The quality of commutator surfaces in DC motors significantly affects the performance and longevity of the motors. Traditional methods of inspecting commutator surface defects, such as roundness and roughness meters, have limitations in detecting subtle and complex surface irregularities. This study proposes an image analysis technique combined with convolutional neural networks (CNNs) to enhance the detection of commutator surface defects. Our method improves the identification and classification of defects, correlating these findings with the assembly quality of DC motors. Although the experimental results are premilitary, it validates the effectiveness of the proposed approach, demonstrating improvements in defect detection accuracy. Future work will focus on expanding the image dataset and refining the CNN model to enhance its accuracy and real-time application capabilities.
This paper explores the transformative impact of AI-powered digital assistants on business operations and the evolving role of secretarial work. As organizations increasingly integrate artificial intelligence into their workflows, digital assistants are becoming essential tools that streamline tasks, enhance efficiency, and support decision-making processes. These AI-driven technologies are not only automating routine administrative functions but also enabling more strategic contributions by secretaries, such as managing complex schedules, data analysis, and personalized communication. The study examines how AI is reshaping the traditional secretarial role, leading to a shift in job responsibilities and skill requirements. It also discusses the potential challenges, such as ethical considerations and the need for upskilling, that come with this technological advancement. The findings suggest that AI-powered digital assistants are set to revolutionize the business landscape, offering both opportunities and challenges for the future of secretarial work.
In an increasingly connected digital era, security has become a critical aspect of online learning, particularly in programs like the TOEFL ITP (Test of English as a Foreign Language Institutional Testing Program) and Massive Open Online Courses (MOOCs). This study explores the implementation of the Internet of Things (IoT) to enhance security through two-factor authentication (2FA) on the TOEFL ITP MOOCs platforms. IoT offers a sophisticated and efficient solution for securing user access by utilizing connected devices, such as smartphones or wearables, which can verify user identity in real-time. The research delves into the IoT technology architecture that enables the integration of 2FA, analyzing its benefits and challenges, and assessing its impact on security and user experience. The implementation of IoT-based 2FA aims to protect personal data and prevent unauthorized access, ensuring that only verified users can access learning materials or take the TOEFL ITP exam. Additionally, this study highlights how enhancing security through IoT can build trust among users and boost the adoption of online learning technologies. The findings indicate that IoT implementation in 2FA not only improves security but also promotes academic integrity in an increasingly complex digital environment.
The present study investigates the monitoring of reservoir water levels to help prevent floods by accurately measuring and controlling the water flow through the reservoir’s door. The system uses multiple technologies, such as the OTT C31 Universal Current Meter, float level switches, and the Yagi-Uda antenna, to gather real-time data from substations and main stations around the dam. The data collected includes water level before and after the dam door, and water velocity at the dam door. Ensuring the efficiency and security of data transmission, the system employs an HT12E encoder and HT12D decoder, which are used to encode and decode data for secured transmission. The data was processed in Arduino Mega 2560 and sent to Raspberry Pi due to its ability to connect to Wi-Fi, which could host a website providing users with real-time data and reducing the damage caused by flooding. The real-time data transmission allows the system to significantly improve the capability for proactive flood management, making it a vital tool for protecting public safety and infrastructure. The simulation and experimental results demonstrate the effectiveness of the proposed system in both controlled and real-world environments. Key findings include the performance of the Yagi-Uda antenna at 433 MHz and the water velocity measurement through varied cross-sectional dam doors.
This work introduces a novel design for a circularly polarized monopolar microstrip antenna with an omnidirectional conical-beam radiation pattern. The antenna consists of hexagonal quasi-circular patches on the top plane, complemented by asymmetric trapezoid slots on the ground, all implemented on an FR4 substrate without the need for shorting vias. By integrating rotated asymmetric trapezoid ground slots, the antenna induces the degeneration of two adjacent TM02 resonant modes, resulting in a significant 10 dB impedance bandwidth covering 590 MHz, representing a 9.7% fractional bandwidth. The antenna achieves a radiation pattern characterized by successful Right-Handed Circularly Polarized (RHCP) co-polarization and axial ratio close to 3dB at the center frequency of 6.02 GHz. The parameters have been optimized using the finite element method-based full-wave electromagnetic simulator Ansys HFSS, ensuring precise design considerations for enhanced performance.
<div style="text-align:justify">Optical advances in skincare technology represent a revolutionary approach to addressing various dermatological concerns and enhancing overall skin health. This&nbsp; &nbsp;study&nbsp;provides an in-depth exploration of the principles, applications, and benefits of optical technologies in skincare. From non-invasive diagnostics to targeted treatments and cosmetic formulations, optical innovations are transforming the landscape of skincare, offering new possibilities for personalized and effective solutions. Optical advances in skincare technology have the potential to transform dermatological practice and improve skin health outcomes for individuals worldwide.&nbsp;Optical advances in skincare technology represent a revolutionary approach to addressing various dermatological concerns and enhancing overall skin health. This study&nbsp;provides an in-depth exploration of the principles, applications, and benefits of optical technologies in skincare. From non-invasive diagnostics to targeted treatments and cosmetic formulations, optical innovations are transforming the landscape of skincare, offering new possibilities for personalized and effective solutions. Optical advances in skincare technology have the potential to transform dermatological practice and improve skin health outcomes for individuals worldwide.</div>
In the rapidly advancing era of 6G networks, an efficient resource allocation (RA) is necessitated. Consequently, our paper reveals a sophisticated mathematical model based on Evolutionary Game Theory and replicator dynamics, designed to optimize and stabilize resource distribution. The model delineates how Evolutionary Stable Strategies (ESS) can be systematically identified and employed to enhance network efficiency and fairness significantly. Further, the integration of strategic interaction analysis and dynamic modelling demonstrates that ESS not only<br />respond adeptly to changing network conditions but also robustly guard against inefficiencies caused by signal degradation and user demand variability. Our empirical simulations validate the<br />model&rsquo;s effectiveness in fostering resilient and equitable RA, thereby setting a foundation for future 6G network designs that prioritize adaptability and sustainability. Furthermore, we<br />proposed a few algorithms, such as ESS sustainability and stabilization criteria for ESS, to depict the change in strategy population, which turns into the strategy fitness change and<br />convergence of strategic population, respectively. Moreover, our paper aims to highlight the innovative approach succinctly, additionally, the theoretical foundation and practical outcomes<br />of our research, focusing to engage and address a wider audience effectively in the upcoming era of next-generation communication technologies.
This research attempts to tackle the prevailing challenges in bandwidth estimation (BWE) for real-time communication systems, with a special emphasis on applying offline reinforcement learning to craft a more accurate neural network for bandwidth estimation than those built using traditional heuristics. The cultivated model, &quot;CQLBWE&quot;, represents a data-driven approach to BWE, operating offline. The model exploits heuristic-based techniques of the past to formulate a proficient BWE policy. Furthermore, the successful usage of CQLBWE underscores the practicability of deploying offline reinforcement learning algorithms in the field of bandwidth estimation.
In recent years, there has been an increasing number of software solutions presented to tackle the issue of energy usage at the application level. Nevertheless, there is little knowledge about the level of concern among software developers over energy use, the specific areas of energy consumption that they deem significant, and the potential solutions they propose for enhancing energy efficiency. In order to address this problem, academics and professionals have been investigating several strategies to enhance energy efficiency in computer systems. It may be an interesting project to use deep learning algorithms, especially those that make use of natural language processing (NLP) methods, to estimate software energy usage based on Stack Overflow data. This study examines the concerns of practitioners about energy consumption on Stack Overflow (SO) via the utilisation of Lexicon-based Sentiment Analysis, a concept in Natural Language Processing (NLP), combined with recurrent neural networks. The objective is to improve energy efficiency by forecasting time series data. The results of this study indicate that the practitioners&#39; desire to start conversations in the field of energy is closely linked to the utilisation of ideas. This analysis of software energy consumption issues may assist academics in identifying the most significant concerns for software developers and end users.
<strong>This work deals with the problem of prison overcrowding in Senegal and the use of electronic bracelets to reduce this overcrowding. Electronic bracelets collect a variety of data such as location, movements, communication data and biometric data. However, data security is a major concern. The aim of the work is to protect this data by using IoT and Fog Computing technologies to limit the data collection perimeter, thereby reducing the transfer of massive amounts of data to remote data offices. The architecture implemented aims to collect only the necessary data from remand and correctional office controlled by departmental courts, to comply with data protection laws and to implement security policies to prevent external attacks. This approach aims to guarantee data confidentiality while enabling the use of electronic bracelets to improve the prison situation in Senegal.</strong><br />&nbsp;
The Internet is integral to modern life, with ISPs offering appealing deals to meet the demand for unlimited data. However, reality often falls short of expectations. While recommendation systems exist, user-centric options are rare. This paper proposes a novel ISP selection methodology using user experience data and a Calibrated Random Forest (CRF) model. Unlike traditional methods that focus on advertised features, this approach emphasizes user-defined criteria such as cost, device connectivity, and technical support experience. By analyzing survey data, the model highlights the critical link between user needs and support quality, enabling users to choose ISPs that prioritize customer service. The model demonstrates promising results with a strong R-squared value and low Mean Squared Error (MSE). This user-centric approach fosters informed decision-making, potentially driving competition and encouraging ISPs to improve service standards, laying a foundation for future developments in ISP selection.
Efficient resource allocation in Device-to-Device (D2D) communication within 6G networks is crucial for enhancing overall network performance and efficiency. This paper presents a novel Deep Learning (DL) based approach for Radio Resource Allocation (RRA), leveraging Distributed Artificial Intelligence (DAI) using Belief-Desire-Intention eXtended (BDIx) agents, dynamic feedback allocation, and a Deep Feedback Neural Network (DFBNN). Additionally, Federated Learning (FL) is integrated to enable distributed training across BDIx agents, serving as D2D Relays (D2DR) or D2D Multihop Relays (D2DMHR), ensuring data privacy and reducing communication overhead. The proposed method is thoroughly evaluated against traditional graph-based and game-theoretic algorithms and Deep Feedforward Neural Networks (DFNN). Results demonstrate significant improvements in interference management, data rate, and execution time. By providing scalable, adaptive, and resilient resource allocation, this proposed method meets the stringent requirements of 6G applications, paving the way for more efficient and reliable network operations.
