scholarly journals Special Issue “Analysis for Power Quality Monitoring”

Energies ◽  
2020 ◽  
Vol 13 (3) ◽  
pp. 514
Author(s):  
Juan-José González de-la-Rosa ◽  
Manuel Pérez-Donsión
Keyword(s):  
Power Quality ◽  
Smart Cities ◽  
Real Data ◽  
Measurement Methods ◽  
Electrical Network ◽  
Economic Losses ◽  
Human Beings ◽  
Special Issue ◽  
Technological Advances ◽  
Industry Practice

We are immersed in the so-called digital energy network, continuously introducing new technological advances for a better way of life. As a consequence, numerous emerging words are relevant to this point: Internet of Things (IoT), big data, smart cities, smart grid, industry 4.0, etc. To achieve this formidable goal, systems should work more efficiently, a fact that inevitably leads to power quality (PQ) assurance. Apart from its economic losses, a bad PQ implies serious risks for machines and, consequently, for people. Many researchers are endeavouring to develop new analysis techniques, instruments, measurement methods, and new indices and norms that match and fulfil the requirements regarding the current operation of the electrical network. This book, and its associated Special Issue, offer a compilation of some of the recent advances in this field. The chapters range from computing to technological implementation, going through event detection strategies and new indices and measurement methods that contribute significantly to the advance of PQ analysis and regulation. Experiments have been developed within the frameworks of research units and projects and deal with real data from industry practice and public buildings. Human beings have an unavoidable commitment to sustainability, which implies adapting PQ monitoring techniques to our dynamic world, defining a digital and smart concept of quality for electricity.

scholarly journals Sustainable Lighting and Light Pollution: A Critical Issue for the Present Generation, a Challenge to the Future

2020 ◽  
Vol 12 (11) ◽  
pp. 4552 ◽  
Author(s):  
Georges Zissis
Keyword(s):  
Human Health ◽  
Critical Issue ◽  
Night Vision ◽  
Stray Light ◽  
Light Pollution ◽  
Human Beings ◽  
Special Issue ◽  
Artificial Lighting ◽  
Technological Advances ◽  
High Level

Human beings’ poor night vision and primitive fear of the dark are reflected in an imperative need to use artificial light to illuminate their environment. Outdoor illumination undoubtedly contributes to the enhancement of practical opportunities for social and economic developments. Considered as a necessity, a means of security, and an attraction or valorization, city lighting growth has been literally exponential in the last half century. Beyond the financial and energy resources that it absorbs, the artificial lighting of urban spaces overflows its objective by polluting our nights to the point that, in our modern megacities, the stars disappear. Apart from the fact that stars are no longer visible, the scientific community is increasingly interested in the direct and indirect impacts of artificial lighting on biodiversity. In parallel, some studies have shown recently that stray light may have direct or indirect effects on human health and mood. The scope of this Special Issue, dedicated to the memory of Prof. Abraham Haim and Dr. Thomas Posch, is to put together a series of high-level papers treating light pollution in a holistic manner that goes from technological advances to policies, passing through impacts on biotopes and human health. Beyond its evident scientific interest, this Special Issue is also contributing to awareness raising, aimed at decision- and policy-makers.

scholarly journals Introduction to special issue about election reporting: Why journalism (still) matters

Journalism ◽  
2019 ◽  
Vol 20 (8) ◽  
pp. 985-993 ◽  
Author(s):  
Stephen Cushion ◽  
Daniel Jackson
Keyword(s):  
Social Media ◽  
Political Parties ◽  
Political Communication ◽  
News Media ◽  
Election Campaigns ◽  
Special Issue ◽  
Technological Advances ◽  
Campaign Coverage ◽  
Election Coverage ◽  
Social Media Platforms

This introduction unpacks the eight articles that make up this Journalism special issue about election reporting. Taken together, the articles ask: How has election reporting evolved over the last century across different media? Has the relationship between journalists and candidates changed in the digital age of campaigning? How do contemporary news values influence campaign coverage? Which voices – politicians, say or journalists – are most prominent? How far do citizens inform election coverage? How is public opinion articulated in the age of social media? Are sites such as Twitter developing new and distinctive election agendas? In what ways does social media interact with legacy media? How well have scholars researched and theorised election reporting cross-nationally? How can research agendas be enhanced? Overall, we argue this Special Issue demonstrates the continued strength of news media during election campaigns. This is in spite of social media platforms increasingly disrupting and recasting the agenda setting power of legacy media, not least by political parties and candidates who are relying more heavily on sites such as Facebook, Instagram and Twitter to campaign. But while debates in recent years have centred on the technological advances in political communication and the associated role of social media platforms during election campaigns (e.g. microtargeting voters, spreading disinformation/misinformation and allowing candidates to bypass media to campaign), our collection of studies signal the enduring influence professional journalists play in selecting and framing of news. Put more simply, how elections are reported still profoundly matters in spite of political parties’ and candidates’ more sophisticated use of digital campaigning.

scholarly journals Predicting the influence of the non-sinusoidal network mode on power transformers

2019 ◽  
Vol 114 ◽  
pp. 04005
Author(s):  
Ngo Van Cuong ◽  
Lidiia I. Kovernikova
Keyword(s):  
Service Life ◽  
Electric Power ◽  
Power Quality ◽  
Electrical Equipment ◽  
Operating Conditions ◽  
Transformer Oil ◽  
Power Transformers ◽  
Electrical Network ◽  
Electrical Networks ◽  
Current Harmonics

The parameters of electrical network modes often do not meet the requirements of Russian GOST 32144-2013 and the guidelines of Vietnam. In the actual operating conditions while there is the non-sinusoidal mode in electrical networks voltage and current harmonics are present. Harmonics result in overheating and damage of power transformers since they cause additional active power losses. Additional losses lead to the additional heat release, accelerating the process of insulating paper, transformer oil and magnetic structure deterioration consequently shortening the service life of a power transformer. In this regard there arises a need to develop certain scientific methods that would help demonstrate that low power quality, for instance could lead to a decrease in the electrical equipment service life. Currently we see a development of automated systems for continuous monitoring of power quality indices and mode parameters of electrical networks. These systems could be supplemented by characteristics calculating programs that give out a warning upon detection of the adverse influence of voltage and current harmonics on various electrical equipment of both electric power providers and electric power consumers. A software program presented in the article may be used to predict the influence of voltage and current harmonics on power transformers.

scholarly journals A Predictive Vehicle Ride Sharing Recommendation System for Smart Cities Commuting

Smart Cities ◽  
2021 ◽  
Vol 4 (1) ◽  
pp. 177-191
Author(s):  
Theodoros Anagnostopoulos
Keyword(s):  
Recommendation System ◽  
Smart Cities ◽  
Urban Pollution ◽  
Real Data ◽  
Movement Behavior ◽  
Daily Movement ◽  
Passenger Transportation ◽  
Ride Sharing ◽  
Mobility Behavior ◽  
User Movement

Smart Cities (or Cities 2.0) are an evolution in citizen habitation. In such cities, transport commuting is changing rapidly with the proliferation of contemporary vehicular technology. New models of vehicle ride sharing systems are changing the way citizens commute in their daily movement schedule. The use of a private vehicle per single passenger transportation is no longer viable in sustainable Smart Cities (SC) because of the vehicles’ resource allocation and urban pollution. The current research on car ride sharing systems is widely expanding in a range of contemporary technologies, however, without covering a multidisciplinary approach. In this paper, the focus is on performing a multidisciplinary research on car riding systems taking into consideration personalized user mobility behavior by providing next destination prediction as well as a recommender system based on riders’ personalized information. Specifically, it proposes a predictive vehicle ride sharing system for commuting, which has impact on the SC green ecosystem. The adopted system also provides a recommendation to citizens to select the persons they would like to commute with. An Artificial Intelligence (AI)-enabled weighted pattern matching model is used to assess user movement behavior in SC and provide the best predicted recommendation list of commuting users. Citizens are then able to engage a current trip to next destination with the more suitable user provided by the list. An experimented is conducted with real data from the municipality of New Philadelphia, in SC of Athens, Greece, to implement the proposed system and observe certain user movement behavior. The results are promising for the incorporation of the adopted system to other SCs.

scholarly journals Canadian smart cities: a case for the circular economy in the age of "smart" innovation

2021 ◽  
Author(s):  
Vickey Simovic
Keyword(s):  
Key Words ◽  
Circular Economy ◽  
Smart City ◽  
Technology Innovation ◽  
Smart Cities ◽  
Digital Tools ◽  
Digital Innovation ◽  
Technological Advances ◽  
A Priority ◽  
Further Development

The Canadian Smart Cities Challenge enabled municipalities across the country to reflect on how smart city technology can be used to solve their unique community challenges, embrace the possibility of impactful projects, create collaborations, and create a suite of digital tools. This paper analyses whether governments can be catalysts in adopting circular economy thinking in the age of digital innovation. In reviewing the SCC applications, five proposal submissions were analysed in depth against a circular economy framework. Recommendations for further development in smart city thinking centre around future Smart Cities Challenges, and building circular assumptions into the challenge questions, whereby ensuring circular principles are a priority for municipalities as they continue to grow and adapt to smart city technological advances. Key words: Smart Cities Challenge, circular economy, smart city technology, innovation, sustainable,​ ​reuse, sharing, remanufacturing and repurposing

scholarly journals Prediction of Solid Garbage Waste Generation in Smart Cities using Naive Bayes Algorithm

2019 ◽  
Vol 9 (2S) ◽  
pp. 53-56
Keyword(s):  
Naive Bayes ◽  
Learning Algorithm ◽  
Smart Cities ◽  
Confusion Matrix ◽  
Daily Basis ◽  
Naïve Bayes ◽  
Human Beings ◽  
Waste Generation ◽  
Future Prediction ◽  
Bayes Algorithm

Smart cities which are becoming overcrowded today are making human beings life miserable and prone to more challenges on daily basis. Overcrowded is leading to vast generation of wastes contributing to air pollution and in turn is affecting health causing various diseases. Even though various measures are taken to recycle wastes, the rate at which it is being produced is becoming higher and higher. This paper deals with prediction of waste generation using Naïve Bayes machine learning algorithm(Classifier) based on the statistics of previous waste datasets. The datasets used for the future prediction are obtained from reliable sources. The implementation of the algorithm is done in Pyspark using Anaconda Jupyter. The performance of the classifier on the datasets is analyzed with confusion matrix and accuracy metric is used to rate the efficiency of the classifier. The accuracy obtained indicates that algorithm can be effectively used for real time prediction and it gives more accurate results for huge input datasets based on independence assumption.

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