scholarly journals Opportunities For Machine Learning in District Heating

2021 ◽  
Vol 11 (13) ◽  
pp. 6112
Author(s):  
Gideon Mbiydzenyuy ◽  
Sławomir Nowaczyk ◽  
Håkan Knutsson ◽  
Dirk Vanhoudt ◽  
Jens Brage ◽  
...  
Keyword(s):  
Machine Learning ◽  
New Technologies ◽  
State Of The Art ◽  
District Heating ◽  
Research Papers ◽  
Research Directions ◽  
Road Map ◽  
The Impact ◽  
Published Research ◽  
Dh Domain

The district heating (DH) industry is facing an important transformation towards more efficient networks that utilise significantly lower water temperatures to distribute the heat. This change requires taking advantage of new technologies, and Machine Learning (ML) is a popular direction. In the last decade, we have witnessed an extreme growth in the number of published research papers that focus on applying ML techniques to the DH domain. However, based on our experience in the field, and an extensive review of the state-of-the-art, we perceive a mismatch between the most popular research directions, such as forecasting, and the challenges faced by the DH industry. In this work, we present our findings, explain and demonstrate the key gaps between the two communities and suggest a road-map ahead towards increasing the impact of ML research in the DH industry.

Publishing Productivity in Sport Psychology 1970–2000: An Exploratory Examination of the Lotka-Price Law

2003 ◽  
Vol 25 (4) ◽  
pp. 477-483 ◽  
Author(s):  
Joseph Baker ◽  
Jennifer Robertson-Wilson ◽  
Whitney Sedgwick
Keyword(s):  
Sport Psychology ◽  
Scientific Productivity ◽  
Eminent Scientist ◽  
Research Papers ◽  
Limited Support ◽  
The Impact ◽  
Published Research ◽  
Shed Light

The current study examined whether the distribution of published research papers in the field of sport psychology followed the Lotka-Price Law of scientific productivity. All authors who had published articles in five sport psychology journals from 1970 to 2000 were considered. The impact of those authors was determined by the total number of published papers in all journals. Results provided limited support for the Lotka-Price Law; however, it appeared that the field of sport psychology was less elitist than other fields. Although these findings suggest that productivity in this field is similar to that in other fields of science, more research is needed to shed light on the role of the eminent scientist and the average researcher in the advancement of knowledge in sport psychology.

scholarly journals How New Technologies Can Improve Prediction, Assessment, and Intervention in Obsessive-Compulsive Disorder (e-OCD): Review

10.2196/11643 ◽  
2019 ◽  
Vol 6 (12) ◽  
pp. e11643 ◽  
Author(s):  
Florian Ferreri ◽  
Alexis Bourla ◽  
Charles-Siegfried Peretti ◽  
Tomoyuki Segawa ◽  
Nemat Jaafari ◽  
...  
Keyword(s):  
Machine Learning ◽  
Virtual Reality ◽  
Obsessive Compulsive Disorder ◽  
New Technologies ◽  
Assessment Tools ◽  
Clinical Practices ◽  
Obsessive Compulsive ◽  
Compulsive Disorder ◽  
Pubmed Database ◽  
The Impact

Background New technologies are set to profoundly change the way we understand and manage psychiatric disorders, including obsessive-compulsive disorder (OCD). Developments in imaging and biomarkers, along with medical informatics, may well allow for better assessments and interventions in the future. Recent advances in the concept of digital phenotype, which involves using computerized measurement tools to capture the characteristics of a given psychiatric disorder, is one paradigmatic example. Objective The impact of new technologies on health professionals’ practice in OCD care remains to be determined. Recent developments could disrupt not just their clinical practices, but also their beliefs, ethics, and representations, even going so far as to question their professional culture. This study aimed to conduct an extensive review of new technologies in OCD. Methods We conducted the review by looking for titles in the PubMed database up to December 2017 that contained the following terms: [Obsessive] AND [Smartphone] OR [phone] OR [Internet] OR [Device] OR [Wearable] OR [Mobile] OR [Machine learning] OR [Artificial] OR [Biofeedback] OR [Neurofeedback] OR [Momentary] OR [Computerized] OR [Heart rate variability] OR [actigraphy] OR [actimetry] OR [digital] OR [virtual reality] OR [Tele] OR [video]. Results We analyzed 364 articles, of which 62 were included. Our review was divided into 3 parts: prediction, assessment (including diagnosis, screening, and monitoring), and intervention. Conclusions The review showed that the place of connected objects, machine learning, and remote monitoring has yet to be defined in OCD. Smartphone assessment apps and the Web Screening Questionnaire demonstrated good sensitivity and adequate specificity for detecting OCD symptoms when compared with a full-length structured clinical interview. The ecological momentary assessment procedure may also represent a worthy addition to the current suite of assessment tools. In the field of intervention, CBT supported by smartphone, internet, or computer may not be more effective than that delivered by a qualified practitioner, but it is easy to use, well accepted by patients, reproducible, and cost-effective. Finally, new technologies are enabling the development of new therapies, including biofeedback and virtual reality, which focus on the learning of coping skills. For them to be used, these tools must be properly explained and tailored to individual physician and patient profiles.

scholarly journals A technologist view of the next decade evolution of the librarian work environment

2018 ◽  
Vol 186 ◽  
pp. 09004
Author(s):  
André Schaaff ◽  
Marc Wenger
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Work Environment ◽  
New Technologies ◽  
State Of The Art ◽  
Working Environment ◽  
Online Resources ◽  
Mining Machine ◽  
Daily Work

The work environment has deeply evolved in recent decades with the generalisation of IT in terms of hardware, online resources and software. Librarians do not escape this movement and their working environment is becoming essentially digital (databases, online publications, Wikis, specialised software, etc.). With the Big Data era, new tools will be available, implementing artificial intelligence, text mining, machine learning, etc. Most of these technologies already exist but they will become widespread and strongly impact our ways of working. The development of social networks that are "business" oriented will also have an increasing influence. In this context, it is interesting to reflect on how the work environment of librarians will evolve. Maintaining interest in the daily work is fundamental and over-automation is not desirable. It is imperative to keep the human-driven factor. We draw on state of the art new technologies which impact their work, and initiate a discussion about how to integrate them while preserving their expertise.

scholarly journals Impact Tech Startups: A Conceptual Framework, Machine-Learning-Based Methodology and Future Research Directions

2021 ◽  
Vol 13 (18) ◽  
pp. 10048
Author(s):  
Benjamin Gidron ◽  
Yael Israel-Cohen ◽  
Kfir Bar ◽  
Dalia Silberstein ◽  
Michael Lustig ◽  
...  
Keyword(s):  
Machine Learning ◽  
Conceptual Framework ◽  
Impact Category ◽  
Private Investment ◽  
Social Enterprises ◽  
Future Research ◽  
Research Directions ◽  
For Profit ◽  
Future Research Directions ◽  
The Impact

The Impact Tech Startup (ITS) is a new, rapidly developing type of organizational category. Based on an entrepreneurial approach and technological foundations, ITSs adopt innovative strategies to tackle a variety of social and environmental challenges within a for-profit framework and are usually backed by private investment. This new organizational category is thus far not discussed in the academic literature. The paper first provides a conceptual framework for studying this organizational category, as a combination of aspects of social enterprises and startup businesses. It then proposes a machine learning (ML)-based algorithm to identify ITSs within startup databases. The UN’s Sustainable Development Goals (SDGs) are used as a referential framework for characterizing ITSs, with indicators relating to those 17 goals that qualify a startup for inclusion in the impact category. The paper concludes by discussing future research directions in studying ITSs as a distinct organizational category through the usage of the ML methodology.

scholarly journals A Survey of the Application of Artifical Intellegence on COVID-19 Diagnosis and Prediction

2021 ◽  
Vol 11 (6) ◽  
pp. 7824-7835
Author(s):  
H. Alalawi ◽  
M. Alsuwat ◽  
H. Alhakami
Keyword(s):  
Machine Learning ◽  
State Of The Art ◽  
Future Research ◽  
Classification Algorithms ◽  
Classification Methods ◽  
Research Directions ◽  
The World ◽  
Modern Methods ◽  
Future Research Directions ◽  
Class Labels

The importance of classification algorithms has increased in recent years. Classification is a branch of supervised learning with the goal of predicting class labels categorical of new cases. Additionally, with Coronavirus (COVID-19) propagation since 2019, the world still faces a great challenge in defeating COVID-19 even with modern methods and technologies. This paper gives an overview of classification algorithms to provide the readers with an understanding of the concept of the state-of-the-art classification algorithms and their applications used in the COVID-19 diagnosis and detection. It also describes some of the research published on classification algorithms, the existing gaps in the research, and future research directions. This article encourages both academics and machine learning learners to further strengthen the basis of classification methods.

Adaptive Interaction for Mass Customisation

2012 ◽  
pp. 2101-2116
Author(s):  
Gulden Uchyigit
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Ubiquitous Computing ◽  
Mass Customization ◽  
New Technologies ◽  
State Of The Art ◽  
Computing Technologies ◽  
One To One ◽  
Marketing Service ◽  
Innovative Techniques

The popularisation of mass customization and the need for integration of the user needs into the design, production and marketing phases has called for more innovative methods to be introduced into this area. At present the continuous growth of the world wide web and its rapid integration into people’s everyday lives and the popularisation of new technologies such as ubiquitous computing making possible the computing everywhere paradigm, offers a more desirable alternative for vendors in reaching their customers using more innovative techniques in an attempt to provide each customer with a one-to-one design, manufacturing and marketing service. The integration of ubiquitous computing technologies with machine learning and data mining techniques, which has been popular in personalization techniques, will serve to bring about innovative changes in this area. In future years this may revolutionise the way in which vendors can reach their customers offering every customer a tailored one-to-one service from design, to manufacturing, to delivery. This chapter will present the state of the art techniques to enable the combination of machine learning, data mining and ubiquitous computing technologies which will serve to provide innovative techniques applications and user interfaces for mass customization systems. This is currently a field of intense research and development activity and some technologies are already on the path to practical application. This chapter will present a state of the art survey of these technologies and their applications.

Sarcopenia: Beyond Muscle Atrophy and into the New Frontiers of Opportunistic Imaging, Precision Medicine, and Machine Learning

2018 ◽  
Vol 22 (03) ◽  
pp. 307-322 ◽  
Author(s):  
Leon Lenchik ◽  
Robert Boutin
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Precision Medicine ◽  
Muscle Atrophy ◽  
State Of The Art ◽  
The State ◽  
Future Directions ◽  
Common Diagnosis ◽  
The Impact

AbstractAs populations continue to age worldwide, the impact of sarcopenia on public health will continue to grow. The clinically relevant and increasingly common diagnosis of sarcopenia is at the confluence of three tectonic shifts in medicine: opportunistic imaging, precision medicine, and machine learning. This review focuses on the state-of-the-art imaging of sarcopenia and provides context for such imaging by discussing the epidemiology, pathophysiology, consequences, and future directions in the field of sarcopenia.

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