Energy Security and Resiliency for the Texas National Guard

The Texas Military Department (TMD) faces energy security issues due to the dependency of electricity from the grid that can be disrupted in case of a natural disaster like Hurricane Harvey hitting Texas. This motivates us to generate electricity at the location using locally available renewable sources, reducing TMDs dependency on the grid and giving a sense of energy security. The fall in the price of renewable energy over the last few years makes them a suitable candidate for harnessing greener energy and establishing an independent micro grid. Most of these renewable energy sources are intermittent in nature which takes our focus on storage options, along with greater reliance on more reliable energy sources such as biomass and natural gas. This study targets the electricity consumption of Camp Swift on an annual basis. From the optimization results we can learn that we can produce over 40% of the energy through renewable sources which is which is higher than the state average of 18%. This results in a total cost of about 2.7 million USD out of which about 62000 USD is kept for running costs while 2.33 million USD is the expected cost of setting up this grid. By using Biomass and Natural Gas, in conjunction with Solar and a Diesel Generator, the system is able to produce 5.5 million kWh of electricity against annual demand of less than 2 million kWh which can be used to sell electricity back to the grid in the event of a grid failure or via net metering enabled smart meters

Using Influence Nets in Financial Informatics: A Case Study of Pakistan

The paper presents an application of Influence Nets (INs) in the field of financial informatics. Influence Nets have primarily been used in war games to model effects based operations but, as shown in this paper, they can prove to be equally useful in other domains requiring decision making under uncertain situations. The primary advantage of INs lies in their ability to acquire knowledge from subject matter experts in problem domains that rely heavily on experts’ opinion. A sample case study from the fields of economics and finance is presented in this paper. The case study models the choices faced by a developing country to recover her economy which is going through a difficult phase due to global financial crisis, internal law and order situation and political instability

Recent status of the understanding of neutrino-nucleus cross section

In this work we have presented current understanding of neutrino-nucleon/nucleus cross sections in the few GeV energy region relevant for a precise determination of neutrino oscillation parameters and CP violation in the leptonic sector. In this energy region various processes like quasielastic and inelastic production of single and multipion production, coherent pion production, kaon, eta, hyperon production, associated particle production as well as deep inelastic scattering processes contribute to the neutrino event rates.Comment: 9-Pages, 4-figures, Talk given at DAE-HEP Symposium held at Delhi University, 12-16 December, 201

Suspicious activity reporting using dynamic bayesian networks

AbstractSuspicious activity reporting has been a crucial part of anti-money laundering systems. Financial transactions are considered suspicious when they deviate from the regular behavior of their customers. Money launderers pay special attention to keep their transactions as normal as possible to disguise their illicit nature. This may deceive the classical deviation based statistical methods for finding anomalies. This study presents an approach, called SARDBN (Suspicious Activity Reporting using Dynamic Bayesian Network), that employs a combination of clustering and dynamic Bayesian network (DBN) to identify anomalies in sequence of transactions. SARDBN applies DBN to capture patterns in a customer’s monthly transactional sequences as well as to compute an anomaly index called AIRE (Anomaly Index using Rank and Entropy). AIRE measures the degree of anomaly in a transaction and is compared against a pre-defined threshold to mark the transaction as normal or suspicious. The presented approach is tested on a real dataset of more than 8 million banking transactions and has shown promising results

Modeling time-varying uncertain situations using Dynamic Influence Nets

AbstractThis paper enhances the Timed Influence Nets (TIN) based formalism to model uncertainty in dynamic situations. The enhancements enable a system modeler to specify persistence and time-varying influences in a dynamic situation that the existing TIN fails to capture. The new class of models is named Dynamic Influence Nets (DIN). Both TIN and DIN provide an alternative easy-to-read and compact representation to several time-based probabilistic reasoning paradigms including Dynamic Bayesian Networks. The Influence Net (IN) based approach has its origin in the Discrete Event Systems modeling. The time delays on arcs and nodes represent the communication and processing delays, respectively, while the changes in the probability of an event at different time instants capture the uncertainty associated with the occurrence of the event over a period of time