A Low Cost and Low Power Consumption Automatic Water Meter Reading System: Hardware Investigation and Network Design

This paper presents a low power consumption and low cost automatic data collection network for water meter application. Based on transmission performance and power consumption, several low cost sub-GHz wireless transceivers are analyzed and compared, and consequently a suitable hardware is chosen. The associated network protocol stack is also examined. To construct the automatic collecting data mechanism, we consider a cluster based wireless sensor network (WSN) where routers and a GPRS gateway are used to link each cluster to a data collection center. Advantages of this proposed configuration are the simple implementation, low cost and low power consumption. By using the Monte Carlo simulation technique, packet delivery ratio and power consumption for different topologies are investigated. Based on obtained results, the optimum network topology for automatic water meter reading in a typical urban environment is finally proposed

Performance improvement of recycled aggregate concrete using fly ash and Portland blast-furnace slag cement

Several countermeasures are implemented in the manufacture of construction materials to avoid negative impacts on the environment. Using concrete debris in construction demolition waste as a recycled aggregate to make recycled aggregate concrete (RAC) is one of the countermeasures. Further, the use of recycled construction waste and industrial by-products, such as fly ash and ground granulated blast-furnace slag, in concrete not only promotes resource circulation and reduces CO2 emissions in the cement manufacturing process but also improves concrete performance. In this study, low-quality recycled aggregate was mixed with normal aggregate at various replacement ratios to produce RAC. Additionally, ground granulated blast-furnace slag in Portland blast-furnace slag cement and fly ash were introduced in concrete to improve concrete performance. Applying the relative quality index method for performance evaluation, it was possible to design a mix proportion of RAC that achieved the requisite performance through the application of Portland blast-furnace slagcement and fly ashas a cement substitute or as a fine aggregate substitute

Recommendation Systems: A Systematic Review

This article presents a comprehensive and objective systematic review of existing research on recommendation systems with regards to core theory, latest studies, various applications, current attitudes, and potential future applications. The research is mainly based on exploring professional peer-reviewed studies and articles and using their abstracts to create a comprehensive and unbiased review of existing research. The following search terms were used to identify articles and studies for the research: recommendation systems; recommender systems; core theory of recommender systems; current attitudes towards recommendation systems; latest studies on recommendation systems; applications of recommendation systems; potential studies on recommendation systems; and future potential applications of recommendation systems. The research also used the advanced search filter to locate recent studies for comparison by limiting the search by year to find studies published from 2021 onwards. Most literature on this area highlights the importance of recommendation systems in almost all aspects of modern life. Specifically, recommendation systems have become critical components in business, health care, education, marketing, and social networking domains. Additionally, most studies identified reinforcement of learning and deep learning techniques as significant developments in the field. These techniques form the backbone of most modern recommendation systems. The primary concern that could hinder further evolution systems is their consequent filter bubble effects which many studies showed to be problematic. Healthcare is a central area that shows tremendous potential for these systems. Although recommender systems have been implemented in this domain, there remains a lot of untapped potential that, if unleashed, could revolutionize medicine and healthcare. But the problems facing these systems have to be tackled first to establish trust. Keywords: Recommendation systems, Recommender systems, Deep learning, Reinforcement learning DOI: 10.7176/CEIS/13-4-04 Publication date:August 31st 202