ELASTICITY: Topological Characterization of Robustness in Complex Networks

Just as a herd of animals relies on its robust social structure to survive in the wild, similarly robustness is a crucial characteristic for the survival of a complex network under attack. The capacity to measure robustness in complex networks defines the resolve of a network to maintain functionality in the advent of classical component failures and at the onset of cryptic malicious attacks. To date, robustness metrics are deficient and unfortunately the following dilemmas exist: accurate models necessitate complex analysis while conversely, simple models lack applicability to our definition of robustness. In this paper, we define robustness and present a novel metric, elasticity- a bridge between accuracy and complexity-a link in the chain of network robustness. Additionally, we explore the performance of elasticity on Internet topologies and online social networks, and articulate results

Developing a Comprehensive Power Simulation Model for the MEMESat-1 CubeSat Using Orbital Dynamics

The University of Georgia’s Small Satellite Research Lab’s Mission for Education and Multimedia Engagement Satellite (MEMESat-1) requires the use of variables such as power generation, power draw, orbital path, packet size, and data processing times. As power generation and charge varies, MEMESat-1 will automatically transition through three operational modes to prevent battery depletion and halt system processes in case of anomalies. Taking these variables and operational modes into account, the MEMESat-1 Mission Operations (MOPS) team will use FreeFlyer software to analyze power generation and draw during MEMESat-1’s orbital cycle. The power limitations of MEMESat-1 are budgeted based on battery and solar cell specifications implying the necessity of power simulations by MOPS

Using GENI for experimental evaluation of Software Defined Networking in smart grids

The North American Electric Reliability Corporation (NERC) envisions a smart grid that aggressively explores advance communication network solutions to facilitate real-time monitoring and dynamic control of the bulk electric power system. At the distribution level, the smart grid integrates renewable generation and energy storage mechanisms to improve the reliability of the grid. Furthermore, dynamic pricing and demand management provide customers an avenue to interact with the power system to determine the electricity usage that best satisfies their lifestyle. At the transmission level, efficient communication and a highly automated architecture provide visibility in the power system and as a result, faults are mitigated faster than they can propagate. However, such higher levels of reliability and efficiency rest on the supporting communication infrastructure. To date, utility companies are moving towards Multiprotocol Label Switching (MPLS) because it supports traffic engineering and virtual private networks (VPNs). Furthermore, it provides Quality of Service (QoS) guarantees and fail-over mechanisms in addition to meeting the requirement of non-routability as stipulated by NERC. However, these benefits come at a cost for the infrastructure that supports the fullMPLS specification. With this realization and given a two week implementation and deployment window in GENI, we explore the modularity and flexibility provided by the low cost OpenFlow Software Defined Networking (SDN) solution. In particular, we use OpenFlow to provide 1.) automatic fail-over mechanisms, 2.) a load balancing, and 3.) Quality of Service guarantees: all essential mechanisms for smart grid networks

Bayesian Time-Series Classifier for Decoding Simple Visual Stimuli from Intracranial Neural Activity

Understanding how external stimuli are encoded in distributed neural activity is of significant interest in clinical and basic neuroscience. To address this need, it is essential to develop analytical tools capable of handling limited data and the intrinsic stochasticity present in neural data. In this study, we propose a straightforward Bayesian time series classifier (BTsC) model that tackles these challenges whilst maintaining a high level of interpretability. We demonstrate the classification capabilities of this approach by utilizing neural data to decode colors in a visual task. The model exhibits consistent and reliable average performance of 75.55% on 4 patients' dataset, improving upon state-of-the-art machine learning techniques by about 3.0 percent. In addition to its high classification accuracy, the proposed BTsC model provides interpretable results, making the technique a valuable tool to study neural activity in various tasks and categories. The proposed solution can be applied to neural data recorded in various tasks, where there is a need for interpretable results and accurate classification accuracy

SAGE Diagram Documentation in Burn Patients in New Mexico: Room for Quality Improvement

Objective The first step in the management of burn patients is an accurate estimation of the body surface area. Based on percentage of body surface area involvement, burns are categorized as major (\u3e20%) and minor ( Methods This is a retrospective study of 327 consecutive patients from 2014-2018 at University of New Mexico Burn Center. Only patients undergoing surgical management were included. We recorded patient demographics, comorbidities, burn characteristics. The primary measure of interest was SAGE documentation and secondary measure of interest was outcomes associated with it. Results We found that SAGE diagram was completed on minority of patients 130 (39.8%). After comparing patients in SAGE group vs. No SAGE group we found that the patients were the same in both groups with regards to the demographics and comorbidities except adult patients with hypertension and hyperlipidemia (p values Conclusion Only a minority of patients get SAGE diagram documented. Although our study did show improved outcomes with the use of SAGE diagram. There is need for prospective studies to validate the utility of SAGE diagram in predicting adverse outcomes in major burns

Leadership for the negentropic online enterprise

This paper focuses on the negative or opposite of entropic disintegration as understood in physical sciences and applies this, metaphorically, to the online enterprise. Negentropic behaviours are those that bring re-integration, renewal, and eventual positive states to the institution. Online learning, which has now been a staple in most college and university strategic plans, has the potential for significant negentropic impact on the enterprise of higher education within individual institutions. Here we focus on how to apply negentropic theoretical constructs to the leadership of online enterprises with the expectation of increased positive forward motion for the higher education institution.Este artículo trata el fenómeno opuesto a la degradación entrópica, tal como se entiende en las ciencias físicas y lo aplica, de manera metafórica, a las empresas online. Las conductas neguentrópicas son aquellas que aportan reintegración, renovación y, finalmente, estados positivos a la organización. El aprendizaje online, que ha resultado ser un pilar básico de los planes estratégicos de la mayoría de universidades e institutos, tiene el potencial para un gran impacto neguentrópico en los centros de educación superior dentro de instituciones individuales. En este artículo nos centramos, fundamentalmente, en cómo la aplicación de la neguentropía contribuye al liderazgo de las empresas online, a la espera de que la tendencia positiva se traslade a los centros de educación superior