Compressed Sensing for Big Data Over Complex Networks

Transductive semi-supervised learning methods aim at automatically labeling large datasets by leveraging information provided by few manually labeled data points and the intrinsic structure of the dataset. Many such methods based on a graph signal representation of a dataset have been proposed, in which the nodes correspond to the data points, the edges connect similar points, and the graph signal is the mapping between the nodes and the labels. Most of the existing methods use deterministic signal models and try to recover the graph signal using a regularized or constrained convex optimization approach, where the regularization/constraint term enforce some sort of smoothness of the graph signal. This thesis takes a different route and investigates a probabilistic graphical modeling approach in which the graph signal is considered a Markov random field defined over the underlying network structure. The measurement process, modeling the initial manually obtained labels, and smoothness assumptions are imposed by a probability distribution defined over the Markov network corresponding to the data graph. Various approximate inference methods such as loopy belief propagation and the mean field methods are studied by means of numerical experiments involving both synthetic and real-world datasets

Iranian Articles in Medical Ethics: An Altmetrics Approach on Social Media Vs. a Bibliometric Study in Scopus Database

Traditional citation analysis has been greatly criticized because the process of citation accumulation requires considerable time after publication. So, the term “altmetrics” was proposed in 2010 to measure the scientific and social impact of a paper. According to the deficiencies of traditional citation analysis, we performed a comprehensive search for medical ethics publications using the altmetrics approach from the beginning until 2019. In this descriptive-analytical study, we retrieved the articles discussing any topics relating to medical ethics that published in the Scopus database from the beginning till 2019 using related medical ethics keywords. A total number of 455 articles with altmetrics scores and citations, included in this study. Altmetrics data were extracted via an altmetrics bookmarklet. Dimensions, Mendeley, and Twitter, had prominent sources of attention on social media platforms. The most number of tweets, and Mendeley’s attentions, in the medical ethics fields, originated from the United States (US) and the United Kingdom (UK). Moreover, master students have the largest share in the citation of articles in Mendeley. Journal of Medical Ethics and History of Medicine has the most proportion of altmetrics score among Iranian papers in medical ethics. The correlation between the altmetrics score and citation index was significant (p <0.05). The medical ethics researchers have to pay more attention to social activities (such as creating and updating their profiles on social media) on the web for wide dissemination and proper evaluation of their scientific publications

Investigating Methods of Transferring Tacit Knowledge among Nursing Experts of Iranian Hospitals

Nursing tacit knowledge is a knowledge that is produced inside the mind of nurses and not saved in any databases. The main challenge of nursing knowledge management is transferring tacit knowledge inside the mind of nurses to the others – especially scholars and researchers. This study evaluates personal, organizational, and technological factors affecting the tacit knowledge transfer among nursing experts. It also assesses the most important ways of tacit knowledge transfer among hospital nurses. This study applied survey method. The population was all nursing experts of Bushehr governmental hospitals nurses which were 480 and finally 215 nurses were selected as the sample. Data collection tool included a researcher-made questionnaire. Descriptive and analytical statistics tests were used. It was revealed that the most important way of tacit knowledge transfer among nursing experts was participating in continuing educational courses. Findings indicated that the most important personal factor affecting tacit knowledge transfer was job security; most important organizational factor was rewards system; and most important technological factor was independent physical environment and educational technologies

When Is network lasso accurate?

The “least absolute shrinkage and selection operator” (Lasso) method has been adapted recently for network-structured datasets. In particular, this network Lasso method allows to learn graph signals from a small number of noisy signal samples by using the total variation of a graph signal for regularization. While efficient and scalable implementations of the network Lasso are available, only little is known about the conditions on the underlying network structure which ensure network Lasso to be accurate. By leveraging concepts of compressed sensing, we address this gap and derive precise conditions on the underlying network topology and sampling set which guarantee the network Lasso for a particular loss function to deliver an accurate estimate of the entire underlying graph signal. We also quantify the error incurred by network Lasso in terms of two constants which reflect the connectivity of the sampled nodes

Information Management: Concepts and Application

  Information Management is the process of information identification, production, classification, storage, dissemination and utilization considering the organizational, cultural, social as well as technological components. Given this, information management would require teamwork and well-defined task description for each and every individual. In order to fulfill their special tasks to secure the four pillars of information management (Production, Organization, Storage and Dissemination and finally application) Information managers are at the mercy of crucial factors such as technology, organization and social-cultural makeup of corporations. The present paper explores the concept of information management and would delve further into strategic information management, personal information management, interpersonal information management as well as effective information management. In the conclusion it would make a case from the standpoint of information ecology

A scientometrics study on informetrics: one decade quantitative researches in Iran (2002-2012)

Background and aim: Present scientometrics study aimed at investigating the scientific collaboration of Iranian scientometrics researchers during 2002-2012.  Material and methods: Using scientometrics approach, this paper studied co-authorship network of Iranian scientometrics researchers. The population includes 779 articles published both in English and Persian and indexed in 4th edition of scientometrics bibliography (last version till now) during 2002-2012. First, the co-authorship network matrix was drawn by using excel software second, the UCINET software and VOSviewer were used for data analysis.  Findings: The study of co-authorship patterns of Iranian scientometrics researchers indicated that Farideh Osareh (47 articles), Mohammad Hasanzadeh (43 articles), and Albdolrreza NouruziChackoli (36 articles) have the highest rank among scientometrics researchers in scientific productivity. According to central indicator, Farshid Danesh (77), Albdolrreza NouruziChackoli (69), and Mohammad Hasanzadeh (66) had the most number of co-authorship with other researchers.  Conclusion: Although clustering coefficient (= 0.767) showed the relative interest of this network members to work with each other and shaping different clusters, the density coefficient (=0.009) revealed the low consistency of the network

Evaluation of River Water Quality Index Using Remote Sensing and Artificial Intelligence Models

To restrict the entry of polluting components into water bodies, particularly rivers, it is critical to undertake timely monitoring and make rapid choices. Traditional techniques of assessing water quality are typically costly and time-consuming. With the advent of remote sensing technologies and the availability of high-resolution satellite images in recent years, a significant opportunity for water quality monitoring has arisen. In this study, the water quality index (WQI) for the Hudson River has been estimated using Landsat 8 OLI-TIRS images and four Artificial Intelligence (AI) models, such as M5 Model Tree (MT), Multivariate Adaptive Regression Spline (MARS), Gene Expression Programming (GEP), and Evolutionary Polynomial Regression (EPR). In this way, 13 water quality parameters (WQPs) (i.e., Turbidity, Sulfate, Sodium, Potassium, Hardness, Fluoride, Dissolved Oxygen, Chloride, Arsenic, Alkalinity, pH, Nitrate, and Magnesium) were measured between 14 March 2021 and 16 June 2021 at a site near Poughkeepsie, New York. First, Multiple Linear Regression (MLR) models were created between these WQPs parameters and the spectral indices of Landsat 8 OLI-TIRS images, and then, the most correlated spectral indices were selected as input variables of AI models. With reference to the measured values of WQPs, the WQI was determined according to the Canadian Council of Ministers of the Environment (CCME) guidelines. After that, AI models were developed through the training and testing stages, and then estimated values of WQI were compared to the actual values. The results of the AI models’ performance showed that the MARS model had the best performance among the other AI models for monitoring WQI. The results demonstrated the high effectiveness and power of estimating WQI utilizing a combination of satellite images and artificial intelligence models