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A national multi‐gigabit‐per‐second research and education network known as the National Research and Education Network is to be established by 1996, according to the High‐Performance Computing Act of 1991 (P.L. 102–194) passed in December 1991. Commonly known as the NREN and referred to as the “information highway,” this electronic network is expected to provide scientific, educational, and economic benefits for the United States and to serve as the basis for an all‐encompassing National Information Infrastructure available to all citizens. The idea of the NREN began in the late 1960s in the Department of Defense and its Defense Advanced Research Projects Agency (DARPA) with the development of ARPANet, the first packet‐switching network. This evolved into the Internet, or Interim NREN, after the National Science Foundation (NSF) linked its national supercomputing centers with the NSFNet. The NSFNet is to be the technological backbone for the NREN, which will continue the networking begun by the Internet. Initially, the NREN is intended to interconnect researchers and resources of research institutions, educational institutions, industry, and government in every state.

COVID-19 was indeed a global epidemic that revolutionized the way of life, especially health-care services. The way health care will be delivered will undergo a dramatic change in the future. The aim is to analyse the increasing usage of health care systems along with digital technology and IoT especially during pandemic.

This research paper deals with users’ perception and their recommendation status of IoT-based smart health-care monitoring devices based on their perception, experience and level of importance to enhance the quality of life. An effective artificial neural networking (ANN)-based predictive model is designed to classify the user’s perception of usage of IoT-based smart health-care monitoring wearables based on their experience and knowledge.

The model developed has 96.7% accuracy. Among the various predictors chosen as inputs for the model, the findings indicate that self-comfort and trusted data from the device are of high priority. The present study focused only on some common factors derived from previous studies.

Although the performance of the proposed system was noticed to be good, the size of the sample is also limited to a few responses. Implications for future research and practices are discussed.

This is a novel study that aims to develop an ANN model on analyzing the user’s perception of IoT-based smart health-care wearables with the effect of COVID-19 pandemic. This paper elaborates on the ongoing efforts to restart the health-care services for survivability in the new normal situations.

InfoMe is an innovative research program that explores and facilitates how ethnic minority youth help members of their social networks, especially elders, with everyday life situations through information and technology.

The project employs mixed methods, iteratively using Teen Design Days and a stratified random, classroom-based survey (n = 500) in six schools, with multiple community partners in King County, WA.

InfoMe inductively demonstrates how ethnic minority youth help others with situations of daily living through information and technology.

The findings are being used to develop InfoMe applications with the youth and InfoMe Train-the-Trainer workshops for professionals who work with youth.

The research is developing a model of how ethnic minority youth engage as information mediaries in different community settings, how individuals and communities benefit; and it is contributing to our general understanding of specific concepts related to the human information experience.

Federal attempts to stimulate technological innovation have been unsuccessful because of the application of an inappropriate policy framework that lacks conceptual and empirical knowledge of the process of technological innovation and fails to acknowledge the relationship between knowledge production, transfer, and use as equally important components of the process of knowledge diffusion. This article argues that the potential contributions of high‐speed computing and networking systems will be diminished unless empirically derived knowledge about the information‐seeking behavior of the members of the social system is incorporated into a new policy framework. Findings from the NASA/DoD Aerospace Knowledge Diffusion Research Project are presented in support of this assertion.

The purpose of this paper is to compare the effectiveness of different analytical approaches, namely artificial neural networks, logistic regression and support vector machines to assess the health of a strainer located at the suction side of the pump.

Data used for simulation included exemplars from clean (represented by datasets after cleaning the suction strainer) and faulty conditions (represented by datasets prior to cleaning the suction strainer). The same datasets were used for modeling in order to compare how different techniques perform when fed with the same information.

Principal component analysis‐based artificial neural networks proved to be better than other techniques in classifying maintenance datasets and predicting flow resistance from a clogged suction strainer.

The work highlights the comparative effectiveness of three predictive analytical techniques in classifying real plant data from a suction strainer. This will provide an opportunity for maintenance experts to see the effectiveness of different techniques as well as revealing valuable information about the relationship between the condition of the suction strainer and the overall performance of the pump.

This paper is an exploratory study of one approach to incorporating situational information into information retrieval systems, drawing on principles and methods of discourse linguistics. A tenet of discourse linguistics is that texts of a specific type possess a structure above the syntactic level, which follows conventions known to the people using such texts to communicate. In some cases, such as literature describing work done, the structure is closely related to situations, and may therefore be a useful representational vehicle for the present purpose. Abstracts of empirical research papers exhibit a well‐defined discourse‐level structure, which is revealed by lexical clues. Two methods of detecting the structure automatically are presented: (i) a Bayesian probabilistic analysis; and (ii) a neural network model. Both methods show promise in preliminary implementations. A study of users' oral problem statements indicates that they are not amenable to the same kind of processing. However, from in‐depth interviews with users and search intermediaries, the following conclusions are drawn: (i) the notion of a generic research script is meaningful to both users and intermediaries as a high‐level description of situation; (ii) a researcher's position in the script is a predictor of the relevance of documents; and (iii) currently, intermediaries can make very little use of situational information. The implications of these findings for system design are discussed, and a system structure presented to serve as a framework for future experimental work on the factors identified in this paper. The design calls for a dialogue with the user on his or her position in a research script and incorporates features permitting discourse‐level components of abstracts to be specified in search strategies.

Identifies key activities that network users can perform in order to use the network effectively. Offers recommended reading, from beginner to expert user status. Explains some commonly used terms (e.g. Turbo Gopher with Veronica!). Lists useful Internet resources.

The purpose of this paper is to identify the efficiency of vibration signals for fault diagnosis system of induction motors.

A fault diagnosis system for induction motors using vibration signals is designed based on pattern recognition. Genetic algorithm is used for feature reduction and neural network tuning.

The usage of genetic algorithm improves the system performance through selecting significant features and optimizing network structure. The efficiency of vibration signals is demonstrated.

Condition monitoring and fault diagnosis for induction motors is one of the main industry maintenance parts. Motors faults usually result in whole production line breakdown. In this paper, one fault diagnosis system is proposed for induction motors based on feature recognition through combination of feature extraction, genetic algorithm and neural network techniques. From the paper, one can learn practically the whole procedure of feature‐based fault diagnosis system and the efficiency of GA and vibration signals for motor fault diagnosis. One real test has been done to validate the system performance. The results indicate that this system is promising for the real application in industry.

The use of genetic algorithm for feature selection and neural network tuning; the choice of vibration analysis for fault diagnosis of induction motor.

In recent years there has been growing discussion in the library community regarding the civic role of the public library. The discussion is rooted in a deep-seated professional commitment to the value of the public library as an institution of democratic society. As a recent president of the American Library Association, Nancy Kranich, wrote in 2001, “Libraries serve the most fundamental ideals of our society as uniquely democratic institutions. As far back as the nineteenth century, libraries were hailed as institutions that schooled citizens in the conduct of democratic life.” (p. vi).