Range Restriction affects Factor Analysis: Normality, Estimation, Fit, Loadings, and Reliability

A sample suffers range restriction (RR) when its variance is reduced comparing to its population variance and, in turn, it fails representing such population. If the RR occurs over the latent factor, not directly over the observed variable, the researcher deals with an indirect RR, common when using convenience samples. This work explores how this problem affects different outputs of the factor analysis: multivariate normality (MVN), estimation process, goodness-of-fit, recovery of factor loadings, and reliability. In doing so, a Monte Carlo study was conducted. Data were generated following the linear selective sampling model, simulating tests varying their sample size (N = 200 and 500 cases), test size (J = 6, 12, 18, 24 items), loading size (L = .50, .70, and .90) and restriction size (from R = 1, .90, .80, and so on till .10 selection ratio). Our results systematically suggest that an interaction between decreasing the loading size and increasing the restriction size affects the MVN assessment, obstructs the estimation process, and leads to an underestimation of the factor loadings and reliability. However, most of the MVN tests and most of the fit indices employed were nonsensitive to the RR problem. We provide some recommendations to applied researchers.

Test-Size Evolution of the planktonic Foraminifera <i>Globorotalia menardii</i> in the Eastern Tropical Atlantic since the Late Miocene

The mean test size of planktonic foraminifera (PF) is known to have increased especially during the last 12 Ma, probably in terms of an adaptive response to an intensification of the surface-water stratification. On geologically short timescales, the test size in PF is related to environmental conditions. In an optimal species-specific environment, individuals exhibit a greater maximum and average test size, while the size decreases the more unfavourable the environment becomes. An interesting case was observed in the late Neogene and Quaternary size evolution of Globorotalia menardii, which seems to be too extreme to be only explained by changes in environmental conditions. In the western tropical Atlantic Ocean (WTAO) and the Caribbean Sea, the test size more than doubles from 2.6 Ma to 1.95 Ma and 1.7 Ma, respectively, following an almost uninterrupted and successive phase of test size decrease from 4 Ma. Two hypotheses have been suggested to explain the sudden occurrence of a giant G. menardii form: it was triggered by either (1) a punctuated, regional evolutionary event or (2) the immigration of specimens from the Indian Ocean via the Agulhas Leakage. Morphometric measurements of tests from sediment samples of the Ocean Drilling Program (ODP) Leg 108 Hole 667A in the eastern tropical Atlantic Ocean (ETAO), show that the giant type already appears 0.1 Ma earlier at this location than in the WTAO, which indicates that the extreme size increase in the early Pleistocene was a tropical-Atlantic-Ocean-wide event. A coinciding change in the predominant coiling direction suggests that probably a new morphotype occurred. If the giant size and the uniform change in the predominant coiling direction are an indicator for this new type, the form already occurred in the eastern tropical Pacific Ocean at the Pliocene/Pleistocene boundary at 2.58 Ma. This finding supports the Agulhas Leakage hypothesis. However, the hypothesis of a regional, punctuated evolutionary event cannot be dismissed due to missing data from the Indian Ocean. This paper presents the AMOC/thermocline hypothesis, which not only suggests an alternative explanation for the sudden test-size increase in the early Pleistocene, but also for the test size evolution within the whole tropical Atlantic Ocean and the Caribbean Sea for the last 8 Ma. The test-size evolution shows a similar trend with indicators for changes in the Atlantic Meridional Overturning Circulation (AMOC) strength. The mechanism behind that might be that changes in the AMOC strength have a major influence on the thermal stratification of the upper water column, which is known to be the habitat of G. menardii.

A Weight Based Feature Extraction Model on Multifaceted Multimedia Bigdata Using Convolutional Neural Network

This paper intends to present main technique for feature extraction on multimeda getting well versed and a challenging task to handle big data. Analyzing and feature extracting valuable data from high dimensional dataset challenges the bounds of measurable methods and strategies. Conventional techniques in general have less performance while managing high dimensional datasets. Lower test size has consistently been an issue in measurable tests, which get bothered in high dimensional information due to more equivalent or higher component size than the quantity of tests. The intensity of any measurable test is legitimately relative to its capacity to lesser an invalid theory, and test size is a significant central factor in producing probabilities of errors for making substantial ends. Thus one of the effective methods for taking care of high dimensional datasets is by lessening its measurement through feature selection and extraction with the goal that substantial accurate data can be practically performed. Clustering is the act of finding hidden or comparable data in information. It is one of the most widely recognized techniques for realizing useful features where a weight is given to each feature without predefining the various classes. In any feature selection and extraction procedures, the three main considerations of concern are measurable exactness, model interpretability and computational multifaceted nature. For any classification model, it is important to ensure that the productivity of any of these three components isn't undermined. In this manuscript, a Weight Based Feature Extraction Model on Multifaceted Multimedia Big Data (WbFEM-MMB) is proposed which extracts useful features from videos. The feature extraction strategies utilize features from the discrete cosine methods and the features are extracted using a pre-prepared Convolutional Neural Network (CNN). The proposed method is compared with traditional methods and the results show that the proposed method exhibits better performance and accuracy in extracting features from multifaceted multimedia data.

Diagnostic of pathology on the vertebral column machine learning - Cluster K-nearest Neighbor (CKNN) part (I)

In this investigation, we have developed a graphical user interface application to perform the diagnostic of pathology on the column vertebral based on the Cluster K-Nearest Neighbor (CKNN) classifier. The system is implemented and simulated in Anaconda, and its performance is tested on real dataset that contains 6 features and two (02) classes. Each class, abnormal and normal class consists of 210 instances, and 100 instances, respectively. A comparison of the performance of the test measurement under various test sizes (10%~50%) is carried out to predict the class label when the nearest neighbor k changes from 1 to 19. The results show that the accuracy depends on both independent parameters, the test size and k-neighbors, which gives better training accuracy than the test accuracy, in the range of [82.5% ~ 100%] and [70%~84%], respectively. When k varies from 1 to 4, a higher training accuracy, larger than 90% is observed. While the test set shows a low accuracy in the range of [74% ~ 82.5%]. Increasing the test size or/and k, does not affect significantly the accuracy. When k is larger 1, the training accuracy is approximately equal to 0.925±0.05, the test accuracy (except for k=6 and 17) is about 0.79±0.05. The prediction of the class status maybe optimized by combining the dataset set size with the k-neighbors parameters. The GUI can be useful to help the medical doctors to diagnostic the patient effectively to take a rapid decision and predict results in a reduced time lapse.

Freshwater Testate Amoebae (Arcellinida) Response to Eutrophication as Revealed by Test Size and Shape Indices

We review the potential for applying traits-based approaches to freshwater testate amoeba, a diverse protist group that are abundant in lakes and are valuable ecological indicators. We investigated the efficacy of geometric morphometric analysis to define Arcellinida test size and shape indices that could summarize freshwater testate amoeba community dynamics along a temporal gradient of eutrophication in Loch Leven, Scotland (United Kingdom). A cluster analysis of test size and shape indices yielded three clusters, each dominated by a single shape: elongate, spherical and ovoid. When plotted stratigraphically, we observed increases in spherical tests, decreases in elongate tests and shrinking of test size coeval with eutrophication in Loch Leven. Decreases in the elongate cluster may reflect benthic conditions with reduced oxygen levels, while increases in the spherical cluster are likely associated with an expanding macrophyte community that promoted pelagic and epibiotic life habits. Shrinking of test size may be a stress response to eutrophication and/or warming temperatures. Tracking community dynamics using test size and shape indices was found to be as effective as using species-based approaches to summarize key palaeolimnological changes, with the added benefits of being free from taxonomic bias and error. The approach thus shows significant potential for future studies of aquatic community change in nutrient impacted lakes.

Response of Intertidal Foraminiferal Assemblages to Salinity Changes in a Laboratory Culture Experiment

This study explored the response to salinity of intertidal foraminiferal assemblages from the Yellow Sea by culturing them for 100 days at six constant salinity levels (17, 22, 27, 32, 37, and 42 psu) in laboratory microcosms with four replicates each. A total of 7,471 live (stained) foraminiferal specimens were obtained and analyzed. The diversity parameters of foraminiferal assemblages (species richness, Margalef index, Shannon-Wiener index, and Fisher's alpha) declined significantly when the salinity was increased or decreased from the field value, but foraminiferal abundance was highly resistant to salinity. In addition, salinity exerted different effects on foraminifera from different orders. Specifically, the proportion of species from Order Miliolida significantly increased whereas that of species from Order Rotaliida decreased with increasing salinity. High salinity-tolerant species Ammonia aomoriensis, Cribrononion gnythosuturatum, Ammonia tepida, and Quinqueloculina seminula could fill unoccupied ecological niches when the proportion of salinity-sensitive species has declined. Furthermore, our morphometric results showed that foraminiferal test size was significantly negatively correlated with salinity, and numerous abnormal specimens appeared in foraminiferal assemblages when salinity deviated from the field value. Our study revealed that intertidal foraminiferal assemblages had high adaptability at different salinities because of the existence of high salinity-tolerant dominant species. In addition, salinity variation can significantly alter foraminiferal morphology in test size and abnormality.

Size variations in foraminifers from the early Permian to the Late Triassic: implications for the Guadalupian–Lopingian and the Permian–Triassic mass extinctions

The final 10 Myr of the Paleozoic saw two of the biggest biological crises in Earth history: the middlePermian extinction (often termed the Guadalupian–Lopingian extinction [GLE]) that was followed 7–8 Myr later by Earth's most catastrophic loss of diversity, the Permian–Triassic mass extinction (PTME). These crises are not only manifest as sharp decreases in biodiversity and—particularly for the PTME—total ecosystem collapse, but they also drove major changes in biological morphological characteristics such as the Lilliput effect. The evolution of test size among different clades of foraminifera during these two extinction events has been less studied. We analyzed a global database of foraminiferal test size (volume) including 20,226 specimens in 464 genera, 98 families, and 9 suborders from 632 publications. Our analyses reveal significant reductions in foraminiferal mean test size across the Guadalupian/Lopingian boundary (GLB) and the Permian/Triassic boundary (PTB), from 8.89 to 7.60 log10 μm3 (lg μm3) and from 7.25 to 5.82 lg μm3, respectively. The decline in test size across the GLB is a function of preferential extinction of genera exhibiting gigantism such as fusulinoidean fusulinids. Other clades show little change in size across the GLB. In contrast, all Lopingian suborders in our analysis (Fusulinina, Lagenina, Miliolina, and Textulariina) experienced a significant decrease in test size across the PTB, mainly due to size-biased extinction and within-lineage change. The PTME was clearly a major catastrophe that affected many groups simultaneously, and the GLE was more selective, perhaps hinting at a subtler, less extreme driver than the later PTME.

Test suites effectiveness evolution in open source systems: empirical study

<span>Test suite code coverage is usually used to indicate the capability of a test suite in detecting faults. Earlier research studies, which explored the relationship among test suite effectiveness and code coverage, have not addressed this relationship evolutionally. Moreover, some of these works were studied small or identical domain systems, which make the result generalization process unclear for other systems. Finally, some of these studies were conducted with automatically generated test suites, which might not present the real situation for studied systems, so the results cannot be generalized to real test suites. In this paper, the authors empirically explore three open-source software systems along with their 11 versions. These versions are evolved over time and might have more sources of code and test suites. This work objective is to study the correlation between test suite effectiveness, the size of the test suite, and coverage for three Java programs during their evolution. In this work, the code coverage, test suite LOC and mutation testing coverage are measured to assess the correlation between the effectiveness of fault detection, code coverage, and test suite size. Based on the result we cannot generalize the assumption that test size is always revealing a positive correlation with its effectiveness, but still weak to the high correlation between test effectiveness, test size, and coverage.</span><p> </p>