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Open access

Jin Zhang, Yanyan Wang, Yuehua Zhao and Xin Cai

regarded as major factors in determining the quality of studies. There are multiple categories of statistical methods, such as inferential statistical methods, predictive statistical methods, parametric statistical methods, and nonparametric statistical methods. Each category contains a lot of statistical approaches. For instance, ANOVA test is an inferential statistical method, while Pearson’s correlation is a predictive statistical method. These various statistical methods have different characteristics and are used differently in studies. Therefore, it is meaningful

Open access

Xiao Hu

1 Introduction Studies of cultural heritages are invaluable in many disciplines including archeology, history, anthropology, linguistics, and religious studies. Among tangible cultural heritage (i.e., those with physical presence), many are non-movable such as monuments, architecture, and archeological sites. To observe non-movable heritages, people have to travel to the heritage site. Moreover, most non-movable heritages are exposed to the natural environment with limited protection, suffering from deformation and deterioration caused by weather or other

Open access

Dar-Zen Chen and Chien-Hsiang Chou

were the main transition patterns analyzed in this study. Because business markets are competitive, assuming proper control of patent characteristics related to transition patterns is desired by diverse types of stakeholders to potentially facilitate monitoring and predicting rivals’ transition patterns at early stages and to provide valuable assistance in adapting strategies ( Aharonson & Schilling, 2016 ). A series of statistical testing procedures was used to conduct a preliminary study to gain insight into the significance and directionality of technology

Open access

Maria Esteva, Ramona L. Walls, Andrew B. Magill, Weijia Xu, Ruizhu Huang, James Carson and Jawon Song

ontological efforts to describe biological data ( Smith et al., 2007 ). Our solution was to devise a generic data model to accommodate all our test cases and many more. Our generic data model ( Fig. 1A) has three key components: 1) processual entities, which include things such as collecting specimens, carrying out assays, and analyzing data; 2) material entities such as physical specimens, reagents, and probes; and 3) data entities, including both individual data objects and datasets. We include a project entity as the umbrella under which the other entities are grouped

Open access

Will R. Thomas, Benjamin Galewsky, Sandeep Puthanveetil Satheesan, Gregory Jansen, Richard Marciano, Shannon Bradley, Jong Lee, Luigi Marini and Kenton McHenry

folder number for the underlying physical record, filename and path assigned at the time of capture, SHA256 fixity information, and format information captured by the identity program in the ImageMagick suite. Figure 1 Storage Pool. C = Cassandra Figure 2 Expanded Storage Pool. We rely on Apache Spark ( Zaharia et al., 2016 ) to distribute the training of the machine-learning models. Training and testing data are stored in Cassandra, as Spark integrates with it as a persistence layer. Although Spark has an ML library (Meng, et al., 2016), the

Open access

Danchen Zhang and Daqing He

. Esfandiari et al. [ 11 ] gave a comprehensive review of the studies in this area. According to their summarization, most studies regarded diagnosis prediction as a classification task. For example, Yeh et al. [ 12 ] used the history of patients’ diseases, blood test results, and physical examination results as the features and used trained classifiers to predict the probability of getting a cerebrovascular disease. Besides, other studies tried to predict the diagnosis with regression methods, clustering methods, association rules, or hybrid systems [ 11 ]. For example

Open access

Liang Hong, Mengqi Luo, Ruixue Wang, Peixin Lu, Wei Lu and Long Lu

physical health data based on artificial ant colony optimization. This method is determined through testing to be an effective and efficient approach to clustering health and medical data for further analysis. Paul and Hoque (2010) proposed to use the background knowledge of medical domain in the clustering process to predict the likelihood of diseases. The developed algorithm can handle both continuous and discrete data and perform clustering based on anticipated likelihood attributes with core attributes of disease in data point. In this paper, its effectiveness has

Open access

Yongqiang Sun, Dina Liu and Nan Wang

information sensitivity based on general information types, it is more appropriate to measure information sensitivity by taking users’ perception of sensitivity into account. Some scholars define information sensitivity as the perceived intimacy level of information ( Lwin, Wirtz, & Williams, 2007 ). More intimate information is perceived as riskier to disclose because it may lead to potential losses, including psychological (e.g., loss of self-esteem), physical (e.g., loss of health), and material (e.g., loss of property and assets) aspects ( Moon, 2000 ). Therefore, this

Open access

Minghong Chen, Jingye Qu, Yuan Xu and Jiangping Chen

, dynamic, infection, dynamical, sepsis, infectious, estimate, hcv, lh, forecast, symposium, influenza, ebola, prevalence, demographic, ass, reference, rop Cluster4 mobility, motion, impairment, physical, elderly, assistive, child, prosthesis, therapy, assistance, movement, caregiver, gait, exoskeleton, cartilage, assist, rehabilitation, energy, gerontechnology, orthosis Cluster5 Social, twitter, behavioral, media, health, measure, wellness, signal, mobility, volume, dynamic, monitor, symptom, personalized, drug, management, condition, theory, identity

Open access

Yiming Zhao, Baitong Chen, Jin Zhang, Ying Ding, Jin Mao and Lihong Zhou

more connected and coherent than that of 2005–2006. That is to say, connections between these core terms increased, while the number of core terms that diabetics used in 2013–2014 was less than in 2005–2006. 4.2 Diabetics are concerned more about daily life than before A remarkable change took place for term use from 2005–2006 to 2013–2014 in terms of the term co-occurrence network metrics. An independent-samples t -test analysis was applied to compare degree centrality and betweenness centrality between the term co-occurrence networks of 2005–2006 and 2013