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An out-branching B u + (in-branching B u −) in a digraph D is a connected spanning subdigraph of D in which every vertex except the vertex u , called the root, has in-degree (out-degree) one. A good (u,v) - pair in D is a pair of branchings B u + , B v − which have no arc in common. Thomassen proved that it is NP-complete to decide if a digraph has any good pair. A digraph is semicomplete if it has no pair of non-adjacent vertices. A semicomplete composition is any digraph D which is obtained from a semicomplete digraph S by substituting an arbitrary digraph H x for each vertex x of S. Recently the authors of this paper gave a complete classification of semicomplete digraphs which have a good (u , v) -pair, where u , v are prescribed vertices. They also gave a polynomial algorithm which for a given semicomplete digraph D and vertices u , v of D , either produces a good (u , v) -pair in D or a certificate that D has no such pair. In this paper we show how to use the result for semicomplete digraphs to completely solve the problem of characterizing semicomplete compositions which have a good (u , v) -pair for given vertices u , v. Our solution implies that the problem of deciding the existence of a good (u , v) -pair and finding such a pair when it exists is polynomially solvable for all semicomplete compositions. We also completely solve the problem of deciding the existence of a good (u , v) -pair and finding one when it exists for digraphs that are compositions of transitive digraphs. Combining these two results we obtain a polynomial algorithm for deciding whether a given quasi-transitive digraph D has a good (u , v) -pair for given vertices u , v of D. This proves a conjecture of Bang-Jensen and Gutin from 1998. [ABSTRACT FROM AUTHOR]

The impacts of climate change and development present significant challenges and complexities that require new solutions, wise choices, and multi-disciplinary integration. In this context, emotional intelligence (EI) plays a crucial role. However, traditional engineering education and practice overlook the importance of understanding and managing emotions. This research aims to determine the impact of EI as a tool to enhance proactive decision-making and implement sustainable measures within the engineering profession.The study makes three main research contributions. First, it confirms a positive relationship between EI and proactive sustainable decision-making among engineers. This means that engineers with high EI are more likely to consider the impacts of their decisions on various stakeholders and dimensions of sustainability. Second, it suggests that EI can enhance creativity and innovative thinking in engineering, helping engineers to develop effective solutions for challenges related to climate change. Third, the study advocates for incorporating EI training and assessment into engineering curriculums to foster a sustainable and ethical engineering culture. By improving EI, engineers can enhance their interpersonal skills, self-awareness, and emotional management, which in turn can significantly improve teamwork in addressing challenges related to climate change. • Innovative Approach: The paper introduces emotional intelligence (EI) as a novel factor in engineering decision-making for climate change adaptation, which provides a fresh perspective within the realm of sustainable practices. • Relevance to Sustainability: It emphasizes the role of EI in fostering proactive and strategic decision-making, which is crucial for mitigating and adapting to a changing climate, aligning with the journal's focus on sustainable solutions1. • Multidisciplinary Integration: The study bridges engineering, psychology, and environmental science, reflecting the journal's interdisciplinary approach to addressing sustainability challenges. • Practical Implications: By exploring the attitudes of engineers and their decision-making tendencies in relation to EI, the paper offers insights that can lead to targeted interventions, contributing to the journal's aim of promoting sustainable practices. • This paper does not constitute a bibliometric study; rather, it provides empirical research on the impact of EI on sustainable decision-making among engineers. [ABSTRACT FROM AUTHOR]

Effective engagement is crucial for enhancing environmental decision-making processes, fostering more sustainable and equitable outcomes. However, the success of engagement is highly variable and context-dependent. While theoretical frameworks have been developed to explain outcome variance in engagement in environmental decision-making, they have not yet been tested in digital contexts, leaving their applicability to digital engagement processes unclear. More broadly, there are unanswered questions about the effectiveness of digital tools in achieving the goals of engagement, which have become increasingly pertinent amidst growing concerns about the potential of digital technologies for exacerbating exclusions, ethical issues, and systematically undermining democratic progress. This paper addresses this evidence gap by presenting findings from interviews with practitioners in UK public, private, and third sector organisations. Our results provide empirical insights into the technical, ethical, and inclusivity debates surrounding digital tools and their effectiveness in promoting accessible engagement, high-quality social interaction, place-based decision-making, and more trustworthy and credible outcomes. Our findings indicate that while current engagement theories are applicable to digital environments, the key explanatory factors acquire new dimensions in digital compared to in-person contexts. Drawing on the findings, this study contributes novel insights to expand current theory for explaining "what works" in engagement in environmental decisions, enhancing its relevance and applicability in the digital age. The paper concludes with evidence-led recommendations for environmental practitioners to improve engagement processes in digital and remote settings. [Display omitted] • There is a lack of evidence on the benefits and drawbacks of digital tools for engagement. • The applicability of current theories for engagement to digital and remote settings is unclear. • Key factors known to shape outcomes in engagement take on new dimensions in digital, remote environments. • There are new considerations which make engagement theory more relevant and applicable in digital contexts. [ABSTRACT FROM AUTHOR]

In order to deal with the fast changing requirements of container based services in clouds, auto-scaling is used as an essential mechanism for adapting the number of provisioned resources with the variable service workloads. However, the latest auto-scaling approaches lack the comprehensive consideration of variable workloads and hybrid auto-scaling for multi-type services. Firstly, the historical data based proactive approaches are widely used to handle complex and variable workloads in advance. The decision-making accuracy of proactive approaches depends on the prediction algorithm, which is affected by the anomalies, missing values and errors in the historical workload data, and the unexpected workload cannot be handled. Secondly, the trigger based reactive approaches are seriously affected by workload fluctuation which causes the frequent invalid scaling of service resources. Besides, due to the existence of scaling time, there are different completion delays of different scaling actions. Thirdly, the latest approaches also ignore the different scaling time of hybrid scaling for multi-type services including stateful services and stateless services. Especially, when the stateful services are scaled horizontally, the neglected long scaling time causes the untimely supply and withdrawal of resources. Consequently, all three issues above can lead to the degradation of Quality of Services (QoS) and the inefficient utilization of resources. This paper proposes a new hybrid auto-scaling approach for multi-type services to resolve the impact of service scaling time on decision making. We combine the proactive scaling strategy with the reactive anomaly detection and correction mechanism. For making a proactive decision, the ensemble learning model with the structure improved deep network is designed to predict the future workload. On the basis of the predicted results and the scaling time of different types of services, the auto-scaling decisions are made by a Deep Reinforcement Learning (DRL) model with heterogeneous action space, which integrates horizontal and vertical scaling actions. Meanwhile, with the anomaly detection and correction mechanism, the workload fluctuation and unexpected workload can be detected and handled. We evaluate our approach against three different proactive and reactive auto-scaling approaches in the cloud environment, and the experimental results show the proposed approach can achieve the better scaling behavior compared to state-of-the-art approaches. • We design a hybrid auto-scaling system architecture for multi-type cloud services. • We propose a workload prediction model based on ensemble learning. • We propose a DRL based auto-scaling mechanism for multi-type cloud services. • We propose a anomaly correction mechanism for alleviating scaling error impact. [ABSTRACT FROM AUTHOR]

Planning under complex uncertainty often asks for plans that can adapt to changing future conditions. To inform plan development during this process, exploration methods have been used to explore the performance of candidate policies given uncertainties. Nevertheless, these methods hardly enable adaptation by themselves, so extra efforts are required to develop the final adaptive plans, hence compromising the overall decision-making efficiency. This paper introduces Reinforcement Learning (RL) that employs closed-loop control as a new exploration method that enables automated adaptive policy-making for planning under uncertainty. To investigate its performance, we compare RL with a widely-used exploration method, Multi-Objective Evolutionary Algorithm (MOEA), in two hypothetical problems via computational experiments. Our results indicate the complementarity of the two methods. RL makes better use of its exploration history, hence always providing higher efficiency and providing better policy robustness in the presence of parameter uncertainty. MOEA quantifies objective uncertainty in a more intuitive way, hence providing better robustness to objective uncertainty. These findings will help researchers choose appropriate methods in different applications. • Introduce reinforcement learning to decision-making under deep uncertainty. • Compare reinforcement learning and evolutionary algorithm. • Reinforcement learning is consistently more computationally efficient. • Reinforcement learning provides better robustness to parameter uncertainty. • Evolutionary algorithm provides better robustness to objective uncertainty. [ABSTRACT FROM AUTHOR]

• An algorithm is designed to reduce the network blocking rate. • Markov Decision Process based state transfer is provided for online RMSA in real-time. • Offline Deep Q-Network based algorithm is provided to get a well-trained Q-network helping the RMSA state transfer. • The experience priority replay mechanism and Sumtree are introduced to speed up Deep Q-Network training. The continuous growth of the network communication demand puts higher requirements on the network infrastructures. The elastic optical network (EON) has great potential to support the continued demands for communication bandwidth. Efficient use of bandwidth resources of EON has been particularly important to alleviate network blocking, which depends on routing, modulation, and spectrum allocation processes (RMSA). However, the time-varying states of EON caused by the uncertainty of future demands make it a lot tougher to realize the online RMSA in real time. To solve the above problem, this paper proposes a kind of Deep Q Network (DQN) algorithm with prioritized experience replay mechanism to perform the RMSA process in real-time. The proposed algorithm includes two parts. One is the Markov Decision Process (MDP) based state transfer for online RMSA by a trained Q-network. The other is an offline DQN-based algorithm for getting a trained Q-network to help the decision-making of RMSA state transfer, where the experience priority replay mechanism and Sumtree are introduced to speed up DQN training. Simulation results show that compared with the traditional Deep Q Network algorithm, the proposed algorithm nearly doubles the Q-network training speed. And compared with the traditional sp + ff algorithm, the trained Q-network reduces the blocking rate by nearly 35 %. [ABSTRACT FROM AUTHOR]