This paper presents a survey of retrieval algorithms used in advertising recommendation and organic content recommendation systems. Modern digital platforms rely on retrieval-based models to efficiently match users with relevant advertisements or personalized content. This survey reviews key techniques including inverted index methods, collaborative filtering, content-based filtering, hybrid recommendation models, and the two-tower neural network architecture widely used in large-scale recommendation systems. The paper compares the objectives, data utilization strategies, and evaluation metrics of ad targeting and organic retrieval systems. Practical challenges such as cold-start problems, data quality, scalability, and privacy considerations are also discussed. This survey further highlights the growing connection between industrial recommendation pipelines and emerging retrieval mechanisms used in large language model (LLM) systems. This survey provides insights into the design principles of modern retrieval systems and outlines future research directions at the intersection of recommendation systems and LLM.
The June 2026 issue of this journal presents a comprehensive body of research advancing efficient engineering intelligence from foundational theory to real-world deployment, with strong alignment to the Sustainable Development Goals (SDGs). A significant cluster addresses SDG 7 (Affordable and Clean Energy) and SDG 9 (Industry, Innovation, and Infrastructure) through innovations in microbial fuel cells, high-voltage insulation reliability, artificial intelligence (AI) based battery management systems, and energy-efficient LoRa/LoRaWAN frameworks. These works emphasize energy sustainability, system resilience, and infrastructure optimization. A second cluster focuses on advanced electronics, control, and communication systems, including memcapacitor design, hybrid model predictive control, reflectarray antennas, and embedded intelligence for autonomous systems, demonstrating efficiency-driven engineering across hardware and system levels. A dominant cluster highlights SDG 3 (Good Health and Well-being), with applications in medical imaging, sepsis detection, breast cancer classification, and mental health analysis, leveraging deep learning, transformers, and hybrid AI models. Finally, contributions aligned with SDG 4 (Quality Education) explore gamified learning systems, virtual reality adoption, and SDG-integrated educational information systems, while complementary studies in agriculture, finance, and Internet of Things (IoT) further demonstrate the societal impact of intelligent systems. Collectively, these works reinforce the role of efficient, scalable, and data-driven engineering in addressing global challenges.
In this study, we test three machine learning methodologies − binary tree, k-nearest neighbors (k-NN), and neural networks (NN) − using a range of hyperparameters. These methods are applied to a dataset consisting of extracted time series characteristics (root mean square (RMS), skewness, and kurtosis from vibration signals of various bearings subjected to different fault conditions from the intelligent maintenance systems (IMS) dataset. We evaluate how effectively these methods classify the condition of the bearings using the provided dataset. We observe the top two methods, artificial neural network (ANN) 99.29% and binary tree 98.84%. With a difference of 0.45%, the binary tree is preferred over the complex ANN due to its ease of interpretation, transparency, and minimal computation requirements. Its integration as code in embedded controllers or electronic control units (ECUs) is more efficient, which makes them faster for real-time processing and safety-critical electric vehicle (EV) systems.
This study investigates the effect of surface pollution on a single-unit 11 kV glass suspension insulator using two-dimensional (2D) axisymmetric simulations in COMSOL Multiphysics. The developed model incorporates the electrical properties of glass, cement, steel electrodes, surrounding air, and a uniform pollution layer, with an applied AC voltage of 11 kV under quasi-static conditions. Simulation results demonstrate pronounced electric field intensification in the polluted configuration, particularly at the air–glass–cap triple junction region, where localized electrical stress is significantly higher compared to the clean condition. While the clean insulator operates within IEC 60383 recommended limits, the polluted model exhibits elevated peak electric field magnitudes, indicating increased flashover vulnerability. The findings highlight the strong influence of surface contamination, material permittivity, and geometric configuration on electric field distribution along the creepage path. This study establishes a reliable and computationally efficient predictive framework for optimizing insulator design, improving maintenance strategies, and enhancing the long-term reliability of high-voltage transmission systems, especially in pollution-prone environments.
In this research, the authors present a study analysis and compares two types of embedded internal permanent magnet synchronous motors (IPMSM) with U-type and V-type magnet configurations using finite element method (FEM) modeling to apply these motors to the currently popular electric vehicle industry. Parameters such as magnetic flux density, torque, cogging torque, back electromotive force (back-EMF), torque oscillation, and harmonic components were analyzed and compared; thereby identifying the advantages and disadvantages of the two IPMSM structures. Specifically, the V-type IPMSM motor offers higher efficiency, more stable torque, and a higher quality back electromotive force waveform with lower losses, making it suitable for high-performance applications such as electric vehicles and industrial automation. Meanwhile, the U-type structure has lower cogging torque, suitable for low-speed applications or those requiring high precision. Simulation results from the ANSYS Maxwell software show that the IPMSM motor is energy-efficient, has high power density, and operates smoothly, allowing for rapid acceleration, long range, compact configuration, and low maintenance; it uses permanent magnets on the rotor to eliminate losses, making electric vehicles lighter and more efficient than traditional motors.
