How construction and demolition waste management has addressed sustainable development goals: Exploring academic and industrial trends.

With the rapid growth of the construction industry and urbanization, the construction and demolition waste (CDW) has constituted the most major solid waste flow in the world. The unsustainable management of CDW causes serious societal and environmental issues, as well as leads to resource waste, which directly and indirectly impact on United Nations' Sustainable Development Goals (SDGs). Due to the awareness of the destructive effect by CDW, the academic and industry have devoted to offer a sustainable pathway for CDWM, which characterizes minimizing carbon footprints as well as proposing circular approaches. In this context, CDW can be reused, recycled and recovery as a value resource. Therefore, this study proposed a unique research method that combines qualitative and quantitative approaches in the form of bigdata analysis and machine learning, which aims to explore the CDWM related knowledge and innovation from academic and industrial perspective respectively. Especially, what is different trends in CDWM-related of academia and industry between pre- and post-SDGs declaration era(s)? What aspects of SDGs have been addressed by academia and industry in pre- and post-SDGs declaration era(s)? The study witnessed that a 350% increase in the growth of academic literature and a 278% increase in the growth in industrial patents compared to pre-SDGs declaration period. In the academia, the emerging topics of research has shifted to management, circular economy, life cycle assessment, and ETC. Similarly, patent citation illustrated that the attention of stakeholders on CDWM in the construction industry has shifted from a linear point to a circular view. Moreover, the result showed that SDG6 (Clean Water and Sanitation), SDG12 (Responsible Consumption and Production) and SDG11 (Sustainable Cities and Communities) have noted as most seriously addressed concerns by academia and industry.

How publications and patents are contributing to the development of municipal solid waste management: Viewing the UN Sustainable Development Goals as ground zero.

Academia and industry have strengthened each other under the guidelines of regulatory institutions to contribute theoretical knowledge and practical solutions for society, which can be presented in a combination of publishing research and filing patents. In the case of municipal solid waste management (MSWM), a great transformation from a linear to a circular view has been in process. In this study, we investigated the role of the United Nations Sustainable Development Goals (SDGs) in MSWM-related development and transformation. The authors examined the contributions of academic and industrial spheres to MSWM in the past 70 years by examining Web of Science's Core Collection and Derwent Innovations Index. The results showed that SDGs not only accelerated the research on MSWM but also pulled MSWM-related knowledge and innovation to new fronts that focus on sustainable and circular methods. Based on the current findings, we derived implications for academia, industry, and policymakers.

A Machine Learning-Based Anomaly Prediction Service for Software-Defined Networks.

Software-defined networking (SDN) has gained tremendous growth and can be exploited in different network scenarios, from data centers to wide-area 5G networks. It shifts control logic from the devices to a centralized entity (programmable controller) for efficient traffic monitoring and flow management. A software-based controller enforces rules and policies on the requests sent by forwarding elements; however, it cannot detect anomalous patterns in the network traffic. Due to this, the controller may install the flow rules against the anomalies, reducing the overall network performance. These anomalies may indicate threats to the network and decrease its performance and security. Machine learning (ML) approaches can identify such traffic flow patterns and predict the systems' impending threats. We propose an ML-based service to predict traffic anomalies for software-defined networks in this work. We first create a large dataset for network traffic by modeling a programmable data center with a signature-based intrusion-detection system. The feature vectors are pre-processed and are constructed against each flow request by the forwarding element. Then, we input the feature vector of each request to a machine learning classifier for training to predict anomalies. Finally, we use the holdout cross-validation technique to evaluate the proposed approach. The evaluation results specify that the proposed approach is highly accurate. In contrast to baseline approaches (random prediction and zero rule), the performance improvement of the proposed approach in average accuracy, precision, recall, and f-measure is (54.14%, 65.30%, 81.63%, and 73.70%) and (4.61%, 11.13%, 9.45%, and 10.29%), respectively.