As part of the European Green Deal, the EU aims to become climate-neutral and reach net-zero greenhouse gas emissions by 2050. Ιn this context, EU member states are required to develop a national strategy to achieve the required emissions reductions under the Paris Agreement and EU climate goals. Western Macedonia is a region in North-western Greece with its economy largely dominated by lignite mining, lignite-fired power plants and district heating systems. In 2019, the Greek Government set the goal of withdrawing all lignite plants by 2028, with most units being withdrawn already by 2023. This decision has had an immense socio-economic impact on the region of Western Macedonia. This research work reflects the current situation at the socio-economic and socio-political level in Western Macedonia and discusses the policies implemented in the context of the lignite phase-out process to ensure a just transition for households and businesses of the region. Although there is not a ‘one-size-fits-all’ blueprint for successful low-carbon transitions of high-carbon intensive regional economies, the main target of our paper is understanding the impacts, challenges and opportunities of decarbonizing Western Macedonia.

Recycling is vital for a sustainable and clean environment. Developed and developing countries are both facing the problem of solid management waste and recycling issues. Waste classification is a good solution to separate the waste from the recycle materials. In this work, we propose a cloud based classification algorithm for automated machines in recycling factories using machine learning. We trained an efficient MobileNet model, able to classify five different types of waste. The inference can be performed in real-time on a cloud server. Various techniques are described and used in order to improve the classification accuracy, such as data augmentation and hyper-parameter tuning. Multiple industrial stations are supported and interconnected via custom data transmission protocols, along with security features. Experimental results indicated that our solution can achieve excellent performance with 96.57% accuracy utilizing a cloud server.

In recent years, the production of municipal solid waste has constantly been increasing. Recycling is becoming more and more important, as it is the only way that we can have a clean and sustainable environment. Recycling, however, is a process that is not fully automated; large volumes of waste materials need to be processed manually. New and novel techniques have to be implemented in order to manage the increased volume of waste materials at recycling factories. In this paper, we propose a novel methodology that can identify common waste materials as they are being processed on a moving belt in waste collection facilities. An efficient waste material detection and classification system is proposed, which can be used in real integrated solid waste management systems. This system is based on a convolutional neural network and is trained using a custom dataset of images, taken on site from actual moving belts in waste collection facilities. The experimental results indicate that the proposed system can outperform existing algorithms found in the literature in real-world conditions, with 92.43% accuracy.

It is evident that over the last years, the usage of robotics in warehouses has been rapidly increasing. The usage of robot vehicles in storage facilities has resulted in increased efficiency and improved productivity levels. The robots, however, are only as efficient as the algorithms that govern them. Many researchers have attempted to improve the efficiency of industrial robots by improving on the internal routing of a warehouse, or by finding the best locations for charging power stations. Because of the popularity of the problem, many research works can be found in the literature regarding warehouse routing. The majority of these algorithms found in the literature, however, are statically designed and cannot handle multi-robot situations, especially when robots have different characteristics. The proposed algorithm of this paper attempts to give the following solution to this issue: utilizing more than one robot simultaneously to allocate tasks and tailor the navigation path of each robot based on its characteristics, such as its speed, type and current location within the warehouse so as to minimize the task delivery timing. Moreover, the algorithm finds the optimal location for the placement of power stations. We evaluated the proposed methodology in a synthetic realistic environment and demonstrated that the algorithm is capable of finding an improved solution within a realistic time frame.

The fostering of empathy among primary school students is an important goal because it enhances the improvement of behavior and the development of positive social contacts. Empathy can contribute to understanding and supporting others’ needs. In most cases, empathy in young children is developed through listening to sad stories, experienced first-hand by others. In the educational scenario presented in this article, the dramatic effects of climate change were conveyed to the pupils through a message said to originate from the future, delivered by an educational robot. The message was expressed by a peer living in Iran in 2050. In addition to delivering the message, the robot called on children to prevent climate change from rapidly worsening by changing their own way of thinking and attitudes. Thus, students called upon a formulated educational problem to understand and handle through their own emotional and cognitive performance through the robot’s storytelling. This performance was intensely affected by empathy towards the Iranian peer’s difficult personal living conditions. The research focused on measuring the evidence of empathy development. Additionally, the design and implementation aspects of the robot are presented, utilizing the implemented teaching intervention as means of demonstrating the innovative nature of the robot.

Most frequently, an FPGA is used as an implementation platform in applications of graphics processing, as its structure can effectively exploit both spatial and temporal parallelism. Such parallelization techniques involve fundamental restrictions, namely being their dependence on both the processing model and the system’s hardware constraints, that can force the designer to restructure the architecture and the implementation. Predesigned accelerators can significantly assist the designer to solve this problem and meet his deadlines. In this paper, we present our accelerators for Grayscale and Sobel Edge Detection, two of the most fundamental algorithms used in digital image processing projects. We have implemented those algorithms with a “bare-metal” VHDL design, written purely by hand, as a portable USB accelerator device, as well as an HLS-based overlay of a similar implementation designed to be used by a Python interface. The comparisons of the two architectures showcase that the HLS generated design can perform equally to or even better than the handwritten HDL equivalent, especially when the correct compiler directives are provided.