Abstract
Worldwide power and energy systems are changing significantly [...]

Abstract
This paper examines the idea of Industry 4.0 from the perspective of the molds industry, a vital industry in today's industrial panorama. Several technologies, particularly in the area of machining equipment, have been introduced as a result of the industry's constant modernization. This technological diversity makes automatic interconnection with production management software extremely difficult, as each brand and model requires different, mostly proprietary, interfaces and communication protocols. In the methodology presented in this paper, a development of monitoring solutions for machining devices is defined supporting the leading equipment and operations used by molds industry companies. OPC UA is employed for high-level communication between the various systems for a standardized approach. The approach combines various machine interfaces on a single system to cover a significant subset of machining equipment currently used by the molds industry, as a key result of this paper and given the variety of monitoring systems and communication protocols. This type of all-in-one approach will provide production managers with the information they need to monitor and improve the complete manufacturing process.

Abstract
Nowadays, cities face severe problems related to traffic management and mobility in general. Therefore, technologies have been developed that can handle these situations and somehow mitigate the caused impact, such as CCTV cameras. However, the techniques for analyzing the images collected by these cameras are increasingly complex and have numerous applications, being dispersed in the literature. Therefore, this article fills an important research gap by presenting a systematic review of the literature on the possible applications of data collected from CCTV cameras and the image analysis and processing techniques that have been developed and proposed in recent years. This systematic review followed the PRISMA statement guidelines and checklist, and three databases were searched, namely Scopus, Web of Science, and Inspec. From the analysis performed, the following applications were identified: Image/video analysis and traffic estimation, pedestrian detection, traffic data analysis, and forecasting, and traffic management. Regarding the image analysis and processing techniques YOLO (only look once), GMM (Gaussian mixture method), morphological methods, fuzzy logic, and other proprietary methods stand out. After a thorough analysis of traffic data, most works still implemented relatively trivial traffic management systems to generate a series of actions to be eventually applied to traffic controllers. Additionally, it was realized that these techniques could be implemented in industrial products from a future perspective. © 2023, Aracne Editrice. All rights reserved.

Abstract
This paper proposes a three-stage model for managing energy communities for local energy sharing and providing grid flexibility services to tackle local distribution grid constraints. The first stage addresses the minimization of each prosumer's individual energy bill by optimizing the schedules of their flexible resources. The second stage optimizes the energy bill of the whole energy community by sharing the prosumers' energy surplus internally and re-dispatching their batteries, while guaranteeing that each prosumer's new energy bill is always be equal to or less than the bill that results for this prosumer from stage one. This collective optimization is designed to ensure an additional collective benefit, without loss for any community member. The third stage, which can be performed by the distribution system operator (DSO), aims to solve the local grid constraints by re-dispatching the flexible resources and, if still necessary, by curtailing local generation or consumption. Stage three minimizes the impact on the schedule obtained at previous stages by minimizing the loss of profit or utility for all prosumers, which are furthermore financially compensated accordingly. This paper describes how the settlement should be performed, including the allocation coefficients to be sent to the DSO to determine the self-consumed and supplied energies of each peer. Finally, some case studies allow an assessment of the performance of the proposed methodology. Results show, among other things, the potential benefits of allowing the allocation coefficients to take negative values to increase the retail market competition; the importance of stage one or, alternatively, the need for a fair internal price to avoid unfair collective benefit sharing among the community members; or how stage three can effectively contribute to grid constraint solving, profiting first from the existing flexible resources.

Abstract
Modern electrical distribution networks are prone to more severe voltage fluctuations due to the presence of variable loads such as electric vehicles and renewable energy generation units. These fluctuations decrease both the quality of power and the hosting capability of the grid. In such a condition, a Dynamic Voltage Compensator (DVC) can be used to stabilize the voltage of the LV networks. DVC is generally designed to resolve voltage fluctuations reflected from MV systems maintaining the voltage on a constant value. However, it will more effectively improve the voltage quality in the grid if the reference voltage is dynamically adjusted based on measurements inside the LV system. On the other hand, the more complex measurement and coordination strategy may lead to the inapplicability of the methods. Hence, voltage reference adjustment strategies should be developed to conform to the availability of data and measurements inside the grid. Accordingly, in this paper, novel voltage reference adjustment strategies have been developed for DVC based on the measurements at the installation point of the device. In order to examine the proposed methods, they are applied to an LV grid with real measured data and the results are discussed. Based on the provided simulation results, the developed dynamic reference voltage adjustment strategies can successfully improve the quality of voltage and improve the hosting capacity of the LV network. © 2023 IEEE.

Abstract
A common approach for improving performance uses FPGAs to accelerate critical code regions, which often involves two processes: hardware/software partitioning, which identifies regions to offload to the FPGA; and optimizing those regions (e.g., through HLS directives). As both processes are separate and usually applied in sequence, the interplay between them is unnatural, and it is unclear how the choices made in one step can benefit the choices made in the other step. This paper presents our work-in-progress for combining partitioning and optimization into a single holistic process. First, our source-to-source compiler builds a task-based representation from the input application. Then, a greedy algorithm builds clusters of tasks and assigns each cluster to either hardware (FPGA) or software (CPU). The algorithm iteratively refines the clusters and offloading decisions by: a) minimizing the communication costs between clusters by assigning tasks that work with shared data to the same cluster; b) reducing the global execution time by applying code optimizations to the tasks in each cluster. We show the impact of our holistic approach to a motivating edge detection example and compare the results when applying partitioning and code optimizations as independent steps. The results show that a holistic partitioning can lead to a speedup of up to 28.7x when compared to a simple offloading of the application to an FPGA.