Decades-old research (by the authors and others) on global Information Technology (IT) trends demonstrates a steady increase in IT’s role in organizational competitiveness. Digital transformation is bringing profound change to everything we do. Nowhere has this been more apparent than the changes demanded by the COVID-19 pandemic. However, the impact on every company began well before and will continue long after the pandemic. What has differentiated the pandemic conundrum is the ubiquity of the impact, a reality that demanded immediate changes to strategies and plans. This research analyzes IT issues and trends before and during the pandemic using data from global IT trends research and surveys to project how IT sustainability will be shaped post-pandemic. The paper examines the top significant management concerns and technology considerations to project the pandemic’s lasting impact on IT and to answer the important question: Will IT revert back to where we were pre-pandemic? We conclude that the changes in IT, and specifically, digital transformation, are not just the result of an emergency-induced pivot, but a broad recognition that IT will sustain its fundamental role in driving/enabling significant economic value across every organization. The research concludes that IT in the post-pandemic era will continue to grow as an essential vehicle for competitive advantage and agility, while promoting sustainability through digital transformation.

Monitoring and forecasting hospitalization rates are of essential significance to public health systems in understanding and managing overall healthcare deliveries and strategizing long-term sustainability. Early-stage prediction of hospitalization rates is crucial to meet the medical needs of numerous patients during emerging epidemic diseases such as COVID-19. Nevertheless, this is a challenging task due to insufficient data and experience. In addition, relevant existing work neglects or fails to exploit the extensive contribution of external factors such as news, policies, and geolocations. In this paper, we demonstrate the significant relationship between hospitalization rates and COVID-19 infection cases. We then adapt a transfer learning architecture with dynamic location-aware sentiment and semantic analysis (TLSS) to a new application scenario: hospitalization rate prediction during COVID-19. This architecture learns and transfers general transmission patterns of existing epidemic diseases to predict hospitalization rates during COVID-19. We combine the learned knowledge with time series features and news sentiment and semantic features in a dynamic propagation process. We conduct extensive experiments to compare the proposed approach with several state-of-the-art machine learning methods with different lead times of ground truth. Our results show that TLSS exhibits outstanding predictive performance for hospitalization rates. Thus, it provides advanced artificial intelligence (AI) techniques for supporting decision-making in healthcare sustainability.

This paper evaluates the effect of energy trade networks on the price volatility of coal, oil, natural gas, and electricity. This research conducts a longitudinal analysis using a time series of static coal trade networks to generate a dynamic trade network. It uses the component causality index as a leading indicator of the price volatility of the energy market. This research finds out that the component causality index, based on degree centrality, anticipates or moves together with coal volatility and, to a lesser degree, with natural gas and electricity volatility for the period 1998–2014. The proposed index could be integrated into a risk management system for investors and regulators. The broad impact of this research lies in the understanding of mechanisms of the instability and risk of the energy sector as a result of a complex interaction of the network of producers and traders.

This study investigates how to design sensor systems in a way that responds to certain factors in the environment. This decision analysis problem focuses on sensor placement: how to place sensors to find an intruder that is affected by environmental elements. The sensors we use are of two types: the first type detects targets, and the second type detects elements in the environment. Techniques from the learning automata literature are used to develop a detection mechanism. The approach proposed in this study is dynamic, and can adjust to environmental variations. And its rate of detection exceeds static approaches, such as evenly spread sensor configuration. This work has implications for the design of any sensor system in which the physical environment shapes the probability of events occurring.

Security issues have received increasing attention in recent years. Due to the difficulty of predicting where a terror event will occur, it is a great challenge to develop methods of detection that preempt attack. Sensors are one such method. However, questions of effective sensor placement remain-it is hard to determine where to place sensors because of uncertainty over the location of an attack. In this paper we use the intruders' behavioral constraints in the face of environmental factors as input to a learning algorithm that optimizes sensor placement. We show through simulation results that this algorithm can dynamically optimize placement by letting sensors make local decisions about where to move in situ. The resulting configurations are more or less equivalent to those achieved by the global optimization of sensor placement. The technique is superior in the sense that re-optimization happens continuously, and can be done with distributed control. Also, in many situations the configurations achieved are better than spacing sensors equally: detection rates are far higher.