Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.8
3.3
Journals
Sustainability
Special Issues
Smart Building: Eco-friendly Technology
sustainability-logo
Submit to Sustainability Review for Sustainability Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Smart Building: Eco-friendly Technology

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Environmental Sustainability and Applications".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 3165

Special Issue Editors

Dr. Wentao Wu

E-Mail Website
Guest Editor
Department of Civil and Architectural Engineering, Tennessee State University, Nashville, TN 37209, USA
Interests: smart building; renewable energy fueled thermal network; healthy urban and built environment
Dr. Zhiwen Luo

E-Mail Website
Guest Editor
School of Construction Management and Engineering, University of Reading, Reading, UK
Interests: smart building; innovative and sustainable technologies; building and urban sustainability
Dr. Jingru Benner

E-Mail Website
Guest Editor
Department of Mechanical Engineering, Western New England University, Springfield, MA 01119, USA
Interests: smart building; heat transfer; computational fluid dynamics

Special Issue Information

Dear Colleagues,

A smart building must be able to create healthy built environment and stabilize faster decarbonization of its energy system using eco-friendly technologies. It is widely accepted that sensor deployment, the Internet of Things, big data analytics, and deep learning algorithms are fundamental technologies for smart buildings.

This Special Issue focuses on eco-friendly technologies for smart buildings and addresses the abovementioned questions. The scope of this Special Issue covers but is not limited to the following topics:

Deploying wireless sensor networks, big data, and machine learning algorithms to advance smart buildings into an integrated cyber–physical system;

Transforming smart buildings into health-cognitive environment to assist in preventing pandemics such as COVID-19;

Integrating digital twin technology into smart buildings for whole-life-cycle performance prediction.

I sincerely invite researchers to contribute to this Special Issue of Sustainability–Smart Building: Eco-friendly Technology by submitting comprehensive reviews or original research articles.

Prof. Dr. Wentao Wu

Prof. Dr. Vincent Luo

Prof. Dr. Jingru Benner


Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Smart building
  • Sensor network
  • Big data
  • Internet of Things
  • Public health
  • Digital twin

Published Papers (1 paper)

Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

19 pages, 5492 KiB  
Article
Prediction of the Hypertension Risk of the Elderly in Built Environments Based on the LSTM Deep Learning and Bayesian Fitting Method
by Rui Zhu, Yang Lv, Zhimeng Wang and Xi Chen
Sustainability 2021, 13(10), 5724; https://doi.org/10.3390/su13105724 - 20 May 2021
Cited by 5 | Viewed by 2321
Abstract
Hypertension has become the greatest risk factor for death in elderly populations. As factors influencing cardiovascular disease, indoor environmental parameters pose potential risks for older adults. In this study, elderly residents in Dalian (Liaoning Province, China) urban dwellings were selected as the research [...] Read more.
Hypertension has become the greatest risk factor for death in elderly populations. As factors influencing cardiovascular disease, indoor environmental parameters pose potential risks for older adults. In this study, elderly residents in Dalian (Liaoning Province, China) urban dwellings were selected as the research subjects, and the environmental parameters of the dwellings’ main activity rooms and the blood pressure parameters of the older adults were measured. Based on the Long Short-Term Memory (LSTM) deep learning algorithm and Bayesian fitting method, a hypertension disease model was established using the long-term environmental parameters to predict the hypertension risk of older adults in their building’s environment. The results showed that temperature, humidity, and some air quality parameters had an impact on blood pressure under single environmental factor, and the comprehensive environmental risks of high systolic blood pressure, high diastolic blood pressure, and high blood pressure were 16.44%, 0%, and 16.44% for the male elderly and 14.11%, 7.14%, and 17.55% for the female elderly, respectively. By comparing the results for the blood pressure measurement and prediction, it can be observed that the risk error of hypertension obtained by the algorithm maintains the variables’ relationship, and the result of the algorithm is reliable in this period. This technology can provide a basis for measuring environmental parameters and will be conducive to the development of an ecological smart building environment. Full article
(This article belongs to the Special Issue Smart Building: Eco-friendly Technology)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-1
Back to TopTop