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)

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)

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)

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)

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)

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 (...) class='Hi'>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)

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)

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)

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)

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)

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)

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)

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)

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 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)

The Philippines is one of the fast-growing economies in the South-East Asian and the Pacific region. This study considered eight factors: HEI PRC rate, crime rate, education, employment, health, poverty, income, and basic family amenities of the 17 regions of the country, all taken from the year 2012 databases of the Philippine Statistics Authority (PSA), the Philippine Institute for Development Studies (PIDS) and Open Data Philippines. Principal Component Analysis (PCA) generated the indices of the six factors and (...) Cluster Analysis (CA) to group regional similarities. Clustering revealed that ARMM and MIMAROPA Regions have clustered independently, in two individual clusters, while the rest of the 15 regions have clustered in two groups of five and 10. The study revealed that crime, poverty and health mortality rates have generally inverse relationships with HEI PRC percentage performance, employment, education, income and basic housing amenities. (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)

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)

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)

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)

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)

Faces carry key personal information about individuals, including cues to their identity, social traits, and emotional state. Much research to date has employed static images of faces taken under tightly controlled conditions yet faces in the real world are dynamic and experienced under ambient conditions. A common approach to studying key dimensions of facial variation is the use of facial caricatures. However, such techniques have again typically relied on static images, and the few examples of dynamic caricatures have relied on (...) animating graphical head models. Here, we present a principal component analysis -based active appearance model for capturing patterns of spatiotemporal variation in videos of natural dynamic facial behaviours. We demonstrate how this technique can be applied to generate dynamic anti-caricatures of biological motion patterns in facial behaviours. This technique could be extended to caricaturing other facial dimensions, or to more general analyses of spatiotemporal variations in dynamic faces. (shrink)

Background: Moral courage as a part of nurses’ moral competence has gained increasing interest as a means to strengthen nurses acting on their moral decisions and offering alleviation to their moral distress. To measure and assess nurses’ moral courage, the development of culturally and internationally validated instruments is needed. Objective: The objective of this study was to validate the Dutch-language version of the four-component Nurses’ Moral Courage Scale originally developed and validated in Finnish data. Research design: This methodological study (...) used non-experimental, cross-sectional exploratory design. Participants and research context: A total of 559 nurses from two hospitals in Flanders, Belgium, completed the Dutch-language version of the Nurses’ Moral Courage Scale. Ethical considerations: Good scientific inquiry guidelines were followed throughout the study. Permission to translate the Nurses’ Moral Courage Scale was obtained from the copyright holder, and the ethical approval and permissions to conduct the study were obtained from the participating university and hospitals, respectively. Findings: The four-component 21-item, Dutch-language version of the Nurses’ Moral Courage Scale proved to be valid and reliable as the original Finnish Nurses’ Moral Courage Scale. The scale’s internal consistency reliability was high (0.91) corresponding with the original Nurses’ Moral Courage Scale validation study (0.93). The principal component analysis confirmed the four-component structure of the original Nurses’ Moral Courage Scale to be valid also in the Belgian data explaining 58.1% of the variance. Confirmatory factor analysis based on goodness-of-fit indices provided evidence of the scale’s construct validity. The use of a comparable sample of Belgian nurses working in speciality care settings as in the Finnish study supported the stability of the structure. Discussion and conclusion: The Dutch-language version of the Nurses’ Moral Courage Scale is a reliable and valid instrument to measure nurses’ self-assessed moral courage in speciality care nursing environments. Further validation studies in other countries, languages and nurse samples representing different healthcare environments would provide additional evidence of the scale’s validity and initiatives for its further development. (shrink)

Both the authorship and the dating of the so-called “Diktat für Schlick” (DFS), once attributed to Ludwig Wittgenstein and assigned by Georg Henrik von Wright to the Wittgenstein Nachlass as item 302, are debated topics in Wittgenstein and Vienna Circle research. Schulte (Waismann as Spokesman for Wittgenstein. In: McGuinness B (ed). Friedrich Waismann - causality and logical positivism. Vienna Circle Institute Yearbook 15. Springer, Dordrecht, pp 225–242, 2011) and Manninen (Waismann’s testimony of Wittgenstein’s fresh starts 1931–35. In: McGuinness B (ed). (...) Friedrich Waismann - causality and logical positivism. Vienna Circle Institute Yearbook 15. Springer, Dordrecht, pp 243–265, 2011) hold that DFS was authored by Friedrich Waismann rather than Wittgenstein. Applying techniques from computational stylometry to the authorship question, the paper concludes that DFS is located stylometrically in the middle between Waismann’s and Wittgenstein’s writings, but slightly closer to Wittgenstein, and so Wittgenstein authorship is hence stylometrically still not unlikely. The paper concludes by presenting a number of factors that speak in favour of the view that DFS might originally indeed have been dictated by Wittgenstein. For the computational stylometry component, the paper uses the Eder et al.’s (Stylometry with R: a package for computational text analysis. R Journal 8/1:107–121. Accessed 21 Oct 2021. https://journal.r-project.org/archive/2016/RJ-2016-007/index.html, 2016) “Stylometry with R” package; the degree of similarity and dissimilarity between documents is calculated by Burrows’ Delta measure; and the results are displayed using Hierarchical Cluster Analysis and Principal Components Analysis. For the text corpus part, the paper uses texts authored by Schlick, Waismann and Wittgenstein. For the archival research part, the paper refers to materials form the Schlick Nachlass in the North Holland Archives, the Waismann Nachlass in the Bodleian Libraries, the Rose Rand Nachlass in the Pittsburgh Archives of Scientific Philosophy, the Ludwig Wittgenstein Nachlass in the Trinity College Cambridge Wren Library, and the Cornell copy of the Ludwig Wittgenstein Nachlass. The paper is a follow-up on Oakes and Pichler (Computational stylometry of Wittgenstein’s ‘Diktat für Schlick’”. In: Hareide L, Johannson C, Oakes M (eds). The many facets of corpus linguistics in Bergen: In honour of Knut Hofland. Bergen Language and Linguistics Series (BeLLS), Bergen, pp 221–240, 2013); for the current paper we have extended the Waismann text corpus with more texts written under the influence of Wittgenstein, a.o. Logik, Sprache, Philosophie (1976). (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)

A portrait recognition system can play an important role in emergency evacuation in mass emergencies. This paper designed a portrait recognition system, analyzed the overall structure of the system and the method of image preprocessing, and used the Single Shot MultiBox Detector (SSD) algorithm for portrait detection. It also designed an improved algorithm combining principal component analysis (PCA) with linear discriminant analysis (LDA) for portrait recognition and tested the system by applying it in a shopping mall (...) to collect and monitor the portrait and establish a data set. The results showed that the missing detection rate and false detection rate of the SSD algorithm were 0.78 and 2.89%, respectively, which were lower than those of the AdaBoost algorithm. Comparisons with PCA, LDA, and PCA + LDA algorithms demonstrated that the recognition rate of the improved PCA + LDA algorithm was the highest, which was 95.8%, the area under the receiver operating characteristic curve was the largest, and the recognition time was the shortest, which was 465 ms. The experimental results show that the improved PCA + LDA algorithm is reliable in portrait recognition and can be used for emergency evacuation in mass emergencies. (shrink)

A Higher education system is closely related to a country’s development. Governments need to make appropriate adjustments to ensure the healthy and sustainable development of higher education. To measure and evaluate the development status of the national higher education system and propose a set of policies, the following work has been done in this paper: First, this paper establishes a comprehensive evaluation index system based on the AHP-FCE model. The healthy development of education in significant countries was compared horizontally and (...) vertically. The results of weighted scores were used to determine the Japanese higher education system as the research object. Secondly, this paper uses factor analysis and principal component analysis to evaluate the level of sustainable development of higher education, detecting development barriers that migrate from the current to the future. This paper proposes a healthy and sustainable development policy blueprint from four aspects. Finally, this article uses the ARMA time series model to predict the development trend of Japanese higher education in the next 30 years and formulates a timetable for corresponding implementation arrangements. In addition, this paper quantitatively evaluates the effectiveness of the policies we propose and their impact on reality and provides references for other countries in the world. (shrink)

Deep learning is widely used in the fields of information technology and education innovation but there are few studies for young children in the preschool stage. Therefore, we aimed to explore factors that affect children’s learning ability through collecting relevant information from teachers in the kindergarten. Literature review, interview, and questionnaire survey methods were used to determine the influencing factors of deep learning. There were five dimensions for these factors: the level of difficulty of academic, communication skills, level of active (...) collaboration, level of in-depth processing, and reflection level evaluation. Reliability and validity tests were used to analyze the data from questionnaires. In total, 100 valid questionnaires were collected. The Cronbach coefficients for academic challenge, communication, active cooperation, deep processing, and reflective evaluation were 0.801, 0.689, 0.770, 0.758, and 0.665, respectively. Principal component analysis revealed that there were three main factors that affect children’s learning depth: the level of deep processing, the level of reflective evaluation, and the active level of collaboration. In conclusion, there were several factors affecting deep learning in children and further studies are warranted to promote the development of this field. (shrink)

Several studies showed evidence supporting the possibility of hand trajectory decoding from low-frequency electroencephalography. However, the decoding in the source space via source localization is scarcely investigated. In this study, we tried to tackle the problem of collinearity due to the higher number of signals in the source space by two folds: first, we selected signals in predefined regions of interest ; second, we applied dimensionality reduction techniques to each ROI. The dimensionality reduction techniques were computing the mean, principal (...) component analysis, and locality preserving projections. We also investigated the effect of decoding between utilizing a template head model and a subject-specific head model during the source localization. The results indicated that applying source-space decoding with PCA yielded slightly higher correlations and signal-to-noise ratios than the sensor-space approach. We also observed slightly higher correlations and SNRs when applying the subject-specific head model than the template head model. However, the statistical tests revealed no significant differences between the source-space and sensor-space approaches and no significant differences between subject-specific and template head models. The decoder with Mean and PCA utilizes information mainly from precuneus and cuneus to decode the velocity kinematics similarly in the subject-specific and template head models. (shrink)

Brain–machine interfaces are systems that allow the control of a device such as a robot arm through a person’s brain activity; such devices can be used by disabled persons to enhance their life and improve their independence. This paper is an extended version of a work that aims at discriminating between left and right imagined hand movements using a support vector machine classifier to control a robot arm in order to help a person to find an object in the environment. (...) The main focus here is to search for the best features that describe efficiently the electroencephalogram data during such imagined gestures by comparing two feature extraction methods, namely the continuous wavelet transform and the empirical modal decomposition, combined with the principal component analysis that were fed through a linear and radial basis function kernel SVM classifier. The experimental results showed high performance achieving an average accuracy across all the subjects of 92.75% with an RBF kernel SVM classifier using CWT and PCA compared to 80.25% accuracy obtained with EMD and PCA. The proposed system has been implemented and tested using data collected from five male subjects and it enabled the control of the robot arm in the right and the left direction. (shrink)

This article investigates the universal values scale, Schwartz Value Survey (SVS) for its applicability to measure cultural context-specific values. The study establishes a need to construct a new scale by identifying and incorporating Indian culture-specific values in SVS. Deriving data using self-assessment questionnaires from 709 respondents in 2 studies and analysing them using principal component analysis and structural equation modelling, the article reconceptualizes Schwartz’s Portrait Values Questionnaire (PVQ) and the 10 motivational value factors and develops a new (...) 76-item Holistic Values Scale (HVS) to measure Indian values using well-established scale development methods. The article further presents the research and policy implications and future research areas in this domain. (shrink)

High-dimensional data analysis has become the most challenging task nowadays. Dimensionality reduction plays an important role here. It focuses on data features, which have proved their impact on accuracy, execution time, and space requirement. In this study, a dimensionality reduction method is proposed based on the convolution of input features. The experiments are carried out on minimal preprocessed nine benchmark datasets. Results show that the proposed method gives an average 38% feature reduction in the original dimensions. The algorithm accuracy (...) is tested using the decision tree, support vector machine, and K-nearest neighbor classifiers and evaluated with the existing principal component analysis algorithm. The average increase in accuracy is 8.06 for DT, 5.80 for SVM, and 18.80 for the KNN algorithm. The most significant characteristic feature of the proposed model is that it reduces attributes, leading to less computation time without loss in classifier accuracy. (shrink)

Genetic and lifestyle factors contribute to cognitive ageing. However, the extent to which the public attribute changes in thinking skills to either genetic or lifestyle factors is largely unknown. This may be important if it impacts engagement in activities deemed beneficial to thinking skills. This study, therefore, explored people’s beliefs about determinants of cognitive ageing and whether those beliefs were associated with engagement in potentially beneficial behaviours. Data were collected through a United Kingdom-wide survey of people aged 40 and over. (...) Participants completed questions about their beliefs regarding cognitive ageing, and specifically the extent to which they believed lifestyle or genetic factors influence those changes, and their engagement in specific behaviours that may be cognitively beneficial. Responses from 3,130 individuals were analysed using chi-square tests of independence, principal component analysis and ANCOVAs to investigate whether their attribution of genetic or lifestyle determinants were associated with their beliefs about cognitive ageing and their participation in brain health-related behaviours. Most respondents believed genes and lifestyle contribute equally to age-related changes in cognitive skills. Respondents who believed genetic factors were more influential were less likely to expect cognitive skills might be improved or maintained with age, less sure what behaviours might be associated with brain health, and less likely to engage in behaviours comprising mental challenge/novelty supported as beneficial for brain health. From this United Kingdom-wide survey about beliefs regarding potential determinants of cognitive ageing, some of our respondents’ views were not aligned with the findings from ageing research. It is important for the public to know how to keep their brains healthy. Our results indicate a need for clearer messaging highlighting the role of lifestyle factors for brain health. (shrink)

With the increasing economic development and gradual opening of China’s aviation market, the Chinese aviation industry is experiencing rapid growth and is facing some problems at the same time. Many scholars have assessed the profitability of the Chinese aviation industry using a single assessment approach. Both theory and methods in assessing the profitability of the airlines need to be developed and improved. This article presents a new integrated approach – a combination of factor analysis, principal component (...) class='Hi'>analysis, and gray relational analysis. Based on the theory of decision making and data mining, this more reasonable and effective model overcomes the one-sidedness and limitations of the individual methods and has significant impact in theory and practice. (shrink)

In China, investors generally make decisions depending on the intonation of executive announcements. A total of 20,328 observations are sampled from the Chinese equity market between 2005 and 2019. We perform principal component analysis to produce monthly sentiment indices and calculate the weighted average of the value over the fiscal year to measure the degree of investor sentiment. The results of the empirical analysis reveal that: there is a significant positive correlation between market-level investor sentiment and (...) executive tone, stock price and trading-volume pressures on executives, and firm-level investor expectation and gambling tendency positively moderate the relationship between investor sentiment and executive tone, executive optimism does not mediate the association between investor sentiment and executive tone, and manipulated and real intonations are non-homogeneous tactics adopted by rational managers to cater to changes in investor sentiment. These findings indicate that executives' intonations are both an extension of firms' current and past performances and managers' decision-making based on sentiments and behaviors of irrational investors, which are consistent with the Catering Theory. In addition, rational executives tend to adopt various intonation tactics to respond to investor sentiment to avoid declines in stock prices and trading volumes. (shrink)