This article explores the design, modeling, and experimental validation of passive antenna arrays (AAs) tailored for unmanned aerial vehicle (UAV) communication systems. Focusing on the technical composition and functionalities of various types of passive antenna arrays, the study delves into different antenna elements that comprise these arrays, discussing their integration into comprehensive systems. Through rigorous modeling aimed at predicting performance in diverse operational conditions and backed by experimental studies, the paper provides practical insights for the development and optimization of AAs. These passive systems leverage the collective strength of multiple antennas to form directed beams, enhancing signal clarity and reducing interference, thereby supporting robust communication links essential for UAV operations. 

This paper is dedicated to a comprehensive analysis of hybrid energy options, with a specific focus on exploring their economic and environmental advantages within the context of an ice cream factory located in Fukuoka, Japan. The study takes a holistic approach, delving into various facets such as power generation, energy expenses, and related factors to uncover the potential benefits associated with specific configurations of hybrid energy solutions.  The analysis presented in this study serves as a valuable tool for assessing the impact of different power generation technologies and energy management strategies. It sheds light on how these choices can influence not only the factory's operational costs but also its environmental footprint. By quantifying these effects, the study provides critical insights  
that can guide decision-makers toward more sustainable and economically sound energy solutions. As a forward-looking application approach, this research envisions the utilization of a PV-wind-diesel-grid-electrolyzer power system. This hybrid configuration serves as a versatile platform for conducting simulation studies, allowing for the exploration of a wide spectrum of potentially viable solutions. The insights derived from these simulations not only facilitate informed decision-making but also pave the way for anticipating and strategically planning future energy implementations. In essence, this study represents a proactive and data-driven approach to energy optimization, offering the ice cream factory in Fukuoka a roadmap to harnessing the benefits of hybrid energy systems, ultimately contributing to both economic efficiency and environmental sustainability. So, at a cost of energy (COE) of 18.313¥ per kWh, this arrangement stands out as an economically advantageous and environmentally friendly solution for the electrification of the ice cream factory.

When you are driving a car and you are being responsible for your co-passenger and other innocent being on the road, you should be extra responsible. Many fatal and minor accidents happen on the road due to the drowsiness of drivers only. Hence, there is a need to detect drowsiness while driving a car. It has become an important requirement for everyone’s safety. The main objective of this study is to create a highly accurate drowsiness detection system using methods that are both affordable and easy for any car manufacturer to include in their cars. The ultimate objective is to increase road user’s protection by raising the level of safety for both drivers and their cars. This study's main contribution is the implementation of a bimodule method for drowsiness detection. The first module effectively detects signs of drowsiness by analyzing a constant stream of images of the driver in real time using a reinforcement learning model. Simultaneously, the car's second module, which is built into the steering wheel grip, keeps track of the driver's hand pressure when performing turns and emergency scenarios. The findings of the study highlight how well the proposed system works to reduce the risks associated with drowsy driving. It further highlights the value of cutting-edge technology in protecting other drivers and improving driving safety, which has the potential to save lives and avoid accidents.

Removing undesirable artifacts in electrocardiogram signals is essential for biological signal processing as the signal gets distorted and makes appropriate investigation challenging. A primary source of distortion affecting recordings is the 50Hz power line interference. To get a high-quality recording, we used a filtering method based on an efficient decomposition technique known as variational mode extraction. This approach is similar to the variational mode decomposition methodology but with a few alterations in mathematical computation. First, it extracts the noise efficiently in a specific frequency band. Then, we apply the discrete wavelet transform to the signal, employing soft thresholding. As a result, it eliminates the extra noise and filters the electrocardiogram signal. We evaluated the efficacy of our proposed method using an arrhythmia database. Furthermore, we compared recent decomposition methods on six random signals using signal-to-noise ratios, mean square errors, correlation coefficients, and other signal features. Our method also efficiently eliminates varying amplitude of powerline noise and finally outperforms decomposition strategies regarding noise reduction and processing complexity across all signal parameters.

The use of indicators is increasingly common, as they allow the visualization and analysis of useful data to assist in decision-making and planning. In this context, this article aimed to evaluate the process of automating indicators in the area of innovation in a multinational company located in Brazil, a transformer manufacturer. To this end, the themes of indicators and the application of Business Intelligence (BI) and process automation for project manager functions were conceptualized. The action research methodology developed automated indicators to facilitate decision-making and improve the productivity of the project team. These results also allowed for the improvement of visual and systemic management, by presenting all current projects and information on the occupancy rate of each team member, in addition to real-time control of whether goals are being achieved in the company. Thus, it was evident that centralizing data helps managers make more assertive decisions and the case reported can be replicated to other industrial sectors. In the academic sphere, the research also contributed to the evolution of the topic of project integration, automation and visual management of indicators using the Power BI tool.

Industry heavyweights like Microsoft, Amazon, and Google are at the forefront of the development and provision of cutting-edge and affordable cloud computing solutions, contributing to the widespread recognition of cloud computing. Without requiring direct human control, this technology provides network services, including data storage and computational power. But security becomes apparent as a major issue, hindering widespread adoption. The present study performs an extensive investigation to investigate security concerns related to cloud computing at several infrastructure levels, including application, network, host, and data. It examines significant issues that could impact the business model for cloud computing and discuss ways to solve security issues at every level that have been documented in the literature. The study identifies open problems, especially when considering cloud capabilities like elasticity, flexibility, and multi-tenancy, which create new problems at every infrastructure tier. Notably, it is found that multi-tenancy has a significant influence, contributing to security issues at all levels including abuse, unavailability, data loss, and privacy violations. The research ends with practical recommendations for additional studies targeted at improving overall cloud computing security. The results highlight the necessity of concentrated effort on mitigating security vulnerabilities resulting from multi-tenancy. This study makes a valuable contribution to the wider discussion on cloud security by identifying particular issues and supporting focused initiatives to strengthen the resilience of cloud infrastructure.

The classification of Fourier Transform Infrared spectra images is crucial in chemometrics. This paper proposes an efficient model based on deep learning approaches for enhancement and classification of the Fourier Transform Infrared Spectra (FTIR) images. The proposed model integrates three deep learning models including ResNet101, EfficientNetB0, and Wavelet Scattering transform (WST) to extract several features from FTIR.  Then the obtained features were fused in conjunction with standard statistical feature extraction. It followed by a subsequent classification phase that employs a Convolutional Neural Network (CNN) architecture, which demonstrates high accuracy in classifying the infrared spectra images into six different classes of ligands and their metal complexes. During the training phase, the network’s weights are iteratively updated using the Adam optimization algorithm. This model addresses the challenge of small and imbalanced datasets through an image oversampling process. Using random over-sampling technique, it enhances the training process and overall classification performance. The extracted features were analyzed using t-distributed Stochastic Neighbor Embedding (t-SNE) to visualize high-dimensional data in two dimensions. The results of the proposed model show high classification accuracy of 0.91%, low error rate of 0.08%, a sensitivity of 0.89% and a precision of 0.89%, false positive rate of 0.01%, F1 score of 0.89, Matthews Correlation Coefficient of 0.87 and Kappa of 0.68.

Simpson's Rule is a widely used numerical integration technique, but it cannot be applied to unequally spaced data. This paper presents a new generalization of Simpson's Rule using both Lagrange and Hermite interpolating polynomials to address this limitation. I provide a geometric interpretation of the method, showing its relationship to the area calculation of a trapezoid and a triangle, where the accuracy is significantly influenced by the chosen interpolating polynomial for midpoint determination. A comprehensive comparative analysis across various functions reveals that the Hermite-based approach consistently exhibits higher accuracy and stability than the Lagrange method, particularly with an increasing number of subintervals. This improved performance stems from the Hermite polynomial's ability to better approximate the function's behavior between data points. The findings highlight the effectiveness of the proposed Hermite-based generalization of Simpson's Rule in improving the accuracy of numerical integration for unequally spaced data, which is commonly encountered in practical applications.

This paper delves into the transformative potential of artificial intelligence (AI), particularly focusing on ChatGPT, within educational realms. By conducting an exhaustive review across various scholarly publications and case studies, this research unveils ChatGPT’s multifaceted role in redefining educational landscapes–ranging from enhancing programming proficiency and fostering creativity in writing, to augmenting student engagement. Our findings illuminate the dual-edged influence of ChatGPT in education, showcasing not only its ability to tailor learning experiences and facilitate programming and creative writing but also its capacity to fortify student-teacher interactions. However, the study does not shy away from highlighting the intricate challenges that accompany the integration of AI in education, including concerns over academic integrity, ethical considerations, and the need for a balanced amalgamation with traditional pedagogical methods. Innovatively, this research proposes a forward-thinking, ethical framework for AI integration in educational settings, advocating for a harmonious blend of ChatGPT’s capabilities with human educators' insights to foster a more engaging, effective, and equitable learning environment. By introducing groundbreaking strategies for integrating interactive learning technologies with ChatGPT, and emphasizing the development of personalized educational trajectories, our study sets a new benchmark for future AI applications in education. The paper’s exploration into the innovative integration of ChatGPT with Virtual Reality (VR) offers a glimpse into the future of immersive learning experiences, opening new avenues for engaging and experiential learning. Through empirical validation and a nuanced discussion on the ethical deployment of AI tools in education, this study marks a significant contribution to the discourse on AI’s role in education, providing valuable insights for educators, policymakers, and technologists alike.

Smart factory represents one of the main Industrial Internet of Things applications that contribute in the industrial activities in the smart cities. This field acquires a special attention with the age of industry 4 from upgrading the passive machine into cyber physical system point of view. However, the process of developing the traditional factory into smart factory faces some issues regarding to handle smart and safe management such as handling the requirements of smart factory applications and the communication networks that should transfer the data among different parts. The current review paper highlights the up-to-date related works in the field of industry 4 in terms of industrial internet of things (IIoT) to filling the gap between the operational technologies and information technologies. Hence, the different architectures of IIoT are taken into consideration of research paper scope in terms of investigation and analysis. This work concentrates on the smart factory application and aims to connect between applications requirements and communication networks in the field of the smart factory in order to hold the optimum management to this aspect. Moreover, the expected cyberthreats and cyberattacks in the smart factory are captured in this work to explain the suitable countermeasures against such cyberattacks.