Environmental benefits of energy poverty alleviation,renewable resources,and urbanization in North Africa

Addressing energy poverty via expanding renewable energy sources and advancing sustainable development strategy is a crucial goal for developing nations. Therefore, the study aims to investigate relationships between carbon dioxide emission, renewable and nonrenewable energy resources, economic growth, urbanization, and energy poverty in the North African region, namely: Tunisia, Sudan, Morocco, Libya, and Egypt from 1993 to 2021, by employing Panel PMG ARDL and Granger causality approaches. According to empirical results, addressing energy poverty (access to electricity), economic growth, and using renewable and nonrenewable energy sources worsen the environment. Two-way causal relationships are founded between environmental deterioration and other variables: economic growth, energy resources, and urbanization. Based on the findings, policymakers should step up their efforts to promote green urbanization and alleviate energy poverty through renewable energy sources.     

When architecture meets AI: A deep reinforcement learning approach for system of systems design

How to design System of Systems has been widely concerned in recent years, especially in military applications. This problem is also known as SoS architecting, which can be boiled down to two subproblems: selecting a number of systems from a set of candidates and specifying the tasks to be completed for each selected system. Essentially, such a problem can be reduced to a combinatorial optimization problem. Traditional exact solvers such as branch-bound algorithm are not efficient enough to deal with large scale cases. Heuristic algorithms are more scalable, but if input changes, these algorithms have to restart the searching process. Re-searching process may take a long time and interfere with the mission achievement of SoS in highly dynamic scenarios, e.g., in the Mosaic Warfare. In this paper, we combine artificial intelligence with SoS architecting and propose a deep reinforcement learning approach DRL-SoSDP for SoS design. Deep neural networks and actor–critic algorithms are used to find the optimal solution with constraints. Evaluation results show that the proposed approach is superior to heuristic algorithms in both solution quality and computation time, especially in large scale cases. DRL-SoSDP can find great solutions in a near real-time manner, showing great potential for cases that require an instant reply. DRL-SoSDP also shows good generalization ability and can find better results than heuristic algorithms even when the scale of SoS is much larger than that in training data.     

So many projects,so little result: The self-perpetuating cycle of inter-institutional projects

This study aims to provide an explanation for the lack of implementation of innovation generated through publicly funded research. While previous scholars have categorized organizational cycles as either virtuous or vicious, cycles of inter-institutional projects can have simultaneous benefits for some organizations while causing drawbacks for others. Such a cycle was observed across inter-institutional projects in port logistics, where the primary objective was to implement innovation. During the investigation of ten projects, it became apparent that an excessive emphasis on certain practices at the expense of others, unintentionally resulted in delays in innovation implementation while collaborations continued to thrive. These practices led to a self-perpetuating cycle of inter-institutional projects that rarely resulted in implemented innovations. In contrast to the solutions proposed in existing literature to address organizational cycles, this study suggests that temporary hybridizing competing logics may be the root cause of cycles of inter-institutional projects.