A Novel Game-Based Intelligent Test for Detecting Elderly Cognitive Function Impairment

Purposes. This research explores the game-based intelligent test (GBIT), predicts the possibilities of Mini-Mental State Examination (MMSE) scores and the risk of cognitive impairment, and then verifies GBIT as one of the reliable and valid cognitive assessment tools. Methods. This study recruited 117 elderly subjects in Taiwan (average age is 79.92 ± 8.68 , average height is 156.91 ± 8.01 , average weight is 59.14 ± 9.67 , and average MMSE score is 23.33 ± 6.16 ). A multiple regression model was used to analyze the GBIT parameters of the elderly’s reaction, attention, coordination, and memory to predict their MMSE performance. The binary logistic regression was then utilized to predict their risk of cognitive impairment. The statistical significance level was set as α = 0.05 . Results. Multiple regression analysis showed that gender, the correct number of reactions, and the correct number of memory have a significantly positive predictive power on MMSE of the elderly ( F = 37.60 , R 2 = 0.69 , and p < 0.05 ). Binary logistic regression analysis noted that the correct average number of reactions falls by one question, and the ratio of cognitive dysfunction risk increases 1.09 times ( p < 0.05 ); the correct average number of memory drops by one question, the ratio of cognitive dysfunction risk increases 3.76 times ( p < 0.05 ), and the overall model predictive power is 88.20% (sensitivity: 84.00%; specificity: 92.30%). Conclusions. This study verifies that GBIT is reliable and can effectively predict the cognitive function and risk of cognitive impairment in the elderly. Therefore, GBIT can be used as one of the feasible tools for evaluating older people’s cognitive function.

Density Functional Theory of Coulombic Excited States Based on Nodal Variational Principle

The density functional theory developed earlier for Coulombic excited states is reconsidered using the nodal variational principle. It is much easier to solve the Kohn–Sham equations, because only the correct number of nodes of the orbitals should be insured instead of the orthogonality.

Kanban in Apparel Manufacturing: A Case Study

Pull system and Kanban are effective lean manufacturing tools. Though many companies in various manufacturing fields have successfully implemented pull manufacturing using Kanban systems, such studies in apparel manufacturing are rarely found. Kanban system was implemented in an apparel manufacturing company, which had implemented some of the lean manufacturing tools. The apparel manufacturing line was identified in four different sub sections and separate Kanbans were used in each sub section. The system was evaluated using two parameters: the number of Kanban cards in each point; the production output. Correct number of Kanban cards were found at all the time of the monitoring period of two weeks-the production efficiency has increased by 22%.

k-MACE Clustering for Gaussian Clusters

Conventional clustering approaches require a preprocessing step that estimates the correct number of cluster prior to the cluster center allocation step. In these approaches, the preprocessing step minimizes one objective function while the second step concentrates on optimization of another objective function. Inspired by MACE-means, we use a single objective function to simultaneously estimate the Correct Number of Cluster (CNC) and acquire the cluster centers. Similarly, we use the Average Central Error (ACE) as ourcost function. The proposed method, denoted by k-minimum ACE (k-MACE), improves MACE-means by rigorous calculation of probabilistic estimate of ACE. While MACE-means (Minimum ACE) only concentrates on Independent Indentically Distributed (IID) clusters, k 􀀀 MACE is a solution for Gaussian clusters with any covariance structure. Simulation results show superiority of k 􀀀 MACE over MACE means and over conven- tional clustering methods such as G-means, DBSCAN, and validity indices methods such as Calinkski Harabaz, Silhoutte, and gap index. Performance is evaluated in terms of

k-MACE Clustering for Gaussian Clusters

Conventional clustering approaches require a preprocessing step that estimates the correct number of cluster prior to the cluster center allocation step. In these approaches, the preprocessing step minimizes one objective function while the second step concentrates on optimization of another objective function. Inspired by MACE-means, we use a single objective function to simultaneously estimate the Correct Number of Cluster (CNC) and acquire the cluster centers. Similarly, we use the Average Central Error (ACE) as ourcost function. The proposed method, denoted by k-minimum ACE (k-MACE), improves MACE-means by rigorous calculation of probabilistic estimate of ACE. While MACE-means (Minimum ACE) only concentrates on Independent Indentically Distributed (IID) clusters, k 􀀀 MACE is a solution for Gaussian clusters with any covariance structure. Simulation results show superiority of k 􀀀 MACE over MACE means and over conven- tional clustering methods such as G-means, DBSCAN, and validity indices methods such as Calinkski Harabaz, Silhoutte, and gap index. Performance is evaluated in terms of

Effects of Verb Network Strengthening Treatment on Sound-Level and Speech Production Errors in Individuals With Aphasia and Acquired Apraxia of Speech

Purpose This retrospective pilot study investigated whether sound-level and speech production errors decreased in confrontation naming following Verb Network Strengthening Treatment (VNeST) for four participants with acquired apraxia of speech (A-AOS) and aphasia for whom lexical retrieval was previously reported. Specifically, we investigated a potential increase in correct number of syllables per word and posttreatment changes across three domains of speech: segmental production, fluency, and prosody. It was hypothesized that treatment shown to increase lexical retrieval in persons with aphasia and A-AOS could potentially facilitate a reduction in sound-level and speech production errors consistent with dual diagnoses of A-AOS and aphasia. Method Naming responses from four participants with aphasia and A-AOS who previously participated in VNeST studies were investigated for correct number of syllables per word and measures of segmental speech, fluency, and prosody. Results Significant gains in at least one measure of speech production were reported for three of the participants. One participant demonstrated decreased segmental speech errors, two showed significant reduction on syllable segmentation, and two demonstrated a significant reduction on false starts and pauses. Significant gains in production of correct number of syllables were limited to one participant, and one participant did not demonstrate increased accuracy on any measure of speech production. Conclusions While speech production errors consistent with motor speech impairment cannot always be definitively distinguished from the confound of aphasia, two participants produced significantly decreased segmentation of syllables, a characteristic unique to A-AOS. The sound-level and speech production changes recorded may be attributed to a combination of interacting motor and language processes and resource allocation. In addition, specific components of VNeST may have contributed to speech production changes. Future work will focus on a prospective study of effects of language therapy (e.g., VNeST) on measures of speech production with investigation beyond the single-word level.

Nonconvergence, Covariance Constraints, and Class Enumeration in Growth Mixture Models

Growth mixture models (GMMs) are a popular method to identify latent classes of growth trajectories. One shortcoming of GMMs is nonconvergence, which often leads researchers to apply covariance equality constraints to simplify estimation. This approach is criticized because it introduces a dubious homoskedasticity assumption across classes. Alternative methods have been proposed to reduce nonconvergence without imposing covariance equality constraints, and though studies have shown that these methods perform well when the correct number of classes is known, research has not examined whether they can accurately identify the number of classes. Given that selecting the number of classes tends to be the most difficult aspect of GMMs, more information about class enumeration performance is crucial to assess the potential utility of these methods. We conduct an extensive simulation based on model characteristics from studies in the PTSD literature to explore class enumeration and classification accuracy of methods for improving nonconvergence. Despite its popularity, results showed that typical approach of applying covariance equality constraints performs quite poorly and is not recommended. However, we recommended covariance pattern GMMs because they (a) had the highest convergence rates, (b) were most likely to identify the correct number of classes, and (c) had the highest classification accuracy in many conditions, even with modest sample sizes. An analysis of empirical PTSD data is provided to show that the typical 4-Class solution found in many empirical PTSD studies may be an artefact of the covariance equality constraint method that has permeated this literature.

On the Detection of the Correct Number of Factors in Two-Facet Models by Means of Parallel Analysis

Methods for optimal factor rotation of two-facet loading matrices have recently been proposed. However, the problem of the correct number of factors to retain for rotation of two-facet loading matrices has rarely been addressed in the context of exploratory factor analysis. Most previous studies were based on the observation that two-facet loading matrices may be rank deficient when the salient loadings of each factor have the same sign. It was shown here that full-rank two-facet loading matrices are, in principle, possible, when some factors have positive and negative salient loadings. Accordingly, the current simulation study on the number of factors to extract for two-facet models was based on rank-deficient and full-rank two-facet population models. The number of factors to extract was estimated from traditional parallel analysis based on the mean of the unreduced eigenvalues as well as from nine other rather traditional versions of parallel analysis (based on the 95th percentile of eigenvalues, based on reduced eigenvalues, based on eigenvalue differences). Parallel analysis based on the mean eigenvalues of the correlation matrix with the squared multiple correlations of each variable with the remaining variables inserted in the main diagonal had the highest detection rates for most of the two-facet factor models. Recommendations for the identification of the correct number of factors are based on the simulation results, on the results of an empirical example data set, and on the conditions for approximately rank-deficient and full-rank two-facet models.

Rovviltforvaltning og reindriftsnæringen: Hvordan kan en todelt målsetting praktiseres i samsvar med internasjonale konvensjoner?

In Norway, both reindeer herders and sheep farmers base their meat production on natural outfield (utmark) pastures during the summer. In winter, most of the sheep are housed and fed indoors, while the reindeer survive on outfield pastures the whole year. During the last few decades, the losses of both sheep and reindeer to protected carnivores has increased in several areas in Norway. In this article, we focus on reindeer herding in the Troms and Nordland reindeer grazing area (reinbeiteområde). Due to large losses of reindeer calves, during the period from their birth in spring until late autumn, several reindeer herding districts hardly have any animals for slaughter. This has a very negative impact on both the economy and the cultural way of life. This article also discusses the reasons why it is difficult to document/prove who or what is responsible for the losses of reindeer calves. It is difficult to get permission to cull extra-active predators if the politically determined quota in each management region is not fulfilled. Documenting the correct number of predators inside each management region is therefore very important for reindeer herding and sheep farming. This has led to criticism and demonstrations by reindeer herders and sheep farmers. Norwegian predator policy formally builds on differentiated management. However, external review reveals that the management model builds on erroneous assumptions. Further, international law also requires that the burden created by predators does not unduly affect indigenous culture. The authors conclude that there is a need for extensive reforms in predator management in Norway.