BackgroundKinarm Standard Tests is a suite of upper limb tasks to assess sensory, motor, and cognitive functions, which produces granular performance data that reflect spatial and temporal aspects of behavior. We have previously used principal component analysis to reduce the dimensionality of multivariate data using the Kinarm End-Point Lab. Here, we performed PCA using data from the Kinarm Exoskeleton Lab, and determined agreement of PCA results across EP and EXO platforms in healthy participants. We additionally examined whether further (...) dimensionality reduction was possible by using PCA across behavioral tasks.MethodsHealthy participants were assessed using the Kinarm EXO and EP. Four behavioral tasks were performed that quantified arm sensory and motor function, including position sense [Arm Position Matching ] and three motor tasks [Visually Guided Reaching, Object Hit, and Object Hit and Avoid ]. The number of components to include per task was determined from scree plots and parallel analysis, and rotation type was decided on a per-task basis. To assess agreement, we compared principal components across platforms using distance correlation. We additionally considered inter-task interactions in EXO data by performing PCA across all six behavioral assessments.ResultsBy applying PCA on a per task basis to data collected using the EXO, the number of behavioral parameters were substantially reduced by 58–75% while accounting for 76–87% of the variance. These results compared well to the EP analysis, and we found good-to-excellent agreement values between PCs from the EXO and those from the EP. Finally, we were able to reduce the dimensionality of the EXO data across tasks down to 16 components out of a total of 76 behavioral parameters, which represents a reduction of 79% while accounting for 73% of the total variance.ConclusionPCA of Kinarm robotic assessment appears to capture similar relationships between kinematic features in healthy individuals and is agnostic to the robotic platform used for collection. Further work is needed to investigate the use of PCA-based data reduction for the characterization of neurological deficits in clinical populations. (shrink)

Principal Component Analysis is one of the data mining methods that can be used to analyze multidimensional datasets. The main objective of this method is a reduction of the number of studied variables with the mainte- nance of as much information as possible, uncovering the structure of the data, its visualization as well as classification of the objects within the space defined by the newly created components. PCA is very often used as a preliminary step in data (...) preparation through the creation of independent components for further analysis. We used the PCA method as a first step in analyzing data from IVF (in vitro fertilization). The next step and main purpose of the analysis was to create models that predict pregnancy. Therefore, 805 different types of IVF cy- cles were analyzed and pregnancy was correctly classified in 61-80% of cases for different analyzed groups in obtained models. (shrink)

The British Columbian Members of the Canadian Guidance and Counselling Association were surveyed to explore their attitudes regarding dual relationships. Of 529 deliverable surveys, 206 usable returns yielded a response rate of 39%. The survey instrument collected data regarding respondents' characteristics and ethicality ratings of 39 dual relationship activity items. An exploratory principal components analysis was performed on responses, resulting in a 4-factor equation, which accounted for 44% of the total variance. The results suggest that, although conceptual (...) considerations of dual relationship typology do underlay the resultant factors, the relative ethicality of each item is also influential. (shrink)

In this research work a novel two-step system for anomaly detection is presented and tested over several real datasets. In the first step the novel Exploratory Projection Pursuit, Beta Hebbian Learning algorithm, is applied over each dataset, either to reduce the dimensionality of the original dataset or to face nonlinear datasets by generating a new subspace of the original dataset with lower, or even higher, dimensionality selecting the right activation function. Finally, in the second step Principal Component Analysis (...) anomaly detection is applied to the new subspace to detect the anomalies and improve its classification capabilities. This new approach has been tested over several different real datasets, in terms of number of variables, number of samples and number of anomalies. In almost all cases, the novel approach obtained better results in terms of area under the curve with similar standard deviation values. In case of computational cost, this improvement is only remarkable when complexity of the dataset in terms of number of variables is high. (shrink)

Assessing physical fitness has emerged as a proxy of the health status of children and adolescents and therefore as relevant from a public health point of view. DAFIS is a project included in Plan Galicia Saudable of the regional government of Galicia. DAFIS consists of an on-line software devoted to record the results of a standard physical fitness protocol carried out as a part of the physical education curriculum. The aims of this study were: to obtain normative values of physical (...) fitness of the Galician school population evaluated in the DAFIS project, and to identify a reduced number of components and tests able to capture a significant amount of the variability in the physical fitness of children and adolescents. From an initial sample of 27784 records, 15287 cases were considered after filtering. Generalized Additive Models for Location, Scale and Shape were used for obtaining percentile curves and tables for each sex. Furthermore, a principal components analysis was performed, selecting the number of components by applying the Kaiser’s rule and selecting a subset of variables considering the correlation between each variable and the components. Percentile curves and normative values are reported for each test and sex. Physical fitness was better in boys than in girls throughout age groups, except for flexibility that was consistently higher in girls. Two main components were detected throughout age groups: the first one representing body composition and partially cardiorespiratory fitness and the second one muscular fitness. For boys and girls, waist to height ratio had the highest correlations with the first component in four out of six age groups. The highest correlation with the second component, was most frequently observed for the handgrip test both in boys and girls. This study provides evidence about the utility of school community actions like DAFIS aimed to track the health-related fitness of children and adolescents. The results suggest that fat mass distribution and muscular performance concentrate a high proportion physical fitness variance. (shrink)

It is important for energy enterprises to research on the investment potential of the energy markets in countries along the “Belt and Road,” which can help them optimize the regional investment structure, reduce investment risks, and conform to the development trend of “going global.” Therefore, we construct an investment potential assessment system of 29 indexes including five dimensions: politics, economy, society, energy, and cooperation and assess energy investment potential of 48 sample countries along the “Belt and Road” using principal (...) component analysis to provide reference meanings for energy enterprises. The results show that the assessment results of investment potential are affected by a combination of multiple indexes. In addition, compared with Central Asia and South Asia, which have weak economic foundations and greater political and legal risks, the investment potential of Central and Eastern Europe and some emerging economies in Southeast Asia is higher. (shrink)

This study compared whole body kinematics of the clean movement when lifting three different loads, implementing two data analysis approaches based on principal component analysis. Nine weightlifters were equipped with 39 markers and their motion captured with 8 Vicon cameras at 100 Hz. Lifts of 60, 85, and 95% of the one repetition maximum were analyzed. The first PCA analyzed variance among time-normed waveforms compiled from subjects and trials; the second PCA analyzed postural positions compiled over time, (...) subjects and trials. Load effects were identified through repeated measures ANOVAs with Bonferroni-corrected post-hocs and through Cousineau-Morey confidence intervals. PCAtrial scores differed in the first and fifth principal component, suggesting that increased barbell load produced higher initial elevation, lower squat position, wider feet position after squatting, and less inclined arms. PCAposture revealed significant timing differences in all components. We conclude, first, barbell load affects specific aspects of the movement pattern of the clean; second, the PCAtrial approach is better suited for detecting deviations from a mean motion trajectory and its results are easier to interpret; the PCAposture approach reveals coordination patterns and facilitates comparisons of postural speeds and accelerations. (shrink)

In the big data era, sequencing technology has produced a large number of biological sequencing data. Different views of the cancer genome data provide sufficient complementary information to explore genetic activity. The identification of differentially expressed genes from multiview cancer gene data is of great importance in cancer diagnosis and treatment. In this paper, we propose a novel method for identifying differentially expressed genes based on tensor robust principal component analysis, which extends the matrix method to the processing (...) of multiway data. To identify differentially expressed genes, the plan is carried out as follows. First, multiview data containing cancer gene expression data from different sources are prepared. Second, the original tensor is decomposed into a sum of a low-rank tensor and a sparse tensor using TRPCA. Third, the differentially expressed genes are considered to be sparse perturbed signals and then identified based on the sparse tensor. Fourth, the differentially expressed genes are evaluated using Gene Ontology and Gene Cards tools. The validity of the TRPCA method was tested using two sets of multiview data. The experimental results showed that our method is superior to the representative methods in efficiency and accuracy aspects. (shrink)

Economy is one of the major issues in the United States presidential election campaign. In order to investigate the impact of the US presidential election on the economy, this paper first constructs an analysis model of the economic impact on the United States based on stepwise regression and principal component analysis to analyze the focus of different candidates’ attention on the economic issues and its possible impact on the US economy in the election year and after the (...) election; secondly, a Chinese economic impact analysis model based on factor analysis and machine learning logistic regression was constructed to analyze the impact of the US presidential election on the Chinese economy. At the same time, the future economic development of the United States and China based on the time series prediction model is forecast and analyzed, respectively. Finally, the countermeasures and policy suggestions on China’s related economic development are put forward. (shrink)

Functionalism has long been the dominant paradigm in systems development practice. However, functionalism promotes an innate and immutable instrumental rationality that is indifferent to human values, rights, society, culture and international stability. It, in essence, lacks empathy. Although alternative paradigms have been promoted for decades in the systems development literature to help address this deficit, functionalism remains dominant. This paper reiterates the call for a fundamental paradigm shift away from myopic functionalism and towards a more empathic and human-centred philosophy. It (...) argues that the human-centred tradition offers a philosophically compatible and mature approach that can be practically harnessed for promoting empathy in systems development. The paper investigates the potential of systems development to become truly human-centred using data originally collected as part of a multi-method critical-interpretive study of privacy in information systems development. Multiple methods are used for the data analysis presented, including principal components analysis, hierarchical clustering, Q methodology and descriptive statistics. The multi-method analysis demonstrates that a marked discreteness exists between human-centred sentiments and instrumentally rational ones in systems development praxis. The paper concludes by presenting recommendations on how human-centred values can be practically fostered and engaged to enable greater empathy in contemporary system development and strengthen international stability. (shrink)

This paper is the result of encounters with cinema in a course entitled Cultural Activities, a required class in the undergraduate education program of the School of Education at Brazil’s Fluminense Federal University. The goal was to produce aesthetic experiences with the moving images of cinematic language that would go beyond the usual didactic and utilitarian ways of exemplifying course content. We counted on the power of moving images to produce affective images, an aesthetic device that drives the relation between (...) thought and language, thereby contributing to a pause in time, thereby allowing us the opportunity to examine more carefully the existential human condition as historically constituted in the world today. This paper was thus the result of using cinema as a tool of social analysis and a methodological resource for the training of undergraduate education students. The cinematic artifact presented here is an animated feature whose story unfolds through the eyes of a child confronting a world tainted by the misfortunes of capitalism. For theoretical support, we focus on authors who analyze the capitalist political and socioeconomic model and its effects on the human condition. Through O menino e o mundo, a student and teacher share their different, but nonetheless powerful, ideas about the field of education, work, and the consumerism and alienation that result from current modes of production. It is particularly important to emphasize that the paper itself is a byproduct of the methodological approach of the course. (shrink)

Tricycles are one of the most popular, most accessible, and least expensive forms of public transit in the Philippines. In addition to being common modes of transportation, motorcycles, and tricycles also contribute significantly to the livelihoods of millions of Filipinos who rely on them for a living. Hence, this study explores the lived experiences and challenges faced by female tricycle drivers. Employing the Interpretative Phenomenological Analysis, the findings of this study were: The participants strive to assist their husbands in (...) providing for their families. Their motivation comes primarily from their families. They each make every effort to provide a better life for their children. Thus, have faced challenges related to their ability to make ends meet, find time to balance job and family obligations, and encounter discrimination and stereotypes in others, and day-to-day life. Moreover, they evade and disregard the problem and discrimination as a coping mechanism. They feel that if they ignore their problem and focus on achieving their goal of providing for their family, it would eventually disappear. (shrink)

Research in face recognition has tended to focus on discriminating between individuals, or “telling people apart.” It has recently become clear that it is also necessary to understand how images of the same person can vary, or “telling people together.” Learning a new face, and tracking its representation as it changes from unfamiliar to familiar, involves an abstraction of the variability in different images of that person's face. Here, we present an application of principal components analysis computed (...) across different photos of the same person. We demonstrate that people vary in systematic ways, and that this variability is idiosyncratic—the dimensions of variability in one face do not generalize well to another. Learning a new face therefore entails learning how that face varies. We present evidence for this proposal and suggest that it provides an explanation for various effects in face recognition. We conclude by making a number of testable predictions derived from this framework. (shrink)

Based on the review and research on the teleworking modality and the variables that impact it at the organizational level, such as quality of life, communication, organizational culture or productivity, a case study is proposed under the innovation and teleworking model of a company. Japanese automotive industry for the application of Multivariate Analysis techniques such as Principal Component Analysis and Linear Regression, in order to condense the information provided by multiple variables into principal components and (...) validate the relationships and impact that exist between them, thus determining the interdependence and correlation of the same with the telework variable, allowing to simplify the complexity of sample spaces with many dimensions. It was possible to identify the main components with the variables' own labels and the dependent and predictive variables of the case were statistically validated, through the use of the IBM SPSS. (shrink)

The diversities in the natural, social, and human environment of various regions often lead to the differences in their regional economic development level and industrial structure and layout; moderate regional differences can mobilize economic vitality and improve development efficiency, but excessive differences may lead to social instability or even turbulence. Intelligent algorithms or their improved and hybrid algorithms can recently achieve more suitable solutions to practical problems of nonlinear, discrete, nondifferentiable, and multiple constraints. Therefore, this paper's main point is on (...) the analysis of regional economic development differences based on intelligent hybrid algorithms. On the basis of summarizing and analyzing previous research works, this paper expounded the research status and significance of regional economic development differences analysis, elaborated the development background, current status, and future challenges of intelligent hybrid algorithms, introduced the methods and principles of principal component distance weighting algorithm and sequential quadratic programming algorithm, constructed a basic model for regional economic development differences based on intelligent hybrid algorithm, conducted the construction of analysis index system and the hybrid of intelligence algorithm, analyzed the regional economic development differences based on intelligent hybrid algorithm, performed the principal component analysis and time-space change analysis of regional economic development, and finally carried out a simulation experiment and its result analysis. The study results show that the intelligent hybrid algorithm can simulate the intelligent behavior of certain groups in nature when analyzing the differences in regional economic development, so that it has essentially parallelism, high accuracy, and convergence speed. The study results of this paper provide a reference for further researches on the regional economic development differences analysis based on intelligent hybrid algorithm. (shrink)

In the automatic detection of epileptic seizures, the monitoring of critically ill patients with time varying EEG signals is an essential procedure in intensive care units. There is an increasing interest in using EEG analysis to detect seizure, and in this study we aim to get a better understanding of how to visualize the information in the EEG time-frequency feature, and design and train a novel random forest algorithm for EEG decoding, especially for multiple-levels of illness. Here, we propose (...) an automatic detection framework for epileptic seizure based on multiple time-frequency analysis approaches; it involves a novel random forest model combined with grid search optimization. The short-time Fourier transformation visualizes seizure features after normalization. The dimensionality of features is reduced through principal component analysis before feeding them into the classification model. The training parameters are optimized using grid search optimization to improve detection performance and diagnostic accuracy by in the recognition of three different levels epileptic of conditions (healthy subjects, seizure-free intervals, seizure activity). Our proposed model was used to classify 500 samples of raw EEG data, and multiple cross-validations were adopted to boost the modeling accuracy. Experimental results were evaluated by an accuracy, a confusion matrix, a receiver operating characteristic curve and an area under the curve. The evaluations indicated that our model achieved the more effective classification than some previous typical methods. Such a scheme for computer-assisted clinical diagnosis of seizures has a potential guiding significance, which not only relieves the suffering of patient with epilepsy to improve quality of life, but also helps neurologists reduce their workload. (shrink)

Philosophical and sociological approaches in technology have increasingly shifted toward describing AI (artificial intelligence) systems as ‘(moral) agents,’ while also attributing ‘agency’ to them. It is only in this way – so their principal argument goes – that the effects of technological components in a complex human-computer interaction can be understood sufficiently in phenomenological-descriptive and ethical-normative respects. By contrast, this article aims to demonstrate that an explanatory model only achieves a descriptively and normatively satisfactory result if the concepts (...) of ‘(moral) agent’ and ‘(moral) agency’ are exclusively related to human agents. Initially, the division between symbolic and sub-symbolic AI, the black box character of (deep) machine learning, and the complex relationship network in the provision and application of machine learning are outlined. Next, the ontological and action-theoretical basic assumptions of an ‘agency’ attribution regarding both the current teleology-naturalism debate and the explanatory model of actor network theory are examined. On this basis, the technical-philosophical approaches of Luciano Floridi, Deborah G. Johnson, and Peter-Paul Verbeek will all be critically discussed. Despite their different approaches, they tend to fully integrate computational behavior into their concept of ‘(moral) agency.’ By contrast, this essay recommends distinguishing conceptually between the different entities, causalities, and relationships in a human-computer interaction, arguing that this is the only way to do justice to both human responsibility and the moral significance and causality of computational behavior. (shrink)

Objectives: Recent fMRI studies have shown that it is possible to reliably identify the defaultmode network (DMN) in the absence of any task, by resting-state connectivity analyses in healthy volunteers. We here aimed to identify the DMN in the challenging patient population of disorders of consciousness encountered following coma. Experimental design: A spatial independent component analysis-based methodology permitted DMN assessment, decomposing connectivity in all its different sources either neuronal or artifactual. Three different selection criteria were introduced assessing anticorrelation-corrected connectivity (...) with or without an automatic masking procedure and calculating connectivity scores encompassing both spatial and temporal properties. These three methods were validated on 10 healthy controls and applied to an independent group of 8 healthy controls and 11 severely brain-damaged patients [locked-in syndrome (n ¼ 2), minimally conscious (n ¼ 1), and vegetative state (n ¼ 8)]. Principal observations: All vegetative patients showed fewer connections in the default-mode areas, when compared with controls, contrary to locked-in patients who showed nearnormal connectivity. In the minimally conscious-state patient, only the two selection criteria considering both spatial and temporal properties were able to identify an intact right lateralized BOLD connectivity pattern, and metabolic PET data suggested its neuronal origin. Conclusions: When assess-. (shrink)

The volatility of Shanghai crude oil futures prices is researched in this paper. The cusp catastrophe analysis of Shanghai crude oil futures price is based on the perspective of volatility influencing factors. Some important factors are selected based on commodity attributes and financial attributes, including certain China’s macrofactors, such as producer price index and macroeconomic prosperity index. The principal component analysis is used to process the factors. The control variables of the cusp catastrophe model are extracted. The (...) discriminant of the cusp catastrophe model and the principal component analysis are combined to determine the month of mutation. The three-dimensional renderings and plane projections of Shanghai crude oil futures price mutations in 2018 and 2019 are presented. The model is tested by comparing the time points of price sudden change nodes and emergencies. The results show that only nine factors can be used to roughly determine whether the Shanghai crude oil futures price has a sudden change. During the analysis period, the mutation months are May 2018, December 2018, February 2019, June 2019, and September 2019. It can be proved that the established model has certain feasibility and accuracy. (shrink)

Social and environmental ratings provided by social rating agencies are multidimensional. The first goal of our paper is to identify a small number of independent and relevant socially responsible (SR) dimensions reflecting a firms’ coherent posture toward social issues. We put forward that these dimensions are not exactly the same as the ESG ones (Environment, Social, and Governance). Using the six sub-ratings provided by the Vigeo rating agency, we perform a principal component analysis and we highlight three main (...) independent SR dimensions related to (1) business stakeholders (employees, customers, and suppliers), (2) societal stakeholders (environment and society), and (3) financial stakeholders (stockholders and debt holders). The second objective of our paper is to explore the link between stock returns and these three SR dimensions. Our most notable finding is that for each SR dimension, investors ask for an additional risk premium when they accept to hold non-socially responsible stocks. The cost of equity is thus lower for SR firms. The average premium over the period 2003–2010 is larger for the components “business stakeholders” and “financial stakeholders” than for the component “societal stakeholders.” The premium for this last component has only existed since the end of 2008. Since that time, environment and community involvement have become important risk factors strongly considered by investors. For the three dimensions, investors notably penalize large non-social firms and reward small social firms. (shrink)

Performance and control of upright bipedal posture requires a constant and dynamic integration of relative contributions of different sensory inputs (i. e., sensory reweighting) to enable effective adaptations as individuals face environmental changes and perturbations. Children with gymnastic experience showed balance performance closer to that of adults during and after proprioceptive alteration than children without gymnastic experience when their center of pressure (COP) was analyzed. However, a particular COP sway can be achieved through performing and coordinating different postural movements. The (...) aim of this study was to assess how children and adults of different gymnastic experience perform and control postural movements while they have to adjust balance during and after bilateral tendon vibration. All participants were equipped with spherical markers attached to their skin and two vibrators strapped over the Achilles tendons. Bipedal stance was performed in three 45-s trials in two visual conditions (eyes open, EO, and eyes closed, EC) ordered randomly in which vibration lasted 10 s. Posture movements were analyzed by a principal component analysis (PCA) calculated on normalized and weighted markers coordinates. The relative standard deviation of each principal movement component (principal position, PP-rSTD) quantified its contribution to the whole postural movements, i.e., quantified the coordinative structure. The first (principal velocities, PV-rSTD) and second (principal accelerations, PA-rSTD) time-derivatives characterized the rate-dependent sensory information associated with and the neuromuscular control of the postural movements, respectively. Children without gymnastic experience showed a different postural coordinative structure and different sensory-motor control characteristics. They used less ankle movements in the anterior-posterior direction but increased ankle movements in medio-lateral direction, presented larger hip and trunk velocities, and exhibited more hip actions. Gymnastic experience during childhood seemed to benefit the development of proprioceptive reweighting processes in children, leading to a more mature form of coordinating and controlling posture similarly to adults. (shrink)

The Fear of COVID-19 Scale (FCV-19S) is a new one-dimensional scale used to measure fear of an individual about the COVID-19. Given the seriousness of the COVID-19 situation in China when our study was taking place, our aim was to translate and examine the applicability of the FCV-19S in Chinese students. The sample used for validation comprised 2,445 Chinese students. The psychometrical characteristics of the Chinese FCV-19S (FCV-19S-C) were tested using Rasch analysis. Principal component analysis (PCA) proved (...) the unidimensional structure of the model. Both infit and outfit mean square (MNSQ) values (0.69–1.31) and point-measure correlations (0.82–0.86) indicated a good model fit. Person-item separation and reliability values indicated good reliability of the scale. The person-item map revealed an acceptable level of match between the persons and the items. Differential item functioning of the FCV-19S-C showed no differences with respect to age or gender. FCV-19S-C scores were significantly associated with anxiety, stress, depression, ego-resilience, and general health. The FCV-19S-C was proven to be effective in measuring fear of Chinese students about the COVID-19. (shrink)

This paper investigates Western professional bankers’ perceptions of Islamic finance. Exploiting data from an original survey, we carry out a principal component analysis to characterize the main dimensions on which financial agents diverge. The PCA extracts five dimensions—accounting for 61 % of the variance in the agents’ answers—that we interpret with the help of a pilot field survey. In addition to confirm the increased association of Islamic financial values with ethical practices in the West, our results allow us (...) to understand how the observed growth of the industry has been conceptualized by conventional agents. The five dimensions identified shed light on the multitude of constructs that have informed the diffusion of Islamic financial ideas to international markets. This supports the fact that Islamic finance cannot be seen as a single movement but is characterized by opposing and concurrent logics in global markets. (shrink)

Electromechanical actuators are more and more widely used as actuation devices in flight control system of aircrafts and helicopters. The reliability of EMAs is vital because it will cause serious accidents if the malfunction of EMAs occurs, so it is significant to detect and diagnose the fault of EMAs timely. However, EMAs often run under variable conditions in realistic environment, and the vibration signals of EMAs are nonlinear and nonstationary, which make it difficult to effectively achieve fault diagnosis. This paper (...) proposed a fault diagnosis method of electromechanical actuators based on variational mode decomposition multifractal detrended fluctuation analysis and probabilistic neural network. First, the vibration signals were decomposed by VMD into a number of intrinsic mode functions. Second, the multifractal features hidden in IMFs were extracted by using MFDFA, and the generalized Hurst exponents were selected as the feature vectors. Then, the principal component analysis was introduced to realize dimension reduction of the extracted feature vectors. Finally, the probabilistic neural network was utilized to classify the fault modes. The experimental results show that this method can effectively achieve the fault diagnosis of EMAs even under diffident working conditions. Simultaneously, the diagnosis performance of the proposed method in this paper has an advantage over that of EMD-MFDFA method for feature extraction. (shrink)

Aesthetic emotions are elicited by different sensory impressions generated by music, visual arts, literature, theater, film, or nature scenes. Recently, the AESTHEMOS scale has been developed to facilitate the empirical assessment of such emotions. In this article we report a semantic profile analysis of aesthetic emotion terms that had been used for the development of this scale, using the GRID approach. This method consists of obtaining ratings of emotion terms on a set of meaning facets which represent five (...) class='Hi'>components of the emotion process. The aims here were to determine the dimensionality of the GRID features when applied to aesthetic emotions and compare it to published results for emotion terms in general, and to examine the internal organization of the domain of aesthetic emotion terms in order to identify salient clusters of these items based on the similarity of their feature profiles on the GRID. Exploratory Principal Component Analyses suggest a four-dimensional structure of the semantic space consisting of valence, power, arousal, and novelty, converging with earlier GRID studies on large sets of standard emotion terms. Using cluster analyses, 15 clusters of aesthetic emotion terms with similar GRID feature profiles were identified, revealing the internal organization of the aesthetic emotion terms domain and meaningful subgroups of aesthetic emotions. While replication for further languages is required, these findings provide a solid basis for further research and methodological development in the realm of aesthetic emotions. (shrink)

In this paper, we propose an adaptive Gaussian incremental expectation stadium parameter estimation algorithm for sports video analysis and prediction through the study and analysis of sports videos. The features with more discriminative power are selected from the set of positive and negative templates using a feature selection mechanism, and a sparse discriminative model is constructed by combining a confidence value metric strategy. The sparse generative model is constructed by combining L1 regularization and subspace representation, which retains sufficient (...) representational power while dealing with outliers. To overcome the shortcomings of the traditional multiplicative fusion mechanism, this paper proposes an adaptive selection mechanism based on Euclidean distance, which aims to detect deteriorating models in time during the dynamic tracking process and adopt corresponding strategies to construct more reasonable likelihood functions. Based on the Bayesian citation framework, the adaptive selection mechanism is used to combine the sparse discriminative model and the sparse generative model. Also, different online updating strategies are adopted for the template set and Principal Component Analysis subspace to alleviate the drift problem while ensuring that the algorithm can adapt to the changes of target appearance in the dynamic tracking environment. Through quantitative and qualitative evaluation of the experimental results, it is verified that the algorithm proposed in this paper has stronger robustness compared with other classical algorithms. Our proposed visual object tracking algorithm not only outperforms existing visual object tracking algorithms in terms of accuracy, success rate, accuracy, and robustness but also achieve the performance required for real-time tracking in terms of execution speed on the central processing unit. This paper provides an in-depth analysis and discussion of the adaptive Gaussian incremental expectation stadium parameter estimation algorithm for sports video analysis. Using a variety of county-level algorithms for analysis and multiple solutions to improve the accuracy of the results, we obtain a more efficient and accurate algorithm. (shrink)

Background The number of medical lawsuits in Japan was between 14 and 21 each year before 1998, but increased to 24 to 35 per year after 1999. There were 210 lawsuits during this 10-year period. There is a need for skills and knowledge related to ethics, which is as fundamental to the practice of medicine as basic sciences or clinical skills. in Japan education in ethics is relatively rare and its importance is not yet recognized. Establishing ethics education using legal (...) precedents, which has already been achieved in Western countries, will be a very important issue in Japan. In the present study, a questionnaire survey was conducted among graduate intern doctors, in order to investigate whether ethics education using precedents might have a positive effect in Japan. Methods In 2002, a questionnaire survey entitled Physicians' Clinical Ethics was carried out in a compulsory orientation lecture given to trainees before they started clinical practice in our hospital. The attendees at this lecture were trainees who came from colleges in various districts of Japan. During the lecture, 102 questionnaires were distributed, completed by attendees and collected. The recovery rate was 100%. The questionnaire consisted of 22 questions (in three categories), of which 20 were answered by multiple choices, and the other two were answered by description. The time required to complete the questionnaire was about 10 minutes. Results The recovered questionnaires were analyzed using statistical analysis software (SPSS for Windows, Release 10.07J-1/June/2000), in addition to simple statistical analysis. answers using multiple choices for the 20 questions in the questionnaire were input into SPSS. The principal component analysis was performed for each question. As a result, the item that came to the fore was "legal precedent". Since many intern doctors were interested in understanding laws and precedents, learning about ethical considerations through education using precedents might better meet with their needs and interests. Conclusion We applied a new method in which the results of principal component analysis and frequencies of answers to other questions were combined. From this we deduced that the precedent education used in Western countries was useful to help doctors acquire ethical sensitivity and was not against their will. A relationship was found between reading precedents and the influence of lawsuits, and it was thought that student participation-type precedent education would be useful for doctors in order to acquire ethical sensitivity. (shrink)

This article concerns the problem of assessing selected body compo- sition parameters after completion of antitumor therapy and comparing them with the same parameters of healthy children. A high percentage of overweight and obesity, as well as abnormal fat distribution in convalescents with cancer shows a significant adverse effect of therapy on body composition and suggests the need for early intervention in terms of diet and exercise, which would help patients to quickly achieve the proper parameters of body composition. Two (...) main problems will be mentioned during the presented data analysis. Firstly, in each group there was a small number of observations. Because of this, the real differences between examined subgroups may have been omitted. Secondarily, many variables are correlated and are not normally distributed. Therefore, be- side the standard statistical tests to compare two groups, principal component analysis was applied to reduce the dimensions of the attribute space and to attempt to classify two groups of patients. (shrink)

Systematic sampling of communities gives rise to large contingency tables summing up possible changes in the assemblages' structure. Such tables are generally analysed by multivariate statistical methods, which are ill-suited for simultaneously analysing rare and common species (Field et al., 1982). In order to separately process species belonging to either of these categories, we propose a statistical method to select common species in a sequence of ecological surveys. It is based on a precise definition of rarity, and depends on a (...) rarity parameter. In this work, this parameter will be optimised so that the sub-table of common species captures the essential features of the complete table as well as possible. In this way we analysed the spatio-temporal evolution of macrobenthic communities from the Algiers harbour to study the pollution influence during a year. The examination of the communities' structuring was done through Principal Components Analysis (PCA) of the species proportions table. Environmental variables were simultaneously sampled. We show that the data structure can be explained by about 25% of the total number of present species. Two environmental gradients were brought to the fore inside the harbour, the first one representing pollution, and the second one representing hydrological instabilities. (shrink)

Background: Risks to healthcare workers have escalated during the pandemic and they are likely to experience a greater level of stress. This cross-sectional study investigated mental distress among healthcare workers during the early phase of Coronavirus disease-2019 outbreak in India.Method: 140 healthcare workers of a tertiary care hospital in India were assessed for perceived stress and insomnia. A factor analysis with principal component method reduced these questions to four components which were categorized as insomnia, stress-related anxiety, stress-related (...) irritability, and stress-related hopelessness. Further statistical analyses were done on these factor scores to identify the predictors and investigate the differences between the different categories of healthcare workers.Result: Doctors had the highest level of anxiety among the healthcare workers. Both doctors and nurses perceived a greater level of irritability than the other HCWs. Compared to doctors and nurses, other HCWs were more likely to experience insomnia. Lower age, higher education, female gender, and urban habitat were associated with greater perception of anxiety. Older age, being quarantined, and single marital status were the significant predictors of irritability. Female gender, single marital-status, and higher number of medical ailments contributed to perceived hopelessness. Quarantine significantly predicted insomnia.Conclusion: Different categories of healthcare workers are experiencing varied mental health problems owing to their heterogeneous socio-demographic backgrounds. Tailored and personalized care, as well as policies, might help in alleviating their problems. Further research is warranted to explore the psychological distress and remedies among these frontline workers during and after the ongoing pandemic crisis. (shrink)

We begin by presenting William of Ockham's various formulations of his principle of parsimony, Ockham's Razor. We then define a reaction mechanism and tell a personal story of how Ockham's Razor entered the study of one such mechanism. A small history of methodologies related to Ockham's Razor, least action and least motion, follows. This is all done in the context of the chemical (and scientific) community's almost unthinking acceptance of the principle as heuristically valuable. Which is not matched, to put (...) it mildly, by current philosophical attitudes toward Ockham's Razor. What ensues is a dialogue, pro and con. We first present a context for questioning, within chemistry, the fundamental assumption that underlies Ockham's Razor, namely that the world is simple. Then we argue that in more than one pragmatic way the Razor proves useful, without at all assuming a simple world. Ockham's Razor is an instruction in an operating manual, not a world view. Continuing the argument, we look at the multiplicity and continuity of concerted reaction mechanisms, and at principal component and Bayesian analysis (two ways in which Ockham's Razor is embedded into modern statistics). The dangers to the chemical imagination from a rigid adherence to an Ockham's Razor perspective, and the benefits of the use of this venerable and practical principle are given, we hope, their due. (shrink)

Categorization and memory for specific items are fundamental processes that allow us to apply knowledge to novel stimuli. This study directly compares categorization and memory using delay match to category (DMC) and delay match to sample (DMS) tasks. In DMC participants view and categorize a stimulus, maintain the category across a delay, and at the probe phase view another stimulus and indicate whether it is in the same category or not. In DMS, a standard item working memory task, participants encode (...) and maintain a specific individual item, and at probe decide if the stimulus is an exact match or not. Constrained Principal Components Analysis was used to identify and compare activity within neural networks associated with these tasks, and we relate these networks to those that have been identified with resting state-fMRI. We found that two frontoparietal networks of particular interest. The first network included regions associated with the dorsal attention network and frontoparietal salience network; this network showed patterns of activity consistent with a role in rapid orienting to and processing of complex stimuli. The second uniquely involved regions of the frontoparietal central executive network; this network responded more slowly following each stimulus and showed a pattern of activity consistent with a general role in role in decision-making across tasks. Additional components were identified that were associated with visual, somatomotor and default mode networks. (shrink)

We analyzed the expressivity of recombinant proteins by using data mining methods. The expression technique of recombinant protein is a key step towards elucidating the functions of genes discovered through genomic sequence projects. We have studied the productive efficiency of recombinant proteins in fission yeast, Schizosaccharomyces pombe, by mining the expression results. We gathered 57 proteins whose expression levels were known roughly in the host. Correlation analysis, principal component analysis and decision tree analysis were applied to (...) these expression data. Analysis featuring codon usage and amino acid composition clarified that the amino acid composition affected to the expression levels of a recombinant protein strongly than the effect of codon usage. Furthermore, analysis of amino acid composition showed that protein solubility and the metabolism cost of amino acids correlated with a protein expressivity. Codon usage was often interesting in the field of recombinant expressions. However, our analysis found the weak correlation codon features with expressivities. These results indicated that ready-made indices of codon bias were irrelevant ones for modeling the expressivities of recombinant proteins. Our data driven approach was an easy and powerful method to improve recombinant protein expression, and this approach should be concentrated attention with the huge amount of expression data accumulating through the post-genome era. (shrink)

Automated neuropsychiatric batteries have been used in research and clinical practice, including for chronic diseases, such as Systemic Lupus Erythematosus. The Pediatric Automated Neuropsychological Assessment Metrics battery, originally developed for use in American-English speaking individuals, allows tracking of cognitive functions. It can be applied to people over 9 years old. The aim of this study was to translate and present initial validation data from the Ped-ANAM into Brazilian-Portuguese. We translated the battery according to Beaton’s guidelines. Psychometric properties were tested, internal (...) consistency was analyzed by Cronbach’s alpha coefficient, test-retest reliability by the intraclass correlation coefficient. Further, we measured the test execution speed at both times as a temporal stability. Principal component analysis was used for structural validity. Evidence of construct validity was assessed through assessment of the relationships with the Wechsler Intelligence Scales. All participants prior to the start of study related activities signed an informed consent form approved by the local ethics committee. A sample of 230 individuals [mean of age: 23 years; 65% females] was included; a subset of 51 individuals [mean of age: 18 years, 59% female] completed the Ped-ANAM twice to assess test-retest reliability, and another subset of 54 individuals [mean of age: 20.4 years; 67% female] completed the Wechsler Intelligence Scales for Children and Adult for assessment of the Ped-ANAM’s construct validity. Our results suggest that the internal consistency of the Ped-ANAM and its subtest test-retest reliability were excellent. There was no clustering in the Principal Components Analysis, suggestive of non-grouping of the evaluated variables. Construct validity assessment to the Wechsler Scales showed expected ranges of low to strong correlations. We concluded that, based on the results of this study, a cross-culturally validated Brazilian-Portuguese version of the Ped-ANAM has been developed and it is a reliable tool for the screening cognitive function. (shrink)

What are the philosophical views of professional philosophers, and how do these views change over time? The 2020 PhilPapers Survey surveyed around 2000 philosophers on 100 philosophical questions. The results provide a snapshot of the state of some central debates in philosophy, reveal correlations and demographic effects involving philosophers' views, and reveal some changes in philosophers' views over the last decade.

Studies show bilingualism entails an advantage in cognitive control tasks. There is evidence of a bilingual advantage in the context of aphasia, resulting in better cognitive outcomes and recovery in bilingual persons with aphasia compared to monolingual peers. This bilingual advantage also results in structural changes in the right hemisphere gray matter. Very few studies have examined the so-called bilingual advantage by reference to specific anomia therapy efficacy. This study aims to compare the effect of French-Phonological Component Analysis (Fr-PCA) (...) in monolinguals and bilingual persons with aphasia, both at the linguistic and cognitive control level, and to examine the structural impact of left hemisphere lesion location and right hemisphere structural data. Eight participants with chronic aphasia received Fr-PCA for a total of 15 h over 5 weeks. The results showed improved accuracy for treated words and generalization to untreated items and discourse in both groups, and improved Flanker task performance for some participants. Bilingual participants improved more than monolinguals for picture-naming tasks and narrative discourse. Damage to the left postcentral gyrus and the middle frontal gyrus was associated with less therapy-induced improvement. Additionally, left hemisphere damage to the inferior parietal gyrus and postcentral gyrus was associated with reduced cognitive control pre-therapy. Undamaged right hemisphere cortical thicknesses were significantly different between groups; the inferior frontal gyrus and the middle frontal gyrus were greater for the bilingual participants and correlated with cognitive control skills. These results suggest a bilingual advantage in anomia recovery following Fr-PCA, potentially resulting from enhanced cognitive control abilities that could be supported by right hemisphere neural reserve. (shrink)

Background:Experiencing moral distress is traumatic for nurses. Ignoring moral distress can lead to job dissatisfaction, improper handling in the care of patients, or even leaving the job. Thus, it is crucial to use valid and reliable instruments to measure moral distress.Objective:The purpose of this study was to determine the reliability and the validity of the Persian version of the Moral Distress Scale–Revised among a sample of Iranian nurses.Research design:In this methodological study, 310 nurses were recruited from all hospitals affiliated with (...) the Qazvin University of Medical Sciences from February 2014 to April 2015. Data were collected using a demographic questionnaire and the Moral Distress Scale–Revised. The construct validity of the Moral Distress Scale–Revised was evaluated using principal component analysis and confirmatory factor analysis. Internal consistency reliability was assessed with Cronbach’s alpha.Ethical considerations:This study was approved by the Regional Committee of Medical Research Ethics. The ethical principles of voluntary participation, anonymity, and confidentiality were considered.Findings:The construct validity of the scale showed four factors with eigenvalues greater than one. The model had a good fit ( χ2(162) = 307.561, χ2/ df = 1.899, goodness-of-fit index =.904, comparative fit index =.927, incremental fit index =.929, and root mean square error of approximation (90% confidence interval) =.049 (.040–.057)) with all factor loadings greater than.5 and statistically significant. Cronbach’s alpha coefficients were.853,.686,.685, and.711for the four factors. Moreover, the model structure was invariant across different income groups.Discussion and conclusion:The Persian version of the Moral Distress Scale–Revised demonstrated suitable validity and reliability among nurses. The factor analysis also revealed that the Moral Distress Scale–Revised has a multidimensional structure. Regarding the proper psychometric characteristics, the validated scale can be used to further research about moral distress in this population. (shrink)

The semantic similarity between words is one of the basic knowledge in Natural Language Processing. There have been several previous studies on measuring the similarity based on word vectors in a multi-dimensional space. In those studies, high dimensional feature vectors of words are made from words' cooccurrence in a corpus or from reference relation in a dictionary, and then the word vectors are calculated from the feature vectors through the method like principal component analysis. This paper proposes a (...) new placement method of nouns into a multi-dimensional space based on words' cooccurrence in a corpus. The proposed method doesn't use the high dimensional feature vectors of words, but is based on the idea that ``vectors corresponding to nouns which cooccur with a word w in a relation f constitute a group in the multi-dimensional space''. Although the whole meaning of nouns isn't reflected in the word vectors obtained by the pro posed method, the semantic similarity between nouns defined with the word vectors is proper for an example-based disambiguation method. (shrink)