Browsing by Issue Date, starting with "2022-05-06"
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- B Impact Assessment as a Sustainable Tool: Analysis of the Certification ModelPublication . Silva, Vítor; Lima, Vanda; Sá, José Carlos; Fonseca, Luís; Santos, GilbertoCurrently, certification is an essential tool for a company’s sustainability and a seal of trust for the stakeholders. The B Corporation (B Corp) certification system is in line with the leading indicators of sustainable development and social responsibility published by the general assembly of the United Nations, namely: environment, community, workers, customers, and governance. Nevertheless, it is essential that academic research should empirically assess the B Corp model’s reliability for its validation and legitimization. In this study, we address the results of the B Impact Assessment of 2262 companies certified by B Corp from the beginning of 2017 to March 2021. The main objective is to analyze the B Impact Assessment, verifying the robustness and consistency of the model to measure and improve the economic, social, and environmental impact of companies. We analyzed the construct’s validity through a confirmatory factorial analysis using AMOS statistical software. The results allowed us to identify some weaknesses and limitations of the B Impact Assessment. This certification system reflects an unadjusted model where the main assessment indicators have problems with regard to the measurement scale. The governance and customer indicators are the most vulnerable. The findings also allow us to state that there are apparently no minimum values established for each of the parameters evaluated, which may cause imbalances in the sustainable development process of B Corp companies. This research contributes to enhancing B Impact Assessment as a sustainability tool, highlighting areas for improvement concerning the indicators’ measurement scales and the assessment process, including the monitoring of evaluators.
- Data-driven Flight Control of Internet-of- Drones for Sensor Data Aggregation using Multi-agent Deep Reinforcement LearningPublication . Li, Kai; Ni, Wei; Emami, Yousef; Dressler, FalkoEnergy-harvesting-powered sensors are increasingly deployed beyond the reach of terrestrial gateways, where there is often no persistent power supply. Making use of the internet of drones (IoD) for data aggregation in such environments is a promising paradigm to enhance network scalability and connectivity. The flexibility of IoD and favorable line-of-sight connections between the drones and ground nodes are exploited to improve data reception at the drones. In this article, we discuss the challenges of online flight control of IoD, where data-driven neural networks can be tailored to design the trajectories and patrol speeds of the drones and their communication schedules, preventing buffer overflows at the ground nodes. In a small-scale IoD, a multi-agent deep reinforcement learning can be developed with long short-term memory to train the continuous flight control of IoD and data aggregation scheduling, where a joint action is generated for IoD via sharing the flight control decisions among the drones. In a large-scale IoD, sharing the flight control decisions in real-time can result in communication overheads and interference. In this case, deep reinforcement learning can be trained with the second-hand visiting experiences, where the drones learn the actions of each other based on historical scheduling records maintained at the ground nodes.