Background-We aim to determine the economic and social impact of typical interventions proposed by the public health officials and preventive behavioral changes adopted by the private citizens in the event of a "flu-like" epidemic.Method-We apply an individual-based simulation model to the New River Valley area of Virginia for addressing this critical problem. The economic costs include not only the loss in productivity due to sickness but also the indirect cost incurred through disease avoidance and caring for dependents.Results-The results show that the most important factor responsible for preventing income loss is the modification of individual behavior; it drops the total income loss by 62% compared to the base case. The next most important factor is the closure of schools which reduces the total income loss by another 40%.Conclusions-The preventive behavior of the private citizens is the most important factor in controlling the epidemic.

We discuss an interaction-based approach to study the coevolution between socio-technical networks, individual behaviors, and contagion processes on these networks. We use epidemics in human population as an example of this phenomenon. The methods consist of developing synthetic yet realistic national-scale networks using a first principles approach. Unlike simple random graph techniques, these methods combine real world data sources with behavioral and social theories to synthesize detailed social contact (proximity) networks. Individual-based models of within-host disease progression and inter-host transmission are then used to model the contagion process. Finally, models of individual behaviors are composed with disease progression models to develop a realistic representation of the complex system in which individual behaviors and the social network adapt to the contagion. These methods are embodied within Simdemics – a general purpose modeling environment to support pandemic planning and response. Simdemics is designed specifically to be scalable to networks with 300 million agents – the underlying algorithms and methods in Simdemics are all high-performance computing oriented methods. New advances in network science, machine learning, high performance computing, data mining and behavioral modeling were necessary to develop Simdemics. Simdemics is combined with two other environments, Simfrastructure and Didactic, to form an integrated cyberenvironment. The integrated cyber-environment provides the end-user flexible and seamless Internet based access to Simdemics. Service-oriented architectures play a critical role in delivering the desired services to the end user. Simdemics, in conjunction with the integrated cyber-environment, has been used in over a dozen user defined case studies. These case studies were done to support specific policy questions that arose in the context of planning the response to pandemics (e.g., H1N1, H5N1) and human initiated bio-terrorism events. These studies played a crucial role in the continual development and improvement of the cyber-environment.

Complaints often expressed about undergraduate computer science or information systems programs,,, is that students graduate with adequate technical skills but often lack an understanding of organizational processes, team project experience, and the ability to integrate information technology in an organizational setting. To address this, educators have historically created service-learning group projects, which leverage local organizations. These types of projects can be very rewarding for students and offer excellent educational opportunities for the students within the group. Educators who have taught service-learning group projects know there are significant problems with using this type of project. These problems include: motivation of students to do their best work, fair individual and overall group assessment, appropriate workload for a semester, and minimizing disruptive outside influences. In addition, the creation of new projects on a continuous semester basis would be very useful for the instructor of a project course. To solve these issues and problems, the following capstone course design was used with success at Grand Valley State University (GVSU). A socially-relevant, community-based assignment with local non-profit organizations provided the basis for the capstone information systems project course described in this paper. This project course produced working applications for actual clients that gave students a unique capstone experience.

Unmanned Aerial Vehicles (UAVs) are becoming more prevalent every day. In addition, advances in battery life and electronic sensors have enabled the development of diverse UAV applications outside their original military domain. For example, Search and Rescue (SAR) operations can benefit greatly from modern UAVs since even the simplest commercial models are equipped with high-resolution cameras and the ability to stream video to a computer or portable device. As a result, autonomous unmanned systems (ground, aquatic, and aerial) have recently been employed for such typical SAR tasks as terrain mapping, task observation, and early supply delivery. However, these systems were developed before advances such as Google Deepmind's breakthrough with the Deep Q-Network (DQN) technology. Therefore, most of them rely heavily on greedy or potential-based heuristics, without the ability to learn. In this research, we present two possible approximations (Partially Observable Markov Decision Processes) for enhancing the performance of autonomous UAVs in SAR by incorporating newly-developed Reinforcement Learning methods. The project utilizes open-source tools such as Microsoft's state-of-the-art UAV simulator AirSim, and Keras, a machine learning framework that can make use of Google's popular tensor library called TensorFlow. The main approach investigated in this research is the Deep Q-Network.

Abstract-Networks are an effective abstraction for representing real systems. Consequently, network science is increasingly used in academia and industry to solve problems in many fields. Computations that determine structure properties and dynamical behaviors of networks are useful because they give insights into the characteristics of real systems. We introduce a newly built and deployed cyberinfrastructure for network science (CINET) that performs such computations, with the following features: (i) it offers realistic networks from the literature and various random and deterministic network generators; (ii) it provides many algorithmic modules and measures to study and characterize networks; (iii) it is designed for efficient execution of complex algorithms on distributed high performance computers so that they scale to large networks; and (iv) it is hosted with web interfaces so that those without direct access to high performance computing resources and those who are not computing experts can still reap the system benefits. It is a combination of application design and cyberinfrastructure that makes these features possible. To our knowledge, these capabilities collectively make CINET novel. We describe the system and illustrative use cases, with a focus on the CINET user.