531 research outputs found
A conceptual model for megaprogramming
Megaprogramming is component-based software engineering and life-cycle management. Magaprogramming and its relationship to other research initiatives (common prototyping system/common prototyping language, domain specific software architectures, and software understanding) are analyzed. The desirable attributes of megaprogramming software components are identified and a software development model and resulting prototype megaprogramming system (library interconnection language extended by annotated Ada) are described
Challenges in Data Intensive Analysis at Scientific Experimental User Facilities
Today's scientific challenges such as routes to a sustainable energy future, materials by design or biological and chemical environmental remediation methods, are complex problems that require the integration of a wide range of complementary expertise to be addressed successfully. Experimental and computational science research methods can hereby offer fundamental insights for their solution. Experimental facilities in particular can contribute through a large variety of investigative methods, which can span length scales from millions of kilometers (radar) to the sub-nucleus (LHC). These methods are used to probe structure, properties, and function of objects from single elements to whole communities. Hereby direct imaging techniques are a powerful means to develop an atomistic understanding of scientific issues. For example, the identification ofmechanisms associated with chemical, material, and biological transformations requires the direct observation of the reactions to build up an understanding of the atom-by-atom structural and chemical changes. Computational science can aid the planning of such experiments, correlate results, explain or predict the phenomena as they would be observed and thus aid their interpretation. Furthermore computational science can be essential for the investigation of phenomena that are difficult to observe due to their scale, reaction time or extreme conditions. Combining experimental and computational techniques provides scientists with the ability to research structures and processes at various levels of theory, e.g. providing molecular 'movies' of complex reactions that show bond breaking and reforming in natural time scales, along with the intermediate states to understand the mechanisms that govern the chemical transformations. This chapter will discuss the critical data intensive analysis challenges faced by the experimental science community at large scale and laboratory based facilities. The chapter will highlight current solutions and lay out perspectives for the future, such as methods to achieve real time analysis capabilities and the challenges and opportunities of data integration across experimental scales, levels of theory, and varying techniques
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Cybersecurity for industrial Internet of Things: architecture, models and lessons learned
Modern industrial systems now, more than ever, require secure and efficient ways of communication. The trend of making connected, smart architectures is beginning to show in various fields of the industry such as manufacturing and logistics. The number of IoT (Internet of Things) devices used in such systems is naturally increasing and industry leaders want to define business processes which are reliable, reproducible, and can be effortlessly monitored. With the rise in number of connected industrial systems, the number of used IoT devices also grows and with that some challenges arise. Cybersecurity in these types of systems is crucial for their wide adoption. Without safety in communication and threat detection and prevention techniques, it can be very difficult to use smart, connected systems in the industry setting. In this paper we describe two real-world examples of such systems while focusing on our architectural choices and lessons learned. We demonstrate our vision for implementing a connected industrial system with secure data flow and threat detection and mitigation strategies on real-world data and IoT devices. While our system is not an off-the-shelf product, our architecture design and results show advantages of using technologies such as Deep Learning for threat detection and Blockchain enhanced communication in industrial IoT systems and how these technologies can be implemented. We demonstrate empirical results of various components of our system and also the performance of our system as-a-whole
Application of Artificial Intelligence in Modern Healthcare System
Artificial intelligence (AI) has the potential of detecting significant interactions in a dataset and also it is widely used in several clinical conditions to expect the results, treat, and diagnose. Artificial intelligence (AI) is being used or trialed for a variety of healthcare and research purposes, including detection of disease, management of chronic conditions, delivery of health services, and drug discovery. In this chapter, we will discuss the application of artificial intelligence (AI) in modern healthcare system and the challenges of this system in detail. Different types of artificial intelligence devices are described in this chapter with the help of working mechanism discussion. Alginate, a naturally available polymer found in the cell wall of the brown algae, is used in tissue engineering because of its biocompatibility, low cost, and easy gelation. It is composed of α-L-guluronic and β-D-manuronic acid. To improve the cell-material interaction and erratic degradation, alginate is blended with other polymers. Here, we discuss the relationship of artificial intelligence with alginate in tissue engineering fields
ME-EM 2017-18 Annual Report
Table of Contents Curriculum Revision The Result: MEP I-IV ME-EM Research Alumni Features Enrollment & Degrees Graduates Faculty & Staff Alumni Donors Contracts & Grants Patents & Publicationshttps://digitalcommons.mtu.edu/mechanical-annualreports/1001/thumbnail.jp
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