Application of Genetic Algorithm and Analytical Method to Determine the Appropriate Locations and Capacities for Distributed Energy System

In this study, the genetic algorithm (GA) and an analytical technique are used to properly connect the distributed energy system (DES) to the distribution network of the Federal Capital Territory (FCT). A power flow solution is used to obtain the losses and voltages assigned to the chromosomes as the fitness value for the GA to determine the best locations for the DES. Subsequently, the analytical method is used to calculate the capacities of the DES, corresponding to each location obtained using the GA. The effectiveness of the technique is examined on IEEE 33 and 69 buses, and the results demonstrate a loss reduction of 69.19%, the least voltage of 0.975 pu for the 33-node, and a 70.22% loss reduction with the least voltage of 0.985 pu for the 69-node. The suggested technique is applied to the FCT distribution network, and the results show a 70% voltage improvement and 14.05% loss reduction

Challenges and Solutions to Criminal Liability for the Actions of Robots and AI

Civil liability legislation is currently being developed, but little attention has been paid to the issue of criminal liability for the actions of robots. The study describes the generations of robots and points out the concerns about robots’ autonomy. The more autonomy robots obtain, the greater capacity they have for self-learning, yet the more difficulty in proving the failure foreseeability when designing and whether culpability or the elements of a specific crime can be considered. In this study, the tort liability depending on the category of robots is described, and the possible solutions are analyzed. It is shown that there is no need to introduce new criminal law constructions, but to focus on the process of proof. Instead of changing the legal system, it is necessary to create the most detailed audit trail telling about the robot’s actions and surroundings or to have a digital twin of the robot

Evolution of Vortex Structures Generated by a Rigid Flapping Wing with a Winglet in Quiescent Water

This study aims to the utilization of vortex structures generated through wing flapping for achieving sustainable flight, and the motivation is elicited by the phenomenon observed in natural flyers. The vortex structures in the flow field generated by a flapping rigid wing are captured using vorticity and the LAMDA2 criterion. The study investigates a comparative analysis between a wing both with and without a winglet. Moreover, the influence of flapping frequency is examined as well. For the experiments, particle image velocimetry (PIV) measurements are employed for the flow field around mechanical flapping motion in a quiescent water condition. The flapping mechanism has one-degree freedom, showing a 1:3 ratio in motion, and tested wings at 1.5 and 2.0 Hz. A “modified” vortex filamentation and fragmentation phenomenon is proposed as a significant finding in the present study, based on a comprehensive analysis of the flow field around the wing with a winglet

Enhanced Design of On-Chip Monopole Antenna Inspired by Partially Reflective Surface at 5.8 GHz

The increasing popularity of compact, chip-based devices has spurred interest in developing on-chip antennas (OCAs). However, OCAs suffer from low gain and poor radiation efficiency due to the silicon substrate’s low resistivity and high permittivity, influencing antenna performance. To avert these challenges, this study aims to enhance an OCA’s gain and radiation efficiency by incorporating a partially reflective surface (PRS) into the antenna structure. The antenna is simulated using 3D CST software, and its performance is evaluated. To validate the simulation, an antenna prototype is fabricated using sputtering and chemical vapor deposition (CVD) technologies. The prototype demonstrates a peak gain of 2.14 dB and radiation efficiency of 72.2%, showing a 24.3% gain increase and a 16.25% efficiency increase compared to the design without PRS. Additionally, it achieves an impedance bandwidth of 0.63 GHz, making it suitable for WiMAX, RFIC, and Wi-Fi 6 applications

Improving Healthcare Communication: AI-Driven Emotion Classification in Imbalanced Patient Text Data with Explainable Models

Sentiment analysis is crucial in healthcare to understand patients’ emotions, automatically identifying the feelings of patients suffering from serious illnesses (cancer, AIDS, or Ebola) with an artificial intelligence model that constitutes a major challenge to help health professionals. This study presents a comparative study on different machine learning (logistic regression, naive Bayes, and LightGBM) and deep learning models: long short-term memory (LSTM) and bidirectional encoder representations from transformers (BERT) for classify health feelings thanks to textual data related to patients with serious illnesses. Considering the class imbalance of the dataset, various resampling techniques are investigated. The approach is complemented by an explainable model, LIME, to understand the shortcomings of the classification results. The results highlight the superior performance of the BERT and LSTM models with an F1-score of 89%

A Fake Profile Detection Model Using Multistage Stacked Ensemble Classification

Fake profile identification on social media platforms is essential for preserving a reliable online community. Previous studies have primarily used conventional classifiers for fake account identification on social networking sites, neglecting feature selection and class balancing to enhance performance. This study introduces a novel multistage stacked ensemble classification model to enhance fake profile detection accuracy, especially in imbalanced datasets. The model comprises three phases: feature selection, base learning, and meta-learning for classification. The novelty of the work lies in utilizing chi-squared feature-class association-based feature selection, combining stacked ensemble and cost-sensitive learning. The research findings indicate that the proposed model significantly enhances fake profile detection efficiency. Employing cost-sensitive learning enhances accuracy on the Facebook, Instagram, and Twitter spam datasets with 95%, 98.20%, and 81% precision, outperforming conventional and advanced classifiers. It is demonstrated that the proposed model has the potential to enhance the security and reliability of online social networks, compared with existing models

Innovative Approach to Enhance Stability: Neural Network Control and Aquila Optimization Integration in Single Machine Infinite Bus Systems

This paper highlights the need to improve the stability of single-machine infinite-bus (SMIB) systems, which is crucial for maintaining the dependability, efficiency, and safety of electrical power systems. The changing energy environment, characterized by a growing use of renewable sources and more intricate power networks, is challenging established stability measures. SMIB systems exhibit dynamic behavior, particularly during faults or unexpected load variations, requiring sophisticated real-time stabilization methods to avert power failures and provide a steady energy supply. This paper suggests a complex approach that combines power system stability analysis with a neural network controller enhanced by the Aquila optimization algorithm (AOA) to address the dynamic issues of SMIB systems. The study shows that the AOA-optimized neural network (AOA-NN) controller outperforms in avoiding disruptions and attaining speedy stabilization by exhaustively examining electrical, mechanical, and rotor dynamics. This method improves power system resilience and operational efficiency as demands and technology expand

Recognition of Ginger Seed Growth Stages Using a Two-Stage Deep Learning Approach

Monitoring the growth of ginger seed relies on human experts due to the lack of salient features for effective recognition. In this study, a region-based convolutional neural network (R-CNN) hybrid detector-classifier model is developed to address the natural variations in ginger sprouts, enabling automatic recognition into three growth stages. Out of 1,746 images containing 2,277 sprout instances, the model predictions revealed significant confusion between growth stages, aligning with the human perception in data annotation, as indicated by Cohen’s Kappa scores. The developed hybrid detector-classifier model achieved an 85.50% mean average precision (mAP) at 0.5 intersections over union (IoU), tested with 402 images containing 561 sprout instances, with an inference time of 0.383 seconds per image. The results confirm the potential of the hybrid model as an alternative to current manual operations. This study serves as a practical case, for extensions to other applications within plant phenotyping communities

Road Repair Delay Costs in Improving the Road Rehabilitation Strategy through a Comprehensive Road User Cost Model

This study delves into quantifying the adverse effects of road damage on users, particularly focusing on the increased travel time and consequent financial burdens stemming from delayed repairs. Utilizing a comparative method, the research underscores notable reductions in speed and prolonged travel times due to damaged roads, leading to substantial economic losses for road users. To streamline the estimation of road user costs (RUC), the study proposes a simulation model that incorporates varying traffic volumes and repair delays. This model demonstrates a high level of accuracy in estimating RUC, revealing heightened sensitivity to fluctuations in traffic volume and repair delays compared to agency costs. Consequently, the research underscores the imperative of implementing effective repair strategies to alleviate these impacts efficiently, thereby emphasizing the significance of timely infrastructure maintenance in mitigating financial burdens on road users

Iris Recognition Scheme Based on Entropy and Convolutional Neural Network

This study presents an advanced iris image segmentation approach to overcome vibration and occlusion from the lashes. The proposed scheme removes the surrounding areas of the iris image to recover the region of interest (ROI) containing the iris images. The entropy function and mathematical morphology are employed as the foundation of the proposed scheme. Initially, the entropy function is applied to the binarization image. Subsequently, the ROI is cropped and extracted from the binary image using the dilation method. Furthermore, a convolutional neural network (CNN) is used in the recognition phase. The database of the Indian Institute of Technology Delhi (IIT Delhi) serves as a test. The results yield a high level of accuracy—up to 93% during segmentation. Using half of the dataset during the recognition phase results in an accuracy of 98.8%, while using the complete database produces an accuracy of 97.5%