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.

Abstract
The paper presents a modification of the Magnitude Optimum Multiple Integration (MOMI) method process non-parametric data in the frequency domain instead of the time domain The required frequency data are obtained directly from the filtered amplitude -shifted process step response and have been shown to be relatively insensitive to normally distributed process noise. All calculations, including the calculation of the PID controller parameters, are performed analytically. The closed loop responses to tested processes with added normally distributed noise were relatively fast with small or no overshoot, all according to the Magnitude Optimum (MO) method. The proposed method is not limited to open loop step responses or to the PID controller structure.

Abstract
Integrating processes can be found in various industries. The main characteristic of such processes is that a limited process input can cause an unlimited process output. In general, they are more difficult to control compared to stable processes. The recently developed Magnitude optimum multiple integration tuning method for integrating processes provides very good closed -loop responses. However, it uses a reference -weighting 2-DOF PI(D) controller structure where the weighting parameters for the P and D term of the controller are equal (therefore the user can only change one parameter). Another drawback of the existing method is that it needs to find the roots of the fourth -order algebraic equation. The method proposed here does not require finding these roots and provides better tracking compared to the original method while maintaining optimal disturbance rejection for different integrating process models.

Abstract
Recently introduced Generalized Pre-trained Transformers (GPT) and conversional chatbots such as ChatGPT are causing deep society transformations. The incorporation of these Artificial Intelligence technologies can be beneficial in multiple science and development areas including Control Engineering. The evaluation of GPTs within Control Engineering Education and PID control is addressed in this work. Different types of interactions with GPTs are evaluated and the use of a personalized GPT for PID tuning explored. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)