Longitudinal Stability and Change in Time-Frequency Measures from an Oddball Task During Adolescence and Early Adulthood

Time-frequency representations of electroencephalographic signals lend themselves to granular analysis of cognitive and psychological processes. Characterizing developmental trajectories of time-frequency measures can thus inform us about the development of the processes involved. We decomposed EEG activity in a large sample of individuals (N = 1692; 917 females) assessed at approximately three-year intervals from the age of 11 to their mid-20s. Participants completed an oddball task that elicits a robust P3 response. Principal component analysis served to identify meaningful dimensions of time-frequency energy. Component loadings were virtually identical across assessment waves. A common and stable set of time-frequency dynamics thus characterized EEG activity throughout this age range. Trajectories of change in component scores suggest that aspects of brain development reflected in these components comprise two distinct phases, with marked decreases in component amplitude throughout much of adolescence followed by smaller yet significant rates of decreases into early adulthood. Although the structure of time-frequency activity was stable throughout adolescence and early adulthood, we observed subtle change in component loadings as well. Our findings suggest that striking developmental change in event-related potentials emerges through gradual change in the magnitude and timing of a stable set of dimensions of time-frequency activity, illustrating the usefulness of time-frequency representations of EEG signals and longitudinal designs for understanding brain development. In addition, two components were associated with childhood externalizing psychopathology, independent of sex, which extends the existing literature and provides proof of concept of the notion that developmental trajectories might serve as candidate endophenotypes for psychiatric disorders.

Factors Influencing the Performance of Cooperative Vis-À-Vis Private Dairy Industries in Andhra Pradesh

A study was carried out to analyze the factors influencing the performance of dairy industries in Andhra Pradesh. Principal component analysis, a multivariate technique was employed to determine the factor influencing dairy industries. The variable of milk procurement with highest loadings of 0.925 under first component was the most influencing factor showing the performance of the industries. The next most dominant factor was milk sold by the industries per year with loadings of 0.912 comes under second component. Likewise, the overall selected variables were represented under four components with high component loadings. Out of the four components considered, milk procurement by the different dairies was the most influencing factor that shows the performance of dairy industry.

Time Series Features and Machine Learning Forecasts

In this study we combine the results of two independent analyses to position Spanish regions according to both the characteristics of the time series of international tourist arrivals and the accuracy of predictions of arrivals at the regional level. We apply a seasonal trend decomposition procedure based on nonparametric regression to isolate the different components of the series and calculate the main time series features. Predictions are generated with several machine learning models in a recursive multistep-ahead forecasting experiment. Finally, we summarize all the information from the two previous experiments using categorical principal component analysis. By overlapping the distribution of the regions and the component loadings of each variable along both dimensions, we observe that entropy and dispersion show an inverse relation with forecast accuracy, but the interactions between the rest of the features and accuracy are heavily dependent on the forecast horizon. On this evidence, we conclude that in order to increase forecast accuracy of tourist arrivals at the regional level, model selection should be region specific and based on the forecast horizon.

Iterative Supervised Principal Component Analysis-Driven Ligand Design for Regioselective Ti-Catalyzed Pyrrole Synthesis

<div> <div> <div> <p>Herein, we describe the use of iterative supervised principal component analysis (ISPCA) in de novo catalyst design. The regioselective synthesis of 2,5-dimethyl-1,3,4-triphenyl-1H- pyrrole (C) via Ti- catalyzed formal [2+2+1] cycloaddition of phenyl propyne and azobenzene was targeted as a proof of principle. The initial reaction conditions led to an unselective mixture of all possible pyrrole regioisomers. ISPCA was conducted on a training set of catalysts, and their performance was regressed against the scores from the top three principal components. Component loadings from this PCA space along with k-means clustering were used to inform the design of new test catalysts. The selectivity of a prospective test set was predicted in silico using the ISPCA model, and only optimal candidates were synthesized and tested experimentally. This data-driven predictive-modeling workflow was iterated, and after only three generations the catalytic selectivity was improved from 0.5 (statistical mixture of products) to over 11 (> 90% C) by incorporating 2,6-dimethyl- 4-(pyrrolidin-1-yl)pyridine as a ligand. The successful development of a highly selective catalyst without resorting to long, stochastic screening processes demonstrates the inherent power of ISPCA in de novo catalyst design and should motivate the general use of ISPCA in reaction development. </p> </div> </div> </div>

Iterative Supervised Principal Component Analysis-Driven Ligand Design for Regioselective Ti-Catalyzed Pyrrole Synthesis

<div> <div> <div> <p>Herein, we describe the use of iterative supervised principal component analysis (ISPCA) in de novo catalyst design. The regioselective synthesis of 2,5-dimethyl-1,3,4-triphenyl-1H- pyrrole (C) via Ti- catalyzed formal [2+2+1] cycloaddition of phenyl propyne and azobenzene was targeted as a proof of principle. The initial reaction conditions led to an unselective mixture of all possible pyrrole regioisomers. ISPCA was conducted on a training set of catalysts, and their performance was regressed against the scores from the top three principal components. Component loadings from this PCA space along with k-means clustering were used to inform the design of new test catalysts. The selectivity of a prospective test set was predicted in silico using the ISPCA model, and only optimal candidates were synthesized and tested experimentally. This data-driven predictive-modeling workflow was iterated, and after only three generations the catalytic selectivity was improved from 0.5 (statistical mixture of products) to over 11 (> 90% C) by incorporating 2,6-dimethyl- 4-(pyrrolidin-1-yl)pyridine as a ligand. The successful development of a highly selective catalyst without resorting to long, stochastic screening processes demonstrates the inherent power of ISPCA in de novo catalyst design and should motivate the general use of ISPCA in reaction development. </p> </div> </div> </div>

Prototypes profiles of managerial performance

This study examines empirically distinct types of police managers. In an application of the typological methodology the present work investigates the performance profiles of police managers according to the similarity of their configurations. This research was exploratory and no formal hypotheses as to the number and nature of the types that will emerge were advanced. A total of 150 police officers occupying managerial positions from a variety of organizational levels participated in a combined psychological and managerial assessment program. Through inverse factor analysis the participants of this study were classified into three type categories: Aloof Technicians, Amiable Mediocres and Warmhearted Influents. The nature of these prototypical profiles was determined by examining the correlations between participants’ component loadings and scores on managerial performance, personality and mental ability variables. Implications addressing predictor-performance relations, implicit theories of performance, gender differences and relative effectiveness of each type in different situations were discussed.

Psychometric Properties of the French Brief Resilience Scale

The Brief Resilience Scale ( BRS) is a reliable and valid assessment of the self-perceived ability to bounce back or recover quickly from stress. The current study translated and validated the French version of the BRS (BRS-F) in a sample of N = 220 midwives. In a confirmatory factor analysis, the unifactorial model fitted acceptably to the data. High levels of Tucker’s φ implied that the component loadings of the BRS-F and of the original BRS are almost equal. The BRS-F demonstrated good levels of reliability and meaningful correlations with mental health symptoms and burnout. The resilience-mental health difficulties link was fully mediated through emotional exhaustion. Thus, the BRS-F is a psychometrically sound assessment of self-perceived resilience, which is now available to researchers and clinicians in French-speaking contexts. The results also suggest that the BRS-F is relevant for use by healthcare professionals who may benefit from interventions aimed at increasing their resilience.

USE AND MULTIFACTORIAL RECONCILIATION OF UNIFACE REDUCTION MEASURES: A PILOT STUDY AT THE NOBLES POND PALEOINDIAN SITE

How much stone tools are reduced and their form changed from first use to discard bears upon issues such as typological integrity, curation rate, and effects of occupation span. But degree of reduction depends partly upon the measures used to gauge it. Most studies involve single indices that gauge reduction in different ways or at different scales, so results are difficult to compare between studies. In this pilot study, we compare four allometric reduction measures—one each based on length, length:thickness ratio, volume, and mass, estimated by comparing observed values in discarded tools to estimated original values—for consistency when applied to an endscraper sample from the Nobles Pond Paleoindian site in Ohio, USA. Fitted to the Weibull distribution, all measures suggest attrition compared to experimental controls, but variation among them demands reconciliation. A multifactorial method that weights individual measures by their principal-component loadings suggests attritional discard at increasing rate as reduction advances. More importantly, it addresses the growing problem of reconciling the many reduction measures in use, a major concern in this expanding research area.