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The main motivation of this paper is to present  the Yosida approximation of a semi-linear backward stochastic differential equation in infinite dimension. Under suitable assumption and condition, an L²-convergence rate is established.

The authors establish a result concerning the L²-convergence rate of the solution of backward stochastic differential equation with jumps with respect to the Yosida approximation.

The authors carry out a convergence rate of Yosida approximation to the semi-linear backward stochastic differential equation in infinite dimension.

In this paper, the authors present the Yosida approximation of a semi-linear backward stochastic differential equation in infinite dimension. Under suitable assumption and condition, an L²-convergence rate is established.

The purpose of this paper is to show the existence results for adapted solutions of infinite horizon doubly reflected backward stochastic differential equations with jumps. These results are applied to get the existence of an optimal impulse control strategy for an infinite horizon impulse control problem.

The main methods used to achieve the objectives of this paper are the properties of the Snell envelope which reduce the problem of impulse control to the existence of a pair of right continuous left limited processes. Some numerical results are provided to show the main results.

In this paper, the authors found the existence of a couple of processes via the notion of doubly reflected backward stochastic differential equation to prove the existence of an optimal strategy which maximizes the expected profit of a firm in an infinite horizon problem with jumps.

In this paper, the authors found new tools in stochastic analysis. They extend to the infinite horizon case the results of doubly reflected backward stochastic differential equations with jumps. Then the authors prove the existence of processes using Envelope Snell to find an optimal strategy of our control problem.

The purpose of this paper is to develop a collaborative agent-based web architecture and an agent-based green IS assessment tool to aid information technology (IT) practitioners in data centers assess their current green information systems (IS) practice toward attaining sustainability.

The methodology comprises that the collaborative agent-based web architecture, agents’ algorithm and the green IS assessment tool, which is validated by employing focus group questionnaire targeting IT practitioners in seven Malaysian-based enterprises that have an in-house data centers. With 105 valid samples at hand, descriptive analysis and exploratory factor analysis was utilized to determine the applicability of the implemented agent-based green IS assessment tool.

Findings reveal that the agent-based green IS assessment tool possesses the capability to evaluate benchmark and rate enterprise data centers current green IS practice. Additional findings indicate that the agent-based green IS assessment tool provide suggestions on how green IS practice can be improved in enterprise data centers.

This study only collected data from 105 IT practitioners in enterprise data centers based in Malaysia; as such results from this research cannot be generalized to other countries. Moreover, the developed collaborative agents for green IS practice assessment can only be fully deployed after domain experts has added green IS practice assessment questions and alternative answers.

This study presents an autonomous agent-based green IS assessment tool that supports the assessment of enterprise toward inclusion of sustainability considerations to enhance enterprise environmental performance.

This study provides empirical evidence for data centers efficacy leading toward a greener society for environmental conservation for future generations to come.

This study creates awareness by presenting the green IS practice to be implemented by IT practitioners in data centers. In addition, the agent-based green IS assessment tool provides a web-based platform for promoting environmental sustainability by supporting data centers toward evaluating, benchmarking and rating their current green IS practices.