Automated integration of real-time and non-real-time defense systems

Various application domains require the integration of distributed real-time or near-real-time systems with non-real-time systems.Smart cities,smart homes,ambient intelligent systems,or network-centric defense systems are among these application domains.Data Distribution Service(DDS)is a communi-cation mechanism based on Data-Centric Publish-Subscribe(DCPS)model.It is used for distributed systems with real-time operational constraints.Java Message Service(JMS)is a messaging standard for enterprise systems using Service Oriented Architecture(SOA)for non-real-time operations.JMS allows Java programs to exchange messages in a loosely coupled fashion.JMS also supports sending and receiving messages using a messaging queue and a publish-subscribe interface.In this article,we pro-pose an architecture enabling the automated integration of distributed real-time and non-real-time systems.We test our proposed architecture using a distributed Command,Control,Communications,Computers,and Intelligence(C4I)system.The system has DDS-based real-time Combat Management System components deployed to naval warships,and SOA-based non-real-time Command and Control components used at headquarters.The proposed solution enables the exchange of data between these two systems efficiently.We compare the proposed solution with a similar study.Our solution is superior in terms of automation support,ease of implementation,scalability,and performance.     

A Nonlinear Model of Military Expenditure Convergence: Evidence From Estar Nonlinear Unit Root Test

The paper builds a model to empirically test military expenditure convergence in a nonlinear set up. We assert that country A chooses a military strategy of catching up with the military expenditure of its rivals, subject to public spending constraints on public investments, including health and education, leading to decrease in long-term economic welfare. This implies nonlinear convergence path: only when the military expenditure gap between countries reaches the threshold level, will it provide incentives to catch up with rival’s military expenditures. We test this nonlinear catching up hypothesis for 37 countries spanning from 1988 to 2012. Results from individual nonlinear cross-sectionally augmented Dickey–Fuller (NCADF) regression indicate that 53% of countries converge to world’s average military expenditure: where 39% of countries converge to Germany; 33% of countries converge to China; 22% of countries converge to the USA, and 11% of countries converge to Russia. Interestingly, USA does not exhibit nonlinear military expenditure convergence toward world’s average level. For panel NCADF regression, the result suggests that on average, there is evidence for countries converging to USA’s military expenditure at 10% significance level. For the convergence to the world’s average, the statistical significance is at the 1% significance level.     

A stochastic programming approach for chemotherapy appointment scheduling

Chemotherapy appointment scheduling is a challenging problem due to the uncertainty in premedication and infusion durations. In this paper, we formulate a two‐stage stochastic mixed integer programming model for the chemotherapy appointment scheduling problem under limited availability of nurses and infusion chairs. The objective is to minimize the expected weighted sum of nurse overtime, chair idle time, and patient waiting time. The computational burden to solve real‐life instances of this problem to optimality is significantly high, even in the deterministic case. To overcome this burden, we incorporate valid bounds and symmetry breaking constraints. Progressive hedging algorithm is implemented in order to solve the improved formulation heuristically. We enhance the algorithm through a penalty update method, cycle detection and variable fixing mechanisms, and a linear approximation of the objective function. Using numerical experiments based on real data from a major oncology hospital, we compare our solution approach with several scheduling heuristics from the relevant literature, generate managerial insights related to the impact of the number of nurses and chairs on appointment schedules, and estimate the value of stochastic solution to assess the significance of considering uncertainty.