The perspective of the use of artificial intelligence in the formation and control of modern and future urban areas

2021 ◽  
Vol 2 (80) ◽  
pp. 309-313
Author(s):  
D.N. Abilev ◽  
◽  
G.S. Dzhakipova ◽  
Keyword(s):  
Artificial Intelligence ◽  
Everyday Life ◽  
Urban Areas ◽  
Perfect Model ◽  
Routine Tasks ◽  
And Control

With the development of technologies and their introduction into everyday life, there is also the possibility of their professional use in various industries and structures. The AI system is a virtual assistant, whose development every year can create a perfect model of urban control and a virtual assistant in helping people with many routine tasks. This article describes the prospects for using AI and examples of its development.

Work and Technological Change

2020 ◽  
Author(s):  
Stephen R. Barley
Keyword(s):  
Artificial Intelligence ◽  
Historical Context ◽  
Energy Transformation ◽  
History Of ◽  
Work And Organizations ◽  
Role Based ◽  
Control Technologies ◽  
And Control ◽  
Work And Employment

The four chapters of this book summarize the results of thirty-five years dedicated to studying how technologies change work and organizations. The first chapter places current developments in artificial intelligence into the historical context of previous technological revolutions by drawing on William Faunce’s argument that the history of technology is one of progressive automation of the four components of any production system: energy, transformation, and transfer and control technologies. The second chapter lays out a role-based theory of how technologies occasion changes in organizations. The third chapter tackles the issue of how to conceptualize a more thorough approach to assessing how intelligent technologies, such as artificial intelligence, can shape work and employment. The fourth chapter discusses what has been learned over the years about the fears that arise when one sets out to study technical work and technical workers and methods for controlling those fears.

Autonomous Vehicles in the Mobility System

2020 ◽  
pp. 500-514
Author(s):  
Thilo von Pape
Keyword(s):  
Artificial Intelligence ◽  
Mobile Phones ◽  
Mobile Communication ◽  
Autonomous Vehicles ◽  
Communication Research ◽  
Collective Control ◽  
System P ◽  
Mobility System ◽  
And Control ◽  
Transportation Technologies

This chapter discusses how autonomous vehicles (AVs) may interact with our evolving mobility system and what they mean for mobile communication research. It juxtaposes a conceptualization of AVs as manifestations of automation and artificial intelligence with an analysis of our mobility system as a historically grown hybrid of communication and transportation technologies. Since the emergence of railroad and telegraph, this system has evolved on two layers: an underlying infrastructure to power and coordinate the movements of objects, people, and ideas in industrially scaled speeds, volumes, and complexity and an interface to seamlessly access this infrastructure and control it. AVs are poised to further enhance the seamlessness which mobile phones and cars already lent to mobility. But in assuming increasingly sophisticated control tasks, AVs also disrupt an established shift toward individual control, demanding new interfaces to enable higher levels of individual and collective control over the mobility infrastructure.

scholarly journals Artificial Intelligence-Based Wearable Robotic Exoskeletons for Upper Limb Rehabilitation: A Review

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2146
Author(s):  
Manuel Andrés Vélez-Guerrero ◽  
Mauro Callejas-Cuervo ◽  
Stefano Mazzoleni
Keyword(s):  
Artificial Intelligence ◽  
Upper Limb ◽  
Main Trend ◽  
Motor Rehabilitation ◽  
Rehabilitation Process ◽  
Upper Limb Rehabilitation ◽  
Multiple Sensors ◽  
Meta Analyses ◽  
Limb Rehabilitation ◽  
And Control

Processing and control systems based on artificial intelligence (AI) have progressively improved mobile robotic exoskeletons used in upper-limb motor rehabilitation. This systematic review presents the advances and trends of those technologies. A literature search was performed in Scopus, IEEE Xplore, Web of Science, and PubMed using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology with three main inclusion criteria: (a) motor or neuromotor rehabilitation for upper limbs, (b) mobile robotic exoskeletons, and (c) AI. The period under investigation spanned from 2016 to 2020, resulting in 30 articles that met the criteria. The literature showed the use of artificial neural networks (40%), adaptive algorithms (20%), and other mixed AI techniques (40%). Additionally, it was found that in only 16% of the articles, developments focused on neuromotor rehabilitation. The main trend in the research is the development of wearable robotic exoskeletons (53%) and the fusion of data collected from multiple sensors that enrich the training of intelligent algorithms. There is a latent need to develop more reliable systems through clinical validation and improvement of technical characteristics, such as weight/dimensions of devices, in order to have positive impacts on the rehabilitation process and improve the interactions among patients, teams of health professionals, and technology.

scholarly journals Sugar-Sweetened Beverage Consumption Status and Its Association with Childhood Obesity among Chinese Children Aged 6–17 Years

Nutrients ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 2211
Author(s):  
Qian Gan ◽  
Peipei Xu ◽  
Titi Yang ◽  
Wei Cao ◽  
Juan Xu ◽  
...  
Keyword(s):  
Childhood Obesity ◽  
Urban Areas ◽  
Study Data ◽  
Chinese Children ◽  
Obesity Prevalence ◽  
Older Children ◽  
Sugar Sweetened Beverage ◽  
School Aged Children ◽  
Chinese School ◽  
And Control

Objective: There is a remarkable growth in sugar-sweetened (SSB) production and obesity prevalence among school-aged children in China. This paper describes SSB consumption and its association with obesity among Chinese children aged 6–17 years in 2012. Methods: in total, 25,553 children aged 6~17 years enrolled in the China Nutrition and Health Surveillance 2010–2013 were included in this study. Data of SSB consumption frequency and quantity were obtained from a food frequency questionnaire, and the children’s nutritional status was assessed. Multivariate logistic regression was used to evaluate the association between SSB consumption and obesity status. Results: SSB intake was estimated as 181.0 g/day, occurring 2.2 times/week. Older children, males, children from urban areas, and children with higher socioeconomic status were more likely to consume SSBs. Children who consumed SSBs 1~<5 times/week (11.7%) and >5 times/week (12.9%) were more likely to be overweight/obesity than those who consumed SSBs less than once/week. Conclusion: SSB consumption was common among Chinese school-aged children, especially among males, older children, and children from urban areas. High consumption of SSBs was associated with a higher prevalence of overweight/obesity. Actions and plans are required to reduce SSB consumption and control childhood obesity in China.

scholarly journals Controlling Safety of Artificial Intelligence-Based Systems in Healthcare

Symmetry ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 102
Author(s):  
Mohammad Reza Davahli ◽  
Waldemar Karwowski ◽  
Krzysztof Fiok ◽  
Thomas Wan ◽  
Hamid R. Parsaei
Keyword(s):  
Artificial Intelligence ◽  
Rating Scale ◽  
Black Box ◽  
Healthcare Industry ◽  
Emergency Plans ◽  
Controlling System ◽  
Box Models ◽  
Key Dimensions ◽  
Value Model ◽  
And Control

In response to the need to address the safety challenges in the use of artificial intelligence (AI), this research aimed to develop a framework for a safety controlling system (SCS) to address the AI black-box mystery in the healthcare industry. The main objective was to propose safety guidelines for implementing AI black-box models to reduce the risk of potential healthcare-related incidents and accidents. The system was developed by adopting the multi-attribute value model approach (MAVT), which comprises four symmetrical parts: extracting attributes, generating weights for the attributes, developing a rating scale, and finalizing the system. On the basis of the MAVT approach, three layers of attributes were created. The first level contained six key dimensions, the second level included 14 attributes, and the third level comprised 78 attributes. The key first level dimensions of the SCS included safety policies, incentives for clinicians, clinician and patient training, communication and interaction, planning of actions, and control of such actions. The proposed system may provide a basis for detecting AI utilization risks, preventing incidents from occurring, and developing emergency plans for AI-related risks. This approach could also guide and control the implementation of AI systems in the healthcare industry.

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