The paper examines the critical role of switch placement and optimal maneuver selection in distribution grids to enhance both voltage quality and reliability. It utilizes the Adaptive Parameter Gaining Sharing Knowledge-based Improved Multi-Operator Differential Evolution (APGSK-IMODE) algorithm to determine the optimal locations and statuses of switches for these improvements. Additionally, the impact of integrating distributed generation sources on grid voltage profiles and reliability is assessed. The research evaluates three distinct scenarios: improving voltage profiles, enhancing reliability indices, and achieving simultaneous improvements in both. The results show that integrating distributed generation sources positively impacts voltage profile enhancement and grid reliability, with significant improvements observed postinstallation. Furthermore, a comparative analysis of optimization algorithms demonstrates the superior performance and accuracy of the APGSK-IMODE algorithm over other methods.

The two service stations have been connected in series in our tandem queueing inventory model, replicating the real-life environment that provides customer service by providing seperated service without congestion. Customers arrive at Station 1, which has infinite servers (self-service), according to the Markovian arrival process, where they get an information about the inventory. After completion of service in Station 1, the customers can choose whether to move to the buffer in front of Station 2, where a single server gives service for buying inventory, or can exit the system with the predetermined probability. Once the customer who demands an inventory enters the buffer, they are instantly attached to an inventory from the unattached inventory of maximum capacity S , and the inventory replenishment follows ( s , Q ) ordering policy. In Station 2, the server provides two services that the customers can choose with a predefined probability. The server in Station 2 may break down at any time during service. When the server experiences the empty buffer, the server automatically moves to vacation until it finds the customer in the system. Exponentially distributed service, lead, breakdown, and repair times have been considered. The joint probability distribution of the number of customers in Station 1, the available inventory level, and the number of customers in the Station 2 buffer has been found. Numerical calculations have been done to show the sensitive analysis of all parameters.

This paper presents an innovative islanding detection method (IDM) that combines interval type-2 fuzzy logic with the Sandia frequency shift (SFS) technique for enhanced microgrid protection. The study examines a hybrid microgrid incorporating photovoltaic and wind farm distributed generation sources, explicitly accounting for uncertainties in solar irradiation, wind speed, and load demand through probabilistic modeling. Using probability density functions (PDFs), multiple uncertainty scenarios were generated, with representative scenarios selected via Monte Carlo simulation (MCS) for IDM development. The proposed approach further integrates a variable-parameter PSO (VP-PSO) algorithm to optimize both fuzzy membership functions and controller parameters. Comprehensive simulations demonstrate 100% detection accuracy for both PV and wind farm islanding scenarios, significantly outperforming conventional methods: standard SFS (84-86%), type-1 fuzzy SFS (89-91%), and neural network approaches (94-95%). This consistent 12-16% performance improvement establishes the method’s superior reliability for practical microgrid applications.

We propose a novel defense mechanism for protecting users from deepfakes by analyzing their behaviors in competitive activities and their social interactions. The model dynamically embeds user behaviors based on their participation in competitive activities, capturing these activities’ temporal dynamics through long short–term memory networks. This allows the model to effectively identify patterns and changes in user behaviors. BeGuard also considers users’ social relationships, embedding the behaviors of their social friends to account for the influence of these connections on their actions. This results in a richer and more contextually aware behavioral representation. To improve detection accuracy, the model uses an attention mechanism to evaluate abnormal values in user behaviors, particularly those indicating potential deepfake content. This attention‐based evaluation enhances the model’s capacity to detect subtle anomalies, providing a more effective defense against deepfakes in competitive activities–related social networks.

We propose a study to predict health abnormalities by analyzing body temperature and the heart rate variability parameters of pulse waves. The research method firstly selects fever by applying a deep learning model to thermal images, and secondly, extracts heart rate variability from pulse waves through a photo-plethysmograph sensor. It analyzes the relevance of body temperature and health status by dividing the presence or absence of fever cases and comparing parameters related to autonomic nerves and stress cases. As a result of the experiment, the control group with normal body temperature had a mean pulse variability of 37.65, SDNN of 64.83, and RMSSD of 42.24. In contrast, in the experimental group, which consisted of individuals with fever, the average pulse variability was 31.91, the SDNN was 42.34, and the RMSSD was 26.80. Our research expects to be applicable to a thermal imaging system that can measure body temperature and bio-signals together and show the predicted results of health status for fever cases.