The increasing demand for electricity in residential areas demands the expansion of efficient and reliable distribution channels. This study aims to analyze the technical, economic, social, and environmental aspects of the expansion of 20 kV electricity distribution channels in housing. From a technical point of view, the use of a voltage of 20 kV was chosen to support the efficiency of transmission and long-distance distribution with minimal power loss. Economically, the project requires a significant initial investment, but can provide long-term benefits in the form of cost efficiencies and reduced power outages. From a social perspective, improving electricity reliability can improve the quality of life of residential communities, although socialization and coordination are needed to minimize disruptions during the construction phase. Environmental analysis shows the importance of selecting environmentally friendly materials and designing paths that minimize the impact on local ecosystems. The results of the analysis show that the expansion of 20 kV electricity distribution channels in housing is a feasible and sustainable solution, with a significant positive impact on socio-economic development and community comfort.

Introduction: With the arrival of Generative Artificial Intelligence in university classrooms, a future with continuous challenges is perceived. Objective: To analyse the results obtained by students of the Clinical Surgical I course when solving a clinical case using different tools of Generative Artificial Intelligence from the point of view of the doctor and the patient. Materials and methods: Descriptive research, with a sample of 50 Surgical Clinic I students from a private university in Mar del Plata. Results: The experience was developed in three stages. In the first stage, a clinical case of a patient with abdominal pain of 24 hours of evolution is presented. The students, in groups, select Generative Artificial Intelligence answer search engines to solve the case, formulate prompts requesting diagnostic impression, treatment of the presumptive diagnosis and consultation on symptoms. In the second stage, they select the main concepts and with the information obtained, they elaborate two conceptual networks from both points of view per group. A multimedia presentation is shared with the results and this generates an exchange of opinions. In the third stage, an online form is sent to consult situations in which the use of IAG would be significant for a doctor, they indicate differential diagnoses 100%, Treatment 80%, Recommendations 60% Anamnesis 40%, as to what Artificial Intelligence recognises about the patient's viewpoint 20% confusion, 50% motivate the patient to attend a consultation, 60% eliminate diagnostic doubts, 80% obtain more information. Conclusions: Medical education has been presented with the challenge of thinking about how to take advantage of the potential of Generative Artificial Intelligence so that professionals in training have the tools to perform in future scenarios, being reflective in their use in the face of ethical dilemmas that may arise.

Motion images are now available everywhere due to the technological advancements and the growing demand for engaging content in social media and communication. The motion images offer better user experience and allow for more effective storytelling and interaction in a visually driven digital landscape. The features from the motion images can be utilized for biometric security, healthcare, and beyond if extracted with high degree of accuracy. This study proposed a novel framework for iris features extraction from a sequence of motion image. The method entails capturing and analyzing iris features while the subject's eye is in motion. The proposed framework has 4 stages: preprocessing, image segmentation, graph representation and features extraction.

The evaluation of user experience (UX) in Augmented Reality (AR) apps is crucial for assessing the quality of interaction between users and applications, particularly within the tourist industry. An AR interactive game application created for The Sila's Agrotourism in Bali seeks to provide a more immersive and instructive tourism experience through the integration of monster hunting game features. Despite employing AR technology to deliver a novel experience, it is crucial to assess the degree to which this app fulfills user expectations. This study is to assess the user experience of the application utilizing the User Experience Questionnaire (UEQ), including six principal dimensions: Attractiveness, Clarity, Efficiency, Accuracy, Stimulation, and Novelty. This research employs data gathering using a UEQ questionnaire administered to 34 participants who have utilized the application. Data analysis was conducted utilizing descriptive statistics, including the computation of the mean and standard deviation. The evaluation results indicate that the app received favorable scores across nearly all categories, particularly in Attractiveness, Clarity, and Novelty, suggesting its efficacy in capturing attention and delivering enjoyable new experiences. Nevertheless, the Stimulation component received a little lower score, suggesting opportunities for enhancing user involvement. The program functioned adequately; however, enhancements to the stimulation component could elevate the overall quality of the user experience.

CV. Global Bali Expert, a tourism-related organization and the focus of this investigation, has applied revenue estimates from the preceding period as a benchmark for predicting future revenue. This method has resulted in marketing strategies that are less mature and ambiguous in terms of the company's development. One potential resolution to this problem is to implement revenue forecasting calculations. This study contrasts the Chen and Singh Fuzzy Time Series models to ascertain which forecasting model is more precise for this organization. The Chen model predicted Rp. 19,229,988 for the total revenue category, with a MAPE accuracy rate of 19.13%, and the Singh model predicted Rp. 20,074,992 for the same category, with a MAPE accuracy rate of 5.17%. The Singh Fuzzy Time Series model outperforms the Chen model, as can be inferred from this research.

Result from seismic analyses performed on 4 RC buildings with two different configurations in horizontal and vertical. In vertical like; step back building and step back-set back building and in horizontal symmetrical and unsymmetrical in the terms of bays are presented. Analysis including torsional effect has been carried out by using response spectrum method. The dynamic response properties i.e. fundamental time period, top story displacement and the base shear induced in columns have been studied with reference to the suitability of a building configuration on sloping ground. It is observed that step back-set back buildings are more suitable on sloping ground as compared to step back building.

The energy industry is significant by digitalization, renewable integration, and automation, making it increasingly reliant on interconnected systems. This evolution exposes the sector to sophisticated cyber threats targeting critical infrastructure, operational technologies (OT), and supply chains, enhancing the cybersecurity landscape of the energy industry, offering several opportunities in the industry. This article explores dual role of AI in shaping the energy industry's cybersecurity framework, emphasizing its impact on threat prevention and performance optimization. Key aspects such as the integration of AI-driven anomaly detection systems, real-time response capabilities, and secure energy grid operations are analyzed. The article also examines the challenges of implementing AI-based cybersecurity, including ethical concerns, data privacy, and the risk of adversarial AI attacks. The article shows the necessity for a fair approach to harness AI’s potential while ensuring robust, secure, and efficient energy operations.