Abstract
Recent device-level cyber-attacks have targeted IoT critical applications in power distribution systems integrated with the Internet communications infrastructure. These systems utilize group domain of interpretation (GDOI) as designated by International Electrotechnical Commission (IEC) power utility standards IEC 61850 and IEC 62351. However, GDOI cannot protect against novel threats, such as IoT device-level attacks that can modify device firmware and configuration files to create command and control malicious communication. As a consequence, the attacks can compromise substations with potentially catastrophic consequences. With this in mind, this article proposes a permissioned/private blockchain-based authentication framework that provides a solution to current security threats such as the IoT device-level attacks. Our work improves the GDOI protocol applied in critical IoT applications by achieving decentralized and distributed device authentication. The security of our proposal is demonstrated against known attacks as well as through formal mechanisms via the joint use of the AVISPA and SPAN tools. The proposed approach adds negligible authentication latency, thus ensuring appropriate scalability as the number of nodes increases. Our work addresses the problem of device-level cyber-attacks such as device identity theft and introduction of fake nodes in GDOI-enabled smart grids. It introduces a permissioned blockchain based device authentication management in the GDOI phase 1 and periodic device integrity check in phase 2 to achieve decentralized authentication and device-level security. image

Abstract
-This article integrates forecasting methods with an optimized tariff model to assess the effectiveness of the schedule proposed by the Brazilian Association of Energy Traders, as outlined in the technical note NT n degrees 10/ 2022-SRM/ANEEL. This note discusses the regulatory measures to fully open the free energy market to all captive consumers by 2026. Several models, including Winters, SARIMA, ARIMA, and trend analysis were compared to determine the most suitable method for each input variable in the TAROT model, aiming to enhance forecasting accuracy. The results show that the new schedule proposed in the technical note successfully maintains the balance between the economic surplus of concessionaires and the socioeconomic welfare. Despite both approaches declining during the migration period of low-voltage consumers, their surpluses remain positive. However, there are negative effects on tariffs, impacting all consumer groups remaining in the regulated market, both high-voltage and low-voltage consumers. A key conclusion is that further regulatory changes are essential to mitigate additional increases in energy tariffs, aligning the proposed schedule with the reduction of legacy contracts.

Abstract
The study of user logs plays a crucial role in understanding user behavior and preferences in various online environments. By analyzing user logs, researchers can gain valuable insights into how users interact with a system and make informed decisions on system improvements. They can also assess the effectiveness of different features and functionalities. In the field of game design, the exploration of user logs becomes even more important as it provides valuable information on player motivations, preferences, and gameplay patterns. This research explores the impact of Bartle Taxonomy on user behavior analysis through a Game with a Purpose (GWAP) named BartleZ. By analyzing user logs and decisions within the game, BartleZ aims to determine the dominant player type according to the Bartle Taxonomy classification. This research also investigates how different player types engage with the game and the implications for user experience design.

Abstract
The literature reveals that science parks offer numerous benefits and support services to the activity of a technological startup. However, the decision of choosing the best science park for the startup tends to be an informal process, technically not very rigorous and planning, arising essentially by affinities with the research centre and university. In this study, a decision support system is presented to support entrepreneurs in the process of selecting a science park for the implementation of their startup. The AHP method is used to compare the importance of the criteria for selecting a science park, which includes factors such as location, activity sector, infrastructure, cost, and size. The findings reveal that the use of this decision support system helps entrepreneurs to find a science park that is suitable for the needs of their startup and allows them to comparatively identify the most relevant criteria when choosing a science park. © 2024 Inderscience Enterprises Ltd.. All rights reserved.

Abstract
Machine Learning models need large amounts of annotated data for training. In the field of medical imaging, labeled data is especially difficult to obtain because the annotations have to be performed by qualified physicians. Natural Language Processing (NLP) tools can be applied to radiology reports to extract labels for medical images automatically. Compared to manual labeling, this approach requires smaller annotation efforts and can therefore facilitate the creation of labeled medical image data sets. In this article, we summarize the literature on this topic spanning from 2013 to 2023, starting with a meta-analysis of the included articles, followed by a qualitative and quantitative systematization of the results. Overall, we found four types of studies on the extraction of labels from radiology reports: those describing systems based on symbolic NLP, statistical NLP, neural NLP, and those describing systems combining or comparing two or more of the latter. Despite the large variety of existing approaches, there is still room for further improvement. This work can contribute to the development of new techniques or the improvement of existing ones.

Abstract
The gastrointestinal tract is part of the digestive system, fundamental to digestion. Digestive problems can be symptoms of chronic illnesses like cancer and should be treated seriously. Endoscopic exams in the tract make detecting these diseases in their initial stages possible, enabling an effective treatment. Modern endoscopy has evolved into the Wireless Capsule Endoscopy procedure, where patients ingest a capsule with a camera. This type of exam usually exports videos up to 8 hours in length. Support systems for specialists to detect and diagnose pathologies in this type of exam are desired. This work uses a rarely used dataset, the ERS dataset, containing 121.399 labelled images, to evaluate three models from the EfficientNet family of architectures for the binary classification of Endoscopic images. The models were evaluated in a 5-fold cross-validation process. In the experiments, the best results were achieved by EfficientNetB0, achieving average accuracy and F1-Score of, respectively, 77.29% and 84.67%. Copyright © 2024 by SCITEPRESS – Science and Technology Publications, Lda.