Data center technology changes the mode of computing. Traditional DCs consist of a single layer and only have Ethernet connections among switches. Those old-fashioned DCs cannot fulfill the high resource demand compared with today’s DCs. The architectural design of the DCs is getting substantial importance and acting as the backbone of the network because of its essential feature of supporting and maintaining the rapidly increasing Internet-based applications which include search engines and social networking applications. Every application has its parameters, (...) like latency and blocking in the DC network. Every data center network has its specialized architecture. It has a specific arrangement of layers and switches, which increase or decrease the DC network’s efficiency. We develop a simulation tool that comprises two different DC architectures: basic tree architecture and c-Through architecture. Using this simulation, we analyze the traffic behavior and the performance of the simulated DCN. Our main purpose is to focus on mean waiting time, load, and blocking with respect to the traffic within the DCN. (shrink)

Introduction: Motor evoked potentials (MEPs) to transcranial magnetic stimulation are known to be susceptible to several sources of variability. However, conflicting evidences on individual characteristics in relatively small sample sizes have been reported. We investigated the effect of age, height, and sex on MEPs of the motor cortex and spinal roots in a large cohort. Methods: A total of 587 subjects clinically and neuroradiologically intact were included. MEPs were recorded during mild tonic contraction through a circular coil applied over the (...) “hot spot” of the first dorsal interosseous and tibialis anterior muscles, bilaterally. Central motor conduction time (CMCT) was estimated as the difference between MEP cortical latency and the peripheral motor conduction time by cervical or lumbar magnetic stimulation. Peak-to-peak MEP amplitude to cortical stimulation and right-to-left difference of each parameter were also measured. Results: After Bonferroni correction, general linear (multiple) regression analysis showed that both MEP cortical latency and peripheral motor conduction time at four limbs positively correlated with age and height. At lower limbs, an independent effect of sex on the same measures was also observed (with females showing smaller values than males). CMCT correlated with both age (negatively) and height (positively) when analyzed by a single regression; however, with a multiple regression analysis this significance disappeared, due to the correction for the multicollinearity within the dataset. Conclusions: Physical individual features need to be considered for a more accurate and meaningful MEPs interpretation. Both in clinical practice and in research setting, patients and controls should be matched for age, height, and sex. (shrink)

Mental health is an important basic condition for college students to become adults. Educators gradually attach importance to strengthening the mental health education of college students. This paper makes a detailed analysis and research on college students’ mental health, expounds the development and application of clustering analysis algorithm, applies the distance formula and clustering criterion function commonly used in clustering analysis, and makes a specific description of some classic algorithms of clustering analysis. Based on expounding the (...) advantages and disadvantages of fast-clustering analysis algorithm and hierarchical clustering analysis algorithm, this paper introduces the concept of the two-step clustering algorithm, discusses the algorithm flow of clustering model in detail, and gives the algorithm flow chart. The main work of this paper is to analyze the clustering algorithm of students’ mental health database formed by mental health assessment tool test, establish a data mining model, mine the database, analyze the state characteristics of different college students’ mental health, and provide corresponding solutions. In order to meet the needs of the psychological management system based on the clustering analysis method, the clustering analysis algorithm is used to cluster the data. Based on the original database, this paper establishes the methods of selecting, cleaning, and transforming the data of students’ psychological archives. Finally, it expounds on the application of data mining in students’ psychological management system and summarizes and prospects the implementation of the system. (shrink)

Based on cloud computing and statistics theory, this paper proposes a reasonable analysis method for big data of film and television. The method selects Hadoop open source cloud platform as the basis, combines the MapReduce distributed programming model and HDFS distributed file storage system and other key cloud computing technologies. In order to cope with different data processing needs of film and television industry, association analysis, cluster analysis, factor analysis, and K-mean + association (...) class='Hi'>analysis algorithm training model were applied to model, process, and analyze the full data of film and TV series. According to the film type, producer, production region, investment, box office, audience rating, network score, audience group, and other factors, the film and television data in recent years are analyzed and studied. Based on the study of the impact of each attribute of film and television drama on film box office and TV audience rating, it is committed to the prediction of film and television industry and constantly verifies and improves the algorithm model. (shrink)

This paper attempts to define Exploratory Data Analysis (EDA) more precisely than usual, and to produce the beginnings of a philosophy of this topical and somewhat novel branch of statistics. A data set is, roughly speaking, a collection of k-tuples for some k. In both descriptive statistics and in EDA, these k-tuples, or functions of them, are represented in a manner matched to human and computer abilities with a view to finding patterns that are not "kinkera". A (...) kinkus is a pattern that has a negligible probability of being even partly potentially explicable. A potentially explicable pattern is one for which there probably exists a hypothesis of adequate "explicativity", which is another technical probabilistic concept. A pattern can be judged to be probably potentially explicable even if we cannot find an explanation. The theory of probability understood here is one of partially ordered (interval-valued), subjective (personal) probabilities. Among other topics relevant to a philosophy of EDA are the "reduction" of data; Francis Bacon's philosophy of science; the automatic formulation of hypotheses; successive deepening of hypotheses; neurophysiology; and rationality of type II. (shrink)

Chow illustrates the important role played by significance testing in the evaluation of research findings. Statistics and the goals of research should be treated as both interrelated and separate parts of the research evaluation process – a message that will benefit all who read Chow's book. The arguments are especially pertinent to the debate over the relative merits of confidence intervals and significance tests.

In this commentary to Napoletani et al. (Found Sci 16:1–20, 2011), we argue that the approach the authors adopt suggests that neural nets are mathematical techniques rather than models of cognitive processing, that the general approach dates as far back as Ptolemy, and that applied mathematics is more than simply applying results from pure mathematics.

The notion of representation in neuroscience has largely been predicated on localizing the components of computational processes that explain cognitive function. On this view, which I call “algorithmic homuncularism,” individual, spatially and temporally distinct parts of the brain serve as vehicles for distinct contents, and the causal relationships between them implement the transformations specified by an algorithm. This view has a widespread influence in philosophy and cognitive neuroscience, and has recently been ably articulated and defended by Shea. Still, I am (...) skeptical about algorithmic homuncularism, and I argue against it by focusing on recent methods for complex data analysis in systems neuroscience. I claim that analyses such as principle components analysis and linear discriminant analysis prevent individuating vehicles as algorithmic homuncularism recommends. Rather, each individual part contributes to a global state space, trajectories of which vary with important task parameters. I argue that, while homuncularism is false, this view still supports a kind of “vehicle realism,” and I apply this view to debates about the explanatory role of representation. (shrink)

Insight is an important and repeated component of most qualitative research studies. Yet insight is often a vague concept that is not well articulated in textbooks and research reports. The late James Loder of Princeton University posited a theologically-based process he termed ‘the transforming moment’ that identifies predictable phases in a wide variety of transformations, including those of a psychological, scientific, and spiritual nature. This process corresponds at many levels with the role of insight in qualitative research. As a result, (...) careful examination of Loder's outline of the transformative process can help clarify what is meant by insight and how it informs research activities. (shrink)

In order to better assist investors in the evaluation and decision-making of financial data, this paper puts forward the need to build a reliable and effective financial data prediction model and, on the basis of financial data analysis, integrates deep learning algorithm to analyze financial data and completes the financial data analysis system based on deep learning. This paper introduces the implementation details of the key modules of the platform in detail. The user (...) interaction module obtains and displays the retrieval results through data parsing, calling the background, and computing engine. The data cleaning module fills, optimizes, and normalizes the data through business experience; the calculation engine module uses the algorithm and extracts the database information to get the similar time series and matching financial model. Finally, the data acquisition module fills the database with historical data at the initialization stage and updates the database every day. The data analysis platform for quantitative trading designed and implemented in this paper has carried out demand analysis, design, implementation, and test. From the perspective of function test and performance test, two functions of similar stock search and financial matching model are selected and tested, and the results are in line with the expected results. (shrink)

The target article is important not only for black-box studies, but also for those interested in tracing cognitive processing and/or subjective experience. I provide two examples taken from my own research. I then proceed to discuss how best to analyze data from the n = 1 study, which has a factorial design.

In this paper we will offer a few examples to illustrate the orientation of contemporary research in data analysis and we will investigate the corresponding role of mathematics. We argue that the modus operandi of data analysis is implicitly based on the belief that if we have collected enough and sufficiently diverse data, we will be able to answer most relevant questions concerning the phenomenon itself. This is a methodological paradigm strongly related, but not limited (...) to, biology, and we label it the microarray paradigm. In this new framework, mathematics provides powerful techniques and general ideas which generate new computational tools. But it is missing any explicit isomorphism between a mathematical structure and the phenomenon under consideration. This methodology used in data analysis suggests the possibility of forecasting and analyzing without a structured and general understanding. This is the perspective we propose to call agnostic science, and we argue that, rather than diminishing or flattening the role of mathematics in science, the lack of isomorphisms with phenomena liberates mathematics, paradoxically making more likely the practical use of some of its most sophisticated ideas. (shrink)

Big Data is a large-sized and complex dataset, which cannot be managed using traditional data processing tools. Mining process of big data is the ability to extract valuable information from these large datasets. Association rule mining is a type of data mining process, which is indented to determine interesting associations between items and to establish a set of association rules whose support is greater than a specific threshold. The classical association rules can only be extracted from (...) binary data where an item exists in a transaction, but it fails to deal effectively with quantitative attributes, through decreasing the quality of generated association rules due to sharp boundary problems. In order to overcome the drawbacks of classical association rule mining, we propose in this research a new neutrosophic association rule algorithm. The algorithm uses a new approach for generating association rules by dealing with membership, indeterminacy, and non-membership functions of items, conducting to an efficient decision-making system by considering all vague association rules. To prove the validity of the method, we compare the fuzzy mining and the neutrosophic mining. The results show that the proposed approach increases the number of generated association rules. (shrink)

Psychologists are directed by ethical guidelines in most areas of their practice. However, there are very few guidelines for conducting data analysis in research. The aim of this article is to address the need for more extensive ethical guidelines for researchers who are post–data collection and beginning their data analyses. Improper data analysis is an ethical issue because it can result in publishing false or misleading conclusions. This article includes a review of ethical implications (...) of improper data analysis and potential causes of unethical practices. In addition, current guidelines in psychology and other areas (e.g., American Psychological Association and American Statistical Association Ethics Codes) were used to inspire a list of recommendations for ethical conduct in data analysis that is appropriate for researchers in psychology. (shrink)

We conducted an integrative data analysis to examine the hedonic character of nostalgia. We combined positive and negative affect measures from 41 experiments manipulating nostalgia. Overall, nostalgia inductions increased positive and ambivalent affect, but did not significantly alter negative affect. The magnitude of nostalgia’s effects varied markedly across different experimental inductions of the emotion. The hedonic character of nostalgia, then, depends on how the emotion is elicited and the benchmark to which it is compared. We discuss implications for (...) theory and research on nostalgia and emotions in general. (shrink)

This paper considers the use of secondary data analysis in educational research. It addresses some of the promises and potential pitfalls that influence its use and explores a possible role for the secondary analysis of numeric data in the 'new' political arithmetic tradition of social research. Secondary data analysis is a relatively under-used technique in Education and in the social sciences more widely, and it is an approach that is not without its critics. Here (...) we consider two main objections to the use of secondary data: that it is full of errors and that because of the socially constructed nature of social data, simply reducing it to a numeric form cannot fully encapsulate its complexity. However, secondary data also offers numerous methodological, theoretical and pedagogical benefits. Indeed by treating secondary data analysis with appropriate scepticism and respect for its limitations, by demanding that tacit assumptions about the unreliability of secondary data are applied equally to other research methods, and crucially by combining secondary data analysis with small-scale in-depth work, this paper argues for a return to prominence of secondary data analysis in its own right as well as becoming a central component of the new political arithmetic tradition of social research. (shrink)

A book aimed at advanced undergraduates and graduates in cognitive science and artificial intelligence, linguistics, applied mathematics and data analysis.

Big data has brought about opportunities for landscape architecture, changing the design thinking of layout optimization simulation, expanding the platform for public participation in layout optimization simulation design, reflecting social and humanistic care, and promoting the integration of discipline cooperation and data. At the same time, it also brings about challenges. The proposal of data theory, the acquisition and analysis of data, and the protection of privacy are all issues that we need to face and (...) solve. First, build a layout optimization simulation program under the background of big data. Follow the procedures of conventional layout optimization simulation, add big data analysis technology to the preliminary analysis, layout optimization simulation design, and later evaluation management, introduce new data processing methods, and build a theoretical framework of landscape optimization simulation methods that integrate new and traditional data. Second, studying the evolution of the landscape image layout of the three-dimensional pastoral complex is different: the overall landscape image layout of the three-dimensional pastoral complex shows a trend of fragmentation and heterogeneity. Among them, the arable land is gradually fragmented, the wetland and water area are simplified in form, the woodland and grassland patches are gradually distributed, the fragmentation is reduced by 76.19%, and the connectivity index is gradually increased. From a spatial perspective, the edge area outside the Fourth Ring Road becomes the most obvious area of fragmentation tendency. Finally, by introducing big data into the landscape layout optimization simulation, a layout optimization simulation method based on big data is constructed and used in the layout optimization simulation of the landscape image layout of the three-dimensional pastoral complex to guide the participatory layout optimization simulation. (shrink)

The outcomes of a bibliographic review on political communication, in particular electoral communication in social networks, are presented here. The electoral campaigning are a crucial test to verify the transformations of the media system and of the forms and uses of the linguistic acts by dominant actors in public sphere – candidates, parties, journalists and Gatekeepers. The aim is to reconstruct the first elements of an analytical model on the transformations of the political public sphere, with which to systematize the (...) results of the main empirical research carried out in recent years, in particular those conducted with a promising methodology: Digital Trace Data Analysis. (shrink)

BackgroundDepression is a prevalent condition that has a significant impact on psychosocial functioning and quality of life. The onset and persistence of depression have been linked to a variety of biological and psychosocial variables. Many of these variables are associated with specific lifestyle characteristics, such as physical activity, diet, and sleep patterns. Some psychosocial determinants have an impact on people’ health-related behavior change. These include personal factors such as sense of coherence, patient activation, health literacy, self-efficacy, and procrastination. This study (...) aims to analyze the association between the severity of depression, lifestyle patterns, and personal factors related to health behavior. It also aims to analyze whether personal factors moderate the relationship between lifestyles and depression.MethodsThis study is a secondary data analysis of baseline data collected at the start of a randomized controlled trial. A sample of 226 patients with subclinical, mild, or moderate depression from primary healthcare centers in two sites in Spain was used, and descriptive, bivariate, multivariate, and moderation analyses were performed. Depression was the primary outcome, measured by Beck II Self-Applied Depression Inventory. Lifestyle variables such as physical exercise, adherence to Mediterranean diet and sleep quality, social support, and personal factors such as self-efficacy, patient activation in their own health, sense of coherence, health literacy, and procrastination were considered secondary outcomes.ResultsLow sense of coherence, poor sleep quality, low patient activation, and sedentarism are predictors of having more depressive symptoms. Moderation analyses were not significant.DiscussionLifestyle and personal factors are related to depressive symptomatology. Our findings reveal that sense of coherence, patient’s activation level, sedentarism, and sleep quality are associated with depression. Further research is needed regarding adherence to Mediterranean diet, minutes walking per week and the interrelationship between lifestyles, personal factors, and depression. (shrink)

Data analysis has become most widespread field of research and it has extended in almost every field of study. Considering the recent trends and developments in the field of communication and information technology, there is a scope of combining the monitoring of substation equipment with big data analysis technology. That will result in an improved data analysis ability, information sharing and utilization rate of monitoring data. In the proposed work, the authors have introduced (...) the big data analysis and its corresponding application in the monitoring of substations. Basic concepts and the procedures of the typical data analysis for general problems are also discussed. As a main part of the paper, different types of distributed data analysis techniques have been proposed, in which two relational online analysis, namely Hive and Impala and one H Base multidimensional online analysis are important. These data analysis techniques are proposed considering the analysis efficiency, storage performance from the business development requirements point of view of the substation. The result obtained depicts that the proposed model has an advantage in storage overhead and roll-up performance, when compared with the traditional method, although the data loading speed is approximately 1.7–1.9 times of the traditional model. Some experiments are carried out in order to verify the validity of the model. (shrink)

To explore the impact of academic burden on the physical and mental health of primary school students, combined with the results of the Programme for International Student Assessment report in 2018, the relationship among the development of mathematical literacy, mathematics academic burden, and the physical and mental health of primary school students is studied. First, the relationship between mathematical literacy and mathematics anxiety is analyzed, and related influencing factors and measurement methods of mathematics anxiety are introduced. A questionnaire is then (...) designed for primary school students’ mathematical stress, and the reliability and validity of the designed questionnaire are tested. Finally, a questionnaire survey is conducted on students, parents, and teachers in the third, fourth, and fifth grades of three standardized public primary schools. The results of the questionnaire survey show that students, teachers, and parents have a general understanding of the mathematics academic burden of primary school students at this stage. A total of 70% of teachers believe that primary school students have a heavy mathematics burden; 50% of parents think that primary school students are under heavy academic stress; 70% of primary school students believe that the heavy mathematics burden leads to reduced sleep time and extracurricular activities, which has a serious impact on the physical and mental health of primary school students. This research provides a reference for improving the current balance between education and students’ physical and mental health in China. (shrink)

The use of big data to investigate the spread of infectious diseases or the impact of the built environment on human wellbeing goes beyond the realm of traditional approaches to epidemiology, and includes a large variety of data objects produced by research communities with different methods and goals. This paper addresses the conditions under which researchers link, search and interpret such diverse data by focusing on “data mash-ups”—that is the linking of data from epidemiology, biomedicine, (...) climate and environmental science, which is typically achieved by holding one or more basic parameters, such as geolocation, as invariant. We argue that this strategy works best when epidemiologists interpret localisation procedures through an idiographic perspective that recognises their context-dependence and supports a critical evaluation of the epistemic value of geolocation data whenever they are used for new research purposes. Approaching invariants as strategic constructs can foster data linkage and re-use, and support carefully-targeted predictions in ways that can meaningfully inform public health. At the same time, it explicitly signals the limitations in the scope and applicability of the original datasets incorporated into big data collections, and thus the situated nature of data linkage exercises and their predictive power. (shrink)

Although the terminology of Big Data has so far gained little traction in economics, the availability of unprecedentedly rich datasets and the need for new approaches – both epistemological and computational – to deal with them is an emerging issue for the discipline. Using interviews conducted with a cross-section of economists, this paper examines perspectives on Big Data across the discipline, the new types of data being used by researchers on economic issues, and the range of responses (...) to this opportunity amongst economists. First, we outline the areas in which it is being used, including the prediction and ‘nowcasting’ of economic trends; mapping and predicting influence in the context of marketing; and acting as a cheaper or more accurate substitute for existing types of data such as censuses or labour market data. We then analyse the broader current and potential contributions of Big Data to economics, such as the ways in which econometric methodology is being used to shed light on questions beyond economics, how Big Data is improving or changing economic models, and the kinds of collaborations arising around Big Data between economists and other disciplines. (shrink)

Teachers’ teaching psychological behavior and classroom development are the current research hotspots in the field of educational psychology. How to realize the data analysis of teachers’ teaching psychological behavior and classroom development is a problem that researchers urgently need to solve. Based on the theory of data correlation analysis, this paper uses modern Internet technology and big data analysis teacher teaching system to quantitatively and qualitatively analyze the potential of students, and build a corresponding (...) model. Through rule correlation technology, the article studies various internal correlations between teachers’ teaching psychological behavior, extracts valuable information from various daily data of students through big data analysis technology, and the WEB teacher’s teaching psychological behavior analysis system based on B/S structure solves the problem that the traditional model cannot measure. In the simulation process, the system is implemented by MVC three-tier architecture, the database uses MYSQL 5.0, the prediction questionnaire is formulated on the basis of the literature method and interviews, and the scale is compiled and tested after repeated revisions. Project analysis and factor analysis are performed on the data obtained from the table test to construct and screen indicators. The experimental results show that the teacher’s classroom teaching behavior index system adopted by the system is practical and feasible, including three first-level indicators, 10 s-level indicators, and 21 third-level indicators. The system has 87.1% completeness, which effectively improves teachers’ teaching psychology. (shrink)

Many decades ago Patrick Suppes argued rather convincingly that theoretical hypotheses are not confronted with the direct, raw results of an experiment, rather, they are typically compared with models of data. What exactly is a data model however? And how do the interactions of particles at the subatomic scale give rise to the huge volumes of data that are then moulded into a polished data model? The aim of this paper is to answer these questions by (...) presenting a detailed case study of the construction of data models at the LHCb for testing Lepton Flavour Universality in rare decays of B-mesons. The close examination of the scientific practice at the LHCb leads to the following four main conclusions: raw data in their pure form are practically useless for the comparison of experimental results with theory, and processed data are in some cases epistemically more reliable, real and simulated data are involved in the co-production of the final data model and cannot be easily distinguished, theory-ladenness emerges at three different levels depending on the scope and the purpose for which background theory guides the overall experimental process and the overall process of acquiring and analysing data in high energy physics is too complicated to be fully captured by a generic methodological description of the experimental practice. (shrink)

Neuroimaging technology is the most widely used tool to study human cognition. While originally a promising tool for mapping the content of cognitive theories onto the structures of the brain, recently developed tools for the analysis, handling and sharing of data have changed the theoretical landscape of cognitive neuroscience. Even with these advancements philosophical analyses of evidence in neuroimaging remain skeptical of the promise of neuroimaging technology. These views often treat the analysis techniques used to make sense (...) of data produced in a neuroimaging experiment as one, attributing the inferential limitations of analysis pipelines to the technology as a whole. Situated against the neuroscientists own critical assessment of their methods and the limitations of those methods, this skepticism appears based on a misunderstanding of the role data analysis techniques play in neuroimaging research. My project picks up here, examining how data analysis techniques, such as pattern classification analysis, are used to assess the evidential value of neuroimaging data. The project takes the form of three papers. In the first I identify the use of multiple data analysis techniques as an important aspect of the data interpretation process that is overlooked by critics. In the second I develop an account of inferences in neuroimaging research that is sensitive to this use of data analysis techniques, arguing that interpreting neuroimaging data is a process of isolating and explaining a variety of data patterns. In the third I argue that the development and uptake of new techniques for analyzing data must be accompanied by changes in research practices and standards of evidence if they are to promote knowledge generation. My approach to this work is both traditionally philosophical, insofar as it involves reading and analyzing the work of philosophers and neuroscientists, and embedded insofar as most of the research was conducted while engaged in attending lab meetings and participating in the work of those scientists whose work is the object of my research. (shrink)

In terms of scenic marketing, big data research also plays an important role in the precise marketing of scenic spots. This paper has focused on the big data related to scenic spots as the research object, explores the relationship between various subdivision big data and the number of tourists in scenic spots, and investigates the difference and influence of the consumption behavior of the secondary consumption items in the scenic area, to find the potential of the scenic (...) area’s business growth and to promote the continuous and stable growth of the scenic area’s sales and tourism economy. Using the relevant theories and analysis methods, such as consumer behavior, big data, and tourism consumer behavior, the content mainly focuses on the establishment of the analysis model of the number of tourists in the scenic spot, the data collection, the estimation of the model parameters, the various types of big data, the calculation of the contribution rate of the data to the number of tourists in the scenic spot, and the difference analysis of the secondary consumption items of different types of tourists in the scenic spot. Results show that a multi-objective analysis model is established based on the relevant econometric theories, and an optimization plan is proposed after the multicollinearity diagnosis of the model; to establish a data envelopment analysis model of the difference and influence of different types of tourists’ consumption behavior in scenic spots and study the consumption behavior characteristics of different types of tourists when they purchase secondary consumption items in scenic spots; the econometric model is used to analyze the big data, adjust the linear relationship of some variables, then adopt the method of gradually adding variables combined with the consumer theory, and finally determine the number of daily tourists as the explained variable, the number of internet protocol, Baidu index, and the virtual value of the weekend, dummy variables for variables, bounce rate, and air pollution as explanatory variables. (shrink)

This is the third of four papers to be published in Research Ethics Review in 2009, that address methodological issues of relevance to research ethics committees. It focuses on three issues: the representativeness of study participants, the size of the study and data analysis. Differences between best practices in qualitative and quantitative research are highlighted. The paper argues that, while lack of representativeness may not be unethical, the ethical implications of unnecessary restrictions on eligibility should be considered by (...) committees. Studies that are either too small or too big can pose problems. Research teams need to have the necessary competence to enable an appropriate analysis focused on the main objectives and interpreted in the context of the limitations of the study design. (shrink)