9,784 research outputs found
Promoting Energy-Conservation Behavior in a Smart Home App: Kano Analysis of User Satisfaction with Feedback Nudges
Smart home technologies and apps are on a rise. This allows to implement digital nudging elements to foster energy-conservation behavior and, thus, contribute to mitigating climate change. Digital nudging via feedback can be effective in improving energy-conservation behavior, as substantial prior research has shown. However, the investigation of users’ preferences concerning feedback nudges is missing. This lack of knowledge is crucial, as user satisfaction influences their continuous app usage, a precondition for achieving positive effects. To close this gap, we perform a structured literature review, categorize the feedback nudge features from extant research, and conduct an online survey. Based on survey data and the Kano model, we analyze the effect of feedback nudge features on user satisfaction. Our study complements the traditional focus on the effectiveness of these nudges with a perspective on user satisfaction. The combination of both perspectives suggests which feedback nudge features should be considered for implementation
Data fusion strategies for energy efficiency in buildings: Overview, challenges and novel orientations
Recently, tremendous interest has been devoted to develop data fusion
strategies for energy efficiency in buildings, where various kinds of
information can be processed. However, applying the appropriate data fusion
strategy to design an efficient energy efficiency system is not
straightforward; it requires a priori knowledge of existing fusion strategies,
their applications and their properties. To this regard, seeking to provide the
energy research community with a better understanding of data fusion strategies
in building energy saving systems, their principles, advantages, and potential
applications, this paper proposes an extensive survey of existing data fusion
mechanisms deployed to reduce excessive consumption and promote sustainability.
We investigate their conceptualizations, advantages, challenges and drawbacks,
as well as performing a taxonomy of existing data fusion strategies and other
contributing factors. Following, a comprehensive comparison of the
state-of-the-art data fusion based energy efficiency frameworks is conducted
using various parameters, including data fusion level, data fusion techniques,
behavioral change influencer, behavioral change incentive, recorded data,
platform architecture, IoT technology and application scenario. Moreover, a
novel method for electrical appliance identification is proposed based on the
fusion of 2D local texture descriptors, where 1D power signals are transformed
into 2D space and treated as images. The empirical evaluation, conducted on
three real datasets, shows promising performance, in which up to 99.68%
accuracy and 99.52% F1 score have been attained. In addition, various open
research challenges and future orientations to improve data fusion based energy
efficiency ecosystems are explored
Video Chat Application for Facebook
This project is mainly written for the facebook users. In today’s world, there are many social networking sites available. Among those social networking web sites, facebook is widely used web site. Like all other social networking web sites, Facebook also provides many features to attract more and more users. But it lacks in providing the most important feature of social networking, i.e. video chat. I explore the different options and requirements needed to build the video chat application. I have also described the integration of the application with the facebook
A survey of recommender systems for energy efficiency in buildings: Principles, challenges and prospects
Recommender systems have significantly developed in recent years in parallel
with the witnessed advancements in both internet of things (IoT) and artificial
intelligence (AI) technologies. Accordingly, as a consequence of IoT and AI,
multiple forms of data are incorporated in these systems, e.g. social,
implicit, local and personal information, which can help in improving
recommender systems' performance and widen their applicability to traverse
different disciplines. On the other side, energy efficiency in the building
sector is becoming a hot research topic, in which recommender systems play a
major role by promoting energy saving behavior and reducing carbon emissions.
However, the deployment of the recommendation frameworks in buildings still
needs more investigations to identify the current challenges and issues, where
their solutions are the keys to enable the pervasiveness of research findings,
and therefore, ensure a large-scale adoption of this technology. Accordingly,
this paper presents, to the best of the authors' knowledge, the first timely
and comprehensive reference for energy-efficiency recommendation systems
through (i) surveying existing recommender systems for energy saving in
buildings; (ii) discussing their evolution; (iii) providing an original
taxonomy of these systems based on specified criteria, including the nature of
the recommender engine, its objective, computing platforms, evaluation metrics
and incentive measures; and (iv) conducting an in-depth, critical analysis to
identify their limitations and unsolved issues. The derived challenges and
areas of future implementation could effectively guide the energy research
community to improve the energy-efficiency in buildings and reduce the cost of
developed recommender systems-based solutions.Comment: 35 pages, 11 figures, 1 tabl
Medical data processing and analysis for remote health and activities monitoring
Recent developments in sensor technology, wearable computing, Internet of Things (IoT), and wireless communication have given rise to research in ubiquitous healthcare and remote monitoring of human\u2019s health and activities. Health monitoring systems involve processing and analysis of data retrieved from smartphones, smart watches, smart bracelets, as well as various sensors and wearable devices. Such systems enable continuous monitoring of patients psychological and health conditions by sensing and transmitting measurements such as heart rate, electrocardiogram, body temperature, respiratory rate, chest sounds, or blood pressure. Pervasive healthcare, as a relevant application domain in this context, aims at revolutionizing the delivery of medical services through a medical assistive environment and facilitates the independent living of patients. In this chapter, we discuss (1) data collection, fusion, ownership and privacy issues; (2) models, technologies and solutions for medical data processing and analysis; (3) big medical data analytics for remote health monitoring; (4) research challenges and opportunities in medical data analytics; (5) examples of case studies and practical solutions
EnCOMPASS - An integrative approach to behavioural change for energy saving
This paper presents the research objectives of the enCOMPASS project, which aims at implementing and validating an integrated socio-technical approach to behavioural change for energy saving. To this end, innovative user-friendly digital tools will be developed to 1) make energy data consumption available and understandable for different types of users and stakeholders (household residents, office employees, school pupils, building managers, utilities, ICT providers) and to 2) empower them to collaborate in order to achieve energy savings and manage their energy needs in efficient, cost-effective and comfort-preserving ways. The project will demonstrate how this can be achieved with a novel approach that integrates user-centered visualisation of energy data from smart sensors and user-generated information with context-aware collaborative recommendations for energy saving, intelligent control and adaptive gamified incentives enabling effective and sustained behavioural change
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The Global academic research organization network: Data sharing to cure diseases and enable learning health systems.
Introduction:Global data sharing is essential. This is the premise of the Academic Research Organization (ARO) Council, which was initiated in Japan in 2013 and has since been expanding throughout Asia and into Europe and the United States. The volume of data is growing exponentially, providing not only challenges but also the clear opportunity to understand and treat diseases in ways not previously considered. Harnessing the knowledge within the data in a successful way can provide researchers and clinicians with new ideas for therapies while avoiding repeats of failed experiments. This knowledge transfer from research into clinical care is at the heart of a learning health system. Methods:The ARO Council wishes to form a worldwide complementary system for the benefit of all patients and investigators, catalyzing more efficient and innovative medical research processes. Thus, they have organized Global ARO Network Workshops to bring interested parties together, focusing on the aspects necessary to make such a global effort successful. One such workshop was held in Austin, Texas, in November 2017. Representatives from Japan, Taiwan, Singapore, Europe, and the United States reported on their efforts to encourage data sharing and to use research to inform care through learning health systems. Results:This experience report summarizes presentations and discussions at the Global ARO Network Workshop held in November 2017 in Austin, TX, with representatives from Japan, Korea, Singapore, Taiwan, Europe, and the United States. Themes and recommendations to progress their efforts are explored. Standardization and harmonization are at the heart of these discussions to enable data sharing. In addition, the transformation of clinical research processes through disruptive innovation, while ensuring integrity and ethics, will be key to achieving the ARO Council goal to overcome diseases such that people not only live longer but also are healthier and happier as they age. Conclusions:The achievement of global learning health systems will require further exploration, consensus-building, funding aligned with incentives for data sharing, standardization, harmonization, and actions that support global interests for the benefit of patients
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