenic effects, thus changing the emotional state of the subjects, state-dependent learning may play a role in caffeine-induced cognitive alterations. Mice were administered 20 mg/kg caffeine be...

Assessing the Depth of Cognitive Processing as the Basis for Potential User-State Adaptation

Directory of Open Access Journals (Sweden)

Irina-Emilia Nicolae

2017-10-01

Full Text Available Objective: Decoding neurocognitive processes on a single-trial basis with Brain-Computer Interface (BCI techniques can reveal the user's internal interpretation of the current situation. Such information can potentially be exploited to make devices and interfaces more user aware. In this line of research, we took a further step by studying neural correlates of different levels of cognitive processes and developing a method that allows to quantify how deeply presented information is processed in the brain.Methods/Approach: Seventeen participants took part in an EEG study in which we evaluated different levels of cognitive processing (no processing, shallow, and deep processing within three distinct domains (memory, language, and visual imagination. Our investigations showed gradual differences in the amplitudes of event-related potentials (ERPs and in the extend and duration of event-related desynchronization (ERD which both correlate with task difficulty. We performed multi-modal classification to map the measured correlates of neurocognitive processing to the corresponding level of processing.Results: Successful classification of the neural components was achieved, which reflects the level of cognitive processing performed by the participants. The results show performances above chance level for each participant and a mean performance of 70–90% for all conditions and classification pairs.Significance: The successful estimation of the level of cognition on a single-trial basis supports the feasibility of user-state adaptation based on ongoing neural activity. There is a variety of potential use cases such as: a user-friendly adaptive design of an interface or the development of assistance systems in safety critical workplaces.

Assessing the Depth of Cognitive Processing as the Basis for Potential User-State Adaptation

Science.gov (United States)

Nicolae, Irina-Emilia; Acqualagna, Laura; Blankertz, Benjamin

2017-01-01

Objective: Decoding neurocognitive processes on a single-trial basis with Brain-Computer Interface (BCI) techniques can reveal the user's internal interpretation of the current situation. Such information can potentially be exploited to make devices and interfaces more user aware. In this line of research, we took a further step by studying neural correlates of different levels of cognitive processes and developing a method that allows to quantify how deeply presented information is processed in the brain. Methods/Approach: Seventeen participants took part in an EEG study in which we evaluated different levels of cognitive processing (no processing, shallow, and deep processing) within three distinct domains (memory, language, and visual imagination). Our investigations showed gradual differences in the amplitudes of event-related potentials (ERPs) and in the extend and duration of event-related desynchronization (ERD) which both correlate with task difficulty. We performed multi-modal classification to map the measured correlates of neurocognitive processing to the corresponding level of processing. Results: Successful classification of the neural components was achieved, which reflects the level of cognitive processing performed by the participants. The results show performances above chance level for each participant and a mean performance of 70–90% for all conditions and classification pairs. Significance: The successful estimation of the level of cognition on a single-trial basis supports the feasibility of user-state adaptation based on ongoing neural activity. There is a variety of potential use cases such as: a user-friendly adaptive design of an interface or the development of assistance systems in safety critical workplaces. PMID:29046625

Assessing the Depth of Cognitive Processing as the Basis for Potential User-State Adaptation.

Science.gov (United States)

Nicolae, Irina-Emilia; Acqualagna, Laura; Blankertz, Benjamin

2017-01-01

Objective: Decoding neurocognitive processes on a single-trial basis with Brain-Computer Interface (BCI) techniques can reveal the user's internal interpretation of the current situation. Such information can potentially be exploited to make devices and interfaces more user aware. In this line of research, we took a further step by studying neural correlates of different levels of cognitive processes and developing a method that allows to quantify how deeply presented information is processed in the brain. Methods/Approach: Seventeen participants took part in an EEG study in which we evaluated different levels of cognitive processing (no processing, shallow, and deep processing) within three distinct domains (memory, language, and visual imagination). Our investigations showed gradual differences in the amplitudes of event-related potentials (ERPs) and in the extend and duration of event-related desynchronization (ERD) which both correlate with task difficulty. We performed multi-modal classification to map the measured correlates of neurocognitive processing to the corresponding level of processing. Results: Successful classification of the neural components was achieved, which reflects the level of cognitive processing performed by the participants. The results show performances above chance level for each participant and a mean performance of 70-90% for all conditions and classification pairs. Significance: The successful estimation of the level of cognition on a single-trial basis supports the feasibility of user-state adaptation based on ongoing neural activity. There is a variety of potential use cases such as: a user-friendly adaptive design of an interface or the development of assistance systems in safety critical workplaces.

Visual steady state in relation to age and cognitive function.

Science.gov (United States)

Horwitz, Anna; Dyhr Thomsen, Mia; Wiegand, Iris; Horwitz, Henrik; Klemp, Marc; Nikolic, Miki; Rask, Lene; Lauritzen, Martin; Benedek, Krisztina

2017-01-01

Neocortical gamma activity is crucial for sensory perception and cognition. This study examines the value of using non-task stimulation-induced EEG oscillations to predict cognitive status in a birth cohort of healthy Danish males (Metropolit) with varying cognitive ability. In particular, we examine the steady-state VEP power response (SSVEP-PR) in the alpha (8Hz) and gamma (36Hz) bands in 54 males (avg. age: 62.0 years) and compare these with 10 young healthy participants (avg. age 27.6 years). Furthermore, we correlate the individual alpha-to-gamma difference in relative visual-area power (ΔRV) with cognitive scores for the older adults. We find that ΔRV decrease with age by just over one standard deviation when comparing young with old participants (pintelligence is significantly negatively correlated with ΔRV in the older adult cohort, even when processing speed, global cognition, executive function, memory, and education (pincrease in ΔRV of one standard deviation is associated with a reduction in intelligence of 48% of a standard deviation (p<0.01). Finally, we conclude that the difference in cerebral rhythmic activity between the alpha and gamma bands is associated with age and cognitive status, and that ΔRV therefore provide a non-subjective clinical tool with which to examine cognitive status in old age.

Traffic State Estimation Using Connected Vehicles and Stationary Detectors

Directory of Open Access Journals (Sweden)

Ellen F. Grumert

2018-01-01

Full Text Available Real-time traffic state estimation is of importance for efficient traffic management. This is especially the case for traffic management systems that require fast detection of changes in the traffic conditions in order to apply an effective control measure. In this paper, we propose a method for estimating the traffic state and speed and density, by using connected vehicles combined with stationary detectors. The aim is to allow fast and accurate estimation of changes in the traffic conditions. The proposed method does only require information about the speed and the position of connected vehicles and can make use of sparsely located stationary detectors to limit the dependence on the infrastructure equipment. An evaluation of the proposed method is carried out by microscopic traffic simulation. The traffic state estimated using the proposed method is compared to the true simulated traffic state. Further, the density estimates are compared to density estimates from one detector-based method, one combined method, and one connected-vehicle-based method. The results of the study show that the proposed method is a promising alternative for estimating the traffic state in traffic management applications.

Evaluation of cognitive load and emotional states during multidisciplinary critical care simulation sessions.

Science.gov (United States)

Pawar, Swapnil; Jacques, Theresa; Deshpande, Kush; Pusapati, Raju; Meguerdichian, Michael J

2018-04-01

The simulation in critical care setting involves a heterogeneous group of participants with varied background and experience. Measuring the impacts of simulation on emotional state and cognitive load in this setting is not often performed. The feasibility of such measurement in the critical care setting needs further exploration. Medical and nursing staff with varying levels of experience from a tertiary intensive care unit participated in a standardised clinical simulation scenario. The emotional state of each participant was assessed before and after completion of the scenario using a validated eight-item scale containing bipolar oppositional descriptors of emotion. The cognitive load of each participant was assessed after the completion of the scenario using a validated subjective rating tool. A total of 103 medical and nursing staff participated in the study. The participants felt more relaxed (-0.28±1.15 vs 0.14±1, Pcognitive load for all participants was 6.67±1.41. There was no significant difference in the cognitive loads among medical staff versus nursing staff (6.61±2.3 vs 6.62±1.7; P>0.05). A well-designed complex high fidelity critical care simulation scenario can be evaluated to identify the relative cognitive load of the participants' experience and their emotional state. The movement of learners emotionally from a more negative state to a positive state suggests that simulation can be an effective tool for improved knowledge transfer and offers more opportunity for dynamic thinking.

State Estimation-based Transmission line parameter identification

Directory of Open Access Journals (Sweden)

Fredy Andrés Olarte Dussán

2010-01-01

Full Text Available This article presents two state-estimation-based algorithms for identifying transmission line parameters. The identification technique used simultaneous state-parameter estimation on an artificial power system composed of several copies of the same transmission line, using measurements at different points in time. The first algorithm used active and reactive power measurements at both ends of the line. The second method used synchronised phasor voltage and current measurements at both ends. The algorithms were tested in simulated conditions on the 30-node IEEE test system. All line parameters for this system were estimated with errors below 1%.

Comparison of montreal cognitive assessment and mini-mental state examination in evaluating cognitive domain deficit following aneurysmal subarachnoid haemorrhage.

Directory of Open Access Journals (Sweden)

George Kwok Chu Wong

Full Text Available Cognitive deficits are common after aneurysmal subarachnoid haemorrhage (aSAH, and clinical evaluation is important for their management. Our hypothesis was that the Montreal Cognitive Assessment (MoCa is superior to the Mini-Mental State Examination (MMSE in screening for cognitive domain deficit in aSAH patients.We carried out a prospective observational and diagnostic accuracy study on Hong Kong aSAH patients aged 21 to 75 years who had been admitted within 96 hours of ictus. The domain-specific neuropsychological assessment battery, the MoCA and MMSE were administered 2-4 weeks and 1 year after ictus. A cognitive domain deficit was defined as a cognitive domain z score <-1.65 (below the fifth percentile. Cognitive impairment was defined as two or more cognitive domain deficits. The study is registered at ClinicalTrials.gov of the US National Institutes of Health (NCT01038193.Both the MoCA and the MMSE were successful in differentiating between patients with and without cognitive domain deficits and cognitive impairment at both assessment periods. At 1 year post-ictus, the MoCA produced higher area under the curve scores for cognitive impairment than the MMSE (MoCA, 0.92; 95% CI, 0.83 to 0.97 versus MMSE, 0.77; 95% CI, 0.66 to 0.83, p = 0.009.Cognitive domain deficits and cognitive impairment in patients with aSAH can be screened with the MoCA in both the subacute and chronic phases.

Multistage optimal PMU placement for hybrid state estimation

DEFF Research Database (Denmark)

Hazra, J.; Das, Kaushik; Roy, B. K. S.

2017-01-01

placed by the proposed method are used in developing a hybrid state estimator (HSE). The HSE estimates the voltage phasor at all the buses of a power system with a limited numbers of PMUs in steady state as well as in the presence of disturbances even in that part of network which is unobservable through...... PMUs. Performance of the proposed phased installation scheme for HSE is evaluated on the number of standard test system and the simulation results shows an improvement in the accuracy of the estimated states as compared to the existing methods in the literature....

Auditory beat stimulation and its effects on cognition and mood States.

Science.gov (United States)

Chaieb, Leila; Wilpert, Elke Caroline; Reber, Thomas P; Fell, Juergen

2015-01-01

Auditory beat stimulation may be a promising new tool for the manipulation of cognitive processes and the modulation of mood states. Here, we aim to review the literature examining the most current applications of auditory beat stimulation and its targets. We give a brief overview of research on auditory steady-state responses and its relationship to auditory beat stimulation (ABS). We have summarized relevant studies investigating the neurophysiological changes related to ABS and how they impact upon the design of appropriate stimulation protocols. Focusing on binaural-beat stimulation, we then discuss the role of monaural- and binaural-beat frequencies in cognition and mood states, in addition to their efficacy in targeting disease symptoms. We aim to highlight important points concerning stimulation parameters and try to address why there are often contradictory findings with regard to the outcomes of ABS.

Using neurophysiological signals that reflect cognitive or affective state

NARCIS (Netherlands)

Brouwer, Anne-Marie; Zander, Thorsten O.; van Erp, Johannes Bernardus Fransiscus

2015-01-01

What can we learn from spontaneously occurring brain and other physiological signals about an individual’s cognitive and affective state and how can we make use of this information? One line of research that is actively involved with this question is Passive Brain-Computer-Interfaces (BCI). To date

Conversion between Addenbrooke's Cognitive Examination III and Mini-Mental State Examination.

Science.gov (United States)

Matías-Guiu, Jordi A; Pytel, Vanesa; Cortés-Martínez, Ana; Valles-Salgado, María; Rognoni, Teresa; Moreno-Ramos, Teresa; Matías-Guiu, Jorge

2017-12-10

We aim to provide a conversion between Addenbrooke's Cognitive Examination III (ACE-III) and Mini-Mental State Examination (MMSE) scores, to predict the MMSE result based on ACE-III, thus avoiding the need for both tests, and improving their comparability. Equipercentile equating method was used to elaborate a conversion table using a group of 400 participants comprising healthy controls and Alzheimer's disease (AD) patients. Then, reliability was assessed in a group of 100 healthy controls and patients with AD, 52 with primary progressive aphasia and 22 with behavioral variant frontotemporal dementia. The conversion table between ACE-III and MMSE denoted a high reliability, with intra-class correlation coefficients of 0.940, 0.922, and 0.902 in the groups of healthy controls and AD, behavioral variant frontotemporal dementia, and primary progressive aphasia, respectively. Our conversion table between ACE-III and MMSE suggests that MMSE may be estimated based on the ACE-III score, which could be useful for clinical and research purposes.

Visual steady state in relation to age and cognitive function.

Directory of Open Access Journals (Sweden)

Anna Horwitz

Full Text Available Neocortical gamma activity is crucial for sensory perception and cognition. This study examines the value of using non-task stimulation-induced EEG oscillations to predict cognitive status in a birth cohort of healthy Danish males (Metropolit with varying cognitive ability. In particular, we examine the steady-state VEP power response (SSVEP-PR in the alpha (8Hz and gamma (36Hz bands in 54 males (avg. age: 62.0 years and compare these with 10 young healthy participants (avg. age 27.6 years. Furthermore, we correlate the individual alpha-to-gamma difference in relative visual-area power (ΔRV with cognitive scores for the older adults. We find that ΔRV decrease with age by just over one standard deviation when comparing young with old participants (p<0.01. Furthermore, intelligence is significantly negatively correlated with ΔRV in the older adult cohort, even when processing speed, global cognition, executive function, memory, and education (p<0.05. In our preferred specification, an increase in ΔRV of one standard deviation is associated with a reduction in intelligence of 48% of a standard deviation (p<0.01. Finally, we conclude that the difference in cerebral rhythmic activity between the alpha and gamma bands is associated with age and cognitive status, and that ΔRV therefore provide a non-subjective clinical tool with which to examine cognitive status in old age.

Effect of Smart Meter Measurements Data On Distribution State Estimation

DEFF Research Database (Denmark)

Pokhrel, Basanta Raj; Nainar, Karthikeyan; Bak-Jensen, Birgitte

2018-01-01

Smart distribution grids with renewable energy based generators and demand response resources (DRR) requires accurate state estimators for real time control. Distribution grid state estimators are normally based on accumulated smart meter measurements. However, increase of measurements in the phy......Smart distribution grids with renewable energy based generators and demand response resources (DRR) requires accurate state estimators for real time control. Distribution grid state estimators are normally based on accumulated smart meter measurements. However, increase of measurements...... in the physical grid can enforce significant stress not only on the communication infrastructure but also in the control algorithms. This paper aims to propose a methodology to analyze needed real time smart meter data from low voltage distribution grids and their applicability in distribution state estimation...

Screening an elderly hearing impaired population for mild cognitive impairment using Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA).

Science.gov (United States)

Lim, Magdalene Yeok Leng; Loo, Jenny Hooi Yin

2018-07-01

To determine if there is an association between hearing loss and poorer cognitive scores on Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) and to determine if poor hearing acuity affects scoring on the cognitive screening tests of MMSE and MoCA. One hundred fourteen elderly patients (Singapore residents) aged between 55 and 86 years were sampled. Participants completed a brief history questionnaire, pure tone audiometry, and 2 cognitive screening tests-the MMSE and MoCA. Average hearing thresholds of the better ear in the frequencies of 0.5, 1, 2, and 4 kHz were used for data analysis. Hearing loss was significantly associated with poorer cognitive scores in Poisson regression models adjusted for age. Mini-Mental State Examination scores were shown to decrease by 2.8% (P = .029), and MoCA scores by 3.5% (P = .013) for every 10 dB of hearing loss. Analysis of hearing-sensitive components of "Registration" and "Recall" in MMSE and MoCA using chi-square tests showed significantly poorer performance in the hearing loss group as compared to the normal hearing group. Phonetic analysis of target words with high error rates shows that the poor performance was likely contributed by decreased hearing acuity, on top of a possible true deficit in cognition in the hearing impaired. Hearing loss is associated with poorer cognitive scores on MMSE and MoCA, and cognitive scoring is likely confounded by poor hearing ability. This highlights an important, often overlooked aspect of sensory impairment during cognitive screening. Provisions should be made when testing for cognition in the hearing-impaired population to avoid over-referral and subsequent misdiagnoses of cognitive impairment. Copyright © 2018 John Wiley & Sons, Ltd.

Montreal Cognitive Assessment and Mini-Mental State Examination are both valid cognitive tools in stroke.

Science.gov (United States)

Cumming, T B; Churilov, L; Linden, T; Bernhardt, J

2013-08-01

To determine the validity of the Montreal Cognitive Assessment (MoCA) and the Mini-Mental State Examination (MMSE) as screening tools for cognitive impairment after stroke. Cognitive assessments were administered over 2 sessions (1 week apart) at 3 months post-stroke. Scores on the MoCA and MMSE were evaluated against a diagnosis of cognitive impairment derived from a comprehensive neuropsychological battery (the criterion standard). Sixty patients participated in the study [mean age 72.1 years (SD = 13.9), mean education 10.5 years (SD = 3.9), median acute NIHSS score 5 (IQR 3-7)]. The MoCA yielded lower scores (median = 21, IQR = 17-24; mean = 20.0, SD = 5.4) than the MMSE (median = 26, IQR = 22-27; mean = 24.2, SD = 4.5). MMSE data were more skewed towards ceiling than MoCA data (skewness = -1.09 vs -0.73). Area under the receiver operator curve was higher for MoCA than for MMSE (0.87 vs 0.84), although this difference was not significant (χ(2) = 0.48, P = 0.49). At their optimal cut-offs, the MoCA had better sensitivity than the MMSE (0.92 vs 0.82) but poorer specificity (0.67 vs 0.76). The MoCA is a valid screening tool for post-stroke cognitive impairment; it is more sensitive but less specific than the MMSE. Contrary to the prevailing view, the MMSE also exhibited acceptable validity in this setting. © 2013 John Wiley & Sons A/S.

Do Individual Differences and Aging Effects in the Estimation of Geographical Slant Reflect Cognitive or Perceptual Effects?

Directory of Open Access Journals (Sweden)

Abigail M. Dean

2016-07-01

Full Text Available Several individual differences including age have been suggested to affect the perception of slant. A cross-sectional study of outdoor hill estimation (N = 106 was analyzed using individual difference measures of age, experiential knowledge, fitness, personality traits, and sex. Of particular note, it was found that for participants who reported any experiential knowledge about slant, estimates decreased (i.e., became more accurate as conscientiousness increased, suggesting that more conscientious individuals were more deliberate about taking their experiential knowledge (rather than perception into account. Effects of fitness were limited to those without experiential knowledge, suggesting that they, too, may be cognitive rather than perceptual. The observed effects of age, which tended to produce lower, more accurate estimates of hill slant, provide more evidence that older adults do not see hills as steeper. The main effect of age was to lower slant estimates; such effects may be due to implicit experiential knowledge acquired over a lifetime. The results indicate the impact of cognitive, rather than perceptual factors on individual differences in slant estimation.

Auditory beat stimulation and its effects on cognition and mood states

Directory of Open Access Journals (Sweden)

Leila eChaieb

2015-05-01

Full Text Available Auditory beat stimulation may be a promising new tool for the manipulation of cognitive processes and the modulation of mood-states. Here we aim to review the literature examining the most current applications of auditory beat stimulation and its targets. We give a brief overview of research on auditory steady-state responses and its relationship to auditory beat stimulation. We have summarized relevant studies investigating the neurophysiological changes related to auditory beat stimulation and how they impact upon the design of appropriate stimulation protocols. Focusing on binaural beat stimulation, we then discuss the role of monaural and binaural beat frequencies in cognition and mood-states, in addition to their efficacy in targeting disease symptoms. We aim to highlight important points concerning stimulation parameters and try to address why there are often contradictory findings with regard to the outcomes of auditory beat stimulation.

Optimal state estimation theory applied to safeguards accounting

International Nuclear Information System (INIS)

Pike, D.H.; Morrison, G.W.

1977-01-01

This paper presents a unified theory for the application of modern state estimation techniques to nuclear material accountability. First a summary of the current MUF/LEMUF approach is detailed. It is shown that when inventory measurement error is large in comparison to transfer measurement error, improved estimates of the losses can be achieved using the cumulative summation technique. However, the optimal estimator is shown to be the Kalman filter. An enhancement of the retrospective estimation of losses can be achieved using linear smoothing. State space models are developed for a mixed oxide fuel fabrication facility and examples are presented

Nonlinear Filtering Techniques Comparison for Battery State Estimation

Directory of Open Access Journals (Sweden)

Aspasia Papazoglou

2014-09-01

Full Text Available The performance of estimation algorithms is vital for the correct functioning of batteries in electric vehicles, as poor estimates will inevitably jeopardize the operations that rely on un-measurable quantities, such as State of Charge and State of Health. This paper compares the performance of three nonlinear estimation algorithms: the Extended Kalman Filter, the Unscented Kalman Filter and the Particle Filter, where a lithium-ion cell model is considered. The effectiveness of these algorithms is measured by their ability to produce accurate estimates against their computational complexity in terms of number of operations and execution time required. The trade-offs between estimators' performance and their computational complexity are analyzed.

Optimization of automation: I. Estimation method of cognitive automation rates reflecting the effects of automation on human operators in nuclear power plants

International Nuclear Information System (INIS)

Lee, Seung Min; Kim, Jong Hyun; Seong, Poong Hyun

2014-01-01

Highlights: • We propose an estimation method of the automation rate by taking the advantages of automation as the estimation measures. • We conduct the experiments to examine the validity of the suggested method. • The higher the cognitive automation rate is, the greater the decreased rate of the working time will be. • The usefulness of the suggested estimation method is proved by statistical analyses. - Abstract: Since automation was introduced in various industrial fields, the concept of the automation rate has been used to indicate the inclusion proportion of automation among all work processes or facilities. Expressions of the inclusion proportion of automation are predictable, as is the ability to express the degree of the enhancement of human performance. However, many researchers have found that a high automation rate does not guarantee high performance. Therefore, to reflect the effects of automation on human performance, this paper proposes a new estimation method of the automation rate that considers the effects of automation on human operators in nuclear power plants (NPPs). Automation in NPPs can be divided into two types: system automation and cognitive automation. Some general descriptions and characteristics of each type of automation are provided, and the advantages of automation are investigated. The advantages of each type of automation are used as measures of the estimation method of the automation rate. One advantage was found to be a reduction in the number of tasks, and another was a reduction in human cognitive task loads. The system and the cognitive automation rate were proposed as quantitative measures by taking advantage of the aforementioned benefits. To quantify the required human cognitive task loads and thus suggest the cognitive automation rate, Conant’s information-theory-based model was applied. The validity of the suggested method, especially as regards the cognitive automation rate, was proven by conducting

Dynamic state estimation assisted power system monitoring and protection

Science.gov (United States)

Cui, Yinan

The advent of phasor measurement units (PMUs) has unlocked several novel methods to monitor, control, and protect bulk electric power systems. This thesis introduces the concept of "Dynamic State Estimation" (DSE), aided by PMUs, for wide-area monitoring and protection of power systems. Unlike traditional State Estimation where algebraic variables are estimated from system measurements, DSE refers to a process to estimate the dynamic states associated with synchronous generators. This thesis first establishes the viability of using particle filtering as a technique to perform DSE in power systems. The utility of DSE for protection and wide-area monitoring are then shown as potential novel applications. The work is presented as a collection of several journal and conference papers. In the first paper, we present a particle filtering approach to dynamically estimate the states of a synchronous generator in a multi-machine setting considering the excitation and prime mover control systems. The second paper proposes an improved out-of-step detection method for generators by means of angular difference. The generator's rotor angle is estimated with a particle filter-based dynamic state estimator and the angular separation is then calculated by combining the raw local phasor measurements with this estimate. The third paper introduces a particle filter-based dual estimation method for tracking the dynamic states of a synchronous generator. It considers the situation where the field voltage measurements are not readily available. The particle filter is modified to treat the field voltage as an unknown input which is sequentially estimated along with the other dynamic states. The fourth paper proposes a novel framework for event detection based on energy functions. The key idea is that any event in the system will leave a signature in WAMS data-sets. It is shown that signatures for four broad classes of disturbance events are buried in the components that constitute the

Minimax estimation of qubit states with Bures risk

Science.gov (United States)

Acharya, Anirudh; Guţă, Mădălin

2018-04-01

The central problem of quantum statistics is to devise measurement schemes for the estimation of an unknown state, given an ensemble of n independent identically prepared systems. For locally quadratic loss functions, the risk of standard procedures has the usual scaling of 1/n. However, it has been noticed that for fidelity based metrics such as the Bures distance, the risk of conventional (non-adaptive) qubit tomography schemes scales as 1/\\sqrt{n} for states close to the boundary of the Bloch sphere. Several proposed estimators appear to improve this scaling, and our goal is to analyse the problem from the perspective of the maximum risk over all states. We propose qubit estimation strategies based on separate adaptive measurements, and collective measurements, that achieve 1/n scalings for the maximum Bures risk. The estimator involving local measurements uses a fixed fraction of the available resource n to estimate the Bloch vector direction; the length of the Bloch vector is then estimated from the remaining copies by measuring in the estimator eigenbasis. The estimator based on collective measurements uses local asymptotic normality techniques which allows us to derive upper and lower bounds to its maximum Bures risk. We also discuss how to construct a minimax optimal estimator in this setup. Finally, we consider quantum relative entropy and show that the risk of the estimator based on collective measurements achieves a rate O(n-1log n) under this loss function. Furthermore, we show that no estimator can achieve faster rates, in particular the ‘standard’ rate n ‑1.

Generalized location-based resource allocation for OFDMA cognitive radio systems

KAUST Repository

Ben Ghorbel, Mahdi

2010-09-01

Cognitive radio is one of the hot topics for emerging and future wireless communication. Cognitive users can share channels with primary users under the condition of non interference. In order to compute this interference, the cognitive system usually use the channel state information of the primary user which is often impractical to obtain. However, using location information, we can estimate this interference by pathloss computation. In this paper, we introduce a low-complexity resource allocation algorithm for orthogonal frequency division multiple access (OFDMA) based cognitive radio systems, which uses relative location information between primary and secondary users to estimate the interference. This algorithm considers interference with multiple primary users having different thresholds. The simulation results show the efficiency of the proposed algorithm by comparing it with an optimal exhaustive search method. © 2010 IEEE.

Fuzzy filter for state estimation of a glucoregulatory system.

Science.gov (United States)

Trajanoski, Z; Wach, P

1996-08-01

A filter based on fuzzy logic for state estimation of a glucoregulatory system is presented. A published non-linear model for the dynamics of glucose and its hormonal control including a single glucose compartment, five insulin compartments and a glucagon compartment was used for simulation. The simulated data were corrupted by an additive white noise with zero mean and a coefficient of variation (CV) of between 2 and 20% and then submitted to the state estimation procedure using a fuzzy filter (FF). The performance of the FF was compared with an extended Kalman filter (EKF) for state estimation. Both the FF and the EKF were evaluated in the following cases: (a) five state variables are measurable; three plasma variables are measurable; only plasma glucose is measurable; (b) for different measurement noise levels (CV of 2-20%); and (c) a mismatch between the glucoregulatory system and the given mathematical model (uncertain or approximate model). In contrast to the FF, in the case of approximate model of the glucose system, the EKF failed to achieve useful state estimation. Moreover, the performance of the FF was independent of the noise level. In conclusion, the FF approach is a viable alternative for state estimation in a noisy environment and with an uncertain mathematical model of the glucoregulatory system.

Estimation methods for nonlinear state-space models in ecology

DEFF Research Database (Denmark)

Pedersen, Martin Wæver; Berg, Casper Willestofte; Thygesen, Uffe Høgsbro

2011-01-01

The use of nonlinear state-space models for analyzing ecological systems is increasing. A wide range of estimation methods for such models are available to ecologists, however it is not always clear, which is the appropriate method to choose. To this end, three approaches to estimation in the theta...... logistic model for population dynamics were benchmarked by Wang (2007). Similarly, we examine and compare the estimation performance of three alternative methods using simulated data. The first approach is to partition the state-space into a finite number of states and formulate the problem as a hidden...... Markov model (HMM). The second method uses the mixed effects modeling and fast numerical integration framework of the AD Model Builder (ADMB) open-source software. The third alternative is to use the popular Bayesian framework of BUGS. The study showed that state and parameter estimation performance...

Using Lego robots to estimate cognitive ability in children who have severe physical disabilities.

Science.gov (United States)

Cook, Albert M; Adams, Kim; Volden, Joanne; Harbottle, Norma; Harbottle, Cheryl

2011-01-01

To determine whether low-cost robots provide a means by which children with severe disabilities can demonstrate understanding of cognitive concepts. Ten children, ages 4 to 10, diagnosed with cerebral palsy and related motor conditions, participated. Participants had widely variable motor, cognitive and receptive language skills, but all were non-speaking. A Lego Invention 'roverbot' was used to carry out a range of functional tasks from single-switch replay of pre-stored movements to total control of the movement in two dimensions. The level of sophistication achieved on hierarchically arranged play tasks was used to estimate cognitive skills. The 10 children performed at one of the six hierarchically arranged levels from 'no interaction' through 'simple cause and effect' to 'development and execution of a plan'. Teacher interviews revealed that children were interested in the robot, enjoyed interacting with it and demonstrated changes in behaviour and social and language skills following interaction. Children with severe physical disabilities can control a Lego robot to perform un-structured play tasks. In some cases, they were able to display more sophisticated cognitive skills through manipulating the robot than in traditional standardised tests. Success with the robot could be a proxy measure for children who have cognitive abilities but cannot demonstrate them in standard testing.

Internal Models, Vestibular Cognition, and Mental Imagery: Conceptual Considerations.

Science.gov (United States)

Mast, Fred W; Ellis, Andrew W

2015-01-01

Vestibular cognition has recently gained attention. Despite numerous experimental and clinical demonstrations, it is not yet clear what vestibular cognition really is. For future research in vestibular cognition, adopting a computational approach will make it easier to explore the underlying mechanisms. Indeed, most modeling approaches in vestibular science include a top-down or a priori component. We review recent Bayesian optimal observer models, and discuss in detail the conceptual value of prior assumptions, likelihood and posterior estimates for research in vestibular cognition. We then consider forward models in vestibular processing, which are required in order to distinguish between sensory input that is induced by active self-motion, and sensory input that is due to passive self-motion. We suggest that forward models are used not only in the service of estimating sensory states but they can also be drawn upon in an offline mode (e.g., spatial perspective transformations), in which interaction with sensory input is not desired. A computational approach to vestibular cognition will help to discover connections across studies, and it will provide a more coherent framework for investigating vestibular cognition.

Time to talk about work-hour impact on anesthesiologists: The effects of sleep deprivation on Profile of Mood States and cognitive tasks.

Science.gov (United States)

Saadat, Haleh; Bissonnette, Bruno; Tumin, Dmitry; Thung, Arlyne; Rice, Julie; Barry, N'Diris; Tobias, Joseph

2016-01-01

A physician's fatigue raises significant concerns regarding personal and patient safety. Effects of sleep deprivation on clinical performance and the quality of patient care are major considerations of today's health care environment. To evaluate the impact of partial sleep deprivation after a 17-h overnight call (3 pm-7 am) on the mood status and cognitive skills of anesthesiologists in an academic clinical hospital setting, as compared to these parameters during regular working hours. Taking circadian rhythm into account, the following measures were assessed in 21 pediatric anesthesiologists at two time points over the course of the study; (i) between 7 and 8 am on a regular non call day, and (ii) between 7 and 8 am after a 17-h in-house call (3 pm-7 am). Six mood states were assessed using the Profile of Mood States. A Total Mood Disturbance (TMD) score was obtained as the sum of all mood scores minus vigor. The total score provides a global estimate of affective state. Simple cognitive tests were similarly administered to assess cognitive skills. A two-tailed paired t-test was used to compare data between regular and post call days. A P sleep deprivation affects the total mood status of anesthesiologists and impacts their cognitive skills. These findings are particularly relevant in a context of increased work expectation, particularly on clinical performance in our modern medical system. Such observations suggest that there may be changes that impact the safety of our patients and the quality of health care that is provided. © 2015 John Wiley & Sons Ltd.

Vehicle State Information Estimation with the Unscented Kalman Filter

Directory of Open Access Journals (Sweden)

Hongbin Ren

2014-01-01

Full Text Available The vehicle state information plays an important role in the vehicle active safety systems; this paper proposed a new concept to estimate the instantaneous vehicle speed, yaw rate, tire forces, and tire kinemics information in real time. The estimator is based on the 3DoF vehicle model combined with the piecewise linear tire model. The estimator is realized using the unscented Kalman filter (UKF, since it is based on the unscented transfer technique and considers high order terms during the measurement and update stage. The numerical simulations are carried out to further investigate the performance of the estimator under high friction and low friction road conditions in the MATLAB/Simulink combined with the Carsim environment. The simulation results are compared with the numerical results from Carsim software, which indicate that UKF can estimate the vehicle state information accurately and in real time; the proposed estimation will provide the necessary and reliable state information to the vehicle controller in the future.

The resting state fMRI study of patients with Parkinson's disease associated with cognitive dysfunction

International Nuclear Information System (INIS)

Feng Jieying; Huang Biao

2013-01-01

Parkinson's disease (PD) is the most common neurodegenerative cause of Parkinsonism, but the high morbidity of PD accompanied cognitive dysfunction hasn't drawn enough attention by the clinicians. With the rapid development of the resting state functional MRI (fMRI) technique, the cause of PD patients with cognitive dysfunction may be associated with the damage of functional connectivity of the motor networks and the cognitive networks. The relationship between neuropathologic mechanism of PD patients with cognitive dysfunction and impaired cognitive circuits will be disclosed by building the changes of brain topological structure in patients. The resting state fMRI study can provide the rationale for prevention, diagnosis and treatment of PD. (authors)

Exponentially convergent state estimation for delayed switched recurrent neural networks.

Science.gov (United States)

Ahn, Choon Ki

2011-11-01

This paper deals with the delay-dependent exponentially convergent state estimation problem for delayed switched neural networks. A set of delay-dependent criteria is derived under which the resulting estimation error system is exponentially stable. It is shown that the gain matrix of the proposed state estimator is characterised in terms of the solution to a set of linear matrix inequalities (LMIs), which can be checked readily by using some standard numerical packages. An illustrative example is given to demonstrate the effectiveness of the proposed state estimator.

Affective problems and decline in cognitive state in older adults: a systematic review and meta-analysis.

Science.gov (United States)

John, A; Patel, U; Rusted, J; Richards, M; Gaysina, D

2018-05-24

Evidence suggests that affective problems, such as depression and anxiety, increase risk for late-life dementia. However, the extent to which affective problems influence cognitive decline, even many years prior to clinical diagnosis of dementia, is not clear. The present study systematically reviews and synthesises the evidence for the association between affective problems and decline in cognitive state (i.e., decline in non-specific cognitive function) in older adults. An electronic search of PubMed, PsycInfo, Cochrane, and ScienceDirect was conducted to identify studies of the association between depression and anxiety separately and decline in cognitive state. Key inclusion criteria were prospective, longitudinal designs with a minimum follow-up period of 1 year. Data extraction and methodological quality assessment using the STROBE checklist were conducted independently by two raters. A total of 34 studies (n = 71 244) met eligibility criteria, with 32 studies measuring depression (n = 68 793), and five measuring anxiety (n = 4698). A multi-level meta-analysis revealed that depression assessed as a binary predictor (OR 1.36, 95% CI 1.05-1.76, p = 0.02) or a continuous predictor (B = -0.008, 95% CI -0.015 to -0.002, p = 0.012; OR 0.992, 95% CI 0.985-0.998) was significantly associated with decline in cognitive state. The number of anxiety studies was insufficient for meta-analysis, and they are described in a narrative review. Results of the present study improve current understanding of the temporal nature of the association between affective problems and decline in cognitive state. They also suggest that cognitive function may need to be monitored closely in individuals with affective disorders, as these individuals may be at particular risk of greater cognitive decline.

Information geometry of density matrices and state estimation

International Nuclear Information System (INIS)

Brody, Dorje C

2011-01-01

Given a pure state vector |x) and a density matrix ρ-hat, the function p(x|ρ-hat)= defines a probability density on the space of pure states parameterised by density matrices. The associated Fisher-Rao information measure is used to define a unitary invariant Riemannian metric on the space of density matrices. An alternative derivation of the metric, based on square-root density matrices and trace norms, is provided. This is applied to the problem of quantum-state estimation. In the simplest case of unitary parameter estimation, new higher-order corrections to the uncertainty relations, applicable to general mixed states, are derived. (fast track communication)

Role of state-dependent learning in the cognitive effects of caffeine in mice.

Science.gov (United States)

Sanday, Leandro; Zanin, Karina A; Patti, Camilla L; Fernandes-Santos, Luciano; Oliveira, Larissa C; Longo, Beatriz M; Andersen, Monica L; Tufik, Sergio; Frussa-Filho, Roberto

2013-08-01

Caffeine is the most widely used psychoactive substance in the world and it is generally believed that it promotes beneficial effects on cognitive performance. However, there is also evidence suggesting that caffeine has inhibitory effects on learning and memory. Considering that caffeine may have anxiogenic effects, thus changing the emotional state of the subjects, state-dependent learning may play a role in caffeine-induced cognitive alterations. Mice were administered 20 mg/kg caffeine before training and/or before testing both in the plus-maze discriminative avoidance task (an animal model that concomitantly evaluates learning, memory, anxiety-like behaviour and general activity) and in the inhibitory avoidance task, a classic paradigm for evaluating memory in rodents. Pre-training caffeine administration did not modify learning, but produced an anxiogenic effect and impaired memory retention. While pre-test administration of caffeine did not modify retrieval on its own, the pre-test administration counteracted the memory deficit induced by the pre-training caffeine injection in both the plus-maze discriminative and inhibitory avoidance tasks. Our data demonstrate that caffeine-induced memory deficits are critically related to state-dependent learning, reinforcing the importance of considering the participation of state-dependency on the interpretation of the cognitive effects of caffeine. The possible participation of caffeine-induced anxiety alterations in state-dependent memory deficits is discussed.

[Mini-Mental State Examination: Screening and Diagnosis of Cognitive Decline, Using New Normative Data].

Science.gov (United States)

Santana, Isabel; Duro, Diana; Lemos, Raquel; Costa, Vanessa; Pereira, Miguel; Simões, Mário R; Freitas, Sandra

2016-04-01

The Mini-Mental State Examination is the most commonly used cognitive screening test. In Portugal, the cut-off scores are defined according to literacy groups, but different proposals have been recommended by more representative studies. We therefore propose to confirm the influence of demographical variables, such as age and education, in the subjectâs performance; evaluating the discriminant ability of the new normative data; and to further examine the diagnostic acuity of the validated cut-off scoring for mild cognitive impairment and for the most prevalent types of dementia. Our study includes 1 441 educated subjects, divided into seven subgroups: Mild cognitive impairment, Alzheimer's disease, frontotemporal dementia, vascular dementia, dementia with Lewy bodies, community-controls and memory clinic-controls. Altogether age and education explain 10.4% of the Mini-Mental State Examination results variance, with both variables contributing significantly to the resultsâ prediction. The diagnostic acuity based on the most recent normative data was always higher than the one obtained through the validation cut-off scoring, revealing an overall excellent specificity (superior to 90%) and different sensitivity values: excellent for mild Alzheimer's disease (91%), good for dementia with Lewy Bodies (78%) and low for mild cognitive impairment (65%), frontotemporal dementia and vascular dementia (55%). The performance on the Mini-Mental State Examination is influenced by age and education, supporting the use of normative data that consider those variables. With this approach, the Mini-Mental State Examination could be a sensitive and specific instrument for the Alzheimer's disease screening among all healthcare levels. Nevertheless, its diagnostic acuity is limited in other conditions frequently seen in memory clinics, such as Mild Cognitive Impairment and other types of dementia.

Estimating annualized earthquake losses for the conterminous United States

Science.gov (United States)

Jaiswal, Kishor S.; Bausch, Douglas; Chen, Rui; Bouabid, Jawhar; Seligson, Hope

2015-01-01

We make use of the most recent National Seismic Hazard Maps (the years 2008 and 2014 cycles), updated census data on population, and economic exposure estimates of general building stock to quantify annualized earthquake loss (AEL) for the conterminous United States. The AEL analyses were performed using the Federal Emergency Management Agency's (FEMA) Hazus software, which facilitated a systematic comparison of the influence of the 2014 National Seismic Hazard Maps in terms of annualized loss estimates in different parts of the country. The losses from an individual earthquake could easily exceed many tens of billions of dollars, and the long-term averaged value of losses from all earthquakes within the conterminous U.S. has been estimated to be a few billion dollars per year. This study estimated nationwide losses to be approximately $4.5 billion per year (in 2012$), roughly 80% of which can be attributed to the States of California, Oregon and Washington. We document the change in estimated AELs arising solely from the change in the assumed hazard map. The change from the 2008 map to the 2014 map results in a 10 to 20% reduction in AELs for the highly seismic States of the Western United States, whereas the reduction is even more significant for Central and Eastern United States.

Using brain-computer interfaces and brain-state dependent stimulation as tools in cognitive neuroscience

Directory of Open Access Journals (Sweden)

Ole eJensen

2011-05-01

Full Text Available Large efforts are currently being made to develop and improve online analysis of brain activity which can be used e.g. for brain-computer interfacing (BCI. A BCI allows a subject to control a device by willfully changing his/her own brain activity. BCI therefore holds the promise as a tool for aiding the disabled and for augmenting human performance. While technical developments obviously are important, we will here argue that new insight gained from cognitive neuroscience can be used to identify signatures of neural activation which reliably can be modulated by the subject at will. This review will focus mainly on oscillatory activity in the alpha band which is strongly modulated by changes in covert attention. Besides developing BCIs for their traditional purpose, they might also be used as a research tool for cognitive neuroscience. There is currently a strong interest in how brain state fluctuations impact cognition. These state fluctuations are partly reflected by ongoing oscillatory activity. The functional role of the brain state can be investigated by introducing stimuli in real time to subjects depending on the actual state of the brain. This principle of brain-state dependent stimulation may also be used as a practical tool for augmenting human behavior. In conclusion, new approaches based on online analysis of ongoing brain activity are currently in rapid development. These approaches are amongst others informed by new insight gained from EEG/MEG studies in cognitive neuroscience and hold the promise of providing new ways for investigating the brain at work.

Steady-state evoked potentials possibilities for mental-state estimation

Science.gov (United States)

Junker, Andrew M.; Schnurer, John H.; Ingle, David F.; Downey, Craig W.

1988-01-01

The use of the human steady-state evoked potential (SSEP) as a possible measure of mental-state estimation is explored. A method for evoking a visual response to a sum-of-ten sine waves is presented. This approach provides simultaneous multiple frequency measurements of the human EEG to the evoking stimulus in terms of describing functions (gain and phase) and remnant spectra. Ways in which these quantities vary with the addition of performance tasks (manual tracking, grammatical reasoning, and decision making) are presented. Models of the describing function measures can be formulated using systems engineering technology. Relationships between model parameters and performance scores during manual tracking are discussed. Problems of unresponsiveness and lack of repeatability of subject responses are addressed in terms of a need for loop closure of the SSEP. A technique to achieve loop closure using a lock-in amplifier approach is presented. Results of a study designed to test the effectiveness of using feedback to consciously connect humans to their evoked response are presented. Findings indicate that conscious control of EEG is possible. Implications of these results in terms of secondary tasks for mental-state estimation and brain actuated control are addressed.

Artificial Neural Network Based State Estimators Integrated into Kalmtool

DEFF Research Database (Denmark)

Bayramoglu, Enis; Ravn, Ole; Poulsen, Niels Kjølstad

2012-01-01

In this paper we present a toolbox enabling easy evaluation and comparison of dierent ltering algorithms. The toolbox is called Kalmtool and is a set of MATLAB tools for state estimation of nonlinear systems. The toolbox now contains functions for Articial Neural Network Based State Estimation as...

Interference Tolerant Functional Near Infrared Spectrometer (fNIRS) for Cognitive State Monitoring

Data.gov (United States)

National Aeronautics and Space Administration — Measuring hemoglobin concentration changes in the brain with Functional Near Infrared Spectroscopy (fNIRS) is a promising technique for monitoring cognitive state...

Low Loss Tapered Fiber Waveguide Modulator for Crew Cognitive State Monitoring (CSM)

Data.gov (United States)

National Aeronautics and Space Administration — Many crew-related errors in aviation and astronautics are caused by hazardous cognitive states including overstress, disengagement, high fatigue and ineffective crew...

Sensitivity of fNIRS to cognitive state and load

Directory of Open Access Journals (Sweden)

Frank Anthony Fishburn

2014-02-01

Full Text Available Functional near-infrared spectroscopy (fNIRS is an emerging low-cost noninvasive neuroimaging technique that measures cortical bloodflow. While fNIRS has gained interest as a potential alternative to fMRI for use with clinical and pediatric populations, it remains unclear whether fNIRS has the necessary sensitivity to serve as a replacement for fMRI. The present study set out to examine whether fNIRS has the sensitivity to detect linear changes in activation and functional connectivity in response to cognitive load, and functional connectivity changes when transitioning from a task-free resting state to a task. Sixteen young adult subjects were scanned with a continuous-wave fNIRS system during a 10-minute resting-state scan followed by a letter n-back task with three load conditions. Five optical probes were placed over frontal and parietal cortices, covering bilateral dorsolateral PFC (dlPFC, bilateral ventrolateral PFC (vlPFC, frontopolar cortex (FP, and bilateral parietal cortex. Activation was found to scale linearly with working memory load in bilateral prefrontal cortex. Functional connectivity increased with increasing n-back loads for fronto-parietal, interhemispheric dlPFC, and local connections. Functional connectivity differed between the resting state scan and the n-back scan, with fronto-parietal connectivity greater during the n-back, and interhemispheric vlPFC connectivity greater during rest. These results demonstrate that fNIRS is sensitive to both cognitive load and state, suggesting that fNIRS is well-suited to explore the full complement of neuroimaging research questions and will serve as a viable alternative to fMRI.

Stated Preference Survey Estimating the Willingness to Pay ...

Science.gov (United States)

A national stated preference survey designed to elicit household willingness to pay for reductions in impinged and entrained fish at cooling water intake structures. To improve estimation of environmental benefits estimation

Multimodality Inferring of Human Cognitive States Based on Integration of Neuro-Fuzzy Network and Information Fusion Techniques

Directory of Open Access Journals (Sweden)

P. Bhattacharya

2007-11-01

Full Text Available To achieve an effective and safe operation on the machine system where the human interacts with the machine mutually, there is a need for the machine to understand the human state, especially cognitive state, when the human's operation task demands an intensive cognitive activity. Due to a well-known fact with the human being, a highly uncertain cognitive state and behavior as well as expressions or cues, the recent trend to infer the human state is to consider multimodality features of the human operator. In this paper, we present a method for multimodality inferring of human cognitive states by integrating neuro-fuzzy network and information fusion techniques. To demonstrate the effectiveness of this method, we take the driver fatigue detection as an example. The proposed method has, in particular, the following new features. First, human expressions are classified into four categories: (i casual or contextual feature, (ii contact feature, (iii contactless feature, and (iv performance feature. Second, the fuzzy neural network technique, in particular Takagi-Sugeno-Kang (TSK model, is employed to cope with uncertain behaviors. Third, the sensor fusion technique, in particular ordered weighted aggregation (OWA, is integrated with the TSK model in such a way that cues are taken as inputs to the TSK model, and then the outputs of the TSK are fused by the OWA which gives outputs corresponding to particular cognitive states under interest (e.g., fatigue. We call this method TSK-OWA. Validation of the TSK-OWA, performed in the Northeastern University vehicle drive simulator, has shown that the proposed method is promising to be a general tool for human cognitive state inferring and a special tool for the driver fatigue detection.

Power system dynamic state estimation using prediction based evolutionary technique

International Nuclear Information System (INIS)

Basetti, Vedik; Chandel, Ashwani K.; Chandel, Rajeevan

2016-01-01

In this paper, a new robust LWS (least winsorized square) estimator is proposed for dynamic state estimation of a power system. One of the main advantages of this estimator is that it has an inbuilt bad data rejection property and is less sensitive to bad data measurements. In the proposed approach, Brown's double exponential smoothing technique has been utilised for its reliable performance at the prediction step. The state estimation problem is solved as an optimisation problem using a new jDE-self adaptive differential evolution with prediction based population re-initialisation technique at the filtering step. This new stochastic search technique has been embedded with different state scenarios using the predicted state. The effectiveness of the proposed LWS technique is validated under different conditions, namely normal operation, bad data, sudden load change, and loss of transmission line conditions on three different IEEE test bus systems. The performance of the proposed approach is compared with the conventional extended Kalman filter. On the basis of various performance indices, the results thus obtained show that the proposed technique increases the accuracy and robustness of power system dynamic state estimation performance. - Highlights: • To estimate the states of the power system under dynamic environment. • The performance of the EKF method is degraded during anomaly conditions. • The proposed method remains robust towards anomalies. • The proposed method provides precise state estimates even in the presence of anomalies. • The results show that prediction accuracy is enhanced by using the proposed model.

State estimation for wave energy converters

Energy Technology Data Exchange (ETDEWEB)

Bacelli, Giorgio; Coe, Ryan Geoffrey

2017-04-01

This report gives a brief discussion and examples on the topic of state estimation for wave energy converters (WECs). These methods are intended for use to enable real-time closed loop control of WECs.

Automatic feedback on cognitive load and emotional state of traffic controllers

NARCIS (Netherlands)

Neerincx, M.A.; Harbers, M.; Lim, D.; Tas, V. van der

2014-01-01

Workload research in command, information and process-control centers, resulted in a modular and formal Cognitive Load and Emotional State (CLES) model with transparent and easy-to-modify classification and assessment techniques. The model distinguishes three representation and analysis layers with

An Integrated Theory of Prospective Time Interval Estimation: The Role of Cognition, Attention, and Learning

Science.gov (United States)

Taatgen, Niels A.; van Rijn, Hedderik; Anderson, John

2007-01-01

A theory of prospective time perception is introduced and incorporated as a module in an integrated theory of cognition, thereby extending existing theories and allowing predictions about attention and learning. First, a time perception module is established by fitting existing datasets (interval estimation and bisection and impact of secondary…

Resting-State Functional Connectivity Predicts Cognitive Impairment Related to Alzheimer's Disease

Directory of Open Access Journals (Sweden)

Qi Lin

2018-04-01

Full Text Available Resting-state functional connectivity (rs-FC is a promising neuromarker for cognitive decline in aging population, based on its ability to reveal functional differences associated with cognitive impairment across individuals, and because rs-fMRI may be less taxing for participants than task-based fMRI or neuropsychological tests. Here, we employ an approach that uses rs-FC to predict the Alzheimer's Disease Assessment Scale (11 items; ADAS11 scores, which measure overall cognitive functioning, in novel individuals. We applied this technique, connectome-based predictive modeling, to a heterogeneous sample of 59 subjects from the Alzheimer's Disease Neuroimaging Initiative, including normal aging, mild cognitive impairment, and AD subjects. First, we built linear regression models to predict ADAS11 scores from rs-FC measured with Pearson's r correlation. The positive network model tested with leave-one-out cross validation (LOOCV significantly predicted individual differences in cognitive function from rs-FC. In a second analysis, we considered other functional connectivity features, accordance and discordance, which disentangle the correlation and anticorrelation components of activity timecourses between brain areas. Using partial least square regression and LOOCV, we again built models to successfully predict ADAS11 scores in novel individuals. Our study provides promising evidence that rs-FC can reveal cognitive impairment in an aging population, although more development is needed for clinical application.

Joint estimation over multiple individuals improves behavioural state inference from animal movement data.

Science.gov (United States)

Jonsen, Ian

2016-02-08

State-space models provide a powerful way to scale up inference of movement behaviours from individuals to populations when the inference is made across multiple individuals. Here, I show how a joint estimation approach that assumes individuals share identical movement parameters can lead to improved inference of behavioural states associated with different movement processes. I use simulated movement paths with known behavioural states to compare estimation error between nonhierarchical and joint estimation formulations of an otherwise identical state-space model. Behavioural state estimation error was strongly affected by the degree of similarity between movement patterns characterising the behavioural states, with less error when movements were strongly dissimilar between states. The joint estimation model improved behavioural state estimation relative to the nonhierarchical model for simulated data with heavy-tailed Argos location errors. When applied to Argos telemetry datasets from 10 Weddell seals, the nonhierarchical model estimated highly uncertain behavioural state switching probabilities for most individuals whereas the joint estimation model yielded substantially less uncertainty. The joint estimation model better resolved the behavioural state sequences across all seals. Hierarchical or joint estimation models should be the preferred choice for estimating behavioural states from animal movement data, especially when location data are error-prone.

Actions States and Communities Can Take to Address Cognitive Health

Centers for Disease Control (CDC) Podcasts

2014-06-09

In this podcast, CDC’s Dr. Lynda Anderson highlights the important roles that states and communities can play in addressing cognitive health as part of overall health.  Created: 6/9/2014 by National Center for Chronic Disease Prevention and Health Promotion (NCCDPHP).   Date Released: 6/9/2014.

Maximum profile likelihood estimation of differential equation parameters through model based smoothing state estimates.

Science.gov (United States)

Campbell, D A; Chkrebtii, O

2013-12-01

Statistical inference for biochemical models often faces a variety of characteristic challenges. In this paper we examine state and parameter estimation for the JAK-STAT intracellular signalling mechanism, which exemplifies the implementation intricacies common in many biochemical inference problems. We introduce an extension to the Generalized Smoothing approach for estimating delay differential equation models, addressing selection of complexity parameters, choice of the basis system, and appropriate optimization strategies. Motivated by the JAK-STAT system, we further extend the generalized smoothing approach to consider a nonlinear observation process with additional unknown parameters, and highlight how the approach handles unobserved states and unevenly spaced observations. The methodology developed is generally applicable to problems of estimation for differential equation models with delays, unobserved states, nonlinear observation processes, and partially observed histories. Crown Copyright © 2013. Published by Elsevier Inc. All rights reserved.

Event-Based $H_\\infty $ State Estimation for Time-Varying Stochastic Dynamical Networks With State- and Disturbance-Dependent Noises.

Science.gov (United States)

Sheng, Li; Wang, Zidong; Zou, Lei; Alsaadi, Fuad E

2017-10-01

In this paper, the event-based finite-horizon H ∞ state estimation problem is investigated for a class of discrete time-varying stochastic dynamical networks with state- and disturbance-dependent noises [also called (x,v) -dependent noises]. An event-triggered scheme is proposed to decrease the frequency of the data transmission between the sensors and the estimator, where the signal is transmitted only when certain conditions are satisfied. The purpose of the problem addressed is to design a time-varying state estimator in order to estimate the network states through available output measurements. By employing the completing-the-square technique and the stochastic analysis approach, sufficient conditions are established to ensure that the error dynamics of the state estimation satisfies a prescribed H ∞ performance constraint over a finite horizon. The desired estimator parameters can be designed via solving coupled backward recursive Riccati difference equations. Finally, a numerical example is exploited to demonstrate the effectiveness of the developed state estimation scheme.

Steady State Visual Evoked Potential Based Brain-Computer Interface for Cognitive Assessment

DEFF Research Database (Denmark)

Westergren, Nicolai; Bendtsen, Rasmus L.; Kjær, Troels W.

2016-01-01

decline is important. Cognitive decline may be detected using fullyautomated computerized assessment. Such systems will provide inexpensive and widely available screenings of cognitive ability. The aim of this pilot study is to develop a real time steady state visual evoked potential (SSVEP) based brain-computer...... interface (BCI) for neurological cognitive assessment. It is intended for use by patients who suffer from diseases impairing their motor skills, but are still able to control their gaze. Results are based on 11 healthy test subjects. The system performance have an average accuracy of 100% ± 0%. The test...... subjects achieved an information transfer rate (ITR) of 14:64 bits/min ± 7:63 bits=min and a subject test performance of 47:22% ± 34:10%. This study suggests that BCI may be applicable in practice as a computerized cognitive assessment tool. However, many improvements are required for the system...

Vision Aided State Estimation for Helicopter Slung Load System

DEFF Research Database (Denmark)

Bisgaard, Morten; Bendtsen, Jan Dimon; la Cour-Harbo, Anders

2007-01-01

This paper presents the design and verification of a state estimator for a helicopter based slung load system. The estimator is designed to augment the IMU driven estimator found in many helicopter UAV s and uses vision based updates only. The process model used for the estimator is a simple 4...

Guidelines for cognitive behavioral training within doctoral psychology programs in the United States: report of the Inter-organizational Task Force on Cognitive and Behavioral Psychology Doctoral Education.

Science.gov (United States)

Klepac, Robert K; Ronan, George F; Andrasik, Frank; Arnold, Kevin D; Belar, Cynthia D; Berry, Sharon L; Christofff, Karen A; Craighead, Linda W; Dougher, Michael J; Dowd, E Thomas; Herbert, James D; McFarr, Lynn M; Rizvi, Shireen L; Sauer, Eric M; Strauman, Timothy J

2012-12-01

The Association for Behavioral and Cognitive Therapies initiated an interorganizational task force to develop guidelines for integrated education and training in cognitive and behavioral psychology at the doctoral level in the United States. Fifteen task force members representing 16 professional associations participated in a year-long series of conferences, and developed a consensus on optimal doctoral education and training in cognitive and behavioral psychology. The recommendations assume solid foundational training that is typical within applied psychology areas such as clinical and counseling psychology programs located in the United States. This article details the background, assumptions, and resulting recommendations specific to doctoral education and training in cognitive and behavioral psychology, including competencies expected in the areas of ethics, research, and practice. Copyright © 2012. Published by Elsevier Ltd.

Estimation of pump operational state with model-based methods

International Nuclear Information System (INIS)

Ahonen, Tero; Tamminen, Jussi; Ahola, Jero; Viholainen, Juha; Aranto, Niina; Kestilae, Juha

2010-01-01

Pumps are widely used in industry, and they account for 20% of the industrial electricity consumption. Since the speed variation is often the most energy-efficient method to control the head and flow rate of a centrifugal pump, frequency converters are used with induction motor-driven pumps. Although a frequency converter can estimate the operational state of an induction motor without external measurements, the state of a centrifugal pump or other load machine is not typically considered. The pump is, however, usually controlled on the basis of the required flow rate or output pressure. As the pump operational state can be estimated with a general model having adjustable parameters, external flow rate or pressure measurements are not necessary to determine the pump flow rate or output pressure. Hence, external measurements could be replaced with an adjustable model for the pump that uses estimates of the motor operational state. Besides control purposes, modelling the pump operation can provide useful information for energy auditing and optimization purposes. In this paper, two model-based methods for pump operation estimation are presented. Factors affecting the accuracy of the estimation methods are analyzed. The applicability of the methods is verified by laboratory measurements and tests in two pilot installations. Test results indicate that the estimation methods can be applied to the analysis and control of pump operation. The accuracy of the methods is sufficient for auditing purposes, and the methods can inform the user if the pump is driven inefficiently.

Identifying the most efficient items from the Mini-Mental State Examination for cognitive function assessment in older Taiwanese patients.

Science.gov (United States)

Lou, Meei-Fang; Dai, Yu-Tzu; Huang, Guey-Shiun; Yu, Po-Jui

2007-03-01

The purpose of the study was to identify the most efficient items from the Mini-Mental State Examination for assessment of cognitive function. The Mini-Mental State Examination is the most frequently used cognitive screening instrument. However, the Mini-Mental State Examination has been criticized for insensitivity to mild cognitive dysfunction, limited memory assessment and variability in level of difficulty of the individual items. This study used secondary data analysis. Item response theory two-parameter model was used to analyse the data from the admission assessment of mental status by the Mini-Mental State Examination for 801 patients. By using item response analysis, 16 items were selected from the original 30-item Mini-Mental State Examination. The 16 items included mainly the measures of orientation, recall and attention and calculation. The internal consistency of the 16-item Mini-Mental State Examination was 0.84. The proposed new cut-off point for the 16-item Mini-Mental State Examination was 11. The correct classification rate was 0.94, the sensitivity was 100% and the specificity was 97.4%, when compared with the original 30-item Mini-Mental State Examination from the cut-off point of 24. This new cut-off point was determined for the purpose of over-identifying patients at risk so as to ensure early detection of and prevention from the onset of cognitive disturbance. Only a few items are needed to describe the subject's cognitive status. Using item response theory analysis, the study found that the Mini-Mental State Examination could be simplified. Deleting the items with less variation makes this assessment tool not only shorter, easier to administer and less strenuous for respondents, but also enables one to maintain validity as a cognitive function test for clinical setting.

Distributed state estimation for multi-agent based active distribution networks

NARCIS (Netherlands)

Nguyen, H.P.; Kling, W.L.

2010-01-01

Along with the large-scale implementation of distributed generators, the current distribution networks have changed gradually from passive to active operation. State estimation plays a vital role to facilitate this transition. In this paper, a suitable state estimation method for the active network

Lagged Associations of Metropolitan Statistical Area- and State-Level Income Inequality with Cognitive Function: The Health and Retirement Study.

Science.gov (United States)

Kim, Daniel; Griffin, Beth Ann; Kabeto, Mohammed; Escarce, José; Langa, Kenneth M; Shih, Regina A

2016-01-01

Much variation in individual-level cognitive function in late life remains unexplained, with little exploration of area-level/contextual factors to date. Income inequality is a contextual factor that may plausibly influence cognitive function. In a nationally-representative cohort of older Americans from the Health and Retirement Study, we examined state- and metropolitan statistical area (MSA)-level income inequality as predictors of individual-level cognitive function measured by the 27-point Telephone Interview for Cognitive Status (TICS-m) scale. We modeled latency periods of 8-20 years, and controlled for state-/metropolitan statistical area (MSA)-level and individual-level factors. Higher MSA-level income inequality predicted lower cognitive function 16-18 years later. Using a 16-year lag, living in a MSA in the highest income inequality quartile predicted a 0.9-point lower TICS-m score (β = -0.86; 95% CI = -1.41, -0.31), roughly equivalent to the magnitude associated with five years of aging. We observed no associations for state-level income inequality. The findings were robust to sensitivity analyses using propensity score methods. Among older Americans, MSA-level income inequality appears to influence cognitive function nearly two decades later. Policies reducing income inequality levels within cities may help address the growing burden of declining cognitive function among older populations within the United States.

Information-geometric measures estimate neural interactions during oscillatory brain states

Directory of Open Access Journals (Sweden)

Yimin eNie

2014-02-01

Full Text Available The characterization of functional network structures among multiple neurons is essential to understanding neural information processing. Information geometry (IG, a theory developed for investigating a space of probability distributions has recently been applied to spike-train analysis and has provided robust estimations of neural interactions. Although neural firing in the equilibrium state is often assumed in these studies, in reality, neural activity is non-stationary. The brain exhibits various oscillations depending on cognitive demands or when an animal is asleep. Therefore, the investigation of the IG measures during oscillatory network states is important for testing how the IG method can be applied to real neural data. Using model networks of binary neurons or more realistic spiking neurons, we studied how the single- and pairwise-IG measures were influenced by oscillatory neural activity. Two general oscillatory mechanisms, externally driven oscillations and internally induced oscillations, were considered. In both mechanisms, we found that the single-IG measure was linearly related to the magnitude of the external input, and that the pairwise-IG measure was linearly related to the sum of connection strengths between two neurons. We also observed that the pairwise-IG measure was not dependent on the oscillation frequency. These results are consistent with the previous findings that were obtained under the equilibrium conditions. Therefore, we demonstrate that the IG method provides useful insights into neural interactions under the oscillatory condition that can often be observed in the real brain.

A Parameter Estimation Method for Dynamic Computational Cognitive Models

NARCIS (Netherlands)

Thilakarathne, D.J.

2015-01-01

A dynamic computational cognitive model can be used to explore a selected complex cognitive phenomenon by providing some features or patterns over time. More specifically, it can be used to simulate, analyse and explain the behaviour of such a cognitive phenomenon. It generates output data in the

How does cognition evolve? Phylogenetic comparative psychology

Science.gov (United States)

Matthews, Luke J.; Hare, Brian A.; Nunn, Charles L.; Anderson, Rindy C.; Aureli, Filippo; Brannon, Elizabeth M.; Call, Josep; Drea, Christine M.; Emery, Nathan J.; Haun, Daniel B. M.; Herrmann, Esther; Jacobs, Lucia F.; Platt, Michael L.; Rosati, Alexandra G.; Sandel, Aaron A.; Schroepfer, Kara K.; Seed, Amanda M.; Tan, Jingzhi; van Schaik, Carel P.; Wobber, Victoria

2014-01-01

Now more than ever animal studies have the potential to test hypotheses regarding how cognition evolves. Comparative psychologists have developed new techniques to probe the cognitive mechanisms underlying animal behavior, and they have become increasingly skillful at adapting methodologies to test multiple species. Meanwhile, evolutionary biologists have generated quantitative approaches to investigate the phylogenetic distribution and function of phenotypic traits, including cognition. In particular, phylogenetic methods can quantitatively (1) test whether specific cognitive abilities are correlated with life history (e.g., lifespan), morphology (e.g., brain size), or socio-ecological variables (e.g., social system), (2) measure how strongly phylogenetic relatedness predicts the distribution of cognitive skills across species, and (3) estimate the ancestral state of a given cognitive trait using measures of cognitive performance from extant species. Phylogenetic methods can also be used to guide the selection of species comparisons that offer the strongest tests of a priori predictions of cognitive evolutionary hypotheses (i.e., phylogenetic targeting). Here, we explain how an integration of comparative psychology and evolutionary biology will answer a host of questions regarding the phylogenetic distribution and history of cognitive traits, as well as the evolutionary processes that drove their evolution. PMID:21927850

How does cognition evolve? Phylogenetic comparative psychology.

Science.gov (United States)

MacLean, Evan L; Matthews, Luke J; Hare, Brian A; Nunn, Charles L; Anderson, Rindy C; Aureli, Filippo; Brannon, Elizabeth M; Call, Josep; Drea, Christine M; Emery, Nathan J; Haun, Daniel B M; Herrmann, Esther; Jacobs, Lucia F; Platt, Michael L; Rosati, Alexandra G; Sandel, Aaron A; Schroepfer, Kara K; Seed, Amanda M; Tan, Jingzhi; van Schaik, Carel P; Wobber, Victoria

2012-03-01

Now more than ever animal studies have the potential to test hypotheses regarding how cognition evolves. Comparative psychologists have developed new techniques to probe the cognitive mechanisms underlying animal behavior, and they have become increasingly skillful at adapting methodologies to test multiple species. Meanwhile, evolutionary biologists have generated quantitative approaches to investigate the phylogenetic distribution and function of phenotypic traits, including cognition. In particular, phylogenetic methods can quantitatively (1) test whether specific cognitive abilities are correlated with life history (e.g., lifespan), morphology (e.g., brain size), or socio-ecological variables (e.g., social system), (2) measure how strongly phylogenetic relatedness predicts the distribution of cognitive skills across species, and (3) estimate the ancestral state of a given cognitive trait using measures of cognitive performance from extant species. Phylogenetic methods can also be used to guide the selection of species comparisons that offer the strongest tests of a priori predictions of cognitive evolutionary hypotheses (i.e., phylogenetic targeting). Here, we explain how an integration of comparative psychology and evolutionary biology will answer a host of questions regarding the phylogenetic distribution and history of cognitive traits, as well as the evolutionary processes that drove their evolution.

Online Synchrophasor-Based Dynamic State Estimation using Real-Time Digital Simulator

DEFF Research Database (Denmark)

Khazraj, Hesam; Adewole, Adeyemi Charles; Udaya, Annakkage

2018-01-01

Dynamic state estimation is a very important control center application used in the dynamic monitoring of state variables. This paper presents and validates a time-synchronized phasor measurement unit (PMU)-based for dynamic state estimation by unscented Kalman filter (UKF) method using the real-...... using the RTDS (real-time digital simulator). The dynamic state variables of multi-machine systems are monitored and measured for the study on the transient behavior of power systems.......Dynamic state estimation is a very important control center application used in the dynamic monitoring of state variables. This paper presents and validates a time-synchronized phasor measurement unit (PMU)-based for dynamic state estimation by unscented Kalman filter (UKF) method using the real......-time digital simulator (RTDS). The dynamic state variables of the system are the rotor angle and speed of the generators. The performance of the UKF method is tested with PMU measurements as inputs using the IEEE 14-bus test system. This test system was modeled in the RSCAD software and tested in real time...

Geometry of perturbed Gaussian states and quantum estimation

International Nuclear Information System (INIS)

Genoni, Marco G; Giorda, Paolo; Paris, Matteo G A

2011-01-01

We address the non-Gaussianity (nG) of states obtained by weakly perturbing a Gaussian state and investigate the relationships with quantum estimation. For classical perturbations, i.e. perturbations to eigenvalues, we found that the nG of the perturbed state may be written as the quantum Fisher information (QFI) distance minus a term depending on the infinitesimal energy change, i.e. it provides a lower bound to statistical distinguishability. Upon moving on isoenergetic surfaces in a neighbourhood of a Gaussian state, nG thus coincides with a proper distance in the Hilbert space and exactly quantifies the statistical distinguishability of the perturbations. On the other hand, for perturbations leaving the covariance matrix unperturbed, we show that nG provides an upper bound to the QFI. Our results show that the geometry of non-Gaussian states in the neighbourhood of a Gaussian state is definitely not trivial and cannot be subsumed by a differential structure. Nevertheless, the analysis of perturbations to a Gaussian state reveals that nG may be a resource for quantum estimation. The nG of specific families of perturbed Gaussian states is analysed in some detail with the aim of finding the maximally non-Gaussian state obtainable from a given Gaussian one. (fast track communication)

Fidelity estimation between two finite ensembles of unknown pure equatorial qubit states

Energy Technology Data Exchange (ETDEWEB)

Siomau, Michael, E-mail: siomau@physi.uni-heidelberg.de [Physikalisches Institut, Heidelberg Universitaet, D-69120 Heidelberg (Germany); Department of Theoretical Physics, Belarussian State University, 220030 Minsk (Belarus)

2011-09-05

Suppose, we are given two finite ensembles of pure qubit states, so that the qubits in each ensemble are prepared in identical (but unknown for us) states lying on the equator of the Bloch sphere. What is the best strategy to estimate fidelity between these two finite ensembles of qubit states? We discuss three possible strategies for the fidelity estimation. We show that the best strategy includes two stages: a specific unitary transformation on two ensembles and state estimation of the output states of this transformation. -- Highlights: → We search for the best strategy for the fidelity estimation. → A measurement-based, a cloning-based and a unified strategies are considered. → The last strategy includes a specific unitary transformation and state estimation. → The unified strategy is shown to be the best among the three.

[Effects of cognitive state on balance disturbances and gait disorders in institutionalised elderly].

Science.gov (United States)

Díaz-Pelegrina, Ana; Cabrera-Martos, Irene; López-Torres, Isabel; Rodríguez-Torres, Janet; Valenza, Marie Carmen

2016-01-01

Ageing has been linked to a high prevalence of cognitive impairment, which, in turn, has been related to balance disturbances and gait disorders. The aim of this study was to identify whether there are differences between subjects with and without cognitive impairment regarding the quality of gait and balance. An observational study was conducted on institutionalised people older than 65 years (n=82). Gait and balance was evaluated after the assessment of cognitive impairment using the Mini-Mental State Examination (MMSE). Single and dual tests were used including, the 6-minute walking, stride length, and gait speed. Timed Up and Go tests were also used to evaluate balance. The participants were divided into three groups: 28 subjects in the group without cognitive impairment (MMSE≥27), 29 subjects with mild (27cognitive impairment (MMSE<21). Gait assessment showed significant between-groups differences in all the variables (P<.05). The variables assessing balance also showed significantly worse values in those groups with cognitive impairment. The severity of cognitive impairment is related to impaired balance and gait, thus the clinical monitoring of these variables in population at risk is needed. Copyright © 2015 SEGG. Published by Elsevier Espana. All rights reserved.

Guidelines for preparation of State water-use estimates for 2015

Science.gov (United States)

Bradley, Michael W.

2017-05-01

The U.S. Geological Survey (USGS) has estimated the use of water in the United States at 5-year intervals since 1950. This report describes the water-use categories and data elements used for the national water-use compilation conducted as part of the USGS National Water-Use Science Project. The report identifies sources of water-use information, provides standard methods and techniques for estimating water use at the county level, and outlines steps for preparing documentation for the United States, the District of Columbia, Puerto Rico, and the U.S. Virgin Islands.As part of this USGS program to document water use on a national scale, estimates of water withdrawals for the categories of public supply, self-supplied domestic, industrial, irrigation, and thermoelectric power are prepared for each county in each State, District, or territory by using the guidelines in this report. County estimates of water withdrawals for aquaculture, livestock, and mining are prepared for each State by using a county-based national model, although water-use programs in each State or Water Science Center have the option of producing independent county estimates of water withdrawals for these categories. Estimates of water withdrawals and consumptive use for thermoelectric power will be aggregated to the county level for each State by the national project; additionally, irrigation consumptive use at the county level will also be provided, although study chiefs in each State have the option of producing independent county estimates of water withdrawals and consumptive use for these categories.Estimates of deliveries of water from public supplies for domestic use by county also will be prepared for each State. As a result, total domestic water use can be determined for each State by combining self-supplied domestic withdrawals and public-supplied domestic deliveries. Fresh groundwater and surface-water estimates will be prepared for all categories of use, and saline groundwater and

Estimating GSP and labor productivity by state

OpenAIRE

Paul W. Bauer; Yoonsoo Lee

2006-01-01

In gauging the health of state economies, arguably the two most important series to track are employment and output. While employment by state is available about three weeks after the end of a month, data on output, as measured by Gross State Product (GSP), are only available annually and with a significant lag. This Policy Discussion Paper details how more current estimates of GSP can be generated using U.S. Gross Domestic Product and personal income along with individual states’ personal in...

State and parameter estimation in biotechnical batch reactors

NARCIS (Netherlands)

Keesman, K.J.

2000-01-01

In this paper the problem of state and parameter estimation in biotechnical batch reactors is considered. Models describing the biotechnical process behaviour are usually nonlinear with time-varying parameters. Hence, the resulting large dimensions of the augmented state vector, roughly > 7, in

An open source framework for tracking and state estimation ('Stone Soup')

Science.gov (United States)

Thomas, Paul A.; Barr, Jordi; Balaji, Bhashyam; White, Kruger

2017-05-01

The ability to detect and unambiguously follow all moving entities in a state-space is important in multiple domains both in defence (e.g. air surveillance, maritime situational awareness, ground moving target indication) and the civil sphere (e.g. astronomy, biology, epidemiology, dispersion modelling). However, tracking and state estimation researchers and practitioners have difficulties recreating state-of-the-art algorithms in order to benchmark their own work. Furthermore, system developers need to assess which algorithms meet operational requirements objectively and exhaustively rather than intuitively or driven by personal favourites. We have therefore commenced the development of a collaborative initiative to create an open source framework for production, demonstration and evaluation of Tracking and State Estimation algorithms. The initiative will develop a (MIT-licensed) software platform for researchers and practitioners to test, verify and benchmark a variety of multi-sensor and multi-object state estimation algorithms. The initiative is supported by four defence laboratories, who will contribute to the development effort for the framework. The tracking and state estimation community will derive significant benefits from this work, including: access to repositories of verified and validated tracking and state estimation algorithms, a framework for the evaluation of multiple algorithms, standardisation of interfaces and access to challenging data sets. Keywords: Tracking,

Distributed State Estimation Using a Modified Partitioned Moving Horizon Strategy for Power Systems.

Science.gov (United States)

Chen, Tengpeng; Foo, Yi Shyh Eddy; Ling, K V; Chen, Xuebing

2017-10-11

In this paper, a distributed state estimation method based on moving horizon estimation (MHE) is proposed for the large-scale power system state estimation. The proposed method partitions the power systems into several local areas with non-overlapping states. Unlike the centralized approach where all measurements are sent to a processing center, the proposed method distributes the state estimation task to the local processing centers where local measurements are collected. Inspired by the partitioned moving horizon estimation (PMHE) algorithm, each local area solves a smaller optimization problem to estimate its own local states by using local measurements and estimated results from its neighboring areas. In contrast with PMHE, the error from the process model is ignored in our method. The proposed modified PMHE (mPMHE) approach can also take constraints on states into account during the optimization process such that the influence of the outliers can be further mitigated. Simulation results on the IEEE 14-bus and 118-bus systems verify that our method achieves comparable state estimation accuracy but with a significant reduction in the overall computation load.

OCCIPITAL SOURCES OF RESTING STATE ALPHA RHYTHMS ARE RELATED TO LOCAL GRAY MATTER DENSITY IN SUBJECTS WITH AMNESIC MILD COGNITIVE IMPAIRMENT AND ALZHEIMER’S DISEASE

Science.gov (United States)

Claudio, Babiloni; Claudio, Del Percio; Marina, Boccardi; Roberta, Lizio; Susanna, Lopez; Filippo, Carducci; Nicola, Marzano; Andrea, Soricelli; Raffaele, Ferri; Ivano, Triggiani Antonio; Annapaola, Prestia; Serenella, Salinari; Rasser Paul, E; Erol, Basar; Francesco, Famà; Flavio, Nobili; Görsev, Yener; Durusu, Emek-Savaş Derya; Gesualdo, Loreto; Ciro, Mundi; Thompson Paul, M; Rossini Paolo, M.; Frisoni Giovanni, B

2014-01-01

Occipital sources of resting state electroencephalographic (EEG) alpha rhythms are abnormal, at the group level, in patients with amnesic mild cognitive impairment (MCI) and Alzheimer’s disease (AD). Here we evaluated the hypothesis that amplitude of these occipital sources is related to neurodegeneration in occipital lobe as measured by magnetic resonance imaging (MRI). Resting-state eyes-closed EEG rhythms were recorded in 45 healthy elderly (Nold), 100 MCI, and 90 AD subjects. Neurodegeneration of occipital lobe was indexed by weighted averages of gray matter density (GMD), estimated from structural MRIs. EEG rhythms of interest were alpha 1 (8–10.5 Hz) and alpha 2 (10.5–13 Hz). EEG cortical sources were estimated by low resolution brain electromagnetic tomography (LORETA). Results showed a positive correlation between occipital GMD and amplitude of occipital alpha 1 sources in Nold, MCI and AD subjects as a whole group (r=0.3, p=0.000004, N=235). Furthermore, there was a positive correlation between amplitude of occipital alpha 1 sources and cognitive status as revealed by Mini Mental State Evaluation (MMSE) score across all subjects (r=0.38, p=0.000001, N=235). Finally, amplitude of occipital alpha 1 sources allowed a moderate classification of individual Nold and AD subjects (sensitivity: 87.8%; specificity: 66.7%; area under the Receiver Operating Characteristic (ROC) curve: 0.81). These results suggest that the amplitude of occipital sources of resting state alpha rhythms is related to AD neurodegeneration in occipital lobe along pathological aging. PMID:25442118

Inline state of health estimation of lithium-ion batteries using state of charge calculation

Science.gov (United States)

Sepasi, Saeed; Ghorbani, Reza; Liaw, Bor Yann

2015-12-01

The determination of state-of-health (SOH) and state-of-charge (SOC) is challenging and remains as an active research area in academia and industry due to its importance for Li-ion battery applications. The estimation process poses more challenges after substantial battery aging. This paper presents an inline SOH and SOC estimation method for Li-ion battery packs, specifically for those based on LiFePO4 chemistry. This new hybridized SOC and SOH estimator can be used for battery packs. Inline estimated model parameters were used in a compounded SOC + SOH estimator consisting of the SOC calculation based on coulomb counting method as an expedient approach and an SOH observer using an extended Kalman filter (EKF) technique for calibrating the estimates from the coulomb counting method. The algorithm's low SOC and SOH estimation error, fast response time, and less-demanding computational requirement make it practical for on-board estimations. The simulation and experimental results, along with the test bed structure, are presented to validate the proposed methodology on a single cell and a 3S1P LiFePO4 battery pack.

Using the Oxford Cognitive Screen to Detect Cognitive Impairment in Stroke Patients: A Comparison with the Mini-Mental State Examination.

Science.gov (United States)

Mancuso, Mauro; Demeyere, Nele; Abbruzzese, Laura; Damora, Alessio; Varalta, Valentina; Pirrotta, Fabio; Antonucci, Gabriella; Matano, Alessandro; Caputo, Marina; Caruso, Maria Giovanna; Pontiggia, Giovanna Teresa; Coccia, Michela; Ciancarelli, Irene; Zoccolotti, Pierluigi

2018-01-01

The Oxford Cognitive Screen (OCS) was recently developed with the aim of describing the cognitive deficits after stroke. The scale consists of 10 tasks encompassing five cognitive domains: attention and executive function, language, memory, number processing, and praxis. OCS was devised to be inclusive and un-confounded by aphasia and neglect. As such, it may have a greater potential to be informative on stroke cognitive deficits of widely used instruments, such as the Mini-Mental State Examination (MMSE) or the Montreal Cognitive Assessment, which were originally devised for demented patients. The present study compared the OCS with the MMSE with regards to their ability to detect cognitive impairments post-stroke. We further aimed to examine performance on the OCS as a function of subtypes of cerebral infarction and clinical severity. 325 first stroke patients were consecutively enrolled in the study over a 9-month period. The OCS and MMSE, as well as the Bamford classification and NIHSS, were given according to standard procedures. About a third of patients (35.3%) had a performance lower than the cutoff (cognitive domains of the OCS. Using the MMSE as a standard of clinical practice, the comparative sensitivity of OCS was 100%. Out of the 208 patients with normal MMSE performance 180 showed impaired performance in at least one domain of the OCS. The discrepancy between OCS and MMSE was particularly strong for patients with milder strokes. As for subtypes of cerebral infarction, fewer patients demonstrated widespread impairments in the OCS in the Posterior Circulation Infarcts category than in the other categories. Overall, the results showed a much higher incidence of cognitive impairment with the OCS than with the MMSE and demonstrated no false negatives for OCS vs MMSE. It is concluded that OCS is a sensitive screen tool for cognitive deficits after stroke. In particular, the OCS detects high incidences of stroke-specific cognitive impairments, not detected

Metric Indices for Performance Evaluation of a Mixed Measurement based State Estimator

Directory of Open Access Journals (Sweden)

Paula Sofia Vide

2013-01-01

Full Text Available With the development of synchronized phasor measurement technology in recent years, it gains great interest the use of PMU measurements to improve state estimation performances due to their synchronized characteristics and high data transmission speed. The ability of the Phasor Measurement Units (PMU to directly measure the system state is a key over SCADA measurement system. PMU measurements are superior to the conventional SCADA measurements in terms of resolution and accuracy. Since the majority of measurements in existing estimators are from conventional SCADA measurement system, it is hard to be fully replaced by PMUs in the near future so state estimators including both phasor and conventional SCADA measurements are being considered. In this paper, a mixed measurement (SCADA and PMU measurements state estimator is proposed. Several useful measures for evaluating various aspects of the performance of the mixed measurement state estimator are proposed and explained. State Estimator validity, performance and characteristics of the results on IEEE 14 bus test system and IEEE 30 bus test system are presented.

Application of radial basis neural network for state estimation of ...

African Journals Online (AJOL)

An original application of radial basis function (RBF) neural network for power system state estimation is proposed in this paper. The property of massive parallelism of neural networks is employed for this. The application of RBF neural network for state estimation is investigated by testing its applicability on a IEEE 14 bus ...

Introduction to State Estimation of High-Rate System Dynamics.

Science.gov (United States)

Hong, Jonathan; Laflamme, Simon; Dodson, Jacob; Joyce, Bryan

2018-01-13

Engineering systems experiencing high-rate dynamic events, including airbags, debris detection, and active blast protection systems, could benefit from real-time observability for enhanced performance. However, the task of high-rate state estimation is challenging, in particular for real-time applications where the rate of the observer's convergence needs to be in the microsecond range. This paper identifies the challenges of state estimation of high-rate systems and discusses the fundamental characteristics of high-rate systems. A survey of applications and methods for estimators that have the potential to produce accurate estimations for a complex system experiencing highly dynamic events is presented. It is argued that adaptive observers are important to this research. In particular, adaptive data-driven observers are advantageous due to their adaptability and lack of dependence on the system model.

Transitions to mild cognitive impairments, dementia, and death: findings from the Nun Study.

Science.gov (United States)

Tyas, Suzanne L; Salazar, Juan Carlos; Snowdon, David A; Desrosiers, Mark F; Riley, Kathryn P; Mendiondo, Marta S; Kryscio, Richard J

2007-06-01

The potential of early interventions for dementia has increased interest in cognitive impairments less severe than dementia. However, predictors of the trajectory from intact cognition to dementia have not yet been clearly identified. The purpose of this study was to determine whether known risk factors for dementia increased the risk of mild cognitive impairments or progression from mild cognitive impairments to dementia. A polytomous logistic regression model was used, with parameters governing transitions within transient states (intact cognition, mild cognitive impairments, global impairment) estimated separately from parameters governing the transition from transient to absorbing state (dementia or death). Analyses were based on seven annual examinations (1991-2002) of 470 Nun Study participants aged > or = 75 years at baseline and living in the United States. Odds of developing dementia increased with age primarily for those with low educational levels. In these women, presence of an apolipoprotein E gene *E4 allele increased the odds more than fourfold by age 95 years. Age, education, and the apolipoprotein E gene were all significantly associated with mild cognitive impairments. Only age, however, was associated with progression to dementia. Thus, risk factors for dementia may operate primarily by predisposing individuals to develop mild cognitive impairments; subsequent progression to dementia then depends on only time and competing mortality.

White matter microstructural damage in small vessel disease is associated with Montreal cognitive assessment but not with mini mental state examination performances: vascular mild cognitive impairment Tuscany study.

Science.gov (United States)

Pasi, Marco; Salvadori, Emilia; Poggesi, Anna; Ciolli, Laura; Del Bene, Alessandra; Marini, Sandro; Nannucci, Serena; Pescini, Francesca; Valenti, Raffaella; Ginestroni, Andrea; Toschi, Nicola; Diciotti, Stefano; Mascalchi, Mario; Inzitari, Domenico; Pantoni, Leonardo

2015-01-01

Montreal Cognitive Assessment (MoCA) has been proposed as a screening tool in vascular cognitive impairment. Diffusion tensor imaging is sensitive to white matter microstructural damage. We investigated if diffusion tensor imaging-derived indices are more strongly associated with performances on MoCA or on the widely used mini mental state examination in patients with mild cognitive impairment and small vessel disease. Mild cognitive impairment patients with moderate/severe degrees of white matter hyperintensities on MRI were enrolled. Lacunar infarcts, cortical atrophy, medial temporal lobe atrophy and median values of mean diffusivity and fractional anisotropy of the cerebral white matter were studied and correlated with cognitive tests performances. Seventy-six patients (mean age 75.1±6.8 years, mean years of education 8.0±4.3) were assessed. In univariate analyses, a significant association of both MoCA and mini mental state examination scores with age, education, cortical atrophy, and medial temporal lobe atrophy was found, whereas mean diffusivity and fractional anisotropy were associated with MoCA. In partial correlation analyses, adjusting for all demographic and neuroimaging variables, both mean diffusivity and fractional anisotropy were associated only with MoCA (mean diffusivity: r= -0.275, P=0.023; fractional anisotropy: r=0.246, P=0.043). In patients with mild cognitive impairment and small vessel disease, diffusion tensor imaging-measured white matter microstructural damage is more related to MoCA than mini mental state examination performances. MoCA is suited for the cognitive screening of patients with small vessel disease. © 2014 American Heart Association, Inc.

Experimental demonstration of a cognitive quality of transmission estimator for optical communication systems

DEFF Research Database (Denmark)

Caballero Jambrina, Antonio; Aguado, Juan Carlos; Borkowski, Robert

2012-01-01

small and not optimized underlying knowledge base, it achieves between 79% and 98.7% successful classifications based on the error vector magnitude (EVM) parameter, and approximately 100% when the classification is based on the optical signal to noise ratio (OSNR).......The impact of physical layer impairments in optical network design and operation has received significant attention in the last years, thereby requiring estimation techniques to predict the quality of transmission (QoT) of optical connections before being established. In this paper, we report...... on the experimental demonstration of a case-based reasoning (CBR) technique to predict whether optical channels fulfill QoT requirements, thus supporting impairment-aware networking. The validation of the cognitive QoT estimator is performed in a WDM 80 Gb/s PDM-QPSK testbed, and we demonstrate that even with a very...

Power system static state estimation using Kalman filter algorithm

Directory of Open Access Journals (Sweden)

Saikia Anupam

2016-01-01

Full Text Available State estimation of power system is an important tool for operation, analysis and forecasting of electric power system. In this paper, a Kalman filter algorithm is presented for static estimation of power system state variables. IEEE 14 bus system is employed to check the accuracy of this method. Newton Raphson load flow study is first carried out on our test system and a set of data from the output of load flow program is taken as measurement input. Measurement inputs are simulated by adding Gaussian noise of zero mean. The results of Kalman estimation are compared with traditional Weight Least Square (WLS method and it is observed that Kalman filter algorithm is numerically more efficient than traditional WLS method. Estimation accuracy is also tested for presence of parametric error in the system. In addition, numerical stability of Kalman filter algorithm is tested by considering inclusion of zero mean errors in the initial estimates.

State energy data report 1992: Consumption estimates

Energy Technology Data Exchange (ETDEWEB)

1994-05-01

This is a report of energy consumption by state for the years 1960 to 1992. The report contains summaries of energy consumption for the US and by state, consumption by source, comparisons to other energy use reports, consumption by energy use sector, and describes the estimation methodologies used in the preparation of the report. Some years are not listed specifically although they are included in the summary of data.

Cognitive decline, mortality, and organophosphorus exposure in aging Mexican Americans.

Science.gov (United States)

Paul, Kimberly C; Ling, Chenxiao; Lee, Anne; To, Tu My; Cockburn, Myles; Haan, Mary; Ritz, Beate

2018-01-01

Cognitive impairment is a major health concern among older Mexican Americans, associated with significant morbidity and mortality, and may be influenced by environmental exposures. To investigate whether agricultural based ambient organophosphorus (OP) exposure influences 1) the rate of cognitive decline and mortality and 2) whether these associations are mediated through metabolic or inflammatory biomarkers. In a subset of older Mexican Americans from the Sacramento Area Latino Study on Aging (n = 430), who completed modified mini-mental state exams (3MSE) up to 7 times (1998-2007), we examined the relationship between estimated ambient OP exposures and cognitive decline (linear repeated measures model) and time to dementia or being cognitively impaired but not demented (CIND) and time to mortality (cox proportional hazards model). We then explored metabolic and inflammatory biomarkers as potential mediators of these relationships (additive hazards mediation). OP exposures at residential addresses were estimated with a geographic information system (GIS) based exposure assessment tool. Participants with high OP exposure in the five years prior to baseline experienced faster cognitive decline (β = 0.038, p = 0.02) and higher mortality over follow-up (HR = 1.91, 95% CI = 1.12, 3.26). The direct effect of OP exposure was estimated at 241 (95% CI = 27-455) additional deaths per 100,000 person-years, and the proportion mediated through the metabolic hormone adiponectin was estimated to be 4% 1.5-19.2). No other biomarkers were associated with OP exposure. Our study provides support for the involvement of OP pesticides in cognitive decline and mortality among older Mexican Americans, possibly through biologic pathways involving adiponectin. Copyright © 2017 Elsevier Inc. All rights reserved.

A stochastic estimation procedure for intermittently-observed semi-Markov multistate models with back transitions.

Science.gov (United States)

Aralis, Hilary; Brookmeyer, Ron

2017-01-01

Multistate models provide an important method for analyzing a wide range of life history processes including disease progression and patient recovery following medical intervention. Panel data consisting of the states occupied by an individual at a series of discrete time points are often used to estimate transition intensities of the underlying continuous-time process. When transition intensities depend on the time elapsed in the current state and back transitions between states are possible, this intermittent observation process presents difficulties in estimation due to intractability of the likelihood function. In this manuscript, we present an iterative stochastic expectation-maximization algorithm that relies on a simulation-based approximation to the likelihood function and implement this algorithm using rejection sampling. In a simulation study, we demonstrate the feasibility and performance of the proposed procedure. We then demonstrate application of the algorithm to a study of dementia, the Nun Study, consisting of intermittently-observed elderly subjects in one of four possible states corresponding to intact cognition, impaired cognition, dementia, and death. We show that the proposed stochastic expectation-maximization algorithm substantially reduces bias in model parameter estimates compared to an alternative approach used in the literature, minimal path estimation. We conclude that in estimating intermittently observed semi-Markov models, the proposed approach is a computationally feasible and accurate estimation procedure that leads to substantial improvements in back transition estimates.

National intelligence estimates and the Failed State Index.

Science.gov (United States)

Voracek, Martin

2013-10-01

Across 177 countries around the world, the Failed State Index, a measure of state vulnerability, was reliably negatively associated with the estimates of national intelligence. Psychometric analysis of the Failed State Index, compounded of 12 social, economic, and political indicators, suggested factorial unidimensionality of this index. The observed correspondence of higher national intelligence figures to lower state vulnerability might arise through these two macro-level variables possibly being proxies of even more pervasive historical and societal background variables that affect both.

On Estimating Marginal Tax Rates for U.S. States

OpenAIRE

Reed, W. Robert; Rogers, Cynthia L; Skidmore, Mark

2011-01-01

This paper presents a procedure for generating state-specific time-varying estimates of marginal tax rates (MTRs). Most estimates of MTRs follow a procedure developed by Koester and Kormendi (1989) (K&K). Unfortunately, the time-invariant nature of the K&K estimates precludes their use as explanatory variables in panel data studies with fixed effects. Furthermore, the associated MTR estimates are not explicitly linked to statutory tax parameters. Our approach addresses both shortcomings. Usin...

Parameter and State Estimation of Large-Scale Complex Systems Using Python Tools

Directory of Open Access Journals (Sweden)

M. Anushka S. Perera

2015-07-01

Full Text Available This paper discusses the topics related to automating parameter, disturbance and state estimation analysis of large-scale complex nonlinear dynamic systems using free programming tools. For large-scale complex systems, before implementing any state estimator, the system should be analyzed for structural observability and the structural observability analysis can be automated using Modelica and Python. As a result of structural observability analysis, the system may be decomposed into subsystems where some of them may be observable --- with respect to parameter, disturbances, and states --- while some may not. The state estimation process is carried out for those observable subsystems and the optimum number of additional measurements are prescribed for unobservable subsystems to make them observable. In this paper, an industrial case study is considered: the copper production process at Glencore Nikkelverk, Kristiansand, Norway. The copper production process is a large-scale complex system. It is shown how to implement various state estimators, in Python, to estimate parameters and disturbances, in addition to states, based on available measurements.

Using the Oxford Cognitive Screen to Detect Cognitive Impairment in Stroke Patients: A Comparison with the Mini-Mental State Examination

Directory of Open Access Journals (Sweden)

Mauro Mancuso

2018-02-01

Full Text Available BackgroundThe Oxford Cognitive Screen (OCS was recently developed with the aim of describing the cognitive deficits after stroke. The scale consists of 10 tasks encompassing five cognitive domains: attention and executive function, language, memory, number processing, and praxis. OCS was devised to be inclusive and un-confounded by aphasia and neglect. As such, it may have a greater potential to be informative on stroke cognitive deficits of widely used instruments, such as the Mini-Mental State Examination (MMSE or the Montreal Cognitive Assessment, which were originally devised for demented patients.ObjectiveThe present study compared the OCS with the MMSE with regards to their ability to detect cognitive impairments post-stroke. We further aimed to examine performance on the OCS as a function of subtypes of cerebral infarction and clinical severity.Methods325 first stroke patients were consecutively enrolled in the study over a 9-month period. The OCS and MMSE, as well as the Bamford classification and NIHSS, were given according to standard procedures.ResultsAbout a third of patients (35.3% had a performance lower than the cutoff (<22 on the MMSE, whereas 91.6% were impaired in at least one OCS domain, indicating higher incidences of impairment for the OCS. More than 80% of patients showed an impairment in two or more cognitive domains of the OCS. Using the MMSE as a standard of clinical practice, the comparative sensitivity of OCS was 100%. Out of the 208 patients with normal MMSE performance 180 showed impaired performance in at least one domain of the OCS. The discrepancy between OCS and MMSE was particularly strong for patients with milder strokes. As for subtypes of cerebral infarction, fewer patients demonstrated widespread impairments in the OCS in the Posterior Circulation Infarcts category than in the other categories.ConclusionOverall, the results showed a much higher incidence of cognitive impairment with the OCS than with the

Guidelines for Cognitive Behavioral Training within Doctoral Psychology Programs in the United States: Report of the Inter-Organizational Task Force on Cognitive and Behavioral Psychology Doctoral Education

Science.gov (United States)

Klepac, Robert K.; Ronan, George F.; Andrasik, Frank; Arnold, Kevin D.; Belar, Cynthia D.; Berry, Sharon L.; Christofff, Karen A.; Craighead, Linda W.; Dougher, Michael J.; Dowd, E. Thomas; Herbert, James D.; McFarr, Lynn M.; Rizvi, Shireen L.; Sauer, Eric M.; Strauman, Timothy J.

2012-01-01

The Association for Behavioral and Cognitive Therapies initiated an interorganizational task force to develop guidelines for integrated education and training in cognitive and behavioral psychology at the doctoral level in the United States. Fifteen task force members representing 16 professional associations participated in a yearlong series of…

Estimation of the number of wild pigs found in the United States

Energy Technology Data Exchange (ETDEWEB)

Mayer, J. [Savannah River Site (SRS), Aiken, SC (United States). Savannah River National Lab. (SRNL)

2014-08-01

Based on a compilation of three estimation approaches, the total nationwide population of wild pigs in the United States numbers approximately 6.3 million animals, with that total estimate ranging from 4.4 up to 11.3 million animals. The majority of these numbers (99 percent), which were encompassed by ten states (i.e., Alabama, Arkansas, California, Florida, Georgia, Louisiana, Mississippi, Oklahoma, South Carolina and Texas), were based on defined estimation methodologies (e.g., density estimates correlated to the total potential suitable wild pig habitat statewide, statewide harvest percentages, statewide agency surveys regarding wild pig distribution and numbers). In contrast to the pre-1990 estimates, none of these more recent efforts, collectively encompassing 99 percent of the total, were based solely on anecdotal information or speculation. To that end, one can defensibly state that the wild pigs found in the United States number in the millions of animals, with the nationwide population estimated to arguably vary from about four million up to about eleven million individuals.

Estimating the state of large spatio-temporally chaotic systems

International Nuclear Information System (INIS)

Ott, E.; Hunt, B.R.; Szunyogh, I.; Zimin, A.V.; Kostelich, E.J.; Corazza, M.; Kalnay, E.; Patil, D.J.; Yorke, J.A.

2004-01-01

We consider the estimation of the state of a large spatio-temporally chaotic system from noisy observations and knowledge of a system model. Standard state estimation techniques using the Kalman filter approach are not computationally feasible for systems with very many effective degrees of freedom. We present and test a new technique (called a Local Ensemble Kalman Filter), generally applicable to large spatio-temporally chaotic systems for which correlations between system variables evaluated at different points become small at large separation between the points

Higher-order Multivariable Polynomial Regression to Estimate Human Affective States

Science.gov (United States)

Wei, Jie; Chen, Tong; Liu, Guangyuan; Yang, Jiemin

2016-03-01

From direct observations, facial, vocal, gestural, physiological, and central nervous signals, estimating human affective states through computational models such as multivariate linear-regression analysis, support vector regression, and artificial neural network, have been proposed in the past decade. In these models, linear models are generally lack of precision because of ignoring intrinsic nonlinearities of complex psychophysiological processes; and nonlinear models commonly adopt complicated algorithms. To improve accuracy and simplify model, we introduce a new computational modeling method named as higher-order multivariable polynomial regression to estimate human affective states. The study employs standardized pictures in the International Affective Picture System to induce thirty subjects’ affective states, and obtains pure affective patterns of skin conductance as input variables to the higher-order multivariable polynomial model for predicting affective valence and arousal. Experimental results show that our method is able to obtain efficient correlation coefficients of 0.98 and 0.96 for estimation of affective valence and arousal, respectively. Moreover, the method may provide certain indirect evidences that valence and arousal have their brain’s motivational circuit origins. Thus, the proposed method can serve as a novel one for efficiently estimating human affective states.

Introduction to using neurophysiological signals that reflect cognitive or affective state (Editorial)

NARCIS (Netherlands)

Erp, J.B.F. van; Brouwer, A.M.; Zander, T.O.

2015-01-01

The central question of this Frontiers Research Topic is: What can we learn from brain and other physiological signals about an individual's cognitive and affective state and how can we use this information? This question reflects three important issues which are addressed by the 22 articles in this

Estimating repetitive spatiotemporal patterns from resting-state brain activity data.

Science.gov (United States)

Takeda, Yusuke; Hiroe, Nobuo; Yamashita, Okito; Sato, Masa-Aki

2016-06-01

Repetitive spatiotemporal patterns in spontaneous brain activities have been widely examined in non-human studies. These studies have reported that such patterns reflect past experiences embedded in neural circuits. In human magnetoencephalography (MEG) and electroencephalography (EEG) studies, however, spatiotemporal patterns in resting-state brain activities have not been extensively examined. This is because estimating spatiotemporal patterns from resting-state MEG/EEG data is difficult due to their unknown onsets. Here, we propose a method to estimate repetitive spatiotemporal patterns from resting-state brain activity data, including MEG/EEG. Without the information of onsets, the proposed method can estimate several spatiotemporal patterns, even if they are overlapping. We verified the performance of the method by detailed simulation tests. Furthermore, we examined whether the proposed method could estimate the visual evoked magnetic fields (VEFs) without using stimulus onset information. The proposed method successfully detected the stimulus onsets and estimated the VEFs, implying the applicability of this method to real MEG data. The proposed method was applied to resting-state functional magnetic resonance imaging (fMRI) data and MEG data. The results revealed informative spatiotemporal patterns representing consecutive brain activities that dynamically change with time. Using this method, it is possible to reveal discrete events spontaneously occurring in our brains, such as memory retrieval. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

The reliability of assigning individuals to cognitive states using the Mini Mental-State Examination: a population-based prospective cohort study

OpenAIRE

Marioni, Riccardo E.; Chatfield, Mark; Brayne, Carol; Matthews, Fiona E.; Med Res Council

2011-01-01

Abstract Background Previous investigations of test re-test reliability of the Mini-Mental State Examination (MMSE) have used correlations and statistics such as Cronbach's α to assess consistency. In practice, the MMSE is usually used to group individuals into cognitive states. The reliability of this grouping (state based approach) has not been fully explored. Methods MMSE data were collected on a subset of 2,275 older participants (≥ 65 years) from the population-based Medical Research Cou...

Estimating Dynamic Connectivity States in fMRI Using Regime-Switching Factor Models

KAUST Repository

Ting, Chee-Ming

2017-12-06

We consider the challenges in estimating state-related changes in brain connectivity networks with a large number of nodes. Existing studies use sliding-window analysis or time-varying coefficient models which are unable to capture both smooth and abrupt changes simultaneously, and rely on ad-hoc approaches to the high-dimensional estimation. To overcome these limitations, we propose a Markov-switching dynamic factor model which allows the dynamic connectivity states in functional magnetic resonance imaging (fMRI) data to be driven by lower-dimensional latent factors. We specify a regime-switching vector autoregressive (SVAR) factor process to quantity the time-varying directed connectivity. The model enables a reliable, data-adaptive estimation of change-points of connectivity regimes and the massive dependencies associated with each regime. We develop a three-step estimation procedure: 1) extracting the factors using principal component analysis, 2) identifying connectivity regimes in a low-dimensional subspace based on the factor-based SVAR model, 3) constructing high-dimensional state connectivity metrics based on the subspace estimates. Simulation results show that our estimator outperforms K-means clustering of time-windowed coefficients, providing more accurate estimate of time-evolving connectivity. It achieves percentage of reduction in mean squared error by 60% when the network dimension is comparable to the sample size. When applied to resting-state fMRI data, our method successfully identifies modular organization in resting-state networks in consistency with other studies. It further reveals changes in brain states with variations across subjects and distinct large-scale directed connectivity patterns across states.

Subclinical thyroid disorders and cognitive performance among adolescents in the United States.

Science.gov (United States)

Wu, Tiejian; Flowers, Joanne W; Tudiver, Fred; Wilson, Jim L; Punyasavatsut, Natavut

2006-04-19

Thyroid hormone plays a crucial role in the growth and function of the central nervous system. The purpose of the study was to examine the relationships between the status of subclinical thyroid conditions and cognition among adolescents in the United States. Study sample included 1,327 adolescents 13 to 16 years old who participated in the Third National Health and Nutrition Examination Survey (NHANES III). Serum thyroxine (T4) and thyroid stimulating hormone (TSH) were measured and subclinical hypothyroidism, subclinical hyperthyroidism, and euthyroid groups were defined. Cognitive performance was assessed using the subscales of the Wide Range Achievement Test-Revised (WRAT-R) and the Wechsler Intelligence Scale for Children-Revised (WISC-R). The age-corrected scaled scores for arithmetic, reading, block design, and digit span were derived from the cognitive assessments. Subclinical hypothyroidism was found in 1.7% and subclinical hyperthyroidism was found in 2.3% of the adolescents. Cognitive assessment scores on average tended to be lower in adolescents with subclinical hyperthyroidism and higher in those with subclinical hypothyroidism than the score for the euthyroid group. Adolescents with subclinical hypothyroidism had significantly better scores in block design and reading than the euthyroid subjects even after adjustment for a number of variables including sex, age, and family income level. Subclinical hypothyroidism was associated with better performance in some areas of cognitive functions while subclinical hyperthyroidism could be a potential risk factor.

Battery state-of-charge estimation using approximate least squares

Science.gov (United States)

Unterrieder, C.; Zhang, C.; Lunglmayr, M.; Priewasser, R.; Marsili, S.; Huemer, M.

2015-03-01

In recent years, much effort has been spent to extend the runtime of battery-powered electronic applications. In order to improve the utilization of the available cell capacity, high precision estimation approaches for battery-specific parameters are needed. In this work, an approximate least squares estimation scheme is proposed for the estimation of the battery state-of-charge (SoC). The SoC is determined based on the prediction of the battery's electromotive force. The proposed approach allows for an improved re-initialization of the Coulomb counting (CC) based SoC estimation method. Experimental results for an implementation of the estimation scheme on a fuel gauge system on chip are illustrated. Implementation details and design guidelines are presented. The performance of the presented concept is evaluated for realistic operating conditions (temperature effects, aging, standby current, etc.). For the considered test case of a GSM/UMTS load current pattern of a mobile phone, the proposed method is able to re-initialize the CC-method with a high accuracy, while state-of-the-art methods fail to perform a re-initialization.

Maximum Correntropy Unscented Kalman Filter for Spacecraft Relative State Estimation

Directory of Open Access Journals (Sweden)

Xi Liu

2016-09-01

Full Text Available A new algorithm called maximum correntropy unscented Kalman filter (MCUKF is proposed and applied to relative state estimation in space communication networks. As is well known, the unscented Kalman filter (UKF provides an efficient tool to solve the non-linear state estimate problem. However, the UKF usually plays well in Gaussian noises. Its performance may deteriorate substantially in the presence of non-Gaussian noises, especially when the measurements are disturbed by some heavy-tailed impulsive noises. By making use of the maximum correntropy criterion (MCC, the proposed algorithm can enhance the robustness of UKF against impulsive noises. In the MCUKF, the unscented transformation (UT is applied to obtain a predicted state estimation and covariance matrix, and a nonlinear regression method with the MCC cost is then used to reformulate the measurement information. Finally, the UT is adopted to the measurement equation to obtain the filter state and covariance matrix. Illustrative examples demonstrate the superior performance of the new algorithm.

Pain treatment for nursing home residents differs according to cognitive state - a cross-sectional study.

Science.gov (United States)

Bauer, Ulrike; Pitzer, Stefan; Schreier, Maria Magdalena; Osterbrink, Jürgen; Alzner, Reinhard; Iglseder, Bernhard

2016-06-17

Communication skills are known to decrease with advancing cognitive impairment. Analgesic treatment in long-term care may be deficient due to the residents' impaired ability to communicate their pain and needs. Undertreated pain frequently leads to rising BPSD in residents with cognitive impairment, resulting in a treatment with antipsychotics. Aim of this study was the analysis of differences in assessment and pharmacological treatment of pain in nursing home residents relative to their cognitive state and ability to articulate pain. Data stems from the baseline of a non-experimental pre-post-study in 12 Austrian nursing homes. Residents' pain prevalence in relation to pain assessment and cognitive decline was assessed, data on medical diagnoses and prescriptions were retrieved from the nursing homes' documentation (n = 425). Residents were first divided into two groups: Residents with MMSE ≥ 18 were selected into group CUS (cognitively unimpaired/slightly impaired), residents with MMSE ≤ 17 were selected into group CI (cognitively moderately to severely impaired). CI residents were then sub-grouped according to their ability to communicate pain via the Verbal Rating Scale (VRS) (i.e. group CI-V, group CI-NV). Pain behavior of CI residents was assessed with a modified German version of PAINAD. Group differences were tested with ANOVA and H-test, 95 % confidence intervals were calculated and associations were tested with log-binomial regression. Pain prevalence in CI residents irrespective of their ability to communicate pain was 80 % and exceeded the CUS group prevalence significantly by 14 %. CI residents had significantly less analgesic prescriptions. Furthermore, CI residents have a significantly higher risk of getting no analgesics when in pain than CUS residents (CI-V: RR =2.6, CI-NV: RR =3.4). Use of antipsychotics was high in all groups (49 - 65 %) with more prescriptions in the cognitively impaired group. Results point toward an

A Novel Methodology for Estimating State-Of-Charge of Li-Ion Batteries Using Advanced Parameters Estimation

Directory of Open Access Journals (Sweden)

Ibrahim M. Safwat

2017-11-01

Full Text Available State-of-charge (SOC estimations of Li-ion batteries have been the focus of many research studies in previous years. Many articles discussed the dynamic model’s parameters estimation of the Li-ion battery, where the fixed forgetting factor recursive least square estimation methodology is employed. However, the change rate of each parameter to reach the true value is not taken into consideration, which may tend to poor estimation. This article discusses this issue, and proposes two solutions to solve it. The first solution is the usage of a variable forgetting factor instead of a fixed one, while the second solution is defining a vector of forgetting factors, which means one factor for each parameter. After parameters estimation, a new idea is proposed to estimate state-of-charge (SOC of the Li-ion battery based on Newton’s method. Also, the error percentage and computational cost are discussed and compared with that of nonlinear Kalman filters. This methodology is applied on a 36 V 30 A Li-ion pack to validate this idea.

Methods for Estimating Water Withdrawals for Mining in the United States, 2005

Science.gov (United States)

Lovelace, John K.

2009-01-01

The mining water-use category includes groundwater and surface water that is withdrawn and used for nonfuels and fuels mining. Nonfuels mining includes the extraction of ores, stone, sand, and gravel. Fuels mining includes the extraction of coal, petroleum, and natural gas. Water is used for mineral extraction, quarrying, milling, and other operations directly associated with mining activities. For petroleum and natural gas extraction, water often is injected for secondary oil or gas recovery. Estimates of water withdrawals for mining are needed for water planning and management. This report documents methods used to estimate withdrawals of fresh and saline groundwater and surface water for mining during 2005 for each county and county equivalent in the United States, Puerto Rico, and the U.S. Virgin Islands. Fresh and saline groundwater and surface-water withdrawals during 2005 for nonfuels- and coal-mining operations in each county or county equivalent in the United States, Puerto Rico, and the U.S. Virgin Islands were estimated. Fresh and saline groundwater withdrawals for oil and gas operations in counties of six states also were estimated. Water withdrawals for nonfuels and coal mining were estimated by using mine-production data and water-use coefficients. Production data for nonfuels mining included the mine location and weight (in metric tons) of crude ore, rock, or mineral produced at each mine in the United States, Puerto Rico, and the U.S. Virgin Islands during 2004. Production data for coal mining included the weight, in metric tons, of coal produced in each county or county equivalent during 2004. Water-use coefficients for mined commodities were compiled from various sources including published reports and written communications from U.S. Geological Survey National Water-use Information Program (NWUIP) personnel in several states. Water withdrawals for oil and gas extraction were estimated for six States including California, Colorado, Louisiana, New

Spin State Estimation of Tumbling Small Bodies

Science.gov (United States)

Olson, Corwin; Russell, Ryan P.; Bhaskaran, Shyam

2016-06-01

It is expected that a non-trivial percentage of small bodies that future missions may visit are in non-principal axis rotation (i.e. "tumbling"). The primary contribution of this paper is the application of the Extended Kalman Filter (EKF) Simultaneous Localization and Mapping (SLAM) method to estimate the small body spin state, mass, and moments of inertia; the spacecraft position and velocity; and the surface landmark locations. The method uses optical landmark measurements, and an example scenario based on the Rosetta mission is used. The SLAM method proves effective, with order of magnitude decreases in the spacecraft and small body spin state errors after less than a quarter of the comet characterization phase. The SLAM method converges nicely for initial small body angular velocity errors several times larger than the true rates (effectively having no a priori knowledge of the angular velocity). Surface landmark generation and identification are not treated in this work, but significant errors in the initial body-fixed landmark positions are effectively estimated. The algorithm remains effective for a range of different truth spin states, masses, and center of mass offsets that correspond to expected tumbling small bodies throughout the solar system.

Distributed Dynamic State Estimator, Generator Parameter Estimation and Stability Monitoring Demonstration

Energy Technology Data Exchange (ETDEWEB)

Meliopoulos, Sakis [Georgia Inst. of Technology, Atlanta, GA (United States); Cokkinides, George [Georgia Inst. of Technology, Atlanta, GA (United States); Fardanesh, Bruce [New York Power Authority, NY (United States); Hedrington, Clinton [U.S. Virgin Islands Water and Power Authority (WAPA), St. Croix (U.S. Virgin Islands)

2013-12-31

This is the final report for this project that was performed in the period: October1, 2009 to June 30, 2013. In this project, a fully distributed high-fidelity dynamic state estimator (DSE) that continuously tracks the real time dynamic model of a wide area system with update rates better than 60 times per second is achieved. The proposed technology is based on GPS-synchronized measurements but also utilizes data from all available Intelligent Electronic Devices in the system (numerical relays, digital fault recorders, digital meters, etc.). The distributed state estimator provides the real time model of the system not only the voltage phasors. The proposed system provides the infrastructure for a variety of applications and two very important applications (a) a high fidelity generating unit parameters estimation and (b) an energy function based transient stability monitoring of a wide area electric power system with predictive capability. Also the dynamic distributed state estimation results are stored (the storage scheme includes data and coincidental model) enabling an automatic reconstruction and “play back” of a system wide disturbance. This approach enables complete play back capability with fidelity equal to that of real time with the advantage of “playing back” at a user selected speed. The proposed technologies were developed and tested in the lab during the first 18 months of the project and then demonstrated on two actual systems, the USVI Water and Power Administration system and the New York Power Authority’s Blenheim-Gilboa pumped hydro plant in the last 18 months of the project. The four main thrusts of this project, mentioned above, are extremely important to the industry. The DSE with the achieved update rates (more than 60 times per second) provides a superior solution to the “grid visibility” question. The generator parameter identification method fills an important and practical need of the industry. The “energy function” based

Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries

International Nuclear Information System (INIS)

Ng, Kong Soon; Moo, Chin-Sien; Chen, Yi-Ping; Hsieh, Yao-Ching

2009-01-01

The coulomb counting method is expedient for state-of-charge (SOC) estimation of lithium-ion batteries with high charging and discharging efficiencies. The charging and discharging characteristics are investigated and reveal that the coulomb counting method is convenient and accurate for estimating the SOC of lithium-ion batteries. A smart estimation method based on coulomb counting is proposed to improve the estimation accuracy. The corrections are made by considering the charging and operating efficiencies. Furthermore, the state-of-health (SOH) is evaluated by the maximum releasable capacity. Through the experiments that emulate practical operations, the SOC estimation method is verified to demonstrate the effectiveness and accuracy.

Number line estimation and complex mental calculation: Is there a shared cognitive process driving the two tasks?

Science.gov (United States)

Montefinese, Maria; Semenza, Carlo

2018-05-17

It is widely accepted that different number-related tasks, including solving simple addition and subtraction, may induce attentional shifts on the so-called mental number line, which represents larger numbers on the right and smaller numbers on the left. Recently, it has been shown that different number-related tasks also employ spatial attention shifts along with general cognitive processes. Here we investigated for the first time whether number line estimation and complex mental arithmetic recruit a common mechanism in healthy adults. Participants' performance in two-digit mental additions and subtractions using visual stimuli was compared with their performance in a mental bisection task using auditory numerical intervals. Results showed significant correlations between participants' performance in number line bisection and that in two-digit mental arithmetic operations, especially in additions, providing a first proof of a shared cognitive mechanism (or multiple shared cognitive mechanisms) between auditory number bisection and complex mental calculation.

Resting-state networks associated with cognitive processing show more age-related decline than those associated with emotional processing.

Science.gov (United States)

Nashiro, Kaoru; Sakaki, Michiko; Braskie, Meredith N; Mather, Mara

2017-06-01

Correlations in activity across disparate brain regions during rest reveal functional networks in the brain. Although previous studies largely agree that there is an age-related decline in the "default mode network," how age affects other resting-state networks, such as emotion-related networks, is still controversial. Here we used a dual-regression approach to investigate age-related alterations in resting-state networks. The results revealed age-related disruptions in functional connectivity in all 5 identified cognitive networks, namely the default mode network, cognitive-auditory, cognitive-speech (or speech-related somatosensory), and right and left frontoparietal networks, whereas such age effects were not observed in the 3 identified emotion networks. In addition, we observed age-related decline in functional connectivity in 3 visual and 3 motor/visuospatial networks. Older adults showed greater functional connectivity in regions outside 4 out of the 5 identified cognitive networks, consistent with the dedifferentiation effect previously observed in task-based functional magnetic resonance imaging studies. Both reduced within-network connectivity and increased out-of-network connectivity were correlated with poor cognitive performance, providing potential biomarkers for cognitive aging. Copyright © 2017 Elsevier Inc. All rights reserved.

Towards a near infrared spectroscopy-based estimation of operator attentional state.

Directory of Open Access Journals (Sweden)

Gérard Derosière

Full Text Available Given the critical risks to public health and safety that can involve lapses in attention (e.g., through implication in workplace accidents, researchers have sought to develop cognitive-state tracking technologies, capable of alerting individuals engaged in cognitively demanding tasks of potentially dangerous decrements in their levels of attention. The purpose of the present study was to address this issue through an investigation of the reliability of optical measures of cortical correlates of attention in conjunction with machine learning techniques to distinguish between states of full attention and states characterized by reduced attention capacity during a sustained attention task. Seven subjects engaged in a 30 minutes duration sustained attention reaction time task with near infrared spectroscopy (NIRS monitoring over the prefrontal and the right parietal areas. NIRS signals from the first 10 minutes of the task were considered as characterizing the 'full attention' class, while the NIRS signals from the last 10 minutes of the task were considered as characterizing the 'attention decrement' class. A two-class support vector machine algorithm was exploited to distinguish between the two levels of attention using appropriate NIRS-derived signal features. Attention decrement occurred during the task as revealed by the significant increase in reaction time in the last 10 compared to the first 10 minutes of the task (p<.05. The results demonstrate relatively good classification accuracy, ranging from 65 to 90%. The highest classification accuracy results were obtained when exploiting the oxyhemoglobin signals (i.e., from 77 to 89%, depending on the cortical area considered rather than the deoxyhemoglobin signals (i.e., from 65 to 66%. Moreover, the classification accuracy increased to 90% when using signals from the right parietal area rather than from the prefrontal cortex. The results support the feasibility of developing cognitive tracking

Call Arrival Rate Prediction and Blocking Probability Estimation for Infrastructure based Mobile Cognitive Radio Personal Area Network

Directory of Open Access Journals (Sweden)

Neeta Nathani

2017-08-01

Full Text Available The Cognitive Radio usage has been estimated as non-emergency service with low volume traffic. Present work proposes an infrastructure based Cognitive Radio network and probability of success of CR traffic in licensed band. The Cognitive Radio nodes will form cluster. The cluster nodes will communicate on Industrial, Scientific and Medical band using IPv6 over Low-Power Wireless Personal Area Network based protocol from sensor to Gateway Cluster Head. For Cognitive Radio-Media Access Control protocol for Gateway to Cognitive Radio-Base Station communication, it will use vacant channels of licensed band. Standalone secondary users of Cognitive Radio Network shall be considered as a Gateway with one user. The Gateway will handle multi-channel multi radio for communication with Base Station. Cognitive Radio Network operators shall define various traffic data accumulation counters at Base Station for storing signal strength, Carrier-to-Interference and Noise Ratio, etc. parameters and record channel occupied/vacant status. The researches has been done so far using hour as interval is too long for parameters like holding time expressed in minutes and hence channel vacant/occupied status time is only probabilistically calculated. In the present work, an infrastructure based architecture has been proposed which polls channel status each minute in contrary to hourly polling of data. The Gateways of the Cognitive Radio Network shall monitor status of each Primary User periodically inside its working range and shall inform to Cognitive Radio- Base Station for preparation of minutewise database. For simulation, the occupancy data for all primary user channels were pulled in one minute interval from a live mobile network. Hourly traffic data and minutewise holding times has been analyzed to optimize the parameters of Seasonal Auto Regressive Integrated Moving Average prediction model. The blocking probability of an incoming Cognitive Radio call has been

State-Level Estimates of Obesity-Attributable Costs of Absenteeism

Science.gov (United States)

Andreyeva, Tatiana; Luedicke, Joerg; Wang, Y. Claire

2014-01-01

Objective To provide state-level estimates of obesity-attributable costs of absenteeism among working adults in the U.S. Methods Nationally-representative data from the National Health and Nutrition Examination Survey (NHANES) for 1998–2008 and from the Behavioral Risk Factor Surveillance System (BRFSS) for 2012 are examined. The outcome is obesity-attributable workdays missed in the previous year due to health, and their costs to states. Results Obesity, but not overweight, is associated with a significant increase in workdays absent, from 1.1 to 1.7 extra days missed annually compared to normal weight employees. Obesity-attributable absenteeism among American workers costs the nation an estimated $8.65 billion per year. Conclusion Obesity imposes a considerable financial burden on states, accounting for 6.5%–12.6% of total absenteeism costs in the workplace. State legislature and employers should seek effective ways to reduce these costs. PMID:25376405

Triangular and Trapezoidal Fuzzy State Estimation with Uncertainty on Measurements

Directory of Open Access Journals (Sweden)

Mohammad Sadeghi Sarcheshmah

2012-01-01

Full Text Available In this paper, a new method for uncertainty analysis in fuzzy state estimation is proposed. The uncertainty is expressed in measurements. Uncertainties in measurements are modelled with different fuzzy membership functions (triangular and trapezoidal. To find the fuzzy distribution of any state variable, the problem is formulated as a constrained linear programming (LP optimization. The viability of the proposed method would be verified with the ones obtained from the weighted least squares (WLS and the fuzzy state estimation (FSE in the 6-bus system and in the IEEE-14 and 30 bus system.

Uncertainty of feedback and state estimation determines the speed of motor adaptation

Directory of Open Access Journals (Sweden)

Kunlin Wei

2010-05-01

Full Text Available Humans can adapt their motor behaviors to deal with ongoing changes. To achieve this, the nervous system needs to estimate central variables for our movement based on past knowledge and new feedback, both of which are uncertain. In the Bayesian framework, rates of adaptation characterize how noisy feedback is in comparison to the uncertainty of the state estimate. The predictions of Bayesian models are intuitive: the nervous system should adapt slower when sensory feedback is more noisy and faster when its state estimate is more uncertain. Here we want to quantitatively understand how uncertainty in these two factors affects motor adaptation. In a hand reaching experiment we measured trial-by-trial adaptation to a randomly changing visual perturbation to characterize the way the nervous system handles uncertainty in state estimation and feedback. We found both qualitative predictions of Bayesian models confirmed. Our study provides evidence that the nervous system represents and uses uncertainty in state estimate and feedback during motor adaptation.

State estimation for integrated vehicle dynamics control

NARCIS (Netherlands)

Zuurbier, J.; Bremmer, P.

2002-01-01

This paper discusses a vehicle controller and a state estimator that was implemented and tested in a vehicle equipped with a combined braking and chassis control system to improve handling. The vehicle dynamics controller consists of a feed forward body roll compensation and a feedback stability

State estimation of spatio-temporal phenomena

Science.gov (United States)

Yu, Dan

This dissertation addresses the state estimation problem of spatio-temporal phenomena which can be modeled by partial differential equations (PDEs), such as pollutant dispersion in the atmosphere. After discretizing the PDE, the dynamical system has a large number of degrees of freedom (DOF). State estimation using Kalman Filter (KF) is computationally intractable, and hence, a reduced order model (ROM) needs to be constructed first. Moreover, the nonlinear terms, external disturbances or unknown boundary conditions can be modeled as unknown inputs, which leads to an unknown input filtering problem. Furthermore, the performance of KF could be improved by placing sensors at feasible locations. Therefore, the sensor scheduling problem to place multiple mobile sensors is of interest. The first part of the dissertation focuses on model reduction for large scale systems with a large number of inputs/outputs. A commonly used model reduction algorithm, the balanced proper orthogonal decomposition (BPOD) algorithm, is not computationally tractable for large systems with a large number of inputs/outputs. Inspired by the BPOD and randomized algorithms, we propose a randomized proper orthogonal decomposition (RPOD) algorithm and a computationally optimal RPOD (RPOD*) algorithm, which construct an ROM to capture the input-output behaviour of the full order model, while reducing the computational cost of BPOD by orders of magnitude. It is demonstrated that the proposed RPOD* algorithm could construct the ROM in real-time, and the performance of the proposed algorithms on different advection-diffusion equations. Next, we consider the state estimation problem of linear discrete-time systems with unknown inputs which can be treated as a wide-sense stationary process with rational power spectral density, while no other prior information needs to be known. We propose an autoregressive (AR) model based unknown input realization technique which allows us to recover the input

Subclinical thyroid disorders and cognitive performance among adolescents in the United States

Directory of Open Access Journals (Sweden)

Wilson Jim L

2006-04-01

Full Text Available Abstract Background Thyroid hormone plays a crucial role in the growth and function of the central nervous system. The purpose of the study was to examine the relationships between the status of subclinical thyroid conditions and cognition among adolescents in the United States. Methods Study sample included 1,327 adolescents 13 to 16 years old who participated in the Third National Health and Nutrition Examination Survey (NHANES III. Serum thyroxine (T4 and thyroid stimulating hormone (TSH were measured and subclinical hypothyroidism, subclinical hyperthyroidism, and euthyroid groups were defined. Cognitive performance was assessed using the subscales of the Wide Range Achievement Test-Revised (WRAT-R and the Wechsler Intelligence Scale for Children-Revised (WISC-R. The age-corrected scaled scores for arithmetic, reading, block design, and digit span were derived from the cognitive assessments. Results Subclinical hypothyroidism was found in 1.7% and subclinical hyperthyroidism was found in 2.3% of the adolescents. Cognitive assessment scores on average tended to be lower in adolescents with subclinical hyperthyroidism and higher in those with subclinical hypothyroidism than the score for the euthyroid group. Adolescents with subclinical hypothyroidism had significantly better scores in block design and reading than the euthyroid subjects even after adjustment for a number of variables including sex, age, and family income level. Conclusion Subclinical hypothyroidism was associated with better performance in some areas of cognitive functions while subclinical hyperthyroidism could be a potential risk factor.

The Alzheimer's prevention initiative composite cognitive test score: sample size estimates for the evaluation of preclinical Alzheimer's disease treatments in presenilin 1 E280A mutation carriers.

Science.gov (United States)

Ayutyanont, Napatkamon; Langbaum, Jessica B S; Hendrix, Suzanne B; Chen, Kewei; Fleisher, Adam S; Friesenhahn, Michel; Ward, Michael; Aguirre, Camilo; Acosta-Baena, Natalia; Madrigal, Lucìa; Muñoz, Claudia; Tirado, Victoria; Moreno, Sonia; Tariot, Pierre N; Lopera, Francisco; Reiman, Eric M

2014-06-01

To identify a cognitive composite that is sensitive to tracking preclinical Alzheimer's disease decline to be used as a primary end point in treatment trials. We capitalized on longitudinal data collected from 1995 to 2010 from cognitively unimpaired presenilin 1 (PSEN1) E280A mutation carriers from the world's largest known early-onset autosomal dominant Alzheimer's disease kindred to identify a composite cognitive test with the greatest statistical power to track preclinical Alzheimer's disease decline and estimate the number of carriers age 30 years and older needed to detect a treatment effect in the Alzheimer's Prevention Initiative's (API) preclinical Alzheimer's disease treatment trial. The mean-to-standard-deviation ratios (MSDRs) of change over time were calculated in a search for the optimal combination of 1 to 7 cognitive tests/subtests drawn from the neuropsychological test battery in cognitively unimpaired mutation carriers during a 2- and 5-year follow-up period (n = 78 and 57), using data from noncarriers (n = 31 and 56) during the same time period to correct for aging and practice effects. Combinations that performed well were then evaluated for robustness across follow-up years, occurrence of selected items within top-performing combinations, and representation of relevant cognitive domains. The optimal test combination included Consortium to Establish a Registry for Alzheimer's Disease (CERAD) Word List Recall, CERAD Boston Naming Test (high frequency items), Mini-Mental State Examination (MMSE) Orientation to Time, CERAD Constructional Praxis, and Raven's Progressive Matrices (Set A), with an MSDR of 1.62. This composite is more sensitive than using either the CERAD Word List Recall (MSDR = 0.38) or the entire CERAD-Col battery (MSDR = 0.76). A sample size of 75 cognitively normal PSEN1 E280A mutation carriers aged 30 years and older per treatment arm allows for a detectable treatment effect of 29% in a 60-month trial (80% power, P = .05). We

Study of the Convergence in State Estimators for LTI Systems with Event Detection

Directory of Open Access Journals (Sweden)

Juan C. Posada

2016-01-01

Full Text Available The methods frequently used to estimate the state of an LTI system require that the precise value of the output variable is known at all times, or at equidistant sampling times. In LTI systems, in which the output signal is measured through binary sensors (detectors, the traditional way of state observers design is not applicable even though the system has a complete observability matrix. This type of state observers design is known as passive. It is necessary, then, to introduce a new state estimation technique, which allows reckoning the state from the information of the variable’s crossing through a detector’s action threshold (switch. This paper seeks, therefore, to study the convergence in this type of estimators in finite time, allowing establishing, theoretically, whether some family of the proposed models can be estimated in a convergent way through the use of the estimation technique based on events.

Tau Protein in Oral Mucosa and Cognitive State: A Cross-sectional Study

Directory of Open Access Journals (Sweden)

Luis Fernando Arredondo

2017-10-01

Full Text Available Neurodegenerative diseases are characterized by the presence of abnormal aggregates of proteins in brain tissue. Among them, the presence of aggregates of phosphorylated Tau protein (p-Tau is the hallmark of Alzheimer’s disease (AD and other major neurodegenerative disorders such as corticobasal degeneration and frontotemporal dementia among others. Although Tau protein has previously been assumed to be exclusive to the central nervous system, it is also found in peripheral tissues. The purpose of this study was to determine whether there is a differential Tau expression in oral mucosa cells according to cognitive impairment. Eighty-one subjects were enrolled in the study and classified per Mini-Mental State Examination test score into control, mild cognitive impairment (MCI, and severe cognitive impairment (SCI groups. Immunocytochemistry and immunofluorescence revealed the presence of Tau and four p-Tau forms in the cytoplasm and nucleus of oral mucosa cells. More positivity was present in subjects with cognitive impairment than in control subjects, both in the nucleus and cytoplasm, in a speckle pattern. The mRNA expression of Tau by quantitative real-time polymerase chain reaction was higher in SCI as compared with the control group (P < 0.01. A significantly higher percentage of immunopositive cells in the SCI group was found via flow cytometry in comparison to controls and the MCI group (P < 0.01. These findings demonstrate the higher presence of p-Tau and Tau transcript in the oral mucosa of cognitively impaired subjects when compared with healthy subjects. The feasibility of p-Tau quantification by flow cytometry supports the prospective analysis of oral mucosa as a support tool for screening of proteinopathies in cognitively impaired patients.

Discrete-time state estimation for stochastic polynomial systems over polynomial observations

Science.gov (United States)

Hernandez-Gonzalez, M.; Basin, M.; Stepanov, O.

2018-07-01

This paper presents a solution to the mean-square state estimation problem for stochastic nonlinear polynomial systems over polynomial observations confused with additive white Gaussian noises. The solution is given in two steps: (a) computing the time-update equations and (b) computing the measurement-update equations for the state estimate and error covariance matrix. A closed form of this filter is obtained by expressing conditional expectations of polynomial terms as functions of the state estimate and error covariance. As a particular case, the mean-square filtering equations are derived for a third-degree polynomial system with second-degree polynomial measurements. Numerical simulations show effectiveness of the proposed filter compared to the extended Kalman filter.

Lithium-Ion Battery Online Rapid State-of-Power Estimation under Multiple Constraints

Directory of Open Access Journals (Sweden)

Shun Xiang

2018-01-01

Full Text Available The paper aims to realize a rapid online estimation of the state-of-power (SOP with multiple constraints of a lithium-ion battery. Firstly, based on the improved first-order resistance-capacitance (RC model with one-state hysteresis, a linear state-space battery model is built; then, using the dual extended Kalman filtering (DEKF method, the battery parameters and states, including open-circuit voltage (OCV, are estimated. Secondly, by employing the estimated OCV as the observed value to build the second dual Kalman filters, the battery SOC is estimated. Thirdly, a novel rapid-calculating peak power/SOP method with multiple constraints is proposed in which, according to the bisection judgment method, the battery’s peak state is determined; then, one or two instantaneous peak powers are used to determine the peak power during T seconds. In addition, in the battery operating process, the actual constraint that the battery is under is analyzed specifically. Finally, three simplified versions of the Federal Urban Driving Schedule (SFUDS with inserted pulse experiments are conducted to verify the effectiveness and accuracy of the proposed online SOP estimation method.

Montreal Cognitive Assessment Performance in Patients with Parkinson’s Disease with “Normal” Global Cognition According to Mini-Mental State Examination Score

Science.gov (United States)

Nazem, Sarra; Siderowf, Andrew D.; Duda, John E.; Have, Tom Ten; Colcher, Amy; Horn, Stacy S.; Moberg, Paul J.; Wilkinson, Jayne R.; Hurtig, Howard I.; Stern, Matthew B.; Weintraub, Daniel

2009-01-01

OBJECTIVES To examine Montreal Cognitive Assessment (MoCA) performance in patients with Parkinson’s disease (PD) with “normal” global cognition according to Mini-Mental State Examination (MMSE) score. DESIGN A cross-sectional comparison of the MoCA and the MMSE. SETTING Two movement disorders centers at the University of Pennsylvania and the Philadelphia Veterans Affairs Medical Center. PARTICIPANTS A convenience sample of 131 patients with idiopathic PD who were screened for cognitive and psychiatric complications. MEASUREMENTS Subjects were administered the MoCA and MMSE, and only subjects defined as having a normal age- and education-adjusted MMSE score were included in the analyses (N = 100). As previously recommended in patients without PD, a MoCA score less than 26 was used to indicate the presence of at least mild cognitive impairment (MCI). RESULTS Mean MMSE and MoCA scores ± standard deviation were 28.8 ± 1.1 and 24.9 ± 3.1, respectively. More than half (52.0%) of subjects with normal MMSE scores had cognitive impairment according to their MoCA score. Impairments were seen in numerous cognitive domains, including memory, visuospatial and executive abilities, attention, and language. Predictors of cognitive impairment on the MoCA using univariate analyses were male sex, older age, lower educational level, and greater disease severity; older age was the only predictor in a multivariate model. CONCLUSION Approximately half of patients with PD with a normal MMSE score have cognitive impairment based on the recommended MoCA cutoff score. These results suggest that MCI is common in PD and that the MoCA is a more sensitive instrument than the MMSE for its detection. PMID:19170786

Dynamic state estimation techniques for large-scale electric power systems

International Nuclear Information System (INIS)

Rousseaux, P.; Pavella, M.

1991-01-01

This paper presents the use of dynamic type state estimators for energy management in electric power systems. Various dynamic type estimators have been developed, but have never been implemented. This is primarily because of dimensionality problems posed by the conjunction of an extended Kalman filter with a large scale power system. This paper precisely focuses on how to circumvent the high dimensionality, especially prohibitive in the filtering step, by using a decomposition-aggregation hierarchical scheme; to appropriately model the power system dynamics, the authors introduce new state variables in the prediction step and rely on a load forecasting method. The combination of these two techniques succeeds in solving the overall dynamic state estimation problem not only in a tractable and realistic way, but also in compliance with real-time computational requirements. Further improvements are also suggested, bound to the specifics of the high voltage electric transmission systems

Real-time measurements and their effects on state estimation of distribution power system

DEFF Research Database (Denmark)

Han, Xue; You, Shi; Thordarson, Fannar

2013-01-01

between the estimated values (voltage and injected power) and the measurements are applied to evaluate the accuracy of the estimated grid states. Eventually, some suggestions are provided for the distribution grid operators on placing the real-time meters in the distribution grid.......This paper aims at analyzing the potential value of using different real-time metering and measuring instruments applied in the low voltage distribution networks for state-estimation. An algorithm is presented to evaluate different combinations of metering data using a tailored state estimator....... It is followed by a case study based on the proposed algorithm. A real distribution grid feeder with different types of meters installed either in the cabinets or at the customer side is selected for simulation and analysis. Standard load templates are used to initiate the state estimation. The deviations...

Method for Estimating Water Withdrawals for Livestock in the United States, 2005

Science.gov (United States)

Lovelace, John K.

2009-01-01

Livestock water use includes ground water and surface water associated with livestock watering, feedlots, dairy operations, and other on-farm needs. The water may be used for drinking, cooling, sanitation, waste disposal, and other needs related to the animals. Estimates of water withdrawals for livestock are needed for water planning and management. This report documents a method used to estimate withdrawals of fresh ground water and surface water for livestock in 2005 for each county and county equivalent in the United States, Puerto Rico, and the U.S. Virgin Islands. Categories of livestock included dairy cattle, beef and other cattle, hogs and pigs, laying hens, broilers and other chickens, turkeys, sheep and lambs, all goats, and horses (including ponies, mules, burros, and donkeys). Use of the method described in this report could result in more consistent water-withdrawal estimates for livestock that can be used by water managers and planners to determine water needs and trends across the United States. Water withdrawals for livestock in 2005 were estimated by using water-use coefficients, in gallons per head per day for each animal type, and livestock-population data. Coefficients for various livestock for most States were obtained from U.S. Geological Survey water-use program personnel or U.S. Geological Survey water-use publications. When no coefficient was available for an animal type in a State, the median value of reported coefficients for that animal was used. Livestock-population data were provided by the National Agricultural Statistics Service. County estimates were further divided into ground-water and surface-water withdrawals for each county and county equivalent. County totals from 2005 were compared to county totals from 1995 and 2000. Large deviations from 1995 or 2000 livestock withdrawal estimates were investigated and generally were due to comparison with reported withdrawals, differences in estimation techniques, differences in livestock

Unconditional and Conditional Standards Using Cognitive Function Curves for the Modified Mini-Mental State Exam: Cross-Sectional and Longitudinal Analyses in Older Chinese Adults in Singapore.

Science.gov (United States)

Cheung, Yin Bun; Xu, Ying; Feng, Lei; Feng, Liang; Nyunt, Ma Shwe Zin; Chong, Mei Sian; Lim, Wee Shiong; Lee, Tih Shih; Yap, Philip; Yap, Keng Bee; Ng, Tze Pin

2015-09-01

The conventional practice of assessing cognitive status and monitoring change over time in older adults using normative values of the Mini-Mental State Exam (MMSE) based on age bands is imprecise. Moreover, population-based normative data on changes in MMSE score over time are scarce and crude because they do not include age- and education-specific norms. This study aims to develop unconditional standards for assessing current cognitive status and conditional standards that take prior MMSE score into account for assessing longitudinal change, with percentile curves as smooth functions of age. Cross-sectional and longitudinal data of a modified version of the MMSE for 2,026 older Chinese adults from the Singapore Longitudinal Aging Study, aged 55-84, in Singapore were used to estimate quantile regression coefficients and create unconditional standards and conditional standards. We presented MMSE percentile curves as a smooth function of age in education strata, for unconditional and conditional standards, based on quantile regression coefficient estimates. We found the 5th and 10th percentiles were more strongly associated with age and education than were higher percentiles. Model diagnostics demonstrated the accuracy of the standards. The development and use of unconditional and conditional standards should facilitate cognitive assessment in clinical practice and deserve further studies. Copyright © 2015 American Association for Geriatric Psychiatry. Published by Elsevier Inc. All rights reserved.

Estimated HIV incidence in the United States, 2006-2009.

Directory of Open Access Journals (Sweden)

Joseph Prejean

Full Text Available BACKGROUND: The estimated number of new HIV infections in the United States reflects the leading edge of the epidemic. Previously, CDC estimated HIV incidence in the United States in 2006 as 56,300 (95% CI: 48,200-64,500. We updated the 2006 estimate and calculated incidence for 2007-2009 using improved methodology. METHODOLOGY: We estimated incidence using incidence surveillance data from 16 states and 2 cities and a modification of our previously described stratified extrapolation method based on a sample survey approach with multiple imputation, stratification, and extrapolation to account for missing data and heterogeneity of HIV testing behavior among population groups. PRINCIPAL FINDINGS: Estimated HIV incidence among persons aged 13 years and older was 48,600 (95% CI: 42,400-54,700 in 2006, 56,000 (95% CI: 49,100-62,900 in 2007, 47,800 (95% CI: 41,800-53,800 in 2008 and 48,100 (95% CI: 42,200-54,000 in 2009. From 2006 to 2009 incidence did not change significantly overall or among specific race/ethnicity or risk groups. However, there was a 21% (95% CI:1.9%-39.8%; p = 0.017 increase in incidence for people aged 13-29 years, driven by a 34% (95% CI: 8.4%-60.4% increase in young men who have sex with men (MSM. There was a 48% increase among young black/African American MSM (12.3%-83.0%; p<0.001. Among people aged 13-29, only MSM experienced significant increases in incidence, and among 13-29 year-old MSM, incidence increased significantly among young, black/African American MSM. In 2009, MSM accounted for 61% of new infections, heterosexual contact 27%, injection drug use (IDU 9%, and MSM/IDU 3%. CONCLUSIONS/SIGNIFICANCE: Overall, HIV incidence in the United States was relatively stable 2006-2009; however, among young MSM, particularly black/African American MSM, incidence increased. HIV continues to be a major public health burden, disproportionately affecting several populations in the United States, especially MSM and racial and

Modulation of steady state functional connectivity in the default mode and working memory networks by cognitive load.

Science.gov (United States)

Newton, Allen T; Morgan, Victoria L; Rogers, Baxter P; Gore, John C

2011-10-01

Interregional correlations between blood oxygen level dependent (BOLD) magnetic resonance imaging (fMRI) signals in the resting state have been interpreted as measures of connectivity across the brain. Here we investigate whether such connectivity in the working memory and default mode networks is modulated by changes in cognitive load. Functional connectivity was measured in a steady-state verbal identity N-back task for three different conditions (N = 1, 2, and 3) as well as in the resting state. We found that as cognitive load increases, the functional connectivity within both the working memory the default mode network increases. To test whether functional connectivity between the working memory and the default mode networks changed, we constructed maps of functional connectivity to the working memory network as a whole and found that increasingly negative correlations emerged in a dorsal region of the posterior cingulate cortex. These results provide further evidence that low frequency fluctuations in BOLD signals reflect variations in neural activity and suggests interaction between the default mode network and other cognitive networks. Copyright © 2010 Wiley-Liss, Inc.

Validity of the mini-mental state examination and the montreal cognitive assessment in the prediction of driving test outcome.

Science.gov (United States)

Hollis, Ann M; Duncanson, Haley; Kapust, Lissa R; Xi, Patricia M; O'Connor, Margaret G

2015-05-01

To evaluate the effectiveness of two cognitive screening measures, the Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA), in predicting driving test outcome for individuals with and without cognitive impairment. Retrospective cohort study. A clinical driving evaluation program at a teaching hospital in the United States. Adult drivers who underwent assessment with the MMSE and MoCA as part of a comprehensive driving evaluation between 2010 and 2014 (N=92). MMSE and MoCA total scores were independent variables. The outcome measure was performance on a standardized road test. A preestablished diagnosis of cognitive impairment enhanced the validity of cognitive screening measures in the identification of at-risk drivers. In individuals with cognitive impairment there was a significant relationship between MoCA score and on-road outcome. Specifically, an individual was 1.36 times as likely to fail the road test with each 1-point decrease in MoCA score. No such relationship was detected in those without a diagnosis of cognitive impairment. For individuals who have not been diagnosed with cognitive impairment, neither the MMSE nor the MoCA can be reliably used as an indicator of driving risk, but for individuals with a preestablished diagnosis of cognitive impairment, the MoCA is a useful tool in this regard. A score on the MoCA of 18 or less should raise concerns about driving safety. © 2015, Copyright the Authors Journal compilation © 2015, The American Geriatrics Society.

Consequences of Learned Helplessness and Recognition of the State of Cognitive Exhaustion in Persons with Mild Intellectual Disability.

Science.gov (United States)

Gacek, Michał; Smoleń, Tomasz; Pilecka, Władysława

2017-01-01

Persons with intellectual disability are a group at risk of being exposed to overly demanding problem-solving situations, which may produce learned helplessness . The research was based on the informational model of learned helplessness. The consequences of exposure to an unsolvable task and the ability to recognize the symptoms of cognitive exhaustion were tested in 120 students with mild intellectual disability. After the exposure to the unsolvable task, persons in the experimental group obtained lower results than the control group in the escape/avoidance learning task, but a similar result was found in the divergent thinking fluency task. Also, participants in the experimental group had difficulties recognizing the symptoms of the cognitive exhaustion state. After a week's time, the difference in escape/avoidance learning performance was still observed. The results indicate that exposure to unsolvable tasks may negatively influence the cognitive performance in persons with intellectual disability, although those persons may not identify the cognitive state related to lowered performance.

Effects of Cognitive Training on Resting-State Functional Connectivity of Default Mode, Salience, and Central Executive Networks.

Science.gov (United States)

Cao, Weifang; Cao, Xinyi; Hou, Changyue; Li, Ting; Cheng, Yan; Jiang, Lijuan; Luo, Cheng; Li, Chunbo; Yao, Dezhong

2016-01-01

Neuroimaging studies have documented that aging can disrupt certain higher cognitive systems such as the default mode network (DMN), the salience network and the central executive network (CEN). The effect of cognitive training on higher cognitive systems remains unclear. This study used a 1-year longitudinal design to explore the cognitive training effect on three higher cognitive networks in healthy older adults. The community-living healthy older adults were divided into two groups: the multi-domain cognitive training group (24 sessions of cognitive training over a 3-months period) and the wait-list control group. All subjects underwent cognitive measurements and resting-state functional magnetic resonance imaging scanning at baseline and at 1 year after the training ended. We examined training-related changes in functional connectivity (FC) within and between three networks. Compared with the baseline, we observed maintained or increased FC within all three networks after training. The scans after training also showed maintained anti-correlation of FC between the DMN and CEN compared to the baseline. These findings demonstrated that cognitive training maintained or improved the functional integration within networks and the coupling between the DMN and CEN in older adults. Our findings suggested that multi-domain cognitive training can mitigate the aging-related dysfunction of higher cognitive networks.

State estimation and control for low-cost unmanned aerial vehicles

CERN Document Server

Hajiyev, Chingiz; Yenal Vural, Sıtkı

2015-01-01

This book discusses state estimation and control procedures for a low-cost unmanned aerial vehicle (UAV). The authors consider the use of robust adaptive Kalman filter algorithms and demonstrate their advantages over the optimal Kalman filter in the context of the difficult and varied environments in which UAVs may be employed. Fault detection and isolation (FDI) and data fusion for UAV air-data systems are also investigated, and control algorithms, including the classical, optimal, and fuzzy controllers, are given for the UAV. The performance of different control methods is investigated and the results compared. State Estimation and Control of Low-Cost Unmanned Aerial Vehicles covers all the important issues for designing a guidance, navigation and control (GNC) system of a low-cost UAV. It proposes significant new approaches that can be exploited by GNC system designers in the future and also reviews the current literature. The state estimation, control and FDI methods are illustrated by examples and MATLAB...

Bridging cognitive screening tests in neurologic disorders: A crosswalk between the short Montreal Cognitive Assessment and Mini-Mental State Examination.

Science.gov (United States)

Roalf, David R; Moore, Tyler M; Mechanic-Hamilton, Dawn; Wolk, David A; Arnold, Steven E; Weintraub, Daniel A; Moberg, Paul J

2017-08-01

To provide a crosswalk between the recently proposed short Montreal Cognitive Assessment (s-MoCA) and Mini-Mental State Examination (MMSE) within a clinical cohort. A total of 791 participants, with and without neurologic conditions, received both the MMSE and the MoCA at the same visit. s-MoCA scores were calculated and equipercentile equating was used to create a crosswalk between the s-MoCA and MMSE. As expected, s-MoCA scores were highly correlated (Pearson r = 0.82, P < .001) with MMSE scores. s-MoCA scores correctly classified 85% of healthy older adults and 91% of individuals with neurologic conditions that impair cognition. In addition, we provide an easy to use table that enables the conversion of s-MoCA score to MMSE score. The s-MoCA is quick to administer, provides high sensitivity and specificity for cognitive impairment, and now can be compared directly with the MMSE. Copyright © 2017 the Alzheimer's Association. Published by Elsevier Inc. All rights reserved.

Dual states estimation of a subsurface flow-transport coupled model using ensemble Kalman filtering

KAUST Repository

El Gharamti, Mohamad

2013-10-01

Modeling the spread of subsurface contaminants requires coupling a groundwater flow model with a contaminant transport model. Such coupling may provide accurate estimates of future subsurface hydrologic states if essential flow and contaminant data are assimilated in the model. Assuming perfect flow, an ensemble Kalman filter (EnKF) can be used for direct data assimilation into the transport model. This is, however, a crude assumption as flow models can be subject to many sources of uncertainty. If the flow is not accurately simulated, contaminant predictions will likely be inaccurate even after successive Kalman updates of the contaminant model with the data. The problem is better handled when both flow and contaminant states are concurrently estimated using the traditional joint state augmentation approach. In this paper, we introduce a dual estimation strategy for data assimilation into a one-way coupled system by treating the flow and the contaminant models separately while intertwining a pair of distinct EnKFs, one for each model. The presented strategy only deals with the estimation of state variables but it can also be used for state and parameter estimation problems. This EnKF-based dual state-state estimation procedure presents a number of novel features: (i) it allows for simultaneous estimation of both flow and contaminant states in parallel; (ii) it provides a time consistent sequential updating scheme between the two models (first flow, then transport); (iii) it simplifies the implementation of the filtering system; and (iv) it yields more stable and accurate solutions than does the standard joint approach. We conducted synthetic numerical experiments based on various time stepping and observation strategies to evaluate the dual EnKF approach and compare its performance with the joint state augmentation approach. Experimental results show that on average, the dual strategy could reduce the estimation error of the coupled states by 15% compared with the

Cognitive and Emotion Regulation Change Processes in Cognitive Behavioural Therapy for Social Anxiety Disorder.

Science.gov (United States)

O'Toole, Mia S; Mennin, Douglas S; Hougaard, Esben; Zachariae, Robert; Rosenberg, Nicole K

2015-01-01

The objective of the study was to investigate variables, derived from both cognitive and emotion regulation conceptualizations of social anxiety disorder (SAD), as possible change processes in cognitive behaviour therapy (CBT) for SAD. Several proposed change processes were investigated: estimated probability, estimated cost, safety behaviours, acceptance of emotions, cognitive reappraisal and expressive suppression. Participants were 50 patients with SAD, receiving a standard manualized CBT program, conducted in groups or individually. All variables were measured pre-therapy, mid-therapy and post-therapy. Lower level mediation models revealed that while a change in most process measures significantly predicted clinical improvement, only changes in estimated probability and cost and acceptance of emotions showed significant indirect effects of CBT for SAD. The results are in accordance with previous studies supporting the mediating role of changes in cognitive distortions in CBT for SAD. In addition, acceptance of emotions may also be a critical component to clinical improvement in SAD during CBT, although more research is needed on which elements of acceptance are most helpful for individuals with SAD. The study's lack of a control condition limits any conclusion regarding the specificity of the findings to CBT. Change in estimated probability and cost, and acceptance of emotions showed an indirect effect of CBT for SAD. Cognitive distortions appear relevant to target with cognitive restructuring techniques. Finding acceptance to have an indirect effect could be interpreted as support for contemporary CBT approaches that include acceptance-based strategies. Copyright © 2014 John Wiley & Sons, Ltd.

Association of Source of Memory Complaints and Increased Risk of Cognitive Impairment and Cognitive Decline: A Community-Based Study.

Science.gov (United States)

Qi, Xue-Mei; Gu, Lin; Tang, Hui-Dong; Chen, Sheng-Di; Ma, Jian-Fang

2018-04-20

Memory complaint is common in the elderly. Recently, it was shown that self-report memory complaint was predictive of cognitive decline. This study aimed to investigate the predictive value of the source of memory complaints on the risk of cognitive impairment and cognitive decline in a community-based cohort. Data on memory complaints and cognitive function were collected among 1840 Chinese participants (aged ≥55 years old) in an urban community at baseline interview and 5-year follow-up. Incident cognitive impairment was identified based on education-adjusted Mini-Mental State Examination score. Logistic regression model was used to estimate the association between the source of memory complaints and risk of cognitive impairment conversion and cognitive decline, after adjusting for covariates. A total of 1840 participants were included into this study including 1713 normal participants and 127 cognitive impairment participants in 2009. Among 1713 normal participants in 2009, 130 participants were converted to cognitive impairment after 5 years of follow-up. In 2014, 606 participants were identified as cognitive decline. Both self- and informant-reported memory complaints were associated with an increased risk of cognitive impairment (odds ratio [OR] = 1.60, 95% confidence interval [CI]: 1.04-2.48) and cognitive decline (OR = 1.30, 95% CI: 1.01-1.68). Furthermore, this association was more significant in males (OR = 2.10, 95% CI: 1.04-4.24 for cognitive impairment and OR = 1.87, 95% CI: 1.20-2.99 for cognitive decline) and in higher education level (OR = 1.79, 95% CI: 1.02-3.15 for cognitive impairment and OR = 1.40, 95% CI: 1.02-1.91 for cognitive decline). Both self- and informant-reported memory complaints were associated with an increased risk of cognitive impairment conversion and cognitive decline, especially in persons with male gender and high educational background.

Estimating the inbreeding depression on cognitive behavior: a population based study of child cohort.

Directory of Open Access Journals (Sweden)

Mohd Fareed

Full Text Available Cognitive ability tests are widely assumed to measure maximal intellectual performance and predictive associations between intelligence quotient (IQ scores and later mental health problems. Very few epidemiologic studies have been done to demonstrate the relationship between familial inbreeding and modest cognitive impairments in children.We aimed to estimate the effect of inbreeding on children's cognitive behavior in comparison with non-inbred children.A cohort of 408 children (6 to 15 years of age was selected from inbred and non-inbred families of five Muslim populations of Jammu region. The Wechsler Intelligence Scales for Children (WISC was used to measure the verbal IQ (VIQ, performance IQ (PIQ and full scale IQ (FSIQ. Family pedigrees were drawn to access the family history and children's inbred status in terms of coefficient of inbreeding (F.We found significant decline in child cognitive abilities due to inbreeding and high frequency of mental retardation among offspring from inbred families. The mean differences (95% C.I. were reported for the VIQ, being -22.00 (-24.82, -19.17, PIQ -26.92 (-29.96, -23.87 and FSIQ -24.47 (-27.35,-21.59 for inbred as compared to non-inbred children (p<0.001 [corrected].The higher risk of being mentally retarded was found to be more obvious among inbred categories corresponding to the degree of inbreeding and the same accounts least for non-inbred children (p<0.0001. We observed an increase in the difference in mean values for VIQ, PIQ and FSIQ with the increase of inbreeding coefficient and these were found to be statistically significant (p<0.05. The regression analysis showed a fitness decline (depression for VIQ (R2 = 0.436, PIQ (R2 = 0.468 and FSIQ (R2 = 0.464 with increasing inbreeding coefficients (p<0.01.Our comprehensive assessment provides the evidence for inbreeding depression on cognitive abilities among children.

Support vector machines for nuclear reactor state estimation

Energy Technology Data Exchange (ETDEWEB)

Zavaljevski, N.; Gross, K. C.

2000-02-14

Validation of nuclear power reactor signals is often performed by comparing signal prototypes with the actual reactor signals. The signal prototypes are often computed based on empirical data. The implementation of an estimation algorithm which can make predictions on limited data is an important issue. A new machine learning algorithm called support vector machines (SVMS) recently developed by Vladimir Vapnik and his coworkers enables a high level of generalization with finite high-dimensional data. The improved generalization in comparison with standard methods like neural networks is due mainly to the following characteristics of the method. The input data space is transformed into a high-dimensional feature space using a kernel function, and the learning problem is formulated as a convex quadratic programming problem with a unique solution. In this paper the authors have applied the SVM method for data-based state estimation in nuclear power reactors. In particular, they implemented and tested kernels developed at Argonne National Laboratory for the Multivariate State Estimation Technique (MSET), a nonlinear, nonparametric estimation technique with a wide range of applications in nuclear reactors. The methodology has been applied to three data sets from experimental and commercial nuclear power reactor applications. The results are promising. The combination of MSET kernels with the SVM method has better noise reduction and generalization properties than the standard MSET algorithm.

Support vector machines for nuclear reactor state estimation

International Nuclear Information System (INIS)

Zavaljevski, N.; Gross, K. C.

2000-01-01

Validation of nuclear power reactor signals is often performed by comparing signal prototypes with the actual reactor signals. The signal prototypes are often computed based on empirical data. The implementation of an estimation algorithm which can make predictions on limited data is an important issue. A new machine learning algorithm called support vector machines (SVMS) recently developed by Vladimir Vapnik and his coworkers enables a high level of generalization with finite high-dimensional data. The improved generalization in comparison with standard methods like neural networks is due mainly to the following characteristics of the method. The input data space is transformed into a high-dimensional feature space using a kernel function, and the learning problem is formulated as a convex quadratic programming problem with a unique solution. In this paper the authors have applied the SVM method for data-based state estimation in nuclear power reactors. In particular, they implemented and tested kernels developed at Argonne National Laboratory for the Multivariate State Estimation Technique (MSET), a nonlinear, nonparametric estimation technique with a wide range of applications in nuclear reactors. The methodology has been applied to three data sets from experimental and commercial nuclear power reactor applications. The results are promising. The combination of MSET kernels with the SVM method has better noise reduction and generalization properties than the standard MSET algorithm

Evaluation and Comparison of Cognitive State and Depression in Elderly Admitted in Sanitarium with Elderly Sited in Personal Home

OpenAIRE

Mohamad-baghere Sohrabi; Pone Zolfaghari; Farzane Mahdizade; Seyd-Mohammad Aghayan; Mojtaba Ghasemian- Aghmashhadi; Zahra Shariati; Ahmad Khosravi

2008-01-01

Introduction: In this study cognitive state and geriatric depression in elderly living in sanatorium and personal home were evaluated. Methods: The present study was conducted on 46 aged subjects living in sanitarium and 90 aged subjects staying in personal homes. Mini mental status examination (MMSE) and geriatric depression scale (GDS) questionnaires were completed according to the standard examination. Results: As for the cognitive state of the aged living in sanitarium, 13 cases (28.3%) s...

Reconfiguration of Brain Network Architectures between Resting-State and Complexity-Dependent Cognitive Reasoning.

Science.gov (United States)

Hearne, Luke J; Cocchi, Luca; Zalesky, Andrew; Mattingley, Jason B

2017-08-30

Our capacity for higher cognitive reasoning has a measurable limit. This limit is thought to arise from the brain's capacity to flexibly reconfigure interactions between spatially distributed networks. Recent work, however, has suggested that reconfigurations of task-related networks are modest when compared with intrinsic "resting-state" network architecture. Here we combined resting-state and task-driven functional magnetic resonance imaging to examine how flexible, task-specific reconfigurations associated with increasing reasoning demands are integrated within a stable intrinsic brain topology. Human participants (21 males and 28 females) underwent an initial resting-state scan, followed by a cognitive reasoning task involving different levels of complexity, followed by a second resting-state scan. The reasoning task required participants to deduce the identity of a missing element in a 4 × 4 matrix, and item difficulty was scaled parametrically as determined by relational complexity theory. Analyses revealed that external task engagement was characterized by a significant change in functional brain modules. Specifically, resting-state and null-task demand conditions were associated with more segregated brain-network topology, whereas increases in reasoning complexity resulted in merging of resting-state modules. Further increments in task complexity did not change the established modular architecture, but affected selective patterns of connectivity between frontoparietal, subcortical, cingulo-opercular, and default-mode networks. Larger increases in network efficiency within the newly established task modules were associated with higher reasoning accuracy. Our results shed light on the network architectures that underlie external task engagement, and highlight selective changes in brain connectivity supporting increases in task complexity. SIGNIFICANCE STATEMENT Humans have clear limits in their ability to solve complex reasoning problems. It is thought that

Thinner cortex in patients with subjective cognitive decline is associated with steeper decline of memory.

Science.gov (United States)

Verfaillie, Sander C J; Slot, Rosalinde E; Tijms, Betty M; Bouwman, Femke; Benedictus, Marije R; Overbeek, Jozefien M; Koene, Teddy; Vrenken, Hugo; Scheltens, Philip; Barkhof, Frederik; van der Flier, Wiesje M

2018-01-01

We aimed to investigate associations between regional cortical thickness and rate of decline over time in 4 cognitive domains in patients with subjective cognitive decline (SCD). We included 233 SCD patients with the total number of 654 neuropsychological assessments (median = 3, range = 2-8) and available baseline magnetic resonance imaging from the Amsterdam Dementia Cohort (125 males, age: 63 ± 9, Mini-Mental State Examination score: 28 ± 2). We assessed longitudinal cognitive functioning at baseline and follow-up in 4 cognitive domains (composite Z-scores): memory, attention, executive function, and language. Thickness (millimeter) was estimated using FreeSurfer for frontal, temporal, parietal, cingulate, and occipital cortices. We used linear mixed models to estimate effects of cortical thickness on cognitive performance (dependent variables). There were no associations between cortical thickness and baseline cognition, but a faster subsequent rate of memory loss was associated with thinner cortex of the frontal [β (SE) = 0.20 (0.07)], temporal [β (SE) = 0.18 (0.07)], and occipital [β (SE) = 0.22 (0.09)] cortices (all p cognitive decline related to neurodegenerative diseases, most prominently Alzheimer's disease. Copyright © 2017 Elsevier Inc. All rights reserved.

Hybrid fuzzy charged system search algorithm based state estimation in distribution networks

Directory of Open Access Journals (Sweden)

Sachidananda Prasad

2017-06-01

Full Text Available This paper proposes a new hybrid charged system search (CSS algorithm based state estimation in radial distribution networks in fuzzy framework. The objective of the optimization problem is to minimize the weighted square of the difference between the measured and the estimated quantity. The proposed method of state estimation considers bus voltage magnitude and phase angle as state variable along with some equality and inequality constraints for state estimation in distribution networks. A rule based fuzzy inference system has been designed to control the parameters of the CSS algorithm to achieve better balance between the exploration and exploitation capability of the algorithm. The efficiency of the proposed fuzzy adaptive charged system search (FACSS algorithm has been tested on standard IEEE 33-bus system and Indian 85-bus practical radial distribution system. The obtained results have been compared with the conventional CSS algorithm, weighted least square (WLS algorithm and particle swarm optimization (PSO for feasibility of the algorithm.

Efficacy of Cognitive Training in Older Adults with and without Subjective Cognitive Decline Is Associated with Inhibition Efficiency and Working Memory Span, Not with Cognitive Reserve.

Science.gov (United States)

López-Higes, Ramón; Martín-Aragoneses, María T; Rubio-Valdehita, Susana; Delgado-Losada, María L; Montejo, Pedro; Montenegro, Mercedes; Prados, José M; de Frutos-Lucas, Jaisalmer; López-Sanz, David

2018-01-01

The present study explores the role of cognitive reserve, executive functions, and working memory (WM) span, as factors that might explain training outcomes in cognitive status. Eighty-one older adults voluntarily participated in the study, classified either as older adults with subjective cognitive decline or cognitively intact. Each participant underwent a neuropsychological assessment that was conducted both at baseline (entailing cognitive reserve, executive functions, WM span and depressive symptomatology measures, as well as the Mini-Mental State Exam regarding initial cognitive status), and then 6 months later, once each participant had completed the training program (Mini-Mental State Exam at the endpoint). With respect to cognitive status the training program was most beneficial for subjective cognitive decline participants with low efficiency in inhibition at baseline (explaining a 33% of Mini-Mental State Exam total variance), whereas for cognitively intact participants training gains were observed for those who presented lower WM span.

Survey of State-Level Cost and Benefit Estimates of Renewable Portfolio Standards

Energy Technology Data Exchange (ETDEWEB)

Heeter, J.; Barbose, G.; Bird, L.; Weaver, S.; Flores-Espino, F.; Kuskova-Burns, K.; Wiser, R.

2014-05-01

Most renewable portfolio standards (RPS) have five or more years of implementation experience, enabling an assessment of their costs and benefits. Understanding RPS costs and benefits is essential for policymakers evaluating existing RPS policies, assessing the need for modifications, and considering new policies. This study provides an overview of methods used to estimate RPS compliance costs and benefits, based on available data and estimates issued by utilities and regulators. Over the 2010-2012 period, average incremental RPS compliance costs in the United States were equivalent to 0.8% of retail electricity rates, although substantial variation exists around this average, both from year-to-year and across states. The methods used by utilities and regulators to estimate incremental compliance costs vary considerably from state to state and a number of states are currently engaged in processes to refine and standardize their approaches to RPS cost calculation. The report finds that state assessments of RPS benefits have most commonly attempted to quantitatively assess avoided emissions and human health benefits, economic development impacts, and wholesale electricity price savings. Compared to the summary of RPS costs, the summary of RPS benefits is more limited, as relatively few states have undertaken detailed benefits estimates, and then only for a few types of potential policy impacts. In some cases, the same impacts may be captured in the assessment of incremental costs. For these reasons, and because methodologies and level of rigor vary widely, direct comparisons between the estimates of benefits and costs are challenging.

Estimates of lifetime infertility from three states: the behavioral risk factor surveillance system.

Science.gov (United States)

Crawford, Sara; Fussman, Chris; Bailey, Marie; Bernson, Dana; Jamieson, Denise J; Murray-Jordan, Melissa; Kissin, Dmitry M

2015-07-01

Knowledge of state-specific infertility is limited. The objectives of this study were to explore state-specific estimates of lifetime prevalence of having ever experienced infertility, sought treatment for infertility, types of treatments sought, and treatment outcomes. Male and female adult residents aged 18-50 years from three states involved in the States Monitoring Assisted Reproductive Technology Collaborative (Florida, Massachusetts, and Michigan) were asked state-added infertility questions as part of the 2012 Behavioral Risk Factor Surveillance System, a state-based, health-related telephone survey. Analysis involved estimation of lifetime prevalence of infertility. The estimated lifetime prevalence of infertility among 1,285 adults in Florida, 1,302 in Massachusetts, and 3,360 in Michigan was 9.7%, 6.0%, and 4.2%, respectively. Among 736 adults in Florida, 1,246 in Massachusetts, and 2,742 in Michigan that have ever tried to get pregnant, the lifetime infertility prevalence was 25.3% in Florida, 9.9% in Massachusetts, and 5.8% in Michigan. Among those with a history of infertility, over half sought treatment (60.7% in Florida, 70.6% in Massachusetts, and 51.6% in Michigan), the most common being non-assisted reproductive technology fertility treatments (61.3% in Florida, 66.0% in Massachusetts, and 75.9% in Michigan). State-specific estimates of lifetime infertility prevalence in Florida, Massachusetts, and Michigan varied. Variations across states are difficult to interpret, as they likely reflect both true differences in prevalence and differences in data collection questionnaires. State-specific estimates are needed for the prevention, detection, and management of infertility, but estimates should be based on a common set of questions appropriate for these goals.

A Best-Estimate Reactor Core Monitor Using State Feedback Strategies to Reduce Uncertainties

International Nuclear Information System (INIS)

Martin, Robert P.; Edwards, Robert M.

2000-01-01

The development and demonstration of a new algorithm to reduce modeling and state-estimation uncertainty in best-estimate simulation codes has been investigated. Demonstration is given by way of a prototype reactor core monitor. The architecture of this monitor integrates a control-theory-based, distributed-parameter estimation technique into a production-grade best-estimate simulation code. The Kalman Filter-Sequential Least-Squares (KFSLS) parameter estimation algorithm has been extended for application into the computational environment of the best-estimate simulation code RELAP5-3D. In control system terminology, this configuration can be thought of as a 'best-estimate' observer. The application to a distributed-parameter reactor system involves a unique modal model that approximates physical components, such as the reactor, by describing both states and parameters by an orthogonal expansion. The basic KFSLS parameter estimation is used to dynamically refine a spatially varying (distributed) parameter. The application of the distributed-parameter estimator is expected to complement a traditional nonlinear best-estimate simulation code by providing a mechanism for reducing both code input (modeling) and output (state-estimation) uncertainty in complex, distributed-parameter systems

Brain reserve and cognitive reserve protect against cognitive decline over 4.5 years in MS.

Science.gov (United States)

Sumowski, James F; Rocca, Maria A; Leavitt, Victoria M; Dackovic, Jelena; Mesaros, Sarlota; Drulovic, Jelena; DeLuca, John; Filippi, Massimo

2014-05-20

Based on the theories of brain reserve and cognitive reserve, we investigated whether larger maximal lifetime brain growth (MLBG) and/or greater lifetime intellectual enrichment protect against cognitive decline over time. Forty patients with multiple sclerosis (MS) underwent baseline and 4.5-year follow-up evaluations of cognitive efficiency (Symbol Digit Modalities Test, Paced Auditory Serial Addition Task) and memory (Selective Reminding Test, Spatial Recall Test). Baseline and follow-up MRIs quantified disease progression: percentage brain volume change (cerebral atrophy), percentage change in T2 lesion volume. MLBG (brain reserve) was estimated with intracranial volume; intellectual enrichment (cognitive reserve) was estimated with vocabulary. We performed repeated-measures analyses of covariance to investigate whether larger MLBG and/or greater intellectual enrichment moderate/attenuate cognitive decline over time, controlling for disease progression. Patients with MS declined in cognitive efficiency and memory (p improve prediction of future cognitive decline in patients with MS. © 2014 American Academy of Neurology.

Response-based estimation of sea state parameters - Influence of filtering

DEFF Research Database (Denmark)

Nielsen, Ulrik Dam

2007-01-01

Reliable estimation of the on-site sea state parameters is essential to decision support systems for safe navigation of ships. The wave spectrum can be estimated from procedures based on measured ship responses. The paper deals with two procedures—Bayesian Modelling and Parametric Modelling...

Nonlinear Adaptive Descriptor Observer for the Joint States and Parameters Estimation

KAUST Repository

2016-08-29

In this note, the joint state and parameters estimation problem for nonlinear multi-input multi-output descriptor systems is considered. Asymptotic convergence of the adaptive descriptor observer is established by a sufficient set of linear matrix inequalities for the noise-free systems. The noise corrupted systems are also considered and it is shown that the state and parameters estimation errors are bounded for bounded noises. In addition, if the noises are bounded and have zero mean, then the estimation errors asymptotically converge to zero in the mean. The performance of the proposed adaptive observer is illustrated by a numerical example.

Nonlinear Adaptive Descriptor Observer for the Joint States and Parameters Estimation

KAUST Repository

Unknown author

2016-01-01

In this note, the joint state and parameters estimation problem for nonlinear multi-input multi-output descriptor systems is considered. Asymptotic convergence of the adaptive descriptor observer is established by a sufficient set of linear matrix inequalities for the noise-free systems. The noise corrupted systems are also considered and it is shown that the state and parameters estimation errors are bounded for bounded noises. In addition, if the noises are bounded and have zero mean, then the estimation errors asymptotically converge to zero in the mean. The performance of the proposed adaptive observer is illustrated by a numerical example.

Neuropsychological profile in Chinese patients with Parkinson's disease and normal global cognition according to Mini-Mental State Examination Score.

Science.gov (United States)

Qiao, Jin; Zheng, Xiyuan; Wang, Xiaoyan; Lu, Wenhui; Cao, Hongmei; Qin, Xing

2015-01-01

Cognitive impairments have been reported to be more common in non-demented patients with Parkinson's disease (PD) and education levels play an important role in intelligence. The studies on cognitive impairments in Chinese PD patients with higher education levels and normal global cognition according to Mini-Mental State Examination Score (MMSE) have not been reported. We enrolled 69 consecutive PD patients with over 6 years education levels and a MMSE score above 24 (of 30) and performed a battery of neuropsychological scales. There are extensive cognitive domain impairments in PD patients with "normal" global cognitive according to MMSE. Montreal Cognitive Assessment (MoCA) is a highly sensitive scale to screen cognitive impairments in PD. The cutoff score of 28 on the MMSE screening for cognitive impairment in Chinese PD patients with high education levels may be more appropriate.

Particle-filtering-based estimation of maximum available power state in Lithium-Ion batteries

International Nuclear Information System (INIS)

Burgos-Mellado, Claudio; Orchard, Marcos E.; Kazerani, Mehrdad; Cárdenas, Roberto; Sáez, Doris

2016-01-01

Highlights: • Approach to estimate the state of maximum power available in Lithium-Ion battery. • Optimisation problem is formulated on the basis of a non-linear dynamic model. • Solutions of the optimisation problem are functions of state of charge estimates. • State of charge estimates computed using particle filter algorithms. - Abstract: Battery Energy Storage Systems (BESS) are important for applications related to both microgrids and electric vehicles. If BESS are used as the main energy source, then it is required to include adequate procedures for the estimation of critical variables such as the State of Charge (SoC) and the State of Health (SoH) in the design of Battery Management Systems (BMS). Furthermore, in applications where batteries are exposed to high charge and discharge rates it is also desirable to estimate the State of Maximum Power Available (SoMPA). In this regard, this paper presents a novel approach to the estimation of SoMPA in Lithium-Ion batteries. This method formulates an optimisation problem for the battery power based on a non-linear dynamic model, where the resulting solutions are functions of the SoC. In the battery model, the polarisation resistance is modelled using fuzzy rules that are function of both SoC and the discharge (charge) current. Particle filtering algorithms are used as an online estimation technique, mainly because these algorithms allow approximating the probability density functions of the SoC and SoMPA even in the case of non-Gaussian sources of uncertainty. The proposed method for SoMPA estimation is validated using the experimental data obtained from an experimental setup designed for charging and discharging the Lithium-Ion batteries.

Estimates of the Resident Nonimmigrant Population in the United States: 2008

Data.gov (United States)

Department of Homeland Security — This report presents estimates on the size and characteristics of the resident nonimmigrant population in the United States in 2008.1 The estimates were based on...

Towards Real-Time Maneuver Detection: Automatic State and Dynamics Estimation with the Adaptive Optimal Control Based Estimator

Science.gov (United States)

Lubey, D.; Scheeres, D.

Tracking objects in Earth orbit is fraught with complications. This is due to the large population of orbiting spacecraft and debris that continues to grow, passive (i.e. no direct communication) and data-sparse observations, and the presence of maneuvers and dynamics mismodeling. Accurate orbit determination in this environment requires an algorithm to capture both a system's state and its state dynamics in order to account for mismodelings. Previous studies by the authors yielded an algorithm called the Optimal Control Based Estimator (OCBE) - an algorithm that simultaneously estimates a system's state and optimal control policies that represent dynamic mismodeling in the system for an arbitrary orbit-observer setup. The stochastic properties of these estimated controls are then used to determine the presence of mismodelings (maneuver detection), as well as characterize and reconstruct the mismodelings. The purpose of this paper is to develop the OCBE into an accurate real-time orbit tracking and maneuver detection algorithm by automating the algorithm and removing its linear assumptions. This results in a nonlinear adaptive estimator. In its original form the OCBE had a parameter called the assumed dynamic uncertainty, which is selected by the user with each new measurement to reflect the level of dynamic mismodeling in the system. This human-in-the-loop approach precludes real-time application to orbit tracking problems due to their complexity. This paper focuses on the Adaptive OCBE, a version of the estimator where the assumed dynamic uncertainty is chosen automatically with each new measurement using maneuver detection results to ensure that state uncertainties are properly adjusted to account for all dynamic mismodelings. The paper also focuses on a nonlinear implementation of the estimator. Originally, the OCBE was derived from a nonlinear cost function then linearized about a nominal trajectory, which is assumed to be ballistic (i.e. the nominal optimal

The Prevalence of Mild Cognitive Impairment in Diverse Geographical and Ethnocultural Regions: The COSMIC Collaboration.

Directory of Open Access Journals (Sweden)

Perminder S Sachdev

Full Text Available Changes in criteria and differences in populations studied and methodology have produced a wide range of prevalence estimates for mild cognitive impairment (MCI.Uniform criteria were applied to harmonized data from 11 studies from USA, Europe, Asia and Australia, and MCI prevalence estimates determined using three separate definitions of cognitive impairment.The published range of MCI prevalence estimates was 5.0%-36.7%. This was reduced with all cognitive impairment definitions: performance in the bottom 6.681% (3.2%-10.8%; Clinical Dementia Rating of 0.5 (1.8%-14.9%; Mini-Mental State Examination score of 24-27 (2.1%-20.7%. Prevalences using the first definition were 5.9% overall, and increased with age (P < .001 but were unaffected by sex or the main races/ethnicities investigated (Whites and Chinese. Not completing high school increased the likelihood of MCI (P ≤ .01.Applying uniform criteria to harmonized data greatly reduced the variation in MCI prevalence internationally.

Intelligence and education as predictors of cognitive state in late life: a 50-year follow-up.

Science.gov (United States)

Plassman, B L; Welsh, K A; Helms, M; Brandt, J; Page, W F; Breitner, J C

1995-08-01

We evaluated the relation of education and intelligence in early adult life to cognitive function in a group of elderly male twins. The Army General Classification Test (AGCT) was administered to US armed forces inductees in the early 1940s. Fifty years later, as part of a study of dementia in twins, we tested the cognitive status of 930 of these men using the modified Telephone Interview for Cognitive Status (TICS-m). TICS-m scores obtained in later life were correlated with AGCT scores (r = 0.457) and with years of education (r = 0.408). Thus, in univariate analyses, the AGCT score accounted for 20.6% and education accounted for 16.7% of variance in cognitive status. However, these two effects were not fully independent. A multivariable model using AGCT score, education, and the interaction of the two variables as predictors of the TICS-m score explained 24.8% of the variance, a slightly but significantly greater proportion than was explained by either factor alone. In a separate analysis based on 604 pairs of twins who took the AGCT, heritability of intelligence (estimated by AGCT score) was 0.503. Although this study does not address the issue of education and premorbid IQ as risk factors for dementia, the findings suggest that basic cognitive abilities in late life are related to cognitive performance measures from early adult life (ie, education and IQ).

[Short evaluation of cognitive state in advanced stages of dementia: preliminary results of the Spanish validation of the Severe Mini-Mental State Examination].

Science.gov (United States)

Buiza, Cristina; Navarro, Ana; Díaz-Orueta, Unai; González, Mari Feli; Alaba, Javier; Arriola, Enrique; Hernández, Carmen; Zulaica, Amaia; Yanguas, José Javier

2011-01-01

The cognitive assessment of patients with advanced dementia needs proper screening instruments that allow obtain information about the cognitive state and resources that these individuals still have. The present work conducts a Spanish validation study of the Severe Mini Mental State Examination (SMMSE). Forty-seven patients with advanced dementia (Mini-Cognitive Examination [MEC]Cognitive Impairment Profile scales. All test items were discriminative. The test showed high internal (α=0.88), test-retest (0.64 to 1.00, Pvalidity was tested through correlations between the instrument and MEC scores (r=0.59, Pinformation on the construct validity was obtained by dividing the sample into groups that scored above or below 5 points in the MEC and recalculating their correlations with SMMSE. The correlation between the scores in the SMMSE and MEC was significant in the MEC 0-5 group (r=0.55, P5 group. Additionally, differences in scores were found in the SMMSE, but not in the MEC, between the three GDS groups (5, 6 and 7) (H=11.1, Pcognitive impairment which prevents the floor effect through an extension of lower measurement range relative to that of the MEC. From our results, this rapid screening tool and easy to administer, can be considered valid and reliable. Copyright © 2010 SEGG. Published by Elsevier Espana. All rights reserved.

Full State Estimation for Helicopter Slung Load System

DEFF Research Database (Denmark)

Bisgaard, Morten; la Cour-Harbo, Anders; Bendtsen, Jan Dimon

This paper presents the design of a state estimator system for a generic helicopter based slung load system. The estimator is designed to deliver full rigid body state information for both helicopter and load and is based on the unscented Kalman filter. Two different approaches are investigated......: One based on a parameter free kinematic model and one based on a full aerodynamic helicopter and slung load model. The kinematic model approach uses acceleration and rate information from two Inertial Measurement Units, one on the helicopter and one on the load, to drive a simple kinematic model....... A simple and effective virtual sensor method is developed to maintain the constraints imposed by the wires in the system. The full model based approach uses a complex aerodynamical model to describe the helicopter together with a generic rigid body model. This rigid body model is based on a redundant...

Full State Estimation for Helicopter Slung Load System

DEFF Research Database (Denmark)

Bisgaard, Morten; la Cour-Harbo, Anders; Bendtsen, Jan Dimon

2007-01-01

This paper presents the design of a state estimator system for a generic helicopter based slung load system. The estimator is designed to deliver full rigid body state information for both helicopter and load and is based on the unscented Kalman filter. Two different approaches are investigated......: One based on a parameter free kinematic model and one based on a full aerodynamic helicopter and slung load model. The kinematic model approach uses acceleration and rate information from two Inertial Measurement Units, one on the helicopter and one on the load, to drive a simple kinematic model....... A simple and effective virtual sensor method is developed to maintain the constraints imposed by the wires in the system. The full model based approach uses a complex aerodynamical model to describe the helicopter together with a generic rigid body model. This rigid body model is based on a redundant...

Automatic Regionalization Algorithm for Distributed State Estimation in Power Systems: Preprint

Energy Technology Data Exchange (ETDEWEB)

Wang, Dexin; Yang, Liuqing; Florita, Anthony; Alam, S.M. Shafiul; Elgindy, Tarek; Hodge, Bri-Mathias

2016-08-01

The deregulation of the power system and the incorporation of generation from renewable energy sources recessitates faster state estimation in the smart grid. Distributed state estimation (DSE) has become a promising and scalable solution to this urgent demand. In this paper, we investigate the regionalization algorithms for the power system, a necessary step before distributed state estimation can be performed. To the best of the authors' knowledge, this is the first investigation on automatic regionalization (AR). We propose three spectral clustering based AR algorithms. Simulations show that our proposed algorithms outperform the two investigated manual regionalization cases. With the help of AR algorithms, we also show how the number of regions impacts the accuracy and convergence speed of the DSE and conclude that the number of regions needs to be chosen carefully to improve the convergence speed of DSEs.

Does the Addenbrooke's Cognitive Examination-revised add to the Mini-Mental State Examination in established Alzheimer disease? Results from a national dementia research register.

Science.gov (United States)

Law, Emma; Connelly, Peter J; Randall, Emma; McNeill, Catriona; Fox, Helen C; Parra, Mario A; Hudson, Justine; Whyte, Leigh-Ann; Johnstone, Jane; Gray, Sarah; Starr, John M

2013-04-01

To evaluate how much the Addenbrooke's Cognitive Examination-revised (ACE-R) improves the estimate of cognitive ability from the Mini-Mental State Examination (MMSE) in people with Alzheimer disease (AD). We examined itemized data in people with AD who were on the Scottish Dementia Research Interest Register drawn from eight centres across Scotland, covering 75% of the Scottish population. ACE-R items that comprise the MMSE and those that did not (non-MMSE items) were summed separately. We residualized MMSE total on non-MMSE total and vice versa to derive a measure of the variance unique to each. Five hundred and one (258 male, 243 female) participants, mean age 75.7 (range 52-94) years were on the register, of whom 329 (160 men, 169 women) had AD. Of those with AD, 309 had a mean MMSE of 20.5 and mean ACE-R of 57.5 measured with Pearson r = 0.92 between MMSE and ACE-R totals, and the regression equation ACE-R score = 3.0 × MMSE - 4.1. The unique non-MMSE items score correlated with ACE-R total r = 0.40 (16% of ACE-R variance). The ACE-R and MMSE total scores are highly correlated. In this clinical sample of people with established AD, for an MMSE score of 24, the predicted ACE-R score was 67.9 with 95% confidence intervals of 61.6-75.4. The extra non-MMSE ACE-R items improve estimates of cognitive ability by 16%. Copyright © 2012 John Wiley & Sons, Ltd.

Estimating Brain Load from the EEG

Directory of Open Access Journals (Sweden)

Anu Holm

2009-01-01

Full Text Available Modern work requires cognitively demanding multitasking and the need for sustained vigilance, which may result in work-related stress and may increase the possibility of human error. Objective methods for estimating cognitive overload and mental fatigue of the brain on-line, during work performance, are needed. We present a two-channel electroencephalography (EEG–based index, theta Fz/alpha Pz ratio, potentially implementable into a compact wearable device. The index reacts to both acute external and cumulative internal load. The index increased with the number of tasks to be performed concurrently (p = 0.004 and with increased time awake, both after normal sleep (p = 0.002 and sleep restriction (p = 0.004. Moreover, the increase of the index was more pronounced in the afternoon after sleep restriction (p = 0.006. As a measure of brain state and its dynamics, the index can be considered equivalent to the heartbeat, an indicator of the cardiovascular state, thus inspiring the name "brainbeat".

An approach for estimating item sensitivity to within-person change over time: An illustration using the Alzheimer's Disease Assessment Scale-Cognitive subscale (ADAS-Cog).

Science.gov (United States)

Dowling, N Maritza; Bolt, Daniel M; Deng, Sien

2016-12-01

When assessments are primarily used to measure change over time, it is important to evaluate items according to their sensitivity to change, specifically. Items that demonstrate good sensitivity to between-person differences at baseline may not show good sensitivity to change over time, and vice versa. In this study, we applied a longitudinal factor model of change to a widely used cognitive test designed to assess global cognitive status in dementia, and contrasted the relative sensitivity of items to change. Statistically nested models were estimated introducing distinct latent factors related to initial status differences between test-takers and within-person latent change across successive time points of measurement. Models were estimated using all available longitudinal item-level data from the Alzheimer's Disease Assessment Scale-Cognitive subscale, including participants representing the full-spectrum of disease status who were enrolled in the multisite Alzheimer's Disease Neuroimaging Initiative. Five of the 13 Alzheimer's Disease Assessment Scale-Cognitive items demonstrated noticeably higher loadings with respect to sensitivity to change. Attending to performance change on only these 5 items yielded a clearer picture of cognitive decline more consistent with theoretical expectations in comparison to the full 13-item scale. Items that show good psychometric properties in cross-sectional studies are not necessarily the best items at measuring change over time, such as cognitive decline. Applications of the methodological approach described and illustrated in this study can advance our understanding regarding the types of items that best detect fine-grained early pathological changes in cognition. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Bridging consciousness and cognition in memory and perception: evidence for both state and strength processes.

Science.gov (United States)

Aly, Mariam; Yonelinas, Andrew P

2012-01-01

Subjective experience indicates that mental states are discrete, in the sense that memories and perceptions readily come to mind in some cases, but are entirely unavailable to awareness in others. However, a long history of psychophysical research has indicated that the discrete nature of mental states is largely epiphenomenal and that mental processes vary continuously in strength. We used a novel combination of behavioral methodologies to examine the processes underlying perception of complex images: (1) analysis of receiver operating characteristics (ROCs), (2) a modification of the change-detection flicker paradigm, and (3) subjective reports of conscious experience. These methods yielded converging results showing that perceptual judgments reflect the combined, yet functionally independent, contributions of two processes available to conscious experience: a state process of conscious perception and a strength process of knowing; processes that correspond to recollection and familiarity in long-term memory. In addition, insights from the perception experiments led to the discovery of a new recollection phenomenon in a long-term memory change detection paradigm. The apparent incompatibility between subjective experience and theories of cognition can be understood within a unified state-strength framework that links consciousness to cognition across the domains of perception and memory.

Dual extended Kalman filter for combined estimation of vehicle state and road friction

Science.gov (United States)

Zong, Changfu; Hu, Dan; Zheng, Hongyu

2013-03-01

Vehicle state and tire-road adhesion are of great use and importance to vehicle active safety control systems. However, it is always not easy to obtain the information with high accuracy and low expense. Recently, many estimation methods have been put forward to solve such problems, in which Kalman filter becomes one of the most popular techniques. Nevertheless, the use of complicated model always leads to poor real-time estimation while the role of road friction coefficient is often ignored. For the purpose of enhancing the real time performance of the algorithm and pursuing precise estimation of vehicle states, a model-based estimator is proposed to conduct combined estimation of vehicle states and road friction coefficients. The estimator is designed based on a three-DOF vehicle model coupled with the Highway Safety Research Institute(HSRI) tire model; the dual extended Kalman filter (DEKF) technique is employed, which can be regarded as two extended Kalman filters operating and communicating simultaneously. Effectiveness of the estimation is firstly examined by comparing the outputs of the estimator with the responses of the vehicle model in CarSim under three typical road adhesion conditions(high-friction, low-friction, and joint-friction). On this basis, driving simulator experiments are carried out to further investigate the practical application of the estimator. Numerical results from CarSim and driving simulator both demonstrate that the estimator designed is capable of estimating the vehicle states and road friction coefficient with reasonable accuracy. The DEKF-based estimator proposed provides the essential information for the vehicle active control system with low expense and decent precision, and offers the possibility of real car application in future.

State estimation of chemical engineering systems tending to multiple solutions

Directory of Open Access Journals (Sweden)

N. P. G. Salau

2014-09-01

Full Text Available A well-evaluated state covariance matrix avoids error propagation due to divergence issues and, thereby, it is crucial for a successful state estimator design. In this paper we investigate the performance of the state covariance matrices used in three unconstrained Extended Kalman Filter (EKF formulations and one constrained EKF formulation (CEKF. As benchmark case studies we have chosen: a a batch chemical reactor with reversible reactions whose system model and measurement are such that multiple states satisfy the equilibrium condition and b a CSTR with exothermic irreversible reactions and cooling jacket energy balance whose nonlinear behavior includes multiple steady-states and limit cycles. The results have shown that CEKF is in general the best choice of EKF formulations (even if they are constrained with an ad hoc clipping strategy which avoids undesired states for such case studies. Contrary to a clipped EKF formulation, CEKF incorporates constraints into an optimization problem, which minimizes the noise in a least square sense preventing a bad noise distribution. It is also shown that, although the Moving Horizon Estimation (MHE provides greater robustness to a poor guess of the initial state, converging in less steps to the actual states, it is not justified for our examples due to the high additional computational effort.

State estimation and synchronization of pendula systems over digital communication channels

Science.gov (United States)

Fradkov, A. L.; Andrievsky, B.; Ananyevskiy, M.

2014-04-01

The recent results on nonlinear systems synchronization and control under communication constraints are applied to the remote state estimation and synchronization for a class of exogenously excited nonlinear Lurie systems. State estimation of the chain of diffusively coupled pendulums over the digital communication channel with limited capacity is experimentally studied. Advantage of the adaptive coding procedure under the conditions of the plant model uncertainty and irregular disturbances is shown. Quality of the estimation is evaluated by means of the experiments with the multi-pendulum set-up. Experimental study of master-slave synchronization over network (local network, wireless network) for the system with two cart-pendulums is presented.

Estimated United States Transportation Energy Use 2005

Energy Technology Data Exchange (ETDEWEB)

Smith, C A; Simon, A J; Belles, R D

2011-11-09

A flow chart depicting energy flow in the transportation sector of the United States economy in 2005 has been constructed from publicly available data and estimates of national energy use patterns. Approximately 31,000 trillion British Thermal Units (trBTUs) of energy were used throughout the United States in transportation activities. Vehicles used in these activities include automobiles, motorcycles, trucks, buses, airplanes, rail, and ships. The transportation sector is powered primarily by petroleum-derived fuels (gasoline, diesel and jet fuel). Biomass-derived fuels, electricity and natural gas-derived fuels are also used. The flow patterns represent a comprehensive systems view of energy used within the transportation sector.

Deformation-Based Atrophy Estimation for Alzheimer’s Disease

DEFF Research Database (Denmark)

Pai, Akshay Sadananda Uppinakudru

Alzheimer’s disease (AD) - the most common form of dementia, is a term used for accelerated memory loss and cognitive abilities enough to severely hamper day-to-day activities. One of the most globally accepted markers for AD is atrophy, in mainly the brain parenchyma. The goal of the PhD project...... and a new way to estimate atrophy from a deformation field. We demonstrate the performance of the proposed solution but applying it on the publicly available Alzheimer’s disease neuroimaging data (ADNI) initiative and compare to existing state-of-art atrophy estimation methods....

State-Space Estimation of Soil Organic Carbon Stock

Science.gov (United States)

Ogunwole, Joshua O.; Timm, Luis C.; Obidike-Ugwu, Evelyn O.; Gabriels, Donald M.

2014-04-01

Understanding soil spatial variability and identifying soil parameters most determinant to soil organic carbon stock is pivotal to precision in ecological modelling, prediction, estimation and management of soil within a landscape. This study investigates and describes field soil variability and its structural pattern for agricultural management decisions. The main aim was to relate variation in soil organic carbon stock to soil properties and to estimate soil organic carbon stock from the soil properties. A transect sampling of 100 points at 3 m intervals was carried out. Soils were sampled and analyzed for soil organic carbon and other selected soil properties along with determination of dry aggregate and water-stable aggregate fractions. Principal component analysis, geostatistics, and state-space analysis were conducted on the analyzed soil properties. The first three principal components explained 53.2% of the total variation; Principal Component 1 was dominated by soil exchange complex and dry sieved macroaggregates clusters. Exponential semivariogram model described the structure of soil organic carbon stock with a strong dependence indicating that soil organic carbon values were correlated up to 10.8m.Neighbouring values of soil organic carbon stock, all waterstable aggregate fractions, and dithionite and pyrophosphate iron gave reliable estimate of soil organic carbon stock by state-space.

Simultaneous Robust Fault and State Estimation for Linear Discrete-Time Uncertain Systems

Directory of Open Access Journals (Sweden)

Feten Gannouni

2017-01-01

Full Text Available We consider the problem of robust simultaneous fault and state estimation for linear uncertain discrete-time systems with unknown faults which affect both the state and the observation matrices. Using transformation of the original system, a new robust proportional integral filter (RPIF having an error variance with an optimized guaranteed upper bound for any allowed uncertainty is proposed to improve robust estimation of unknown time-varying faults and to improve robustness against uncertainties. In this study, the minimization problem of the upper bound of the estimation error variance is formulated as a convex optimization problem subject to linear matrix inequalities (LMI for all admissible uncertainties. The proportional and the integral gains are optimally chosen by solving the convex optimization problem. Simulation results are given in order to illustrate the performance of the proposed filter, in particular to solve the problem of joint fault and state estimation.

An improved fuzzy Kalman filter for state estimation of nonlinear systems

International Nuclear Information System (INIS)

Zhou, Z-J; Hu, C-H; Chen, L; Zhang, B-C

2008-01-01

The extended fuzzy Kalman filter (EFKF) is developed recently and used for state estimation of the nonlinear systems with uncertainty. Based on extension of the orthogonality principle and the extended fuzzy Kalman filter, an improved fuzzy Kalman filters (IFKF) is proposed in this paper, which is more applicable and can deal with the state estimation of the nonlinear systems better than the EFKF. A simulation study is provided to verify the efficiency of the proposed method

Combined Audience and Video Feedback With Cognitive Review Improves State Anxiety and Self-Perceptions During Speech Tasks in Socially Anxious Individuals.

Science.gov (United States)

Chen, Junwen; McLean, Jordan E; Kemps, Eva

2018-03-01

This study investigated the effects of combined audience feedback with video feedback plus cognitive preparation, and cognitive review (enabling deeper processing of feedback) on state anxiety and self-perceptions including perception of performance and perceived probability of negative evaluation in socially anxious individuals during a speech performance. One hundred and forty socially anxious students were randomly assigned to four conditions: Cognitive Preparation + Video Feedback + Audience Feedback + Cognitive Review (CP+VF+AF+CR), Cognitive Preparation + Video Feedback + Cognitive Review (CP+VF+CR), Cognitive Preparation + Video Feedback only (CP+VF), and Control. They were asked to deliver two impromptu speeches that were evaluated by confederates. Participants' levels of anxiety and self-perceptions pertaining to the speech task were assessed before and after feedback, and after the second speech. Compared to participants in the other conditions, participants in the CP+VF+AF+CR condition reported a significant decrease in their state anxiety and perceived probability of negative evaluation scores, and a significant increase in their positive perception of speech performance from before to after the feedback. These effects generalized to the second speech. Our results suggest that adding audience feedback to video feedback plus cognitive preparation and cognitive review may improve the effects of existing video feedback procedures in reducing anxiety symptoms and distorted self-representations in socially anxious individuals. Copyright © 2017. Published by Elsevier Ltd.

Linear discrete-time state space realization of a modified quadruple tank system with state estimation using Kalman filter

DEFF Research Database (Denmark)

Mohd. Azam, Sazuan Nazrah

2017-01-01

In this paper, we used the modified quadruple tank system that represents a multi-input-multi-output (MIMO) system as an example to present the realization of a linear discrete-time state space model and to obtain the state estimation using Kalman filter in a methodical mannered. First, an existing...... part of the Kalman filter is used to estimates the current state, based on the model and the measurements. The static and dynamic Kalman filter is compared and all results is demonstrated through simulations....

Implicit Particle Filter for Power System State Estimation with Large Scale Renewable Power Integration.

Science.gov (United States)

Uzunoglu, B.; Hussaini, Y.

2017-12-01

Implicit Particle Filter is a sequential Monte Carlo method for data assimilation that guides the particles to the high-probability by an implicit step . It optimizes a nonlinear cost function which can be inherited from legacy assimilation routines . Dynamic state estimation for almost real-time applications in power systems are becomingly increasingly more important with integration of variable wind and solar power generation. New advanced state estimation tools that will replace the old generation state estimation in addition to having a general framework of complexities should be able to address the legacy software and able to integrate the old software in a mathematical framework while allowing the power industry need for a cautious and evolutionary change in comparison to a complete revolutionary approach while addressing nonlinearity and non-normal behaviour. This work implements implicit particle filter as a state estimation tool for the estimation of the states of a power system and presents the first implicit particle filter application study on a power system state estimation. The implicit particle filter is introduced into power systems and the simulations are presented for a three-node benchmark power system . The performance of the filter on the presented problem is analyzed and the results are presented.

Location-Based Resource Allocation for OFDMA Cognitive Radio Systems

KAUST Repository

Ghorbel, Mahdi

2011-05-01

Cognitive radio is one of the hot topics for emerging and future wireless communication. It has been proposed as a suitable solution for the spectrum scarcity caused by the increase in frequency demand. The concept is based on allowing unlicensed users, called cognitive or secondary users, to share the unoccupied frequency bands with their owners, called the primary users, under constraints on the interference they cause to them. In order to estimate this interference, the cognitive system usually uses the channel state information to the primary user, which is often impractical to obtain. However, we propose to use location information, which is easier to obtain, to estimate this interference. The purpose of this work is to propose a subchannel and power allocation method which maximizes the secondary users\\' total capacity under the constraints of limited budget power and total interference to the primary under certain threshold. We model the problem as a constrained optimization problem for both downlink and uplink cases. Then, we propose low-complexity resource allocation schemes based on the waterfilling algorithm. The simulation results show the efficiency of the proposed method with comparison to the exhaustive search algorithm.

Multiple Resting-State Networks Are Associated With Tremors and Cognitive Features in Essential Tremor.

Science.gov (United States)

Fang, Weidong; Chen, Huiyue; Wang, Hansheng; Zhang, Han; Liu, Mengqi; Puneet, Munankami; Lv, Fajin; Cheng, Oumei; Wang, Xuefeng; Lu, Xiurong; Luo, Tianyou

2015-12-01

The heterogeneous clinical features of essential tremor indicate that the dysfunctions of this syndrome are not confined to motor networks, but extend to nonmotor networks. Currently, these neural network dysfunctions in essential tremor remain unclear. In this study, independent component analysis of resting-state functional MRI was used to study these neural network mechanisms. Thirty-five essential tremor patients and 35 matched healthy controls with clinical and neuropsychological tests were included, and eight resting-state networks were identified. After considering the structure and head-motion factors and testing the reliability of the selected resting-state networks, we assessed the functional connectivity changes within or between resting-state networks. Finally, image-behavior correlation analysis was performed. Compared to healthy controls, essential tremor patients displayed increased functional connectivity in the sensorimotor and salience networks and decreased functional connectivity in the cerebellum network. Additionally, increased functional network connectivity was observed between anterior and posterior default mode networks, and a decreased functional network connectivity was noted between the cerebellum network and the sensorimotor and posterior default mode networks. Importantly, the functional connectivity changes within and between these resting-state networks were correlated with the tremor severity and total cognitive scores of essential tremor patients. The findings of this study provide the first evidence that functional connectivity changes within and between multiple resting-state networks are associated with tremors and cognitive features of essential tremor, and this work demonstrates a potential approach for identifying the underlying neural network mechanisms of this syndrome. © 2015 International Parkinson and Movement Disorder Society.

Impaired insight into illness and cognitive insight in schizophrenia spectrum disorders: Resting state functional connectivity

Science.gov (United States)

Gerretsen, Philip; Menon, Mahesh; Mamo, David C.; Fervaha, Gagan; Remington, Gary; Pollock, Bruce G.; Graff-Guerrero, Ariel

2015-01-01

Background Impaired insight into illness (clinical insight) in schizophrenia has negative effects on treatment adherence and clinical outcomes. Schizophrenia is described as a disorder of disrupted brain connectivity. In line with this concept, resting state networks (RSNs) appear differentially affected in persons with schizophrenia. Therefore, impaired clinical, or the related construct of cognitive insight (which posits that impaired clinical insight is a function of metacognitive deficits), may reflect alterations in RSN functional connectivity (fc). Based on our previous research, which showed that impaired insight into illness was associated with increased left hemisphere volume relative to right, we hypothesized that impaired clinical insight would be associated with increased connectivity in the DMN with specific left hemisphere brain regions. Methods Resting state MRI scans were acquired for participants with schizophrenia or schizoaffective disorder (n = 20). Seed-to-voxel and ROI-to-ROI fc analyses were performed using the CONN-fMRI fc toolbox v13 for established RSNs. Clinical and cognitive insight were measured with the Schedule for the Assessment of Insight—Expanded Version and Beck Cognitive Insight Scale, respectively, and included as the regressors in fc analyses. Results As hypothesized, impaired clinical insight was associated with increased connectivity in the default mode network (DMN) with the left angular gyrus, and also in the self-referential network (SRN) with the left insula. Cognitive insight was associated with increased connectivity in the dorsal attention network (DAN) with the right inferior frontal cortex (IFC) and left anterior cingulate cortex (ACC). Conclusion Increased connectivity in DMN and SRN with the left angular gyrus and insula, respectively, may represent neural correlates of impaired clinical insight in schizophrenia spectrum disorders, and is consistent with the literature attributing impaired insight to left

Diagnostic Inspection of Pipelines for Estimating the State of Stress in Them

Science.gov (United States)

Subbotin, V. A.; Kolotilov, Yu. V.; Smirnova, V. Yu.; Ivashko, S. K.

2017-12-01

The diagnostic inspection used to estimate the technical state of a pipeline is described. The problems of inspection works are listed, and a functional-structural scheme is developed to estimate the state of stress in a pipeline. Final conclusions regarding the actual loading of a pipeline section are drawn upon a cross analysis of the entire information obtained during pipeline inspection.

Model-based state estimator for an intelligent tire

NARCIS (Netherlands)

Goos, J.; Teerhuis, A. P.; Schmeitz, A. J.C.; Besselink, I.; Nijmeijer, H.

2017-01-01

In this work a Tire State Estimator (TSE) is developed and validated using data from a tri-axial accelerometer, installed at the inner liner of the tire. The Flexible Ring Tire (FRT) model is proposed to calculate the tire deformation. For a rolling tire, this deformation is transformed into

Model-based State Estimator for an Intelligent Tire

NARCIS (Netherlands)

Goos, J.; Teerhuis, A.P.; Schmeitz, A.J.C.; Besselink, I.J.M.; Nijmeijer, H.

2016-01-01

In this work a Tire State Estimator (TSE) is developed and validated using data from a tri-axial accelerometer, installed at the inner liner of the tire. The Flexible Ring Tire (FRT) model is proposed to calculate the tire deformation. For a rolling tire, this deformation is transformed into

The Montreal Cognitive Assessment (MoCA) is superior to the Mini Mental State Examination (MMSE) in detection of korsakoffs syndrome

NARCIS (Netherlands)

Oudman, Erik; Postma, Albert; Van Der Stigchel, Stefan; Appelhof, Britt; Wijnia, Jan W.; Nijboer, Tanja C W

2014-01-01

The Montreal Cognitive Assessment (MoCA) and Mini Mental State Examination (MMSE) are brief screening instruments for cognitive disorders. Although these instruments have frequently been used in the detection of dementia, there is currently little knowledge on the validity to detect Korsakoffs

Mixture estimation with state-space components and Markov model of switching

Czech Academy of Sciences Publication Activity Database

Nagy, Ivan; Suzdaleva, Evgenia

2013-01-01

Roč. 37, č. 24 (2013), s. 9970-9984 ISSN 0307-904X R&D Projects: GA TA ČR TA01030123 Institutional support: RVO:67985556 Keywords : probabilistic dynamic mixtures, * probability density function * state-space models * recursive mixture estimation * Bayesian dynamic decision making under uncertainty * Kerridge inaccuracy Subject RIV: BC - Control Systems Theory Impact factor: 2.158, year: 2013 http://library.utia.cas.cz/separaty/2013/AS/nagy-mixture estimation with state-space components and markov model of switching.pdf

State estimation bias induced by optimization under uncertainty and error cost asymmetry is likely reflected in perception.

Science.gov (United States)

Shimansky, Y P

2011-05-01

It is well known from numerous studies that perception can be significantly affected by intended action in many everyday situations, indicating that perception and related decision-making is not a simple, one-way sequence, but a complex iterative cognitive process. However, the underlying functional mechanisms are yet unclear. Based on an optimality approach, a quantitative computational model of one such mechanism has been developed in this study. It is assumed in the model that significant uncertainty about task-related parameters of the environment results in parameter estimation errors and an optimal control system should minimize the cost of such errors in terms of the optimality criterion. It is demonstrated that, if the cost of a parameter estimation error is significantly asymmetrical with respect to error direction, the tendency to minimize error cost creates a systematic deviation of the optimal parameter estimate from its maximum likelihood value. Consequently, optimization of parameter estimate and optimization of control action cannot be performed separately from each other under parameter uncertainty combined with asymmetry of estimation error cost, thus making the certainty equivalence principle non-applicable under those conditions. A hypothesis that not only the action, but also perception itself is biased by the above deviation of parameter estimate is supported by ample experimental evidence. The results provide important insights into the cognitive mechanisms of interaction between sensory perception and planning an action under realistic conditions. Implications for understanding related functional mechanisms of optimal control in the CNS are discussed.

A physics-based fractional order model and state of energy estimation for lithium ion batteries. Part II: Parameter identification and state of energy estimation for LiFePO4 battery

Science.gov (United States)

Li, Xiaoyu; Pan, Ke; Fan, Guodong; Lu, Rengui; Zhu, Chunbo; Rizzoni, Giorgio; Canova, Marcello

2017-11-01

State of energy (SOE) is an important index for the electrochemical energy storage system in electric vehicles. In this paper, a robust state of energy estimation method in combination with a physical model parameter identification method is proposed to achieve accurate battery state estimation at different operating conditions and different aging stages. A physics-based fractional order model with variable solid-state diffusivity (FOM-VSSD) is used to characterize the dynamic performance of a LiFePO4/graphite battery. In order to update the model parameter automatically at different aging stages, a multi-step model parameter identification method based on the lexicographic optimization is especially designed for the electric vehicle operating conditions. As the battery available energy changes with different applied load current profiles, the relationship between the remaining energy loss and the state of charge, the average current as well as the average squared current is modeled. The SOE with different operating conditions and different aging stages are estimated based on an adaptive fractional order extended Kalman filter (AFEKF). Validation results show that the overall SOE estimation error is within ±5%. The proposed method is suitable for the electric vehicle online applications.

On state estimation and fusion with elliptical constraints

Energy Technology Data Exchange (ETDEWEB)

Rao, Nageswara S. [ORNL; Liu, Qiang [ORNL

2017-11-01

We consider tracking of a target with elliptical nonlinear constraints on its motion dynamics. The state estimates are generated by sensors and sent over long-haul links to a remote fusion center for fusion. We show that the constraints can be projected onto the known ellipse and hence incorporated into the estimation and fusion process. In particular, two methods based on (i) direct connection to the center, and (ii) shortest distance to the ellipse are discussed. A tracking example is used to illustrate the tracking performance using projection-based methods with various fusers in the lossy long-haul tracking environment.

State and Substate Estimates of Nonmedical Use of Prescription Pain Relievers

Science.gov (United States)

... with other local area data to enhance statistical power and analytic capability. 10 Delete Template National, Regional, and State Estimates In this section, estimates of past year nonmedical use of prescription pain relievers among people aged 12 or older are ...

Estimation of diagnostic performance of dementia screening tests: Mini-Mental State Examination, Mini-Cog, Clock Drawing test and Ascertain Dementia 8 questionnaire.

Science.gov (United States)

Yang, Li; Yan, Jing; Jin, Xiaoqing; Jin, Yu; Yu, Wei; Xu, Shanhu; Wu, Haibin; Xu, Ying; Liu, Caixia

2017-05-09

Dementia is one of the leading causes of dependence in the elderly. This study was conducted to estimate diagnostic performance of dementia screening tests including Mini-Mental State Examination (MMSE), Mini-Cog, Clock Drawing Test (CDT) and Ascertain Dementia 8 questionnaire (AD8) by Bayesian models. A total of 2015 participants aged 65 years or more in eastern China were enrolled. The four screening tests were administered and scored by specifically trained psychiatrists. The prior information of sensitivity and specificity of every screening test was updated via Bayes' theorem to a posterior distribution. Then the results were compared with the estimation based on National Institute of Aging-Alzheimer's Association criteria (NIA-AA). The diagnostic characteristics of Mini-Cog, including sensitivity, specificity, PPV, NPV, especially the Youden index, performed well, even better than the combinations of several screening tests. The Mini-Cog with excellent screening characteristics, spending less time, could be considered to be used as a screening test to help to screen patients with cognitive impairment or dementia early. And Bayesian method was shown to be a suitable tool for evaluating dementia screening tests. The Mini-Cog with excellent screening characteristics, spending less time, could be considered to be used as a screening test to help to screen patients with cognitive impairment or dementia early. And Bayesian method was shown to be a suitable tool for evaluating dementia screening tests.

System state estimation and optimal energy control framework for multicell lithium-ion battery system

International Nuclear Information System (INIS)

Wei, Jingwen; Dong, Guangzhong; Chen, Zonghai; Kang, Yu

2017-01-01

Highlights: • Employed a dual-scale EKF based estimator for in-pack cells’ SOC values. • Proposed a two-stage hybrid state-feedback and output-feedback equalization algorithm. • A switchable balance current mode is designed in the equalization topology. • Verified the performance of proposed method under two conditions. - Abstract: Cell variations caused by the inevitable inconsistency during manufacture and use of battery cells have significant impacts on battery capacity, security and durability for battery energy storage systems. Thus, the battery equalization systems are essentially required to reduce variations of in-pack cells and increase battery pack capability. In order to protect all in-pack cells from damaging, estimate battery state and reduce variations, a system state estimation and energy optimal control framework for multicell lithium-ion battery system is proposed. The state-of-charge (SOC) values of all in-pack cells are firstly estimated using a dual-scale extended Kalman filtering (EKF) to improve estimation accuracy and reduce computation simultaneously. These estimated SOC values provide specific details of battery system, which cannot only be used to protect cells from over-charging/over-discharging, but also be employed to design state-feedback controller for battery equalization system. A two-stage hybrid state-feedback and output-feedback equalization algorithm is proposed. The state-feedback controller is firstly employed for coarse-grained adjustment to reduce equalization time cost with large current. However, due to the inevitable SOC estimation errors, the output-feedback controller is then used for fine-grained adjustment with trickle current. Experimental results show that the proposed framework can provide an effectively estimation and energy control for multicell battery systems. Finally, the implementation of the proposed method is further discussed for the real applications.

Estimation of the Level of Cognitive Development of a Preschool Child Using the System of Situations with Mathematical Contents

Science.gov (United States)

Gorev, Pavel M.; Bichurina, Svetlana Y.; Yakupova, Rufiya M.; Khairova, Irina V.

2016-01-01

Cognitive development of personality can be considered as one of the key directions of preschool education presented in the world practice, where preschool programs are educational ones, and preschool education is the first level of the general education. Thereby the purpose of the research is to create a model of reliable estimation of cognitive…

State Estimation for Landing Maneuver on High Performance Aircraft

Science.gov (United States)

Suresh, P. S.; Sura, Niranjan K.; Shankar, K.

2018-01-01

State estimation methods are popular means for validating aerodynamic database on aircraft flight maneuver performance characteristics. In this work, the state estimation method during landing maneuver is explored for the first of its kind, using upper diagonal adaptive extended Kalman filter (UD-AEKF) with fuzzy based adaptive tunning of process noise matrix. The mathematical model for symmetrical landing maneuver consists of non-linear flight mechanics equation representing Aircraft longitudinal dynamics. The UD-AEKF algorithm is implemented in MATLAB environment and the states with bias is considered to be the initial conditions just prior to the flare. The measurement data is obtained from a non-linear 6 DOF pilot in loop simulation using FORTRAN. These simulated measurement data is additively mixed with process and measurement noises, which are used as an input for UD-AEKF. Then, the governing states that dictate the landing loads at the instant of touch down are compared. The method is verified using flight data wherein, the vertical acceleration at the aircraft center of gravity (CG) is compared. Two possible outcome of purely relying on the aircraft measured data is highlighted. It is observed that, with the implementation of adaptive fuzzy logic based extended Kalman filter tuned to adapt for aircraft landing dynamics, the methodology improves the data quality of the states that are sourced from noisy measurements.

Resting state glucose utilization and the CERAD cognitive battery in patients with Alzheimer's disease.

Science.gov (United States)

Teipel, S J; Willoch, F; Ishii, K; Bürger, K; Drzezga, A; Engel, R; Bartenstein, P; Möller, H-J; Schwaiger, M; Hampel, H

2006-05-01

The present study examined the cortical functional representation of neuropsychological domains in Alzheimer's disease (AD) using positron emission tomography (PET) and the neuropsychological assessment battery of the Consortium to Establish a Registry of Alzheimer's Disease (CERAD). Thirty patients with clinical probable AD and 10 elderly healthy controls underwent (18)FDG brain PET imaging during a resting state. Correlations between metabolic values and cognitive measures were determined using a region of interest analysis with NEUROSTAT (University of Michigan, USA) and a voxel-based analysis with SPM96 (Wellcome Department, London, UK). Specific correlations were seen between measures of episodic memory, verbal fluency and naming and left hemispheric temporal and prefrontal metabolism. Drawing was correlated with metabolism in left prefrontal and left inferior parietal regions. The presented data support the use of metabolic-cognitive correlations to demonstrate the neuronal substrates of cognitive impairment in AD. Subtests of the CERAD battery give a good representation of left, but not of right hemisphere function in AD.

Learning to Estimate Dynamical State with Probabilistic Population Codes.

Directory of Open Access Journals (Sweden)

Joseph G Makin

2015-11-01

Full Text Available Tracking moving objects, including one's own body, is a fundamental ability of higher organisms, playing a central role in many perceptual and motor tasks. While it is unknown how the brain learns to follow and predict the dynamics of objects, it is known that this process of state estimation can be learned purely from the statistics of noisy observations. When the dynamics are simply linear with additive Gaussian noise, the optimal solution is the well known Kalman filter (KF, the parameters of which can be learned via latent-variable density estimation (the EM algorithm. The brain does not, however, directly manipulate matrices and vectors, but instead appears to represent probability distributions with the firing rates of population of neurons, "probabilistic population codes." We show that a recurrent neural network-a modified form of an exponential family harmonium (EFH-that takes a linear probabilistic population code as input can learn, without supervision, to estimate the state of a linear dynamical system. After observing a series of population responses (spike counts to the position of a moving object, the network learns to represent the velocity of the object and forms nearly optimal predictions about the position at the next time-step. This result builds on our previous work showing that a similar network can learn to perform multisensory integration and coordinate transformations for static stimuli. The receptive fields of the trained network also make qualitative predictions about the developing and learning brain: tuning gradually emerges for higher-order dynamical states not explicitly present in the inputs, appearing as delayed tuning for the lower-order states.

Estimates of state-level health-care expenditures associated with disability.

Science.gov (United States)

Anderson, Wayne L; Armour, Brian S; Finkelstein, Eric A; Wiener, Joshua M

2010-01-01

We estimated state-level disability-associated health-care expenditures (DAHE) for the U.S. adult population. We used a two-part model to estimate DAHE for the noninstitutionalized U.S. civilian adult population using data from the 2002-2003 Medical Expenditure Panel Survey and state-level data from the Behavioral Risk Factor Surveillance System. Administrative data for people in institutions were added to generate estimates for the total adult noninstitutionalized population. Individual-level data on total health-care expenditures along with demographic, socioeconomic, geographic, and payer characteristics were used in the models. The DAHE for all U.S. adults totaled $397.8 billion in 2006, with state expenditures ranging from $598 million in Wyoming to $40.1 billion in New York. Of the national total, the DAHE were $118.9 billion for the Medicare population, $161.1 billion for Medicaid recipients, and $117.8 billion for the privately insured and uninsured populations. For the total U.S. adult population, 26.7% of health-care expenditures were associated with disability, with proportions by state ranging from 16.9% in Hawaii to 32.8% in New York. This proportion varied greatly by payer, with 38.1% for Medicare expenditures, 68.7% for Medicaid expenditures, and 12.5% for nonpublic health-care expenditures associated with disability. DAHE vary greatly by state and are borne largely by the public sector, and particularly by Medicaid. Policy makers need to consider initiatives that will help reduce the prevalence of disabilities and disability-related health disparities, as well as improve the lives of people with disabilities.

State Estimation of Permanent Magnet Synchronous Motor Using Improved Square Root UKF

Directory of Open Access Journals (Sweden)

Bo Xu

2016-06-01

Full Text Available This paper focuses on an improved square root unscented Kalman filter (SRUKF and its application for rotor speed and position estimation of permanent magnet synchronous motor (PMSM. The approach, which combines the SRUKF and strong tracking filter, uses the minimal skew simplex transformation to reduce the number of the sigma points, and utilizes the square root filtering to reduce computational errors. The time-varying fading factor and softening factor are introduced to self-adjust the gain matrices and the state forecast covariance square root matrix, which can realize the residuals orthogonality and force the SRUKF to track the real state rapidly. The theoretical analysis of the improved SRUKF and implementation details for PMSM state estimation are examined. The simulation results show that the improved SRUKF has higher nonlinear approximation accuracy, stronger numerical stability and computational efficiency, and it is an effective and powerful tool for PMSM state estimation under the conditions of step response or load disturbance.

A method for state of energy estimation of lithium-ion batteries based on neural network model

International Nuclear Information System (INIS)

Dong, Guangzhong; Zhang, Xu; Zhang, Chenbin; Chen, Zonghai

2015-01-01

The state-of-energy is an important evaluation index for energy optimization and management of power battery systems in electric vehicles. Unlike the state-of-charge which represents the residual energy of the battery in traditional applications, state-of-energy is integral result of battery power, which is the product of current and terminal voltage. On the other hand, like state-of-charge, the state-of-energy has an effect on terminal voltage. Therefore, it is hard to solve the nonlinear problems between state-of-energy and terminal voltage, which will complicate the estimation of a battery's state-of-energy. To address this issue, a method based on wavelet-neural-network-based battery model and particle filter estimator is presented for the state-of-energy estimation. The wavelet-neural-network based battery model is used to simulate the entire dynamic electrical characteristics of batteries. The temperature and discharge rate are also taken into account to improve model accuracy. Besides, in order to suppress the measurement noises of current and voltage, a particle filter estimator is applied to estimate cell state-of-energy. Experimental results on LiFePO_4 batteries indicate that the wavelet-neural-network based battery model simulates battery dynamics robustly with high accuracy and the estimation value based on the particle filter estimator converges to the real state-of-energy within an error of ±4%. - Highlights: • State-of-charge is replaced by state-of-energy to determine cells residual energy. • The battery state-space model is established based on a neural network. • Temperature and current influence are considered to improve the model accuracy. • The particle filter is used for state-of-energy estimation to improve accuracy. • The robustness of new method is validated under dynamic experimental conditions.

Implementation of a Simplified State Estimator for Wind Turbine Monitoring on an Embedded System

DEFF Research Database (Denmark)

Rasmussen, Theis Bo; Yang, Guangya; Nielsen, Arne Hejde

2017-01-01

system, including individual DER, is time consuming and numerically challenging. This paper presents the approach and results of implementing a simplified state estimator onto an embedded system for improving DER monitoring. The implemented state estimator is based on numerically robust orthogonal......The transition towards a cyber-physical energy system (CPES) entails an increased dependency on valid data. Simultaneously, an increasing implementation of renewable generation leads to possible control actions at individual distributed energy resources (DERs). A state estimation covering the whole...

Iterative Observer-based Estimation Algorithms for Steady-State Elliptic Partial Differential Equation Systems

KAUST Repository

Majeed, Muhammad Usman

2017-07-19

Steady-state elliptic partial differential equations (PDEs) are frequently used to model a diverse range of physical phenomena. The source and boundary data estimation problems for such PDE systems are of prime interest in various engineering disciplines including biomedical engineering, mechanics of materials and earth sciences. Almost all existing solution strategies for such problems can be broadly classified as optimization-based techniques, which are computationally heavy especially when the problems are formulated on higher dimensional space domains. However, in this dissertation, feedback based state estimation algorithms, known as state observers, are developed to solve such steady-state problems using one of the space variables as time-like. In this regard, first, an iterative observer algorithm is developed that sweeps over regular-shaped domains and solves boundary estimation problems for steady-state Laplace equation. It is well-known that source and boundary estimation problems for the elliptic PDEs are highly sensitive to noise in the data. For this, an optimal iterative observer algorithm, which is a robust counterpart of the iterative observer, is presented to tackle the ill-posedness due to noise. The iterative observer algorithm and the optimal iterative algorithm are then used to solve source localization and estimation problems for Poisson equation for noise-free and noisy data cases respectively. Next, a divide and conquer approach is developed for three-dimensional domains with two congruent parallel surfaces to solve the boundary and the source data estimation problems for the steady-state Laplace and Poisson kind of systems respectively. Theoretical results are shown using a functional analysis framework, and consistent numerical simulation results are presented for several test cases using finite difference discretization schemes.

Practical global oceanic state estimation

Science.gov (United States)

Wunsch, Carl; Heimbach, Patrick

2007-06-01

The problem of oceanographic state estimation, by means of an ocean general circulation model (GCM) and a multitude of observations, is described and contrasted with the meteorological process of data assimilation. In practice, all such methods reduce, on the computer, to forms of least-squares. The global oceanographic problem is at the present time focussed primarily on smoothing, rather than forecasting, and the data types are unlike meteorological ones. As formulated in the consortium Estimating the Circulation and Climate of the Ocean (ECCO), an automatic differentiation tool is used to calculate the so-called adjoint code of the GCM, and the method of Lagrange multipliers used to render the problem one of unconstrained least-squares minimization. Major problems today lie less with the numerical algorithms (least-squares problems can be solved by many means) than with the issues of data and model error. Results of ongoing calculations covering the period of the World Ocean Circulation Experiment, and including among other data, satellite altimetry from TOPEX/POSEIDON, Jason-1, ERS- 1/2, ENVISAT, and GFO, a global array of profiling floats from the Argo program, and satellite gravity data from the GRACE mission, suggest that the solutions are now useful for scientific purposes. Both methodology and applications are developing in a number of different directions.

Comparison between mini mental state examination (MMSE) and Montreal cognitive assessment Indonesian version (MoCA-Ina) as an early detection of cognitive impairments in post-stroke patients

Science.gov (United States)

Lestari, S.; Mistivani, I.; Rumende, C. M.; Kusumaningsih, W.

2017-08-01

Mild cognitive impairment (MCI) is defined as cognitive impairment that may never develop into dementia. Cognitive impairment is one long-term complication of a stroke. The Mini Mental State Examination (MMSE), which is commonly used as a screening tool for cognitive impairment, has a low sensitivity to detect cognitive impairment, especially MCI. Alternatively, the Montreal Cognitive Assessment Indonesian version (MoCA-Ina) has been reported to have a higher sensitivity than the MMSE. The aim of this study was to compare the proportion of MCI identified between the MMSE and MoCA-Ina in stroke patients. This was a cross-sectional study of stroke outpatients who attended the Polyclinic Neuromuscular Division, Rehabilitation Department, and Polyclinic Stroke, Neurology Department Cipto Mangunkusumo General Hospital, Jakarta. The proportion of MCI identified using the MMSE was 31.03% compared to 72.41% when using the MoCA-Ina. This difference was statistically significant (Fisher’s exact test, p = 0.033). The proportion of MCI in stroke patients was higher when using the MoCA-Ina compared to the MMSE. The MoCA-Ina should be used as an alternative in the early detection of MCI in stroke patients, especially those undergoing rehabilitation.

Cognitive relaying and power allocation under channel state uncertainties

KAUST Repository

Pandarakkottilil, Ubaidulla

2013-04-01

In this paper, we present robust joint relay precoder designs and transceiver power allocations for a cognitive radio network under imperfect channel state information (CSI). The secondary (or cognitive) network consists of a pair of single-antenna transceiver nodes and a non-regenerative two-way relay with multiple antennas which aids the communication process between the transceiver pair. The secondary nodes share the spectrum with a licensed primary user (PU) while guaranteeing that the interference to the PU receiver is maintained below a specified threshold. We consider two robust designs: the first is based on the minimization of the total transmit power of the secondary relay node required to provide the minimum quality of service, measured in terms of mean-square error (MSE) of the transceiver nodes, and the second is based on the minimization of the sum-MSE of the transceiver nodes. The robust designs are based on worst-case optimization and take into account known parameters of the error in the CSI to render the performance immune to the presence of errors in the CSI. Though the original problem is non-convex, we show that the proposed designs can be reformulated as tractable convex optimization problems that can be solved efficiently. We illustrate the performance of the proposed designs through some selected numerical simulations. © 2013 IEEE.

State Estimation for a Biological Phosphorus Removal Process using an Asymptotic Observer

DEFF Research Database (Denmark)

Larose, Claude Alain; Jørgensen, Sten Bay

2001-01-01

This study investigated the use of an asymptotic observer for state estimation in a continuous biological phosphorus removal process. The estimated states are the concentration of heterotrophic, autotrophic, and phosphorus accumulating organisms, polyphosphate, glycogen and PHA. The reaction scheme...... if the convergence, driven by the dilution rate, was slow (from 15 to 60 days). The propagation of the measurement noise and a bias in the estimation of glycogen and PHA could be the result of the high condition number of one of the matrices used in the algorithm of the asymptotic observer for the aerated tanks....

The state of cognitive functions in patients with adult onset type 2 diabetes mellitus depending on sex, age and level of education

Directory of Open Access Journals (Sweden)

N.M. Zherdova

2017-08-01

Full Text Available Background. According to the meta-analysis of studies, patients with diabetes mellitus (DM have worse memory performance, information processing speed, executive functions compared to patients without DM. The level of education, the age of the patient are also important factors in the development of dementia. But research was conducted on selected populations, and the impact of demographic and social factors was studied in a particular area. The purpose of our work was to study the influence of sex, age, education level on the state of cognitive function in patients with type 2 DM in the Ukrainian population. Materials and methods. Eighty one patients with type 2 DM were examined, including 43 women and 38 men. Evaluation of the cognitive impairment was carried out in the morning using the following methods: a “5 words” test, a mental status assessment scale, a battery of tests for frontal dysfunction, an assessment of a clock drawing test. Results. The average age of patients was 55.03 ± 0.37 years, the average level of HbA1c — 8.75 ± 0.16 %, the average duration of DM — 10.03 ± 1.03 years. There was no reliable difference between men and women in the performance of neuropsychological tests. Also, no differences were found between the groups by age and level of education and the state of cognitive function. But the negative effect of age on cognitive function was found according to the Mini-Mental State Examination. Conclusions. A negative relationship between age and the state of cognitive functions was found. It is necessary to carry out further studies on the effect of other socio-cultural factors on the state of cognitive function in patients with type 2 DM.

Optic Flow Based State Estimation for an Indoor Micro Air Vehicle

NARCIS (Netherlands)

Verveld, M.J.; Chu, Q.P.; De Wagter, C.; Mulder, J.A.

2010-01-01

This work addresses the problem of indoor state estimation for autonomous flying vehicles with an optic flow approach. The paper discusses a sensor configuration using six optic flow sensors of the computer mouse type augmented by a three-axis accelerometer to estimate velocity, rotation, attitude

Strategic Cognitive Sequencing: A Computational Cognitive Neuroscience Approach

Directory of Open Access Journals (Sweden)

Seth A. Herd

2013-01-01

Full Text Available We address strategic cognitive sequencing, the “outer loop” of human cognition: how the brain decides what cognitive process to apply at a given moment to solve complex, multistep cognitive tasks. We argue that this topic has been neglected relative to its importance for systematic reasons but that recent work on how individual brain systems accomplish their computations has set the stage for productively addressing how brain regions coordinate over time to accomplish our most impressive thinking. We present four preliminary neural network models. The first addresses how the prefrontal cortex (PFC and basal ganglia (BG cooperate to perform trial-and-error learning of short sequences; the next, how several areas of PFC learn to make predictions of likely reward, and how this contributes to the BG making decisions at the level of strategies. The third models address how PFC, BG, parietal cortex, and hippocampus can work together to memorize sequences of cognitive actions from instruction (or “self-instruction”. The last shows how a constraint satisfaction process can find useful plans. The PFC maintains current and goal states and associates from both of these to find a “bridging” state, an abstract plan. We discuss how these processes could work together to produce strategic cognitive sequencing and discuss future directions in this area.

Predicting Long-Term Cognitive Outcome Following Breast Cancer with Pre-Treatment Resting State fMRI and Random Forest Machine Learning.

Science.gov (United States)

Kesler, Shelli R; Rao, Arvind; Blayney, Douglas W; Oakley-Girvan, Ingrid A; Karuturi, Meghan; Palesh, Oxana

2017-01-01

We aimed to determine if resting state functional magnetic resonance imaging (fMRI) acquired at pre-treatment baseline could accurately predict breast cancer-related cognitive impairment at long-term follow-up. We evaluated 31 patients with breast cancer (age 34-65) prior to any treatment, post-chemotherapy and 1 year later. Cognitive testing scores were normalized based on data obtained from 43 healthy female controls and then used to categorize patients as impaired or not based on longitudinal changes. We measured clustering coefficient, a measure of local connectivity, by applying graph theory to baseline resting state fMRI and entered these metrics along with relevant patient-related and medical variables into random forest classification. Incidence of cognitive impairment at 1 year follow-up was 55% and was predicted by classification algorithms with up to 100% accuracy ( p breast cancer. This information could inform treatment decision making by identifying patients at highest risk for long-term cognitive impairment.

Real-Time Radar-Based Tracking and State Estimation of Multiple Non-Conformant Aircraft

Science.gov (United States)

Cook, Brandon; Arnett, Timothy; Macmann, Owen; Kumar, Manish

2017-01-01

In this study, a novel solution for automated tracking of multiple unknown aircraft is proposed. Many current methods use transponders to self-report state information and augment track identification. While conformant aircraft typically report transponder information to alert surrounding aircraft of its state, vehicles may exist in the airspace that are non-compliant and need to be accurately tracked using alternative methods. In this study, a multi-agent tracking solution is presented that solely utilizes primary surveillance radar data to estimate aircraft state information. Main research challenges include state estimation, track management, data association, and establishing persistent track validity. In an effort to realize these challenges, techniques such as Maximum a Posteriori estimation, Kalman filtering, degree of membership data association, and Nearest Neighbor Spanning Tree clustering are implemented for this application.

Development and quantitative effect estimation of an integrated decision support system to aid operator's cognitive activities for NPP advanced main control rooms

International Nuclear Information System (INIS)

Lee, Seung Jun

2007-02-01

As digital and computer technologies have grown, human-machine interfaces (HMIs) have evolved. In safety critical systems, especially in nuclear power plants (NPPs), HMIs are important for reducing operational costs, for reducing the number of necessary operators, and for reducing the probability of accident occurrence. Efforts have been made to improve main control room (MCR) interface design and to develop automation or support systems to ensure convenient operation and maintenance. In this paper, an integrated decision support system to aid the cognitive activities of operators is proposed for advanced MCRs in future NPPs. The proposed system supports not merely a particular task, but also the entire operation process based on a human cognitive process model. It supports the operator's entire cognitive process by integrating decision support systems that support each cognitive activity. In this paper, the operator's operation processes are analyzed based on a human cognitive process model and appropriate support systems that support each activity of the human cognitive process are suggested. Two decision support systems were developed in this paper. The first one is the fault diagnosis advisory system (FDAS) which detects faults and diagnoses them. The FDAS provides a list of possible faults and expected causes to operators. It was implemented using two kinds of neural networks for more reliable diagnosis results. The second system is the multifunctional operator support system for operation guidance, which includes the FDAS and the operation guidance system. The operation guidance system is to prevent operator's commission errors and omission errors. Furthermore, the effect of the proposed system was estimated because to evaluate decision support systems in order to validate their efficiency is as important as to design highly reliable decision support systems. The effect estimations were performed theoretically and experimentally. The Bayesian

State estimation for networked control systems using fixed data rates

Science.gov (United States)

Liu, Qing-Quan; Jin, Fang

2017-07-01

This paper investigates state estimation for linear time-invariant systems where sensors and controllers are geographically separated and connected via a bandwidth-limited and errorless communication channel with the fixed data rate. All plant states are quantised, coded and converted together into a codeword in our quantisation and coding scheme. We present necessary and sufficient conditions on the fixed data rate for observability of such systems, and further develop the data-rate theorem. It is shown in our results that there exists a quantisation and coding scheme to ensure observability of the system if the fixed data rate is larger than the lower bound given, which is less conservative than the one in the literature. Furthermore, we also examine the role that the disturbances have on the state estimation problem in the case with data-rate limitations. Illustrative examples are given to demonstrate the effectiveness of the proposed method.

Clinical relevance of specific cognitive complaints in determining Mild Cognitive Impairment from Cognitively Normal States in a study of Healthy Elderly Controls

Directory of Open Access Journals (Sweden)

Marina Avila Villanueva

2016-10-01

Full Text Available Introduction: Subjective memory complaints in the elderly have been suggested as an early sign of dementia. This study aims at investigating whether specific cognitive complaints are more useful than others to discriminate Mild Cognitive Impairment (MCI by examining the dimensional structure of the Everyday Memory Questionnaire (EMQ.Material and Methods: A sample of community-dwelling elderly individuals was recruited (766 controls and 78 MCI. The Everyday Memory Questionnaire (EMQ was administered to measure self-perception of cognitive complaints. All participants also underwent a comprehensive clinical and neuropsychological battery. Combined exploratory factor analysis and item response theory were performed to identify the underlying structure of the EMQ. Furthermore, logistic regression analyses were conducted to study whether single cognitive complaints were able to predict MCI.Results: A suitable five-factor solution was found. Each factor focused on a different cognitive domain. Interestingly, just three of them, namely forgetfulness of immediate information, executive functions and prospective memory proved to be effective in distinguishing between cognitively healthy individuals and MCI. Based on these results we propose a shortened EMQ version comprising 10 items (EMQ-10.Discussion: Not all cognitive complaints have the same clinical relevance. Only subjective complaints on specific cognitive domains are able to discriminate MCI. We encourage clinicians to the EMQ-10 as a useful tool to quantify and monitor the progression of individuals who report cognitive complaints.

Survey of cognitive rehabilitation practices in the state of Kuwait.

Science.gov (United States)

Manee, Fahad S; Nadar, Mohammed Sh; Jassem, Zainab; Chavan, Shashidhar Rao

2017-03-01

Background Rehabilitation professionals must be astute at recognizing, assessing, and treating individuals with cognitive deficits. No research is available to examine cognitive rehabilitation practices applied to individuals with neurological conditions in Kuwait. To identify the use of cognitive assessments, the availability of resources, and the barriers to cognitive rehabilitation practices in Kuwait. Methods Face-to-face interviews were conducted with health care professionals working with adult individuals with neurological conditions. These professionals included occupational therapists, speech-language pathologists, psychiatrists, and neurologists. Results The most commonly used cognitive based assessments are MMSE (41%), and MoCA and LOTCA (15.2%). The only clinical assessment used is the Line-Bisection Test (2.2%). The most used occupation-based assessments are FIM (6.5%), COPM (4.3%), the Interest Checklist (2.2%), and the Barthel Index (2.2%). Resources related to cognitive rehabilitation in Kuwait that are unavailable to practitioners include journal clubs (91%), special interest groups (89%), and continuing education programmes (82.6%). Barriers to cognitive rehabilitation practice included lack of sufficient funds for continuing education, lack of time, lack of standardized assessments, and lack of inter-professional teamwork. Conclusion Many adults in Kuwait live with cognitive impairment. There is a need to develop appropriate evidence-based cognitive rehabilitation clinical guidelines in Kuwait.

A multi-timescale estimator for battery state of charge and capacity dual estimation based on an online identified model

International Nuclear Information System (INIS)

Wei, Zhongbao; Zhao, Jiyun; Ji, Dongxu; Tseng, King Jet

2017-01-01

Highlights: •SOC and capacity are dually estimated with online adapted battery model. •Model identification and state dual estimate are fully decoupled. •Multiple timescales are used to improve estimation accuracy and stability. •The proposed method is verified with lab-scale experiments. •The proposed method is applicable to different battery chemistries. -- Abstract: Reliable online estimation of state of charge (SOC) and capacity is critically important for the battery management system (BMS). This paper presents a multi-timescale method for dual estimation of SOC and capacity with an online identified battery model. The model parameter estimator and the dual estimator are fully decoupled and executed with different timescales to improve the model accuracy and stability. Specifically, the model parameters are online adapted with the vector-type recursive least squares (VRLS) to address the different variation rates of them. Based on the online adapted battery model, the Kalman filter (KF)-based SOC estimator and RLS-based capacity estimator are formulated and integrated in the form of dual estimation. Experimental results suggest that the proposed method estimates the model parameters, SOC, and capacity in real time with fast convergence and high accuracy. Experiments on both lithium-ion battery and vanadium redox flow battery (VRB) verify the generality of the proposed method on multiple battery chemistries. The proposed method is also compared with other existing methods on the computational cost to reveal its superiority for practical application.

Resting States Are Resting Traits – An fMRI Study of Sex Differences and Menstrual Cycle Effects in Resting State Cognitive Control Networks

OpenAIRE

Hjelmervik, Helene; Hausmann, Markus; Osnes, Berge; Westerhausen, René; Specht, Karsten

2014-01-01

To what degree resting state fMRI is stable or susceptible to internal mind states of the individual is currently an issue of debate. To address this issue, the present study focuses on sex differences and investigates whether resting state fMRI is stable in men and women or changes within relative short-term periods (i.e., across the menstrual cycle). Due to the fact that we recently reported menstrual cycle effects on cognitive control based on data collected during the same sessions, the c...

The relationship between depressive disorders and the state of cognitive function in elderly patients with type 2 diabetes mellitus

Directory of Open Access Journals (Sweden)

N.N. Zherdоva

2017-03-01

Full Text Available Background. Depression is a risk factor of diabetes mellitus (DM and negatively affects its course with an increased risk of complications. The aim of the study was to assess the state of cognitive function in elderly patients with type 2 DM (DM2 with and without depression. Materials and methods. 115 patients with DM2 were examined, including 78 women and 37 men. Patients were divided into 2 groups, with and without depressive disorders. Assessment of depressive symptoms was performed using self-questionnaire of depression (CES-D. Evaluation of cognitive manifestations was carried out in the morning with the following methods: “the 5 words” test, the Mini-Mental State Examination, Frontal Assessment Battery, clock drawing test. Results. In 68.7 % of elderly patients with DM2, we have not found depressive disorders according to CES-D, mild manifestations were observed in 17.4 % of patients, moderate — in 10.4 % and 3.5 % of patients had severe symptoms of depression. Persons with depressive disorders had a significant decline in cognitive function. In patients with DM with moderate to severe symptoms of depression, the state of cognitive function was significantly worse compared with patients, who have mild symptoms of depression or no depression. Conclusions. All patients with DM2 should be screened for the presence of depressive disorders in order to prevent violations of cognitive functions.

Subclinical thyroid disorders and cognitive performance among adolescents in the United States

OpenAIRE

Wilson Jim L; Tudiver Fred; Flowers Joanne W; Wu Tiejian; Punyasavatsut Natavut

2006-01-01

Abstract Background Thyroid hormone plays a crucial role in the growth and function of the central nervous system. The purpose of the study was to examine the relationships between the status of subclinical thyroid conditions and cognition among adolescents in the United States. Methods Study sample included 1,327 adolescents 13 to 16 years old who participated in the Third National Health and Nutrition Examination Survey (NHANES III). Serum thyroxine (T4) and thyroid stimulating hormone (TSH...

Sensitive Constrained Optimal PMU Allocation with Complete Observability for State Estimation Solution

Directory of Open Access Journals (Sweden)

R. Manam

2017-12-01

Full Text Available In this paper, a sensitive constrained integer linear programming approach is formulated for the optimal allocation of Phasor Measurement Units (PMUs in a power system network to obtain state estimation. In this approach, sensitive buses along with zero injection buses (ZIB are considered for optimal allocation of PMUs in the network to generate state estimation solutions. Sensitive buses are evolved from the mean of bus voltages subjected to increase of load consistently up to 50%. Sensitive buses are ranked in order to place PMUs. Sensitive constrained optimal PMU allocation in case of single line and no line contingency are considered in observability analysis to ensure protection and control of power system from abnormal conditions. Modeling of ZIB constraints is included to minimize the number of PMU network allocations. This paper presents optimal allocation of PMU at sensitive buses with zero injection modeling, considering cost criteria and redundancy to increase the accuracy of state estimation solution without losing observability of the whole system. Simulations are carried out on IEEE 14, 30 and 57 bus systems and results obtained are compared with traditional and other state estimation methods available in the literature, to demonstrate the effectiveness of the proposed method.

Estimating inpatient hospital prices from state administrative data and hospital financial reports.

Science.gov (United States)

Levit, Katharine R; Friedman, Bernard; Wong, Herbert S

2013-10-01

To develop a tool for estimating hospital-specific inpatient prices for major payers. AHRQ Healthcare Cost and Utilization Project State Inpatient Databases and complete hospital financial reporting of revenues mandated in 10 states for 2006. Hospital discharge records and hospital financial information were merged to estimate revenue per stay by payer. Estimated prices were validated against other data sources. Hospital prices can be reasonably estimated for 10 geographically diverse states. All-payer price-to-charge ratios, an intermediate step in estimating prices, compare favorably to cost-to-charge ratios. Estimated prices also compare well with Medicare, MarketScan private insurance, and the Medical Expenditure Panel Survey prices for major payers, given limitations of each dataset. Public reporting of prices is a consumer resource in making decisions about health care treatment; for self-pay patients, they can provide leverage in negotiating discounts off of charges. Researchers can also use prices to increase understanding of the level and causes of price differentials among geographic areas. Prices by payer expand investigational tools available to study the interaction of inpatient hospital price setting among public and private payers--an important asset as the payer mix changes with the implementation of the Affordable Care Act. © Published 2013. This article is a U.S. Government work and is in the public domain in the USA.

Series load induction heating inverter state estimator using Kalman filter

Directory of Open Access Journals (Sweden)

Szelitzky T.

2011-12-01

Full Text Available LQR and H2 controllers require access to the states of the controlled system. The method based on description function with Fourier series results in a model with immeasurable states. For this reason, we proposed a Kalman filter based state estimator, which not only filters the input signals, but also computes the unobservable states of the system. The algorithm of the filter was implemented in LabVIEW v8.6 and tested on recorded data obtained from a 10-40 kHz series load frequency controlled induction heating inverter.

Cognitive Workload and Psychophysiological Parameters During Multitask Activity in Helicopter Pilots.

Science.gov (United States)

Gaetan, Sophie; Dousset, Erick; Marqueste, Tanguy; Bringoux, Lionel; Bourdin, Christophe; Vercher, Jean-Louis; Besson, Patricia

2015-12-01

Helicopter pilots are involved in a complex multitask activity, implying overuse of cognitive resources, which may result in piloting task impairment or in decision-making failure. Studies usually investigate this phenomenon in well-controlled, poorly ecological situations by focusing on the correlation between physiological values and either cognitive workload or emotional state. This study aimed at jointly exploring workload induced by a realistic simulated helicopter flight mission and emotional state, as well as physiological markers. The experiment took place in the helicopter full flight dynamic simulator. Six participants had to fly on two missions. Workload level, skin conductance, RMS-EMG, and emotional state were assessed. Joint analysis of psychological and physiological parameters associated with workload estimation revealed particular dynamics in each of three profiles. 1) Expert pilots showed a slight increase of measured physiological parameters associated with the increase in difficulty level. Workload estimates never reached the highest level and the emotional state for this profile only referred to positive emotions with low emotional intensity. 2) Non-Expert pilots showed increasing physiological values as the perceived workload increased. However, their emotional state referred to either positive or negative emotions, with a greater variability in emotional intensity. 3) Intermediate pilots were similar to Expert pilots regarding emotional states and similar to Non-Expert pilots regarding physiological patterns. Overall, high interindividual variability of these results highlight the complex link between physiological and psychological parameters with workload, and question whether physiology alone could predict a pilot's inability to make the right decision at the right time.

On the specificity of face cognition compared with general cognitive functioning across adult age.

Science.gov (United States)

Hildebrandt, Andrea; Wilhelm, Oliver; Schmiedek, Florian; Herzmann, Grit; Sommer, Werner

2011-09-01

Face cognition is considered a specific human ability, clearly differentiable from general cognitive functioning. Its specificity is primarily supported by cognitive-experimental and neuroimaging research, but recently also from an individual differences perspective. However, no comprehensive behavioral data are available, which would allow estimating lifespan changes of the covariance structure of face-cognition abilities and general cognitive functioning as well as age-differences in face cognition after accounting for interindividual variability in general cognition. The present study aimed to fill this gap. In an age-heterogeneous (18-82 years) sample of 448 adults, we found no factorial dedifferentiation between face cognition and general cognition. Age-related differences in face memory were still salient after taking into account changes in general cognitive functioning. Face cognition thus remains a specific human ability compared with general cognition, even until old age. We discuss implications for models of cognitive aging and suggest that it is necessary to include more explicitly special social abilities in those models.

Cognitive and default-mode resting state networks: do male and female brains "rest" differently?

Science.gov (United States)

Weissman-Fogel, Irit; Moayedi, Massieh; Taylor, Keri S; Pope, Geoff; Davis, Karen D

2010-11-01

Variability in human behavior related to sex is supported by neuroimaging studies showing differences in brain activation patterns during cognitive task performance. An emerging field is examining the human connectome, including networks of brain regions that are not only temporally-correlated during different task conditions, but also networks that show highly correlated spontaneous activity during a task-free state. Both task-related and task-free network activity has been associated with individual task performance and behavior under certain conditions. Therefore, our aim was to determine whether sex differences exist during a task-free resting state for two networks associated with cognitive task performance (executive control network (ECN), salience network (SN)) and the default mode network (DMN). Forty-nine healthy subjects (26 females, 23 males) underwent a 5-min task-free fMRI scan in a 3T MRI. An independent components analysis (ICA) was performed to identify the best-fit IC for each network based on specific spatial nodes defined in previous studies. To determine the consistency of these networks across subjects we performed self-organizing group-level ICA analyses. There were no significant differences between sexes in the functional connectivity of the brain areas within the ECN, SN, or the DMN. These important findings highlight the robustness of intrinsic connectivity of these resting state networks and their similarity between sexes. Furthermore, our findings suggest that resting state fMRI studies do not need to be controlled for sex. © 2010 Wiley-Liss, Inc.

Mothers' use of cognitive state verbs in picture-book reading and the development of children's understanding of mind: a longitudinal study.

Science.gov (United States)

Adrián, Juan E; Clemente, Rosa Ana; Villanueva, Lidón

2007-01-01

Mothers read stories to their children (N=41) aged between 3.3 years and 5.11 years old, and children then completed two false-belief tasks. One year later, mothers read a story to 37 of those children who were also given four tasks to assess their advanced understanding of mental states. Mothers' early use of cognitive verbs in picture-book reading correlated with their children's later understanding of mental states. Some pragmatic aspects of maternal input correlated with children's later outcomes. Two different factors in mothers' cognitive discourse were identified, suggesting a zone of proximal development in children's understanding of mental states.

State of Charge and State of Health Estimation of AGM VRLA Batteries by Employing a Dual Extended Kalman Filter and an ARX Model for Online Parameter Estimation

Directory of Open Access Journals (Sweden)

Ngoc-Tham Tran

2017-01-01

Full Text Available State of charge (SOC and state of health (SOH are key issues for the application of batteries, especially the absorbent glass mat valve regulated lead-acid (AGM VRLA type batteries used in the idle stop start systems (ISSs that are popularly integrated into conventional engine-based vehicles. This is due to the fact that SOC and SOH estimation accuracy is crucial for optimizing battery energy utilization, ensuring safety and extending battery life cycles. The dual extended Kalman filter (DEKF, which provides an elegant and powerful solution, is widely applied in SOC and SOH estimation based on a battery parameter model. However, the battery parameters are strongly dependent on operation conditions such as the SOC, current rate and temperature. In addition, battery parameters change significantly over the life cycle of a battery. As a result, many experimental pretests investigating the effects of the internal and external conditions of a battery on its parameters are required, since the accuracy of state estimation depends on the quality of the information regarding battery parameter changes. In this paper, a novel method for SOC and SOH estimation that combines a DEKF algorithm, which considers hysteresis and diffusion effects, and an auto regressive exogenous (ARX model for online parameters estimation is proposed. The DEKF provides precise information concerning the battery open circuit voltage (OCV to the ARX model. Meanwhile, the ARX model continues monitoring parameter variations and supplies information on them to the DEKF. In this way, the estimation accuracy can be maintained despite the changing parameters of a battery. Moreover, online parameter estimation from the ARX model can save the time and effort used for parameter pretests. The validation of the proposed algorithm is given by simulation and experimental results.

Optimal estimate of a pure qubit state from Uhlmann-Josza fidelity

Energy Technology Data Exchange (ETDEWEB)

Aoki, Manuel Avila, E-mail: manvlk@yahoo.com [Centro Universitario UAEM Valle de Chalco, UAEMex, Edo. de Mexico (Mexico)

2012-04-15

In the framework of collective measurements, efforts have been made to reconstruct one-qubit states. Such schemes find an obstacle in the no-cloning theorem, which prevents full reconstruction of a quantum state. Quantum Mechanics thus restricts to obtain estimates of the reconstruction of a pure qubit. We discuss the optimal estimate on the basis of the Uhlmann-Josza fidelity, respecting the limitations imposed by the no-cloning theorem. We derive a realistic optimal expression for the average fidelity. Our formalism also introduces an optimization parameter L. Values close to zero imply full reconstruction of the qubit (i. e., the classical limit), while larger L's represent good quantum optimization of the qubit estimate. The parameter L is interpreted as the degree of quantumness of the average fidelity associated with the reconstruction. (author)

Remaining lifetime modeling using State-of-Health estimation

Science.gov (United States)

Beganovic, Nejra; Söffker, Dirk

2017-08-01

Technical systems and system's components undergo gradual degradation over time. Continuous degradation occurred in system is reflected in decreased system's reliability and unavoidably lead to a system failure. Therefore, continuous evaluation of State-of-Health (SoH) is inevitable to provide at least predefined lifetime of the system defined by manufacturer, or even better, to extend the lifetime given by manufacturer. However, precondition for lifetime extension is accurate estimation of SoH as well as the estimation and prediction of Remaining Useful Lifetime (RUL). For this purpose, lifetime models describing the relation between system/component degradation and consumed lifetime have to be established. In this contribution modeling and selection of suitable lifetime models from database based on current SoH conditions are discussed. Main contribution of this paper is the development of new modeling strategies capable to describe complex relations between measurable system variables, related system degradation, and RUL. Two approaches with accompanying advantages and disadvantages are introduced and compared. Both approaches are capable to model stochastic aging processes of a system by simultaneous adaption of RUL models to current SoH. The first approach requires a priori knowledge about aging processes in the system and accurate estimation of SoH. An estimation of SoH here is conditioned by tracking actual accumulated damage into the system, so that particular model parameters are defined according to a priori known assumptions about system's aging. Prediction accuracy in this case is highly dependent on accurate estimation of SoH but includes high number of degrees of freedom. The second approach in this contribution does not require a priori knowledge about system's aging as particular model parameters are defined in accordance to multi-objective optimization procedure. Prediction accuracy of this model does not highly depend on estimated SoH. This model

Training Nurses in Cognitive Assessment: Uses and Misuses of the Mini-Mental State Examination

Science.gov (United States)

Koder, Deborah-Anne; Klahr, Amanda

2010-01-01

The Mini-Mental State Examination (MMSE) is one of the most commonly used instruments to screen for cognitive deficits within the hospital setting. However training in how to administer this widely used tool is scarce with little, if any, formal training for nursing staff. Scores are also often misused with over reliance on results and cut-offs to…

The effectiveness of cognitive- behavior therapy on illness representations of multiple-sclerosis and improving their emotional states

Directory of Open Access Journals (Sweden)

Farhad Hazhir

2012-01-01

Full Text Available Background: Illness representations (based on Leventhal's model are associated with chronic illness outcomes. It has been suggested that targeting these cognitive components improves illness outcomes. Multiple sclerosis is a common disorder between neural and immune systems that creates physical and psychological consequences. There are few pre psychological trails on these patients. The aim of this study was to determine effectiveness of cognitive-behavior therapy on altering illness representations and improving emotional states of the patients.Methods: By using a randomized controlled trial design, among 52 selected patients, 35 volunteers randomly were allocated into intervention and control groups. An extensive interventional cognitive behavior therapy based package was conducted to intervention group in 10 weekly sessions. The control group stayed in waiting list and participated in 5 group meeting sessions. (IPQR and (DASS-42 psychological scales were administered, Leven and T statistical tests were applied for dat analysis.Results: The results showed positive changes in four illness representation components of patients including illness (identity, consequences, coherence and personal control. Associated improvement occurred in depression, anxiety, stress and emotional representations.Conclusion: Mooney and Padeskey's theoretically based cognitive-behavior therapy, is effective on illness representations modification and improving emotional states of the patients. The findings are less similar to Goodman's trial on Systemic Lupus Erythematosus patients and more similar to Petrie's trail on cardiac patients.

Functional Trajectories, Cognition, and Subclinical Cerebrovascular Disease.

Science.gov (United States)

Dhamoon, Mandip S; Cheung, Ying-Kuen; Gutierrez, Jose; Moon, Yeseon P; Sacco, Ralph L; Elkind, Mitchell S V; Wright, Clinton B

2018-03-01

Cognition and education influence functional trajectories, but whether associations differ with subclinical brain infarcts (SBI) or white matter hyperintensity volume (WMHV) is unknown. We hypothesized that SBI and WMHV moderated relationships between cognitive performance and education and functional trajectories. A total of 1290 stroke-free individuals underwent brain magnetic resonance imaging and were followed for 7.3 years (mean) with annual functional assessments with the Barthel index (range, 0-100). Magnetic resonance imaging measurements included pathology-informed SBI (PI-SBI) and WMHV (% total cranial volume). Generalized estimating equation models tested associations between magnetic resonance imaging variables and baseline Barthel index and change in Barthel index, adjusting for demographic, vascular, cognitive, and social risk factors, and stroke and myocardial infarction during follow-up. We tested interactions among education level, baseline cognitive performance (Mini-Mental State score), and functional trajectories and ran models stratified by levels of magnetic resonance imaging variables. Mean age was 70.6 (SD, 9.0) years; 19% had PI-SBI, and mean WMHV was 0.68%. Education did not modify associations between cognition and functional trajectories. PI-SBI modified associations between cognition and functional trajectories ( P =0.04) with a significant protective effect of better cognition on functional decline seen only in those without PI-SBI. There was no significant interaction for WMHV ( P =0.8). PI-SBI, and greater WMHV, were associated with 2- to 3-fold steeper functional decline, holding cognition constant. PI-SBI moderated the association between cognition and functional trajectories, with 3-fold greater decline among those with PI-SBI (compared with no PI-SBI) and normal baseline cognition. This highlights the strong and independent association between subclinical markers and patient-centered trajectories over time. © 2018 American Heart

Cognitive two-way relay beamforming: Design with resilience to channel state uncertainties

KAUST Repository

Ubaidulla, P.

2016-07-26

In this paper, we propose a robust distributed relay beamformer design for cognitive radio network operating under uncertainties in the available channel state information. The cognitive network consists of a pair of transceivers and a set of non-regenerative two-way relays that assist the communication between the transceiver pair. The secondary nodes share the spectrum with a licensed primary user node while ensuring that the interference to the primary receiver is maintained below a certain threshold. The proposed robust design maximizes the worst-case signal-to-interference-plus-noise ratio at the secondary transceivers while satisfying constraints on the interference to the primary user and on the total relay transmit power. Though the robust design problem is not a convex problem in its original form, we show that it can be reformulated as a convex optimization problem, which can be solved efficiently. Numerical results are provided and illustrate the merits of the proposed design for various operating conditions and parameters. © 2016 IEEE.

Unauthorized Immigration to the United States: Annual Estimates and Components of Change, by State, 1990 to 2010

Science.gov (United States)

Warren, Robert; Warren, John Robert

2013-01-01

We describe a method for producing annual estimates of the unauthorized immigrant population in the United Sates and components of population change, for each state and D.C., for 1990 to 2010. We quantify a sharp drop in the number of unauthorized immigrants arriving since 2000, and we demonstrate the role of departures from the population (emigration, adjustment to legal status, removal by the Department of Homeland Security (DHS), and deaths) in reducing population growth from one million in 2000 to population losses in 2008 and 2009. The number arriving in the U.S. peaked at more than one million in 1999 to 2001, and then declined rapidly through 2009. We provide evidence that population growth stopped after 2007 primarily because entries declined and not because emigration increased during the economic crisis. Our estimates of the total unauthorized immigrant population in the U.S. and in the top ten states are comparable to those produced by DHS and the Pew Hispanic Center. For the remaining states and D.C., our data and methods produce estimates with smaller ranges of sampling error. PMID:23956482

Unauthorized Immigration to the United States: Annual Estimates and Components of Change, by State, 1990 to 2010.

Science.gov (United States)

Warren, Robert; Warren, John Robert

2013-06-01

We describe a method for producing annual estimates of the unauthorized immigrant population in the United Sates and components of population change, for each state and D.C., for 1990 to 2010. We quantify a sharp drop in the number of unauthorized immigrants arriving since 2000, and we demonstrate the role of departures from the population (emigration, adjustment to legal status, removal by the Department of Homeland Security (DHS), and deaths) in reducing population growth from one million in 2000 to population losses in 2008 and 2009. The number arriving in the U.S. peaked at more than one million in 1999 to 2001, and then declined rapidly through 2009. We provide evidence that population growth stopped after 2007 primarily because entries declined and not because emigration increased during the economic crisis. Our estimates of the total unauthorized immigrant population in the U.S. and in the top ten states are comparable to those produced by DHS and the Pew Hispanic Center. For the remaining states and D.C., our data and methods produce estimates with smaller ranges of sampling error.

H∞ state estimation of stochastic memristor-based neural networks with time-varying delays.

Science.gov (United States)

Bao, Haibo; Cao, Jinde; Kurths, Jürgen; Alsaedi, Ahmed; Ahmad, Bashir

2018-03-01

This paper addresses the problem of H ∞ state estimation for a class of stochastic memristor-based neural networks with time-varying delays. Under the framework of Filippov solution, the stochastic memristor-based neural networks are transformed into systems with interval parameters. The present paper is the first to investigate the H ∞ state estimation problem for continuous-time Itô-type stochastic memristor-based neural networks. By means of Lyapunov functionals and some stochastic technique, sufficient conditions are derived to ensure that the estimation error system is asymptotically stable in the mean square with a prescribed H ∞ performance. An explicit expression of the state estimator gain is given in terms of linear matrix inequalities (LMIs). Compared with other results, our results reduce control gain and control cost effectively. Finally, numerical simulations are provided to demonstrate the efficiency of the theoretical results. Copyright © 2018 Elsevier Ltd. All rights reserved.

Prevalence of Dementia and Cognitive Complaints in the Context of High Cognitive Reserve: A Population-Based Study

Science.gov (United States)

Perquin, Magali; Diederich, Nico; Pastore, Jessica; Lair, Marie-Lise; Stranges, Saverio; Vaillant, Michel

2015-01-01

Objectives This study aimed to assess the prevalence of dementia and cognitive complaints in a cross-sectional sample of Luxembourg seniors, and to discuss the results in the societal context of high cognitive reserve resulting from multilingualism. Methods A population sample of 1,377 people representative of Luxembourg residents aged over 64 years was initially identified via the national social insurance register. There were three different levels of contribution: full participation in the study, partial participation, and non-participation. We examined the profiles of these three different samples so that we could infer the prevalence estimates in the Luxembourgish senior population as a whole using the prevalence estimates obtained in this study. Results After careful attention to the potential bias and of the possibility of underestimation, we considered the obtained prevalence estimates of 3.8% for dementia (with corresponding 95% confidence limits (CL) of 2.8% and 4.8%) and 26.1% for cognitive complaints (CL = [17.8–34.3]) as trustworthy. Conclusion Based on these findings, we postulate that high cognitive reserve may result in surprisingly low prevalence estimates of cognitive complaints and dementia in adults over the age of 64 years, which thereby corroborates the longer disability-free life expectancy observed in the Luxembourg population. To the best of our knowledge, this study is the first to report such Luxembourgish public health data. PMID:26390288

Simultaneous estimation of multiple phases in digital holographic interferometry using state space analysis

Science.gov (United States)

Kulkarni, Rishikesh; Rastogi, Pramod

2018-05-01

A new approach is proposed for the multiple phase estimation from a multicomponent exponential phase signal recorded in multi-beam digital holographic interferometry. It is capable of providing multidimensional measurements in a simultaneous manner from a single recording of the exponential phase signal encoding multiple phases. Each phase within a small window around each pixel is appproximated with a first order polynomial function of spatial coordinates. The problem of accurate estimation of polynomial coefficients, and in turn the unwrapped phases, is formulated as a state space analysis wherein the coefficients and signal amplitudes are set as the elements of a state vector. The state estimation is performed using the extended Kalman filter. An amplitude discrimination criterion is utilized in order to unambiguously estimate the coefficients associated with the individual signal components. The performance of proposed method is stable over a wide range of the ratio of signal amplitudes. The pixelwise phase estimation approach of the proposed method allows it to handle the fringe patterns that may contain invalid regions.

Motor cognitive processing speed estimation among the primary schoolchildren by deriving prediction formula: A cross-sectional study

Directory of Open Access Journals (Sweden)

Vencita Priyanka Aranha

2017-01-01

Full Text Available Objectives: Motor cognitive processing speed (MCPS is often reported in terms of reaction time. In spite of being a significant indicator of function, behavior, and performance, MCPS is rarely used in clinics and schools to identify kids with slowed motor cognitive processing. The reason behind this is the lack of availability of convenient formula to estimate MCPS. Thereby, the aim of this study is to estimate the MCPS in the primary schoolchildren. Materials and Methods: Two hundred and four primary schoolchildren, aged 6–12 years, were recruited by the cluster sampling method for this cross-sectional study. MCPS was estimated by the ruler drop method (RDM. By this method, a metallic stainless steel ruler was suspended vertically such that 5 cm graduation of the lower was aligned between the web space of the child's hand, and the child was asked to catch the moving ruler as quickly as possible, once released from the examiner's hand. Distance the ruler traveled was recorded and converted into time, which is the MCPS. Multiple regression analysis of variables was performed to determine the influence of independent variables on MCPS. Results: Mean MCPS of the entire sample of 204 primary schoolchildren is 230.01 ms ± 26.5 standard deviation (95% confidence interval; 226.4–233.7 ms that ranged from 162.9 to 321.6 ms. By stepwise regression analysis, we derived the regression equation, MCPS (ms = 279.625–5.495 × age, with 41.3% (R = 0.413 predictability and 17.1% (R2 = 0.171 and adjusted R2 = 0.166 variability. Conclusion: MCPS prediction formula through RDM in the primary schoolchildren has been established.

Drug use and AIDS: estimating injection prevalence in a rural state.

Science.gov (United States)

Leukefeld, Carl G; Logan, T K; Farabee, David; Clayton, Richard

2002-01-01

This paper presents approaches used in one rural U.S. state to describe the level of injecting drug use and to estimate the number of injectors not receiving drug-user treatment. The focus is on two broad areas of estimation that were used to present the prevalence of injecting drug use in Kentucky. The first estimation approach uses available data from secondary data sources. The second approach involves three small community studies.

Estimating irrigation water use in the humid eastern United States

Science.gov (United States)

Levin, Sara B.; Zarriello, Phillip J.

2013-01-01

Accurate accounting of irrigation water use is an important part of the U.S. Geological Survey National Water-Use Information Program and the WaterSMART initiative to help maintain sustainable water resources in the Nation. Irrigation water use in the humid eastern United States is not well characterized because of inadequate reporting and wide variability associated with climate, soils, crops, and farming practices. To better understand irrigation water use in the eastern United States, two types of predictive models were developed and compared by using metered irrigation water-use data for corn, cotton, peanut, and soybean crops in Georgia and turf farms in Rhode Island. Reliable metered irrigation data were limited to these areas. The first predictive model that was developed uses logistic regression to predict the occurrence of irrigation on the basis of antecedent climate conditions. Logistic regression equations were developed for corn, cotton, peanut, and soybean crops by using weekly irrigation water-use data from 36 metered sites in Georgia in 2009 and 2010 and turf farms in Rhode Island from 2000 to 2004. For the weeks when irrigation was predicted to take place, the irrigation water-use volume was estimated by multiplying the average metered irrigation application rate by the irrigated acreage for a given crop. The second predictive model that was developed is a crop-water-demand model that uses a daily soil water balance to estimate the water needs of a crop on a given day based on climate, soil, and plant properties. Crop-water-demand models were developed independently of reported irrigation water-use practices and relied on knowledge of plant properties that are available in the literature. Both modeling approaches require accurate accounting of irrigated area and crop type to estimate total irrigation water use. Water-use estimates from both modeling methods were compared to the metered irrigation data from Rhode Island and Georgia that were used to

Adaptive unscented Kalman filtering for state of charge estimation of a lithium-ion battery for electric vehicles

International Nuclear Information System (INIS)

Sun, Fengchun; Hu, Xiaosong; Zou, Yuan; Li, Siguang

2011-01-01

An accurate battery State of Charge estimation is of great significance for battery electric vehicles and hybrid electric vehicles. This paper presents an adaptive unscented Kalman filtering method to estimate State of Charge of a lithium-ion battery for battery electric vehicles. The adaptive adjustment of the noise covariances in the State of Charge estimation process is implemented by an idea of covariance matching in the unscented Kalman filter context. Experimental results indicate that the adaptive unscented Kalman filter-based algorithm has a good performance in estimating the battery State of Charge. A comparison with the adaptive extended Kalman filter, extended Kalman filter, and unscented Kalman filter-based algorithms shows that the proposed State of Charge estimation method has a better accuracy. -- Highlights: → Adaptive unscented Kalman filtering is proposed to estimate State of Charge of a lithium-ion battery for electric vehicles. → The proposed method has a good performance in estimating the battery State of Charge. → A comparison with three other Kalman filtering algorithms shows that the proposed method has a better accuracy.

A concise account of techniques available for shipboard sea state estimation

DEFF Research Database (Denmark)

Nielsen, Ulrik Dam

2017-01-01

This article gives a review of techniques applied to make sea state estimation on the basis of measured responses on a ship. The general concept of the procedures is similar to that of a classical wave buoy, which exploits a linear assumption between waves and the associated motions. In the frequ......This article gives a review of techniques applied to make sea state estimation on the basis of measured responses on a ship. The general concept of the procedures is similar to that of a classical wave buoy, which exploits a linear assumption between waves and the associated motions...

Conversion between mini-mental state examination, montreal cognitive assessment, and dementia rating scale-2 scores in Parkinson's disease.

Science.gov (United States)

van Steenoven, Inger; Aarsland, Dag; Hurtig, Howard; Chen-Plotkin, Alice; Duda, John E; Rick, Jacqueline; Chahine, Lama M; Dahodwala, Nabila; Trojanowski, John Q; Roalf, David R; Moberg, Paul J; Weintraub, Daniel

2014-12-01

Cognitive impairment is one of the earliest, most common, and most disabling non-motor symptoms in Parkinson's disease (PD). Thus, routine screening of global cognitive abilities is important for the optimal management of PD patients. Few global cognitive screening instruments have been developed for or validated in PD patients. The Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), and Dementia Rating Scale-2 (DRS-2) have been used extensively for cognitive screening in both clinical and research settings. Determining how to convert the scores between instruments would facilitate the longitudinal assessment of cognition in clinical settings and the comparison and synthesis of cognitive data in multicenter and longitudinal cohort studies. The primary aim of this study was to apply a simple and reliable algorithm for the conversion of MoCA to MMSE scores in PD patients. A secondary aim was to apply this algorithm for the conversion of DRS-2 to both MMSE and MoCA scores. The cognitive performance of a convenience sample of 360 patients with idiopathic PD was assessed by at least two of these cognitive screening instruments. We then developed conversion scores between the MMSE, MoCA, and DRS-2 using equipercentile equating and log-linear smoothing. The conversion score tables reported here enable direct and easy comparison of three routinely used cognitive screening assessments in PD patients. © 2014 International Parkinson and Movement Disorder Society.

Three screening methods for cognitive dysfunction using the Mini-Mental State Examination and Korean Dementia Screening Questionnaire.

Science.gov (United States)

Choi, Seong Hye; Park, Moon Ho

2016-02-01

To screen for and determine cognitive dysfunction, cognitive tests and/or informant reports are commonly used. However, these cognitive tests and informant reports are not always available. The present study investigated three screening methods using the Mini-Mental State Examination (MMSE) as the cognitive test, and the Korean dementia screening questionnaire (KDSQ) as the informant report. Participants were recruited from the Korea Clinical Research Center for Dementia of South Korea, and included 2861 patients with Alzheimer's disease (dementia), 3519 patients with mild cognitive impairment and 1375 controls with no cognitive dysfunction. Three screening methods were tested: (i) MMSE alone (MMSE(cut-off) ); (ii) a conventional combination of MMSE and KDSQ (MMSE+KDSQ(cut-off) ); and (iii) a decision tree with MMSE and KDSQ (MMSE+KDSQ(decision tree) ). For discriminating any cognitive dysfunction from controls, MMSE+KDSQ(cut-off) had the highest area under the receiver operating characteristic curve (0.784). For discriminating dementia from controls, MMSE+KDSQ(cut-off) had the highest area under the receiver operating characteristic curve (0.899). For discriminating mild cognitive impairment from controls, MMSE(cut-off) had the highest area under the receiver operating characteristic curve (0.683). MMSE+KDSQ(decision tree) showed the highest sensitivity for all discriminations. For overall classification accuracy, MMSE+KDSQ(decision tree) had the highest value (70.0%). These three methods had different advantageous properties for screening and staging cognitive dysfunction. As there might be different availability across clinical settings, these three methods can be selected and used according to situational needs. © 2015 Japan Geriatrics Society.

A model predictive control approach combined unscented Kalman filter vehicle state estimation in intelligent vehicle trajectory tracking

Directory of Open Access Journals (Sweden)

Hongxiao Yu

2015-05-01

Full Text Available Trajectory tracking and state estimation are significant in the motion planning and intelligent vehicle control. This article focuses on the model predictive control approach for the trajectory tracking of the intelligent vehicles and state estimation of the nonlinear vehicle system. The constraints of the system states are considered when applying the model predictive control method to the practical problem, while 4-degree-of-freedom vehicle model and unscented Kalman filter are proposed to estimate the vehicle states. The estimated states of the vehicle are used to provide model predictive control with real-time control and judge vehicle stability. Furthermore, in order to decrease the cost of solving the nonlinear optimization, the linear time-varying model predictive control is used at each time step. The effectiveness of the proposed vehicle state estimation and model predictive control method is tested by driving simulator. The results of simulations and experiments show that great and robust performance is achieved for trajectory tracking and state estimation in different scenarios.

Cognitive event-related potentials in comatose and post-comatose states.

Science.gov (United States)

Vanhaudenhuyse, Audrey; Laureys, Steven; Perrin, Fabien

2008-01-01

We review the interest of cognitive event-related potentials (ERPs) in comatose, vegetative, or minimally conscious patients. Auditory cognitive ERPs are useful to investigate residual cognitive functions, such as echoic memory (MMN), acoustical and semantic discrimination (P300), and incongruent language detection (N400). While early ERPs (such as the absence of cortical responses on somatosensory-evoked potentials) predict bad outcome, cognitive ERPs (MMN and P300) are indicative of recovery of consciousness. In coma-survivors, cognitive potentials are more frequently obtained when using stimuli that are more ecologic or have an emotional content (such as the patient's own name) than when using classical sine tones.

Distinguishing cognitive state with multifractal complexity of hippocampal interspike interval sequences

Directory of Open Access Journals (Sweden)

Dustin eFetterhoff

2015-09-01

Full Text Available Fractality, represented as self-similar repeating patterns, is ubiquitous in nature and the brain. Dynamic patterns of hippocampal spike trains are known to exhibit multifractal properties during working memory processing; however, it is unclear whether the multifractal properties inherent to hippocampal spike trains reflect active cognitive processing. To examine this possibility, hippocampal neuronal ensembles were recorded from rats before, during and after a spatial working memory task following administration of tetrahydrocannabinol (THC, a memory-impairing component of cannabis. Multifractal detrended fluctuation analysis was performed on hippocampal interspike interval sequences to determine characteristics of monofractal long-range temporal correlations (LRTCs, quantified by the Hurst exponent, and the degree/magnitude of multifractal complexity, quantified by the width of the singularity spectrum. Our results demonstrate that multifractal firing patterns of hippocampal spike trains are a marker of functional memory processing, as they are more complex during the working memory task and significantly reduced following administration of memory impairing THC doses. Conversely, LRTCs are largest during resting state recordings, therefore reflecting different information compared to multifractality. In order to deepen conceptual understanding of multifractal complexity and LRTCs, these measures were compared to classical methods using hippocampal frequency content and firing variability measures. These results showed that LRTCs, multifractality, and theta rhythm represent independent processes, while delta rhythm correlated with multifractality. Taken together, these results provide a novel perspective on memory function by demonstrating that the multifractal nature of spike trains reflects hippocampal microcircuit activity that can be used to detect and quantify cognitive, physiological and pathological states.

Dynamic state estimation and prediction for real-time control and operation

NARCIS (Netherlands)

Nguyen, P.H.; Venayagamoorthy, G.K.; Kling, W.L.; Ribeiro, P.F.

2013-01-01

Real-time control and operation are crucial to deal with increasing complexity of modern power systems. To effectively enable those functions, it is required a Dynamic State Estimation (DSE) function to provide accurate network state variables at the right moment and predict their trends ahead. This

Advanced sensing techniques for cognitive radio

CERN Document Server

Zhao, Guodong; Li, Shaoqian

2017-01-01

This SpringerBrief investigates advanced sensing techniques to detect and estimate the primary receiver for cognitive radio systems. Along with a comprehensive overview of existing spectrum sensing techniques, this brief focuses on the design of new signal processing techniques, including the region-based sensing, jamming-based probing, and relay-based probing. The proposed sensing techniques aim to detect the nearby primary receiver and estimate the cross-channel gain between the cognitive transmitter and primary receiver. The performance of the proposed algorithms is evaluated by simulations in terms of several performance parameters, including detection probability, interference probability, and estimation error. The results show that the proposed sensing techniques can effectively sense the primary receiver and improve the cognitive transmission throughput. Researchers and postgraduate students in electrical engineering will find this an exceptional resource.

Kalman-Filter-Based State Estimation for System Information Exchange in a Multi-bus Islanded Microgrid

DEFF Research Database (Denmark)

Wang, Yanbo; Tian, Yanjun; Wang, Xiongfei

2014-01-01

State monitoring and analysis of distribution systems has become an urgent issue, and state estimation serves as an important tool to deal with it. In this paper, a Kalman-Filter-based state estimation method for a multi-bus islanded microgrid is presented. First, an overall small signal model wi...

Assessment of the efficiency of cognitive function of people over 65

Directory of Open Access Journals (Sweden)

Katarzyna Tomaszewska

2018-05-01

Full Text Available Demographic estimates for Poland indicate that the average life expectancy of the population will increase further over the next decades. Weakness of memory is a physiological phenomenon affecting about 60% of people over 65, whereas the prevalence of dementia in the population is estimated at 10-11%. Identification of people at high risk of developing dementia is becoming a significant medical and social problem. Cognitive impairment is one of the first symptoms of dementia. The Mini-Mental State Examination (MMSE is a screening method that allows initial assessment of cognitive performance. Correct diagnosis of symptoms, determining the health needs of seniors in all dimensions sets its further paths and optimal medical treatment. Aim of the paper: The aim of this paper is to assess the efficiency of cognitive function of people over 65. Material and methods: The analysis included 135 questionnaires of the Mini-Mental State Examination (MMSE and respondents’ particulars. Additionally, the functional efficiency of the subjects was assessed using the Barthel scale. The research was conducted among people over 65 in the Podkarpackie Voivodeship in 2017. For the purpose of this work, especially for verification of hypotheses, the following tests were used for questions on nominal scales: V Kramer (2x3, 4x5 tables etc., Phi (2x2 tables. Tb-Kendall or Tc tests were used for questions on ordinal scales. The statistical analysis was carried out using the SPSS program and all relationships were statistically significant when p

A Multi-Sensor Fusion MAV State Estimation from Long-Range Stereo, IMU, GPS and Barometric Sensors.

Science.gov (United States)

Song, Yu; Nuske, Stephen; Scherer, Sebastian

2016-12-22

State estimation is the most critical capability for MAV (Micro-Aerial Vehicle) localization, autonomous obstacle avoidance, robust flight control and 3D environmental mapping. There are three main challenges for MAV state estimation: (1) it can deal with aggressive 6 DOF (Degree Of Freedom) motion; (2) it should be robust to intermittent GPS (Global Positioning System) (even GPS-denied) situations; (3) it should work well both for low- and high-altitude flight. In this paper, we present a state estimation technique by fusing long-range stereo visual odometry, GPS, barometric and IMU (Inertial Measurement Unit) measurements. The new estimation system has two main parts, a stochastic cloning EKF (Extended Kalman Filter) estimator that loosely fuses both absolute state measurements (GPS, barometer) and the relative state measurements (IMU, visual odometry), and is derived and discussed in detail. A long-range stereo visual odometry is proposed for high-altitude MAV odometry calculation by using both multi-view stereo triangulation and a multi-view stereo inverse depth filter. The odometry takes the EKF information (IMU integral) for robust camera pose tracking and image feature matching, and the stereo odometry output serves as the relative measurements for the update of the state estimation. Experimental results on a benchmark dataset and our real flight dataset show the effectiveness of the proposed state estimation system, especially for the aggressive, intermittent GPS and high-altitude MAV flight.

Abnormal resting-state connectivity of motor and cognitive networks in early manifest Huntington's disease.

Science.gov (United States)

Wolf, R C; Sambataro, F; Vasic, N; Depping, M S; Thomann, P A; Landwehrmeyer, G B; Süssmuth, S D; Orth, M

2014-11-01

Functional magnetic resonance imaging (fMRI) of multiple neural networks during the brain's 'resting state' could facilitate biomarker development in patients with Huntington's disease (HD) and may provide new insights into the relationship between neural dysfunction and clinical symptoms. To date, however, very few studies have examined the functional integrity of multiple resting state networks (RSNs) in manifest HD, and even less is known about whether concomitant brain atrophy affects neural activity in patients. Using MRI, we investigated brain structure and RSN function in patients with early HD (n = 20) and healthy controls (n = 20). For resting-state fMRI data a group-independent component analysis identified spatiotemporally distinct patterns of motor and prefrontal RSNs of interest. We used voxel-based morphometry to assess regional brain atrophy, and 'biological parametric mapping' analyses to investigate the impact of atrophy on neural activity. Compared with controls, patients showed connectivity changes within distinct neural systems including lateral prefrontal, supplementary motor, thalamic, cingulate, temporal and parietal regions. In patients, supplementary motor area and cingulate cortex connectivity indices were associated with measures of motor function, whereas lateral prefrontal connectivity was associated with cognition. This study provides evidence for aberrant connectivity of RSNs associated with motor function and cognition in early manifest HD when controlling for brain atrophy. This suggests clinically relevant changes of RSN activity in the presence of HD-associated cortical and subcortical structural abnormalities.

A Bayesian consistent dual ensemble Kalman filter for state-parameter estimation in subsurface hydrology

KAUST Repository

Ait-El-Fquih, Boujemaa; El Gharamti, Mohamad; Hoteit, Ibrahim

2016-01-01

Ensemble Kalman filtering (EnKF) is an efficient approach to addressing uncertainties in subsurface ground-water models. The EnKF sequentially integrates field data into simulation models to obtain a better characterization of the model's state and parameters. These are generally estimated following joint and dual filtering strategies, in which, at each assimilation cycle, a forecast step by the model is followed by an update step with incoming observations. The joint EnKF directly updates the augmented state-parameter vector, whereas the dual EnKF empirically employs two separate filters, first estimating the parameters and then estimating the state based on the updated parameters. To develop a Bayesian consistent dual approach and improve the state-parameter estimates and their consistency, we propose in this paper a one-step-ahead (OSA) smoothing formulation of the state-parameter Bayesian filtering problem from which we derive a new dual-type EnKF, the dual EnKF(OSA). Compared with the standard dual EnKF, it imposes a new update step to the state, which is shown to enhance the performance of the dual approach with almost no increase in the computational cost. Numerical experiments are conducted with a two-dimensional (2-D) synthetic groundwater aquifer model to investigate the performance and robustness of the proposed dual EnKFOSA, and to evaluate its results against those of the joint and dual EnKFs. The proposed scheme is able to successfully recover both the hydraulic head and the aquifer conductivity, providing further reliable estimates of their uncertainties. Furthermore, it is found to be more robust to different assimilation settings, such as the spatial and temporal distribution of the observations, and the level of noise in the data. Based on our experimental setups, it yields up to 25% more accurate state and parameter estimations than the joint and dual approaches.

A Bayesian consistent dual ensemble Kalman filter for state-parameter estimation in subsurface hydrology

KAUST Repository

Ait-El-Fquih, Boujemaa

2016-08-12

Ensemble Kalman filtering (EnKF) is an efficient approach to addressing uncertainties in subsurface ground-water models. The EnKF sequentially integrates field data into simulation models to obtain a better characterization of the model\\'s state and parameters. These are generally estimated following joint and dual filtering strategies, in which, at each assimilation cycle, a forecast step by the model is followed by an update step with incoming observations. The joint EnKF directly updates the augmented state-parameter vector, whereas the dual EnKF empirically employs two separate filters, first estimating the parameters and then estimating the state based on the updated parameters. To develop a Bayesian consistent dual approach and improve the state-parameter estimates and their consistency, we propose in this paper a one-step-ahead (OSA) smoothing formulation of the state-parameter Bayesian filtering problem from which we derive a new dual-type EnKF, the dual EnKF(OSA). Compared with the standard dual EnKF, it imposes a new update step to the state, which is shown to enhance the performance of the dual approach with almost no increase in the computational cost. Numerical experiments are conducted with a two-dimensional (2-D) synthetic groundwater aquifer model to investigate the performance and robustness of the proposed dual EnKFOSA, and to evaluate its results against those of the joint and dual EnKFs. The proposed scheme is able to successfully recover both the hydraulic head and the aquifer conductivity, providing further reliable estimates of their uncertainties. Furthermore, it is found to be more robust to different assimilation settings, such as the spatial and temporal distribution of the observations, and the level of noise in the data. Based on our experimental setups, it yields up to 25% more accurate state and parameter estimations than the joint and dual approaches.

Distributed and decentralized state estimation in gas networks as distributed parameter systems.

Science.gov (United States)

Ahmadian Behrooz, Hesam; Boozarjomehry, R Bozorgmehry

2015-09-01

In this paper, a framework for distributed and decentralized state estimation in high-pressure and long-distance gas transmission networks (GTNs) is proposed. The non-isothermal model of the plant including mass, momentum and energy balance equations are used to simulate the dynamic behavior. Due to several disadvantages of implementing a centralized Kalman filter for large-scale systems, the continuous/discrete form of extended Kalman filter for distributed and decentralized estimation (DDE) has been extended for these systems. Accordingly, the global model is decomposed into several subsystems, called local models. Some heuristic rules are suggested for system decomposition in gas pipeline networks. In the construction of local models, due to the existence of common states and interconnections among the subsystems, the assimilation and prediction steps of the Kalman filter are modified to take the overlapping and external states into account. However, dynamic Riccati equation for each subsystem is constructed based on the local model, which introduces a maximum error of 5% in the estimated standard deviation of the states in the benchmarks studied in this paper. The performance of the proposed methodology has been shown based on the comparison of its accuracy and computational demands against their counterparts in centralized Kalman filter for two viable benchmarks. In a real life network, it is shown that while the accuracy is not significantly decreased, the real-time factor of the state estimation is increased by a factor of 10. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

The effect of embodied emotive states on cognitive categorization.

Science.gov (United States)

Price, Tom F; Harmon-Jones, Eddie

2010-12-01

Research has uncovered that positive affect broadens cognitive categorization. The motivational dimensional model, however, posits that positive affect is not a unitary construct with only one cognitive consequence. Instead, this model puts forth that there are different positive affects varying in approach motivational intensity. According to this model, only positive affects lower in motivational intensity should broaden cognitive processes, whereas positive affects higher in motivational intensity should narrow cognitive processes. Consistent with these predictions, high approach positive affect has been shown to narrow attention, whereas low approach positive affect has been shown to broaden it (Gable & Harmon-Jones, 2008). High approach positive affect, therefore, might narrow categorization. Two experiments investigated this possibility by having participants respond to cognitive categorization tasks in 3 body postures designed to elicit different levels of approach motivation: reclining backward, which should evoke low approach motivation; sitting upright, which should evoke moderate approach motivation; and leaning forward, which should evoke high approach motivation. Participants smiled while in each posture in order to experience positive affect. Experiment 1 provided initial support for the idea that high approach positive affect narrows categorization and low approach positive affect broadens categorization. Experiment 2 replicated these findings with improved smiling instructions. These results extend previous work by showing that the motivational model's predictions hold for basic attentional processes as well as higher level cognitive processes such as categorization.

Adaptive Disturbance Tracking Theory with State Estimation and State Feedback for Region II Control of Large Wind Turbines

Science.gov (United States)

Balas, Mark J.; Thapa Magar, Kaman S.; Frost, Susan A.

2013-01-01

A theory called Adaptive Disturbance Tracking Control (ADTC) is introduced and used to track the Tip Speed Ratio (TSR) of 5 MW Horizontal Axis Wind Turbine (HAWT). Since ADTC theory requires wind speed information, a wind disturbance generator model is combined with lower order plant model to estimate the wind speed as well as partial states of the wind turbine. In this paper, we present a proof of stability and convergence of ADTC theory with lower order estimator and show that the state feedback can be adaptive.

Comparative Study of Online Open Circuit Voltage Estimation Techniques for State of Charge Estimation of Lithium-Ion Batteries

Directory of Open Access Journals (Sweden)

Hicham Chaoui

2017-04-01

Full Text Available Online estimation techniques are extensively used to determine the parameters of various uncertain dynamic systems. In this paper, online estimation of the open-circuit voltage (OCV of lithium-ion batteries is proposed by two different adaptive filtering methods (i.e., recursive least square, RLS, and least mean square, LMS, along with an adaptive observer. The proposed techniques use the battery’s terminal voltage and current to estimate the OCV, which is correlated to the state of charge (SOC. Experimental results highlight the effectiveness of the proposed methods in online estimation at different charge/discharge conditions and temperatures. The comparative study illustrates the advantages and limitations of each online estimation method.

Connections of geometric measure of entanglement of pure symmetric states to quantum state estimation

International Nuclear Information System (INIS)

Chen Lin; Zhu Huangjun; Wei, Tzu-Chieh

2011-01-01

We study the geometric measure of entanglement (GM) of pure symmetric states related to rank 1 positive-operator-valued measures (POVMs) and establish a general connection with quantum state estimation theory, especially the maximum likelihood principle. Based on this connection, we provide a method for computing the GM of these states and demonstrate its additivity property under certain conditions. In particular, we prove the additivity of the GM of pure symmetric multiqubit states whose Majorana points under Majorana representation are distributed within a half sphere, including all pure symmetric three-qubit states. We then introduce a family of symmetric states that are generated from mutually unbiased bases and derive an analytical formula for their GM. These states include Dicke states as special cases, which have already been realized in experiments. We also derive the GM of symmetric states generated from symmetric informationally complete POVMs (SIC POVMs) and use it to characterize all inequivalent SIC POVMs in three-dimensional Hilbert space that are covariant with respect to the Heisenberg-Weyl group. Finally, we describe an experimental scheme for creating the symmetric multiqubit states studied in this article and a possible scheme for measuring the permanence of the related Gram matrix.

Power System Real-Time Monitoring by Using PMU-Based Robust State Estimation Method

DEFF Research Database (Denmark)

Zhao, Junbo; Zhang, Gexiang; Das, Kaushik

2016-01-01

Accurate real-time states provided by the state estimator are critical for power system reliable operation and control. This paper proposes a novel phasor measurement unit (PMU)-based robust state estimation method (PRSEM) to real-time monitor a power system under different operation conditions...... the system real-time states with good robustness and can address several kinds of BD.......-based bad data (BD) detection method, which can handle the smearing effect and critical measurement errors, is presented. We evaluate PRSEM by using IEEE benchmark test systems and a realistic utility system. The numerical results indicate that, in short computation time, PRSEM can effectively track...

The cumulative load of depressive illness is associated with cognitive function in the remitted state of unipolar depressive disorder

DEFF Research Database (Denmark)

Hasselbalch, BJ; Knorr, U; Hasselbalch, S G

2013-01-01

OBJECTIVE: To investigate whether the cumulative number, duration and subtypes (severity and presence of psychotic features) of previous episodes of depression in patients with unipolar depressive disorder in a remitted state are associated with decreased global cognitive function. METHODS: Via...... with the Cambridge Cognitive Examination (CAMCOG), which provides a composite measure of global cognitive function. RESULTS: A total of 88 patients and 50 controls accepted our invitation to participate, fulfilled the selection criteria and were included in the study. The cumulative duration of depressive episodes...... episodes with psychotic features, respectively. CONCLUSION: Our findings suggest that cognitive dysfunction is associated with the cumulative duration of depressive episodes, and that, in particular, depressive episodes with psychotic features in the course of illness may be a significant predictor...

A state-space Bayesian framework for estimating biogeochemical transformations using time-lapse geophysical data

Energy Technology Data Exchange (ETDEWEB)

Chen, J.; Hubbard, S.; Williams, K.; Pride, S.; Li, L.; Steefel, C.; Slater, L.

2009-04-15

We develop a state-space Bayesian framework to combine time-lapse geophysical data with other types of information for quantitative estimation of biogeochemical parameters during bioremediation. We consider characteristics of end-products of biogeochemical transformations as state vectors, which evolve under constraints of local environments through evolution equations, and consider time-lapse geophysical data as available observations, which could be linked to the state vectors through petrophysical models. We estimate the state vectors and their associated unknown parameters over time using Markov chain Monte Carlo sampling methods. To demonstrate the use of the state-space approach, we apply it to complex resistivity data collected during laboratory column biostimulation experiments that were poised to precipitate iron and zinc sulfides during sulfate reduction. We develop a petrophysical model based on sphere-shaped cells to link the sulfide precipitate properties to the time-lapse geophysical attributes and estimate volume fraction of the sulfide precipitates, fraction of the dispersed, sulfide-encrusted cells, mean radius of the aggregated clusters, and permeability over the course of the experiments. Results of the case study suggest that the developed state-space approach permits the use of geophysical datasets for providing quantitative estimates of end-product characteristics and hydrological feedbacks associated with biogeochemical transformations. Although tested here on laboratory column experiment datasets, the developed framework provides the foundation needed for quantitative field-scale estimation of biogeochemical parameters over space and time using direct, but often sparse wellbore data with indirect, but more spatially extensive geophysical datasets.

Conversion Between Mini-Mental State Examination, Montreal Cognitive Assessment, and Dementia Rating Scale-2 Scores in Parkinson’s Disease

Science.gov (United States)

van Steenoven, Inger; Aarsland, Dag; Hurtig, Howard; Chen-Plotkin, Alice; Duda, John E.; Rick, Jacqueline; Chahine, Lama M.; Dahodwala, Nabila; Trojanowski, John Q.; Roalf, David R.; Moberg, Paul J.; Weintraub, Daniel

2015-01-01

Cognitive impairment is one of the earliest, most common, and most disabling non-motor symptoms in Parkinson’s disease (PD). Thus, routine screening of global cognitive abilities is important for the optimal management of PD patients. Few global cognitive screening instruments have been developed for or validated in PD patients. The Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), and Dementia Rating Scale-2 (DRS-2) have been used extensively for cognitive screening in both clinical and research settings. Determining how to convert the scores between instruments would facilitate the longitudinal assessment of cognition in clinical settings and the comparison and synthesis of cognitive data in multicenter and longitudinal cohort studies. The primary aim of this study was to apply a simple and reliable algorithm for the conversion of MoCA to MMSE scores in PD patients. A secondary aim was to apply this algorithm for the conversion of DRS-2 to both MMSE and MoCA scores. The cognitive performance of a convenience sample of 360 patients with idiopathic PD was assessed by at least two of these cognitive screening instruments. We then developed conversion scores between the MMSE, MoCA, and DRS-2 using equipercentile equating and log-linear smoothing. The conversion score tables reported here enable direct and easy comparison of three routinely used cognitive screening assessments in PD patients. PMID:25381961

Cognitive cooperation groups mediated by computers and internet present significant improvement of cognitive status in older adults with memory complaints: a controlled prospective study

Directory of Open Access Journals (Sweden)

Rodrigo de Rosso Krug

Full Text Available ABSTRACT Objective To estimate the effect of participating in cognitive cooperation groups, mediated by computers and the internet, on the Mini-Mental State Examination (MMSE percent variation of outpatients with memory complaints attending two memory clinics. Methods A prospective controlled intervention study carried out from 2006 to 2013 with 293 elders. The intervention group (n = 160 attended a cognitive cooperation group (20 sessions of 1.5 hours each. The control group (n = 133 received routine medical care. Outcome was the percent variation in the MMSE. Control variables included gender, age, marital status, schooling, hypertension, diabetes, dyslipidaemia, hypothyroidism, depression, vascular diseases, polymedication, use of benzodiazepines, exposure to tobacco, sedentary lifestyle, obesity and functional capacity. The final model was obtained by multivariate linear regression. Results The intervention group obtained an independent positive variation of 24.39% (CI 95% = 14.86/33.91 in the MMSE compared to the control group. Conclusion The results suggested that cognitive cooperation groups, mediated by computers and the internet, are associated with cognitive status improvement of older adults in memory clinics.

Mental workload and cognitive task automaticity: an evaluation of subjective and time estimation metrics.

Science.gov (United States)

Liu, Y; Wickens, C D

1994-11-01

The evaluation of mental workload is becoming increasingly important in system design and analysis. The present study examined the structure and assessment of mental workload in performing decision and monitoring tasks by focusing on two mental workload measurements: subjective assessment and time estimation. The task required the assignment of a series of incoming customers to the shortest of three parallel service lines displayed on a computer monitor. The subject was either in charge of the customer assignment (manual mode) or was monitoring an automated system performing the same task (automatic mode). In both cases, the subjects were required to detect the non-optimal assignments that they or the computer had made. Time pressure was manipulated by the experimenter to create fast and slow conditions. The results revealed a multi-dimensional structure of mental workload and a multi-step process of subjective workload assessment. The results also indicated that subjective workload was more influenced by the subject's participatory mode than by the factor of task speed. The time estimation intervals produced while performing the decision and monitoring tasks had significantly greater length and larger variability than those produced while either performing no other tasks or performing a well practised customer assignment task. This result seemed to indicate that time estimation was sensitive to the presence of perceptual/cognitive demands, but not to response related activities to which behavioural automaticity has developed.

The methodical approach to determining the heterogeneity of cognitive function in preschool children requiring correction of speech impediments

Directory of Open Access Journals (Sweden)

N.B. Petrenko

2016-04-01

Full Text Available Introduction. It has been confirmed that children who suffer from speech impediments may experience difficulties in their cognitive activity, limitations in communication, asociality and sense detachment. It is also clear that these children require not only logopedic treatment, but also assistance in developing functions of their cognitive and mental activities. Aims. To identify the case of uniformity lack of cognitive and somatomental functions of 5-6 year old children with speech impediments in a group; to evaluate the method used for this research. Methods. Use estimates of major mental and cognitive activities by means of tests increases the difficulty. Scores from 1 to 10 were given. Such factors as movement coordination, musicality and body plasticity were taken into consideration too. StatSoft STATISTICA10.0. programme was used to run the statistical analysis. Results. The changes of the group with uniformity of physical, cognitive, somatomental and dance abilities were analysed and estimated at the beginning and at the end of the academic year. The results of the claster analysis have shown that the children managed to develop their cognitive and somatomental abilities. Also it was estimated that the level of uniformity has increased in the group. Conclusions. Having done the research we can state that with the help of cluster analysis children with speech impediments can be grouped according to their physical, cognitive, somatomental and dance abilities. With the help of the results of the claster analysis that notifies that the children managed to develop their cognitive and somatomental abilities, we can observe the positivie effects of the suggested dance-cognitive teaching elements in an educational programme.

Multiple Brain Markers are Linked to Age-Related Variation in Cognition

Science.gov (United States)

Hedden, Trey; Schultz, Aaron P.; Rieckmann, Anna; Mormino, Elizabeth C.; Johnson, Keith A.; Sperling, Reisa A.; Buckner, Randy L.

2016-01-01

Age-related alterations in brain structure and function have been challenging to link to cognition due to potential overlapping influences of multiple neurobiological cascades. We examined multiple brain markers associated with age-related variation in cognition. Clinically normal older humans aged 65–90 from the Harvard Aging Brain Study (N = 186) were characterized on a priori magnetic resonance imaging markers of gray matter thickness and volume, white matter hyperintensities, fractional anisotropy (FA), resting-state functional connectivity, positron emission tomography markers of glucose metabolism and amyloid burden, and cognitive factors of processing speed, executive function, and episodic memory. Partial correlation and mediation analyses estimated age-related variance in cognition shared with individual brain markers and unique to each marker. The largest relationships linked FA and striatum volume to processing speed and executive function, and hippocampal volume to episodic memory. Of the age-related variance in cognition, 70–80% was accounted for by combining all brain markers (but only ∼20% of total variance). Age had significant indirect effects on cognition via brain markers, with significant markers varying across cognitive domains. These results suggest that most age-related variation in cognition is shared among multiple brain markers, but potential specificity between some brain markers and cognitive domains motivates additional study of age-related markers of neural health. PMID:25316342

State-of-charge estimation in lithium-ion batteries: A particle filter approach

Science.gov (United States)

Tulsyan, Aditya; Tsai, Yiting; Gopaluni, R. Bhushan; Braatz, Richard D.

2016-11-01

The dynamics of lithium-ion batteries are complex and are often approximated by models consisting of partial differential equations (PDEs) relating the internal ionic concentrations and potentials. The Pseudo two-dimensional model (P2D) is one model that performs sufficiently accurately under various operating conditions and battery chemistries. Despite its widespread use for prediction, this model is too complex for standard estimation and control applications. This article presents an original algorithm for state-of-charge estimation using the P2D model. Partial differential equations are discretized using implicit stable algorithms and reformulated into a nonlinear state-space model. This discrete, high-dimensional model (consisting of tens to hundreds of states) contains implicit, nonlinear algebraic equations. The uncertainty in the model is characterized by additive Gaussian noise. By exploiting the special structure of the pseudo two-dimensional model, a novel particle filter algorithm that sweeps in time and spatial coordinates independently is developed. This algorithm circumvents the degeneracy problems associated with high-dimensional state estimation and avoids the repetitive solution of implicit equations by defining a 'tether' particle. The approach is illustrated through extensive simulations.

Pre-Trained Neural Networks used for Non-Linear State Estimation

DEFF Research Database (Denmark)

Bayramoglu, Enis; Andersen, Nils Axel; Ravn, Ole

2011-01-01

of the paramters in the distribution. This transformation is approximated by a neural network using offline training, which is based on monte carlo sampling. In the paper, there will also be presented a method to construct a flexible distributions well suited for covering the effect of the non-linearities......The paper focuses on nonlinear state estimation assuming non-Gaussian distributions of the states and the disturbances. The posterior distribution and the aposteriori distribution is described by a chosen family of paramtric distributions. The state transformation then results in a transformation...

Robust stability and ℋ ∞ -estimation for uncertain discrete systems with state-delay

Directory of Open Access Journals (Sweden)

Mahmoud Magdi S.

2001-01-01

Full Text Available In this paper, we investigate the problems of robust stability and ℋ ∞ -estimation for a class of linear discrete-time systems with time-varying norm-bounded parameter uncertainty and unknown state-delay. We provide complete results for robust stability with prescribed performance measure and establish a version of the discrete Bounded Real Lemma. Then, we design a linear estimator such that the estimation error dynamics is robustly stable with a guaranteed ℋ ∞ -performance irrespective of the parameteric uncertainties and unknown state delays. A numerical example is worked out to illustrate the developed theory.

Iterative Observer-based Estimation Algorithms for Steady-State Elliptic Partial Differential Equation Systems

KAUST Repository

Majeed, Muhammad Usman

2017-01-01

the problems are formulated on higher dimensional space domains. However, in this dissertation, feedback based state estimation algorithms, known as state observers, are developed to solve such steady-state problems using one of the space variables as time

The Telephone Interview for Cognitive Status: Creating a crosswalk with the Mini-Mental State Exam

Science.gov (United States)

Fong, Tamara G.; Fearing, Michael A.; Jones, Richard N.; Shi, Peilin; Marcantonio, Edward R.; Rudolph, James L.; Yang, Frances M.; Kiely, Dan K.; Inouye, Sharon K.

2009-01-01

Background Brief cognitive screening measures are valuable tools for both research and clinical applications. The most widely used instrument, the Mini-Mental State Examination (MMSE) is limited in that it must be administered face-to-face, cannot be used in participants with visual or motor impairments, and is protected by copyright. Alternative screening instruments, such as the Telephone Interview for Cognitive Status (TICS) have been developed and may provide a valid alternative with comparable cut point scores to rate global cognitive function. Methods MMSE, TICS-30, and TICS-40 scores from 746 community dwelling elders who participated in the Aging, Demographics, and Memory Study (ADAMS) were analyzed with equipercentile equating, a statistical process of determining comparable scores based on percentile equivalents on different forms of an examination. Results Scores from the MMSE and the TICS-30 and TICS-40 corresponded well and clinically relevant cut point scores were determined; for example, an MMSE score of 23 is equivalent to 17 and 20 on the TICS-30 and TICS-40, respectively. Conclusions These findings provide scores that can be used to link TICS and MMSE scores directly. Clinically relevant and important MMSE cut points and the respective ADAMS TICS-30 and TICS-40 cut point scores have been included to identify the degree of cognitive impairment among respondents with any type of cognitive disorder. These results will help with the widespread application of the TICS in both research and clinical practice. PMID:19647495

Supporting Multiple Cognitive Processing Styles Using Tailored Support Systems

International Nuclear Information System (INIS)

Tuan Q. Tran; Karen M. Feigh; Amy R. Pritchett

2007-01-01

According to theories of cognitive processing style or cognitive control mode, human performance is more effective when an individual's cognitive state (e.g., intuition/scramble vs. deliberate/strategic) matches his/her ecological constraints or context (e.g., utilize intuition to strive for a 'good-enough' response instead of deliberating for the 'best' response under high time pressure). Ill-mapping between cognitive state and ecological constraints are believed to lead to degraded task performance. Consequently, incorporating support systems which are designed to specifically address multiple cognitive and functional states e.g., high workload, stress, boredom, and initiate appropriate mitigation strategies (e.g., reduce information load) is essential to reduce plant risk. Utilizing the concept of Cognitive Control Models, this paper will discuss the importance of tailoring support systems to match an operator's cognitive state, and will further discuss the importance of these ecological constraints in selecting and implementing mitigation strategies for safe and effective system performance. An example from the nuclear power plant industry illustrating how a support system might be tailored to support different cognitive states is included

Performances on a cognitive theory of mind task: specific decline or general cognitive deficits? Evidence from normal aging.

Science.gov (United States)

Fliss, Rafika; Lemerre, Marion; Mollard, Audrey

2016-06-01

Compromised theory of mind (ToM) can be explained either by a failure to implement specific representational capacities (mental state representations) or by more general executive selection demands. In older adult populations, evidence supporting affected executive functioning and cognitive ToM in normal aging are reported. However, links between these two functions remain unclear. In the present paper, we address these shortcomings by using a specific task of ToM and classical executive tasks. We studied, using an original cognitive ToM task, the effect of age on ToM performances, in link with the progressive executive decline. 96 elderly participants were recruited. They were asked to perform a cognitive ToM task, and 5 executive tests (Stroop test and Hayling Sentence Completion Test to appreciate inhibitory process, Trail Making Test and Verbal Fluency for shifting assessment and backward span dedicated to estimate working memory capacity). The results show changes in cognitive ToM performance according to executive demands. Correlational studies indicate a significant relationship between ToM performance and the selected executive measures. Regression analyzes demonstrates that level of vocabulary and age as the best predictors of ToM performance. The results are consistent with the hypothesis that ToM deficits are related to age-related domain-general decline rather than as to a breakdown in specialized representational system. The implications of these findings for the nature of social cognition tests in normal aging are also discussed.

Examining Cognitive Status of Elderly Iranians: Farsi Version of the Modified Mini-Mental State Examination.

Science.gov (United States)

Gharaeipour, Manouchehr; Andrew, Melissa K

2013-02-27

The Modified Mini-Mental State Examination (3MS) is an expanded and modified version of the Mini-Mental State Examination (MMSE). Research demonstrates that the reliability, validity, specificity, and sensitivity of the 3MS are superior to that of the MMSE in detecting cognitive impairment. The Farsi version of the 3MS (F-3MS) was examined as a screening tool for dementia among elderly Iranians. The F-3MS and the Clock Drawing Test (CDT) were administered to 58 patients with dementia and 145 control subjects with normal cognition, aged 60 to 85 years. The difference between groups on the mean total scores of the F-3MS was statistically significant (dementia = 60.65 ± 9.89, control = 80.73 ± 8.26; df = 201, t = 14.75, p validity of the F-3MS. The F-3MS can be used as a valid and reliable measure for dementia screening among elderly Iranians.

Enhancing interferometer phase estimation, sensing sensitivity, and resolution using robust entangled states

Science.gov (United States)

Smith, James F.

2017-11-01

With the goal of designing interferometers and interferometer sensors, e.g., LADARs with enhanced sensitivity, resolution, and phase estimation, states using quantum entanglement are discussed. These states include N00N states, plain M and M states (PMMSs), and linear combinations of M and M states (LCMMS). Closed form expressions for the optimal detection operators; visibility, a measure of the state's robustness to loss and noise; a resolution measure; and phase estimate error, are provided in closed form. The optimal resolution for the maximum visibility and minimum phase error are found. For the visibility, comparisons between PMMSs, LCMMS, and N00N states are provided. For the minimum phase error, comparisons between LCMMS, PMMSs, N00N states, separate photon states (SPSs), the shot noise limit (SNL), and the Heisenberg limit (HL) are provided. A representative collection of computational results illustrating the superiority of LCMMS when compared to PMMSs and N00N states is given. It is found that for a resolution 12 times the classical result LCMMS has visibility 11 times that of N00N states and 4 times that of PMMSs. For the same case, the minimum phase error for LCMMS is 10.7 times smaller than that of PMMS and 29.7 times smaller than that of N00N states.

H∞ state estimation for discrete-time memristive recurrent neural networks with stochastic time-delays

Science.gov (United States)

Liu, Hongjian; Wang, Zidong; Shen, Bo; Alsaadi, Fuad E.

2016-07-01

This paper deals with the robust H∞ state estimation problem for a class of memristive recurrent neural networks with stochastic time-delays. The stochastic time-delays under consideration are governed by a Bernoulli-distributed stochastic sequence. The purpose of the addressed problem is to design the robust state estimator such that the dynamics of the estimation error is exponentially stable in the mean square, and the prescribed ? performance constraint is met. By utilizing the difference inclusion theory and choosing a proper Lyapunov-Krasovskii functional, the existence condition of the desired estimator is derived. Based on it, the explicit expression of the estimator gain is given in terms of the solution to a linear matrix inequality. Finally, a numerical example is employed to demonstrate the effectiveness and applicability of the proposed estimation approach.

Periodontitis and Cognitive Decline in Alzheimer's Disease.

Directory of Open Access Journals (Sweden)

Mark Ide

Full Text Available Periodontitis is common in the elderly and may become more common in Alzheimer's disease because of a reduced ability to take care of oral hygiene as the disease progresses. Elevated antibodies to periodontal bacteria are associated with an increased systemic pro-inflammatory state. Elsewhere raised serum pro-inflammatory cytokines have been associated with an increased rate of cognitive decline in Alzheimer's disease. We hypothesized that periodontitis would be associated with increased dementia severity and a more rapid cognitive decline in Alzheimer's disease. We aimed to determine if periodontitis in Alzheimer's disease is associated with both increased dementia severity and cognitive decline, and an increased systemic pro inflammatory state. In a six month observational cohort study 60 community dwelling participants with mild to moderate Alzheimer's Disease were cognitively assessed and a blood sample taken for systemic inflammatory markers. Dental health was assessed by a dental hygienist, blind to cognitive outcomes. All assessments were repeated at six months. The presence of periodontitis at baseline was not related to baseline cognitive state but was associated with a six fold increase in the rate of cognitive decline as assessed by the ADAS-cog over a six month follow up period. Periodontitis at baseline was associated with a relative increase in the pro-inflammatory state over the six month follow up period. Our data showed that periodontitis is associated with an increase in cognitive decline in Alzheimer's Disease, independent to baseline cognitive state, which may be mediated through effects on systemic inflammation.

Bayesian inference for an illness-death model for stroke with cognition as a latent time-dependent risk factor

NARCIS (Netherlands)

Hout A. van den; Fox J.P.; Klein Entink R.H.

2015-01-01

Longitudinal data can be used to estimate the transition intensities between healthy and unhealthy states prior to death. An illness-death model for history of stroke is presented, where time-dependent transition intensities are regressed on a latent variable representing cognitive function. The

Investigating healthy life expectancy using a multi-state model in the presence of missing data and misclassification

Directory of Open Access Journals (Sweden)

Ardo van den Hout

2014-04-01

Full Text Available Background: A continuous-time three-state model can be used to describe change in cognitive function in the older population. State 1 corresponds to normal cognitive function, state 2 to cognitive impairment, and state 3 to dead. For statistical inference, longitudinal data are available from the UK Medical Research Council Cognitive Function and Ageing Study. Objective: The aim is statistical analysis of longitudinal multi-state data taking into account missing data and potential misclassification of state. In addition, methods for long-term prediction of the transition process are of interest, specifically when applied to the study of healthy life expectancy. Methods: Cognitive function in the older population is assumed to be stable or declining. For this reason, observed improvement of cognitive function is assumed to be caused by misclassification of either state 1 or 2. Regression models for the transition intensities are formulated to incorporate covariate information. Maximum likelihood is used for statistical inference. Results: It is shown that missing values for the state at a pre-scheduled time can easily be taken into account. Long-term prediction is explained and illustrated by the estimation of statespecific life expectancies. In addition, it is shown how microsimulation can be used to further explore predictions based on a fitted multi-state model. Conclusions: Statistical analysis of longitudinal multi-state data can take into account missing data and potential misclassification of state. With respect to long-term prediction, microsimulation is a useful tool for summarising and displaying characteristics of cognitive decline and survival. Comments: ---

The effects of healthy aging, amnestic mild cognitive impairment, and Alzheimer's disease on recollection, familiarity and false recognition, estimated by an associative process-dissociation recognition procedure.