scholarly journals Developing a Modern Greenhouse Scientific Research Facility—A Case Study

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2575
Author(s):  
Davor Cafuta ◽  
Ivica Dodig ◽  
Ivan Cesar ◽  
Tin Kramberger
Keyword(s):  
Artificial Intelligence ◽  
Plant Growth ◽  
Optimal Solution ◽  
Plant Health ◽  
Sensor Data ◽  
Nutrient Level ◽  
Future Research ◽  
Time Data ◽  
Real Time Data Processing ◽  
Ebb And Flow

Multidisciplinary approaches in science are still rare, especially in completely different fields such as agronomy science and computer science. We aim to create a state-of-the-art floating ebb and flow system greenhouse that can be used in future scientific experiments. The objective is to create a self-sufficient greenhouse with sensors, cloud connectivity, and artificial intelligence for real-time data processing and decision making. We investigated various approaches and proposed an optimal solution that can be used in much future research on plant growth in floating ebb and flow systems. A novel microclimate pocket-detection solution is proposed using an automatically guided suspended platform sensor system. Furthermore, we propose a methodology for replacing sensor data knowledge with artificial intelligence for plant health estimation. Plant health estimation allows longer ebb periods and increases the nutrient level in the final product. With intelligent design and the use of artificial intelligence algorithms, we will reduce the cost of plant research and increase the usability and reliability of research data. Thus, our newly developed greenhouse would be more suitable for plant growth research and production.

scholarly journals A Framework for Real Time Processing of Sensor Data in the Cloud

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Supun Kamburugamuve ◽  
Leif Christiansen ◽  
Geoffrey Fox
Keyword(s):  
Data Processing ◽  
Real Time ◽  
Cloud Services ◽  
Sensor Data ◽  
Smart Devices ◽  
Time Data ◽  
Real Time Processing ◽  
Real Time Data ◽  
Autonomous Robot Navigation ◽  
Real Time Data Processing

We describe IoTCloud, a platform to connect smart devices to cloud services for real time data processing and control. A device connected to IoTCloud can communicate with real time data analysis frameworks deployed in the cloud via messaging. The platform design is scalable in connecting devices as well as transferring and processing data. With IoTCloud, a user can develop real time data processing algorithms in an abstract framework without concern for the underlying details of how the data is distributed and transferred. For this platform, we primarily consider real time robotics applications such as autonomous robot navigation, where there are strict requirements on processing latency and demand for scalable processing. To demonstrate the effectiveness of the system, a robotic application is developed on top of the framework. The system and the robotics application characteristics are measured to show that data processing in central servers is feasible for real time sensor applications.

scholarly journals The Symphony of Team Flow in Virtual Teams. Using Artificial Intelligence for Its Recognition and Promotion

2021 ◽  
Vol 12 ◽  
Author(s):  
Corinna Peifer ◽  
Anita Pollak ◽  
Olaf Flak ◽  
Adrian Pyszka ◽  
Muhammad Adeel Nisar ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Real Time ◽  
Virtual Teams ◽  
Team Effectiveness ◽  
Learning System ◽  
Self Report ◽  
Sensor Data ◽  
Future Research ◽  
Flow Measurements

More and more teams are collaborating virtually across the globe, and the COVID-19 pandemic has further encouraged the dissemination of virtual teamwork. However, there are challenges for virtual teams – such as reduced informal communication – with implications for team effectiveness. Team flow is a concept with high potential for promoting team effectiveness, however its measurement and promotion are challenging. Traditional team flow measurements rely on self-report questionnaires that require interrupting the team process. Approaches in artificial intelligence, i.e., machine learning, offer methods to identify an algorithm based on behavioral and sensor data that is able to identify team flow and its dynamics over time without interrupting the process. Thus, in this article we present an approach to identify team flow in virtual teams, using machine learning methods. First of all, based on a literature review, we provide a model of team flow characteristics, composed of characteristics that are shared with individual flow and characteristics that are unique for team flow. It is argued that those characteristics that are unique for team flow are represented by the concept of collective communication. Based on that, we present physiological and behavioral correlates of team flow which are suitable – but not limited to – being assessed in virtual teams and which can be used as input data for a machine learning system to assess team flow in real time. Finally, we suggest interventions to support team flow that can be implemented in real time, in virtual environments and controlled by artificial intelligence. This article thus contributes to finding indicators and dynamics of team flow in virtual teams, to stimulate future research and to promote team effectiveness.

scholarly journals Research directions in policy modeling: Insights from comparative analysis of recent projects

Data & Policy ◽  
2021 ◽  
Vol 3 ◽  
Author(s):  
Alexander Ronzhyn ◽  
Maria A. Wimmer
Keyword(s):  
Stakeholder Involvement ◽  
Theoretical Models ◽  
Future Research ◽  
Time Data ◽  
Specific Domain ◽  
Policy Modeling ◽  
Research And Innovation ◽  
Area Of Interest ◽  
Technological Advances ◽  
Real Time Data Processing

Abstract With the increased availability of data and the capacity to make sense of these data, computational approaches to analyze, model and simulate public policy evolved toward viable instruments to deliberate, plan, and evaluate them in different areas of application. Such examples include infrastructure, mobility, monetary, or austerity policies, policies on different aspects of societies (health, pandemic, skills, inclusion, etc.). Technological advances along with the evolution of theoretical models and frameworks open valuable opportunities, while at the same time, posing new challenges. The paper investigates the current state of research in the domain and aims at identifying the most pressing areas for future research. This is done through both literature research of policy modeling and the analysis of research and innovation projects that either focus on policy modeling or involve it as a significant component of the research design. In the paper, 16 recent projects involving the keyword policy modeling were analyzed. The majority of projects concern the application of policy modeling to a specific domain or area of interest, while several projects tackled the cross-cutting topics (risk and crisis management). The detailed analysis of the projects led to topics of future research in the domain of policy modeling. Most prominent future research topics in policy modeling include stakeholder involvement approaches, applicability of research results, handling complexity of models, integration of models from different modeling and simulation paradigms and approaches, visualization of simulation results, real-time data processing, and scalability. These aspects require further research to appropriately contribute to further advance the field.

Plant Growth Promoting Bacteria (PGPB) - A Versatile Tool for Plant Health Management

2019 ◽  
Vol 1 (1) ◽  
pp. 1
Author(s):  
Salah Eddin Khabbaz ◽  
D. Ladhalakshmi ◽  
Merin Babu ◽  
A. Kandan ◽  
V. Ramamoorthy ◽  
...  
Keyword(s):  
Plant Growth ◽  
Health Management ◽  
Plant Health ◽  
Plant Growth Promoting Bacteria ◽  
Versatile Tool ◽  
Plant Growth Promoting ◽  
Growth Promoting

scholarly journals Review on the Use of Artificial Intelligence to Predict Fire Performance of Construction Materials and Their Flame Retardancy

Molecules ◽  
2021 ◽  
Vol 26 (4) ◽  
pp. 1022
Author(s):  
Hoang T. Nguyen ◽  
Kate T. Q. Nguyen ◽  
Tu C. Le ◽  
Guomin Zhang
Keyword(s):  
Artificial Intelligence ◽  
Flame Retardancy ◽  
Material Science ◽  
Construction Materials ◽  
Engineering Problem ◽  
Future Research ◽  
Fire Performance ◽  
Structural Components ◽  
Comprehensive Overview ◽  
Fire Engineering

The evaluation and interpretation of the behavior of construction materials under fire conditions have been complicated. Over the last few years, artificial intelligence (AI) has emerged as a reliable method to tackle this engineering problem. This review summarizes existing studies that applied AI to predict the fire performance of different construction materials (e.g., concrete, steel, timber, and composites). The prediction of the flame retardancy of some structural components such as beams, columns, slabs, and connections by utilizing AI-based models is also discussed. The end of this review offers insights on the advantages, existing challenges, and recommendations for the development of AI techniques used to evaluate the fire performance of construction materials and their flame retardancy. This review offers a comprehensive overview to researchers in the fields of fire engineering and material science, and it encourages them to explore and consider the use of AI in future research projects.

scholarly journals Artificial intelligence in hospitals: providing a status quo of ethical considerations in academia to guide future research

AI & Society ◽  
2021 ◽  
Author(s):  
Milad Mirbabaie ◽  
Lennart Hofeditz ◽  
Nicholas R. J. Frick ◽  
Stefan Stieglitz
Keyword(s):  
Artificial Intelligence ◽  
Citation Network ◽  
Status Quo ◽  
Biomedical Ethics ◽  
Future Research ◽  
Ethical Considerations ◽  
Ethical Discourse ◽  
The Status ◽  
Clinical Environments ◽  
Research Questions

AbstractThe application of artificial intelligence (AI) in hospitals yields many advantages but also confronts healthcare with ethical questions and challenges. While various disciplines have conducted specific research on the ethical considerations of AI in hospitals, the literature still requires a holistic overview. By conducting a systematic discourse approach highlighted by expert interviews with healthcare specialists, we identified the status quo of interdisciplinary research in academia on ethical considerations and dimensions of AI in hospitals. We found 15 fundamental manuscripts by constructing a citation network for the ethical discourse, and we extracted actionable principles and their relationships. We provide an agenda to guide academia, framed under the principles of biomedical ethics. We provide an understanding of the current ethical discourse of AI in clinical environments, identify where further research is pressingly needed, and discuss additional research questions that should be addressed. We also guide practitioners to acknowledge AI-related benefits in hospitals and to understand the related ethical concerns.

scholarly journals C4 Bacterial Volatiles Improve Plant Health

Pathogens ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 682
Author(s):  
Bruno Henrique Silva Dias ◽  
Sung-Hee Jung ◽  
Juliana Velasco de Castro Oliveira ◽  
Choong-Min Ryu
Keyword(s):  
Plant Growth ◽  
Volatile Compounds ◽  
Large Scale ◽  
Growth Promotion ◽  
Plant Growth Promoting Rhizobacteria ◽  
Plant Health ◽  
Systemic Resistance ◽  
Potential Applications ◽  
Plant Growth Promoting ◽  
Bacterial Volatiles

Plant growth-promoting rhizobacteria (PGPR) associated with plant roots can trigger plant growth promotion and induced systemic resistance. Several bacterial determinants including cell-wall components and secreted compounds have been identified to date. Here, we review a group of low-molecular-weight volatile compounds released by PGPR, which improve plant health, mostly by protecting plants against pathogen attack under greenhouse and field conditions. We particularly focus on C4 bacterial volatile compounds (BVCs), such as 2,3-butanediol and acetoin, which have been shown to activate the plant immune response and to promote plant growth at the molecular level as well as in large-scale field applications. We also disc/ uss the potential applications, metabolic engineering, and large-scale fermentation of C4 BVCs. The C4 bacterial volatiles act as airborne signals and therefore represent a new type of biocontrol agent. Further advances in the encapsulation procedure, together with the development of standards and guidelines, will promote the application of C4 volatiles in the field.

scholarly journals The Importance of Implementing Cyber Physical Systems to Acquire Real-Time Data and Indicators

J ◽  
2021 ◽  
Vol 4 (2) ◽  
pp. 147-153
Author(s):  
Paula Morella ◽  
María Pilar Lambán ◽  
Jesús Antonio Royo ◽  
Juan Carlos Sánchez
Keyword(s):  
Decision Making ◽  
Data Acquisition ◽  
Real Time ◽  
Failure Detection ◽  
Cyber Physical Systems ◽  
Future Research ◽  
Time Data ◽  
Physical Systems ◽  
The Real ◽  
Real Time Data

Among the new trends in technology that have emerged through the Industry 4.0, Cyber Physical Systems (CPS) and Internet of Things (IoT) are crucial for the real-time data acquisition. This data acquisition, together with its transformation in valuable information, are indispensable for the development of real-time indicators. Moreover, real-time indicators provide companies with a competitive advantage over the competition since they enhance the calculus and speed up the decision-making and failure detection. Our research highlights the advantages of real-time data acquisition for supply chains, developing indicators that would be impossible to achieve with traditional systems, improving the accuracy of the existing ones and enhancing the real-time decision-making. Moreover, it brings out the importance of integrating technologies 4.0 in industry, in this case, CPS and IoT, and establishes the main points for a future research agenda of this topic.

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