Intelligent Computing: The Latest Advances, Challenges and Future

Computing is a critical driving force in the development of human civilization. In recent years, we have witnessed the emergence of intelligent computing, a new computing paradigm that is reshaping traditional computing and promoting digital revolution in the era of big data, artificial intelligence and internet-of-things with new computing theories, architectures, methods, systems, and applications. Intelligent computing has greatly broadened the scope of computing, extending it from traditional computing on data to increasingly diverse computing paradigms such as perceptual intelligence, cognitive intelligence, autonomous intelligence, and human-computer fusion intelligence. Intelligence and computing have undergone paths of different evolution and development for a long time but have become increasingly intertwined in recent years: intelligent computing is not only intelligence-oriented but also intelligence-driven. Such cross-fertilization has prompted the emergence and rapid advancement of intelligent computing. Intelligent computing is still in its infancy and an abundance of innovations in the theories, systems, and applications of intelligent computing are expected to occur soon. We present the first comprehensive survey of literature on intelligent computing, covering its theory fundamentals, the technological fusion of intelligence and computing, important applications, challenges, and future perspectives. We believe that this survey is highly timely and will provide a comprehensive reference and cast valuable insights into intelligent computing for academic and industrial researchers and practitioners

Rangeland Systems: Processes, Management and Challenges

environmental management; environmental law; ecojustice; ecolog

Edge IoT Driven Framework for Experimental Investigation and Computational Modeling of Integrated Food, Energy, and Water System

As the global population soars from today’s 7.3 billion to an estimated 10 billion by 2050, the demand for Food, Energy, and Water (FEW) resources is expected to more than double. Such a sharp increase in demand for FEW resources will undoubtedly be one of the biggest global challenges. The management of food, energy, water for smart, sustainable cities involves a multi-scale problem. The interactions of these three dynamic infrastructures require a robust mathematical framework for analysis. Two critical solutions for this challenge are focused on technology innovation on systems that integrate food-energy-water and computational models that can quantify the FEW nexus. Information Communication Technology (ICT) and the Internet of Things (IoT) technologies are innovations that will play critical roles in addressing the FEW nexus stress in an integrated way. The use of sensors and IoT devices will be essential in moving us to a path of more productivity and sustainability. Recent advancements in IoT, Wireless Sensor Networks (WSN), and ICT are one lever that can address some of the environmental, economic, and technical challenges and opportunities in this sector. This dissertation focuses on quantifying and modeling the nexus by proposing a Leontief input-output model unique to food-energy-water interacting systems. It investigates linkage and interdependency as demand for resource changes based on quantifiable data. The interdependence of FEW components was measured by their direct and indirect linkage magnitude for each interaction. This work contributes to the critical domain required to develop a unique integrated interdependency model of a FEW system shying away from the piece-meal approach. The physical prototype for the integrated FEW system is a smart urban farm that is optimized and built for the experimental portion of this dissertation. The prototype is equipped with an automated smart irrigation system that uses real-time data from wireless sensor networks to schedule irrigation. These wireless sensor nodes are allocated for monitoring soil moisture, temperature, solar radiation, humidity utilizing sensors embedded in the root area of the crops and around the testbed. The system consistently collected data from the three critical sources; energy, water, and food. From this physical model, the data collected was structured into three categories. Food data consists of: physical plant growth, yield productivity, and leaf measurement. Soil and environment parameters include; soil moisture and temperature, ambient temperature, solar radiation. Weather data consists of rainfall, wind direction, and speed. Energy data include voltage, current, watts from both generation and consumption end. Water data include flow rate. The system provides off-grid clean PV energy for all energy demands of farming purposes, such as irrigation and devices in the wireless sensor networks. Future reliability of the off-grid power system is addressed by investigating the state of charge, state of health, and aging mechanism of the backup battery units. The reliability assessment of the lead-acid battery is evaluated using Weibull parametric distribution analysis model to estimate the service life of the battery under different operating parameters and temperatures. Machine learning algorithms are implemented on sensor data acquired from the experimental and physical models to predict crop yield. Further correlation analysis and variable interaction effects on crop yield are investigated

Economics for ecology ISCS'2012

Demand for electricity in Iraq has been stimulated by a growing economy and increasing number of population. In addition, electricity is subsidized in Iraq, which leads to increased demand. Nowadays the output of electricity sector in Iraq averages more than 8500 MW, while the demand is typically more than 14000 MW. Energy deficit in Iraq increased since 2003, when in the war was destroyed electricity network When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/2645

Decision analysis to inform invasive alien plant management in the Garden Route Biosphere Reserve

Invasive alien plants (IAP) pose significant threats to global economies and biodiversity and are often considered as wicked problems. With an increasing number of IAP and limited resources, their management and decision-making processes are becoming difficult because of uncertainty, multiple and conflicting objectives, and diverse stakeholder views, facts and values. This is particularly challenging given the complex interactions between economic, ecological, and social elements that exist in invaded areas. Consequently, it is important to incorporate new ways of thinking and novel methodologies to improve our understanding of IAP management and the decision-making processes around them, which are currently inadequate. Decision analysis can help with dealing with these challenges and support decision-making under uncertainty. Drawing on the systems thinking approach and the concepts of leverage points, transition management and transformational change, the aim of this thesis was to explore the effectiveness of IAP management and the decision-making process in the Garden Route Biosphere Reserve (GRBR). This was achieved using a mixed methods approach involving: social-ecological inventory (identifying relevant stakeholders); review of literature on the available decision support tools; key informant interviews (stakeholder perspectives on the current decision-making process); and stakeholder workshop and expert consultation (casual loop modelling). The results of this thesis provide evidence that application of the proposed principles of robust decision-making has the potential to overcome the weaknesses of the current decision-making process and as such, enables decision-makers to efficiently allocate resources towards IAS management. A novel causal loop diagram (CLD) was developed to highlight the interconnections between key variables in IAP management and decision-making. This revealed that to transcend ‘policy resistance’ and ‘quickfixes that fail’ archetypes, and improve IAP management, the stakeholders need to consider deep leverage points, for example, fostering trust and shared understanding among different stakeholder groups. These can be realistically maintained over the long-term and can cause a fundamental change in IAP management, rather than focusing on shallow leverage points that are relatively easy to implement but do not result in significant systemic change. The findings of this thesis are flexible and could guide various stakeholder groups at local, national, and international scales in improving the effectiveness of IAP management and decision-making

Decision analysis to inform invasive alien plant management in the Garden Route Biosphere Reserve

Invasive alien plants (IAP) pose significant threats to global economies and biodiversity and are often considered as wicked problems. With an increasing number of IAP and limited resources, their management and decision-making processes are becoming difficult because of uncertainty, multiple and conflicting objectives, and diverse stakeholder views, facts and values. This is particularly challenging given the complex interactions between economic, ecological, and social elements that exist in invaded areas. Consequently, it is important to incorporate new ways of thinking and novel methodologies to improve our understanding of IAP management and the decision-making processes around them, which are currently inadequate. Decision analysis can help with dealing with these challenges and support decision-making under uncertainty. Drawing on the systems thinking approach and the concepts of leverage points, transition management and transformational change, the aim of this thesis was to explore the effectiveness of IAP management and the decision-making process in the Garden Route Biosphere Reserve (GRBR). This was achieved using a mixed methods approach involving: social-ecological inventory (identifying relevant stakeholders); review of literature on the available decision support tools; key informant interviews (stakeholder perspectives on the current decision-making process); and stakeholder workshop and expert consultation (casual loop modelling). The results of this thesis provide evidence that application of the proposed principles of robust decision-making has the potential to overcome the weaknesses of the current decision-making process and as such, enables decision-makers to efficiently allocate resources towards IAS management. A novel causal loop diagram (CLD) was developed to highlight the interconnections between key variables in IAP management and decision-making. This revealed that to transcend ‘policy resistance’ and ‘quickfixes that fail’ archetypes, and improve IAP management, the stakeholders need to consider deep leverage points, for example, fostering trust and shared understanding among different stakeholder groups. These can be realistically maintained over the long-term and can cause a fundamental change in IAP management, rather than focusing on shallow leverage points that are relatively easy to implement but do not result in significant systemic change. The findings of this thesis are flexible and could guide various stakeholder groups at local, national, and international scales in improving the effectiveness of IAP management and decision-making