Abstract
The advent of Industry 4.0 has created a need for more flexible and adaptable manufacturing systems. This paper proposes the integration of AAS (Asset Administration Shells), SBM (Skill-based manufacturing) and OPC UA (Open Platform Communications Unified Architecture), to enable more flexible manufacturing systems. The integration of these concepts provides a solution for achieving faster and easier dynamic reconfiguration in manufacturing systems, which is essential for fulfilling the demand of customization and flexibility in modern production systems. An Asset Administration Shell provides a standardized structure for describing assets and their administration, while Skill-based manufacturing enables the deployment of task-oriented machines that can self-configure, self-diagnose, and self-optimize their performance. The use of OPC UA as a communication protocol ensures that these systems can communicate with one another in a secure and reliable way. This paper presents a conceptual framework for the integration of these three open technologies. This framework contributes to having a single interface and source of information for every asset, which can lead to increased efficiency by reducing changeover times, thus reducing the overall cost in flexible manufacturing system scenarios. Future work will focus on the implementation and validation of this framework in a real-world manufacturing setting.

Abstract
The concept of Industry 4.0 and the introduction of the Internet of Things (IoT) on industrial applications, known as Industrial Internet of Things (IIoT), have been changing the scenario of industrial automation. This new paradigm is expected to optimize industrial processes, increase productivity, lower costs and improve operations integration. For that, structured Machine-to-Machine (M2M) communication is key to ensure agility, interoperability and reliability, with several solutions currently available in the literature and in industry. This paper reviews the state of the art on industrial communication protocols and architectures, providing a classification and comparison of these different solutions based on their most relevant features in the context of Industry 4.0. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

Abstract
This paper describes the relevant research activities that are being carried out on the development of a novel shotcrete technology capable of applying, autonomously and in real time, fibre reinforced shotcrete (FRS) with tailored properties regarding the optimum structural strengthening of railway tunnels (RT). This technique allows to apply fibre reinforced concrete (FRC) of strain softening (SSFRC) and strain hardening (SHFRC) according to a multi -level advanced numerical simulation that considers the relevant nonlinear features of these FRC, as well as their interaction with the surrounding soil, for an intended strengthening performance of the RT. Building information modelling (BIM) is used for assisting on the development of data files of the involved design software, integrating geometric assessment of a RT, damages from inspection and diagnosis, and the characteristics of the FRS strengthening solution. A dedicated computational tool was developed to design FRC with target properties. The preliminary experimental results on the evaluation of the relevant mechanical properties of the FRS are presented and discussed, as well as the experimental tests on the bond between FRS and current substrates found in RT. Representative numerical simulations were performed to demonstrate the structural performance of the proposed FRS -based strengthening technique. Computational tools capable of assuring, in real time, the aimed thickness of the layers forming the FRS strengthening shell were also developed. The first generation of a mechanical device for controlling the amount of fibres to be added, in real time, to the FRS mixture was conceived, built and tested. A mechanism is also being developed to improve the fibre distribution during its introduction through the mechanical device to avoid fibre balling. This work describes the relevant achievements already attained, as introduces the planned future initiatives in the scope of this project.

Abstract
Despite the ubiquitous term climate neutral cities, there is a distinct lack of quantifiable and meaningful municipal decarbonization goals in terms of the targeted energy balance and composition that collectively connect to national scenarios. In this paper we present a simple but useful allocation approach to derive municipal targets for energy demand reduction and renewable expansion based on national energy transition strategies in combination with local potential estimators. The allocation uses local and regional potential estimates for demand reduction and the expansion of renewables and differentiates resulting municipal needs of action accordingly. The resulting targets are visualized and opened as a decision support system (DSS) on a web-platform to facilitate the discussion on effort sharing and potential realization in the decarbonization of society. With the proposed framework, different national scenarios, and their implications for municipal needs for action can be compared and their implications made explicit.

Abstract
The paper presents the comparison of accuracy in the Speech Emotion Recognition task using the Hamming and Hanning windows for framing the speech and determining the spectrogram to be used as input of a convolutional neural network. The detection of between 4 and 10 emotional states was tested for both windows. The results show significant differences in accuracy between the two window types and provide valuable insights for the development of more efficient emotional state detection systems. The best accuracy between 4 and 10 emotions was 64.1% (4 emotions), 57.8% (5 emotions), 59.8% (6 emotions), 48.4% (7 emotions), 47.8% (8 emotions), 51.4% (9 emotions), and 45.9% (10 emotions). These accuracy is at the state-of-the art level.

Abstract
The increasing complexity of integrated energy systems with the electric power grid requires innovative control solutions for efficient management of smart buildings and distributed energy resources. Accurately predicting weather conditions and electricity demand is crucial to make such informed decisions. Machine learning has emerged as a powerful solution to enhance prediction accuracy by harnessing advanced algorithms, but often requires complex parameterizations and ongoing model updates. The Lawrence Berkeley National Laboratory's Autonomous Forecast Framework (AFF) was developed to greatly simplify this process, providing reliable and accurate forecasts with minimal user interaction, by automatically selecting the best model out of a library of candidate models. This work expands on the AFF by not only selecting the best model, but assembling a blend of multiple models into a hybrid forecast model. The validation within this work has shown that this combination of models outperformed the selected best model of the AFF 31%, while providing greater resilience to individual model's forecast error.