Open Access

Advancing digitization has greatly increased the need for usable and secure identity management solutions in e-commerce. Digital identity wallets offer a promising solution by allowing users to securely store their personal data at one place on their smartphones and share it on demand in a self-determined way. In order to ensure the acceptance of digital identity wallets, the digital sovereignty of various stakeholders must be safeguarded. This not only promotes trust in digital technologies and services, but also protects against misuse and unauthorized access, which is crucial for maintaining privacy and security in the digital world. This article describes the elicitation of requirements in the area of digital sovereignty when integrating digital identity wallets into e-commerce. In particular, the requirements of three stakeholder groups—online shoppers, online shop operators, and intermediaries—were analyzed. For this purpose, the study used the MERDigS method for eliciting requirements in the area of digital sovereignty. As a result, 31 unique requirements through workshops and 16 unique requirements through interviews were elicited and grouped into seven key requirements. In addition, the quality of the method results and the method feasibility were evaluated using various evaluation criteria.

The paper discusses the process of block parallelization in the Advanced Encryption Standard (AES) cipher, focusing on the Counter (CTR) mode. It details the benefits of this process, including increased data processing performance and effective resource utilization; emphasizes the independent encryption of each data block in CTR mode, which allows for effective parallelization, especially when handling large data volumes. This work outlines the steps involved in the AES operation scheme in CTR mode, from splitting data into blocks to generating the final ciphertext. It further explains the concept of a unique "counter"or "initialization vector"for each block, which, combined with the key, generates a unique encryption key, enabling parallel processing. The idea implementation delves into the programming of the block parallelization algorithm using services on the Java Spring Boot platform. It describes the roles of the purposed Client Service and Server Service in encrypting and transmitting messages and files and decrypting received messages. This work presents an experiment that tests the hypothesis that blocks parallelization in AES cipher using CTR mode increases performance during the processing of large data volumes. The experiment involves different data volumes and compares the processing speeds of the AES algorithm with and without parallelization. The results confirm the hypothesis, showing that block parallelization in AES for large data volumes can double the data processing speed compared to the non-parallel approach. The paper concludes that block parallelization might be effective not only for the AES algorithm but also for any block symmetric algorithm. It also suggests that parallelization allows for more efficient use of multi-core systems and reduces the execution time to complete the encryption operation. © 2021 Copyright for this paper try its authors.

With climate change and global rising temperatures heat health warning systems have become important in accurately predicting heat waves. However, most heat health warning systems rely on the ambient temperature forecast and do not take indoor building conditions into consideration. Moreover, a general heat warning system cannot accurately predict the heat stress conditions in individual buildings. To implement the prediction algorithms the study also proposes a Raspberry Pi based measurement system. Furthermore, to reduce the computational load on Raspberry Pi a Transfer learning technique is implemented from a pre trained Long Short-Term Memory (LSTM) neural network. The results show prediction accuracy of 97% with an RMSE of 0.218 for indoor temperature prediction.

An algorithm for the integral calculation of efficiency maps of variable flux machines under consideration of the relative flux level is presented. The general idea and the algorithm logic are presented in detail and the efficiency map calculation is validated against a conventional gradient based efficiency map calculation algorithm. Instead of calculating efficiency maps for discrete flux levels and interpolating these maps afterwards, the algorithm presented here directly includes the relative magnet flux in the efficiency calculation. The presented approach uses torque and voltage surfaces in a plane of current, current angle and flux level. As results, the an exemplary efficiency map calculated with the algorithm is presented and the effect of different numbers of underlying data sets are discussed. Finally, a short outlook on practical applications is given.

This study applies semi-supervised learning to automate the differen-tiation of mold colonies, thereby reducing the time and cost associated with airquality assessments. EfficientNet V2 and Normalization-Free Net (NfNet) weretrained on a dataset of mold colony images, created in a semi-supervised way.NfNet demonstrated superior performance, particularly on non-padded images,with explainable AI techniques enhancing interpretability. The models exhibitedgeneralization capabilities to environmental samples, indicating the potential forautomating mold identification and streamlining air quality monitoring, therebyreducing manual effort and costs. Future work will focus on refining species han-dling and integrating the system into laboratory workflows.