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Continuous developments in the electronics industry have led to constantly changing career roles and graduate skills requirements. Students in the University of Electronic Science and Technology of China (UESTC) have complained that numerous courses in electronic engineering are heavily focused on theoretical knowledge that is disconnected from the needs of the industry. Thus, in an effort toward delivering student-centered educational programmes that meet the needs of the industry, this article introduces an innovative course that was developed using the project-based learning (PBL) method and situated in the electronic engineering undergraduate programme at UESTC. Since real-world engineering projects require teams to collaborate on ill-defined problems, we focused this innovative course on developing professional and technical skills, drawing from a range of more typical electronic engineering courses. We provide full details of two projects that were created for this PBL approach and evaluate them as practice examples that demonstrate the impact of this practical pedagogic innovation in UESTC. According to our evaluation, completed by all 40 of our enrolled students, our innovative course based on interdisciplinary PBL exercises demonstrated a significant improvement in student satisfaction and 65% of students preferred the interdisciplinary PBL course in comparison to traditional lecture-based courses.

This article demonstrates the integration of sensor fusion for pose estimation and data collection in tennis balls, aiming to create a smaller, less intrusive form factor for use in progressive learning during tennis practice. The study outlines the design and implementation of the Bosch BNO055 smart sensor, which features built-in managed sensor fusion capabilities. The article also discusses deriving additional data using various mathematical and simulation methods to present relevant orientation information from the sensor in Unity. Embedded within a Vermont practice foam tennis ball, the final prototype product communicates with Unity on a laptop via Bluetooth. The Unity interface effectively visualizes the ball's rotation, the resultant acceleration direction, rotations per minute (RPM), and the orientation relative to gravity. The system successfully demonstrates accurate RPM measurement, provides real-time visualization of ball spin and offers a pathway for innovative applications in tennis training technology. [ABSTRACT FROM AUTHOR]

In this research article, the objective is to determine the return on investment (ROI) of photovoltaic (PV) power plants by employing machine learning (ML) techniques. Special focus is done on the levelized cost of electricity (LCOE) as a pivotal economic parameter crucial for facilitating economic decision‐making and enabling quantitative comparisons among different energy generation technologies. Traditional methods of calculating LCOE often rely on fixed singular input values, which may fall short in addressing uncertainties associated with assessing the financial feasibility of PV projects. In response, a dynamic model that integrates essential demographic, energy, and policy data, is introduced encompassing factors such as interest rates, inflation rates, and energy yield, which are anticipated to undergo changes over the lifetime of a PV system. This dynamic model provides a more accurate estimation of LCOE. The comparative analysis of ML algorithms indicates that the auto‐regression integration moving average (ARIMA) model exhibits a high accuracy of 93.8% in predicting consumer electricity prices. The validation of the model is highlighted through two case studies in the United States and the Philippines underscores the potential impact on LCOE values. For instance, in California, LCOE values could vary by nearly 30% (5.03 cents kWh−1 for singular values vs 7.09 cents kWh−1 using our ML model), influencing the perceived risk or economic feasibility of a PV power plant. Additionally, the ML model estimates the ROI for a grid‐connected PV plant in the Philippines at 5.37 years, in contrast to 4.23 years using traditional methods. [ABSTRACT FROM AUTHOR]

In today's interconnected world, teamwork and collaboration are becoming essential competencies across all disciplines. This review examines various pedagogical strategies aimed at nurturing these skills, with a specific focus on integrating algorithms into educational practices. While traditional approaches classify teamwork strategies as either instructor-led or student-led, this review introduces a third method that is based on ML algorithms, which are promising methods for optimizing team composition based on both static and dynamic student characteristics. We investigate the effectiveness of these algorithms in enhancing collaborative learning outcomes compared to conventional grouping methods. In fact, this review synthesizes the findings from 20 key studies on the implementation of these technologies in educational settings, evaluating their impact on learning outcomes, student motivation and overall satisfaction. Our findings suggest that computer-enhanced strategies not only improve the academic and collaborative experience but also pave the way for more personalized and dynamic educational environments. This review aims to provide educators and curriculum developers with comprehensive insights into leveraging advanced computational tools to foster effective teamwork and interdisciplinary collaboration, thereby enhancing the overall quality of education and preparing students for the collaborative demands of the professional world. [ABSTRACT FROM AUTHOR]

Beamforming, an integral component of modern mobile networks, enables spatial selectivity and improves network quality. However, many beamforming techniques are iterative, introducing unwanted latency to the system. In recent times, there has been a growing interest in leveraging mobile users' location information to expedite beamforming processes. This paper explores the concept of contextual beamforming, discussing its advantages, disadvantages, and implications. Notably, we demonstrate an impressive 53% improvement in the signal-to-interference-plus-noise ratio by implementing the adaptive beamforming maximum ratio transmission (MRT) algorithm compared to scenarios without beamforming. It further elucidates how MRT contributes to contextual beamforming. The importance of localization in implementing contextual beamforming is also examined. Additionally, the paper delves into the use of artificial intelligence (AI) schemes, including machine learning and deep learning, in implementing contextual beamforming techniques that leverage user location information. Based on the comprehensive review, the results suggest that the combination of MRT and zero-forcing techniques, alongside deep neural networks employing Bayesian optimization, represents the most promising approach for contextual beamforming. Furthermore, the study discusses the future potential of programmable switches, such as Tofino—an innovative switch developed by Barefoot Networks (now a part of Intel)—in enabling location-aware beamforming. This paper highlights the significance of contextual beamforming for improving wireless telecommunications performance. By capitalizing on location information and employing advanced AI techniques, the field can overcome challenges and unlock new possibilities for delivering reliable and efficient mobile networks. [ABSTRACT FROM AUTHOR]

Silver nanowire (AgNW) emerges as a next‐generation transparent electrode material, offering enhanced flexibility and ease of fabrication compared to traditional transparent electrode materials, such as metallic oxides. In the previous research, the uniform deposition of AgNWs on flat surfaces is demonstrated, exhibiting high conductivity, flexibility, and excellent transmittance. However, the evolution of nano‐electronics technology necessitates the fabrication of transparent electrodes on non‐flat surfaces, such as those found in zenithal bistable devices and reconfigurable Fresnel lenses. In this study, a method is proposed to deposit AgNW material on uneven surfaces using the Mylar‐bar‐coating method, with a polyvinyl chloride (PVC)‐based Fresnel lens serving as the target surface. Additionally, the impact of varying AgNW suspension concentrations on transmittance and sheet resistance is investigated. To further reduce sheet resistance, a layer of conductive polymer, poly(3,4‐ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS), is deposited on the AgNWs. The measurements reveal that a 2 mg mL−1 sample exhibits a sheet resistance of 187.83 W W−1 with 87.4% transmittance at 550 nm. After the PEDOT:PSS process, the sheet resistance decreases to 122.84 W W−1 with 84.4% transmittance. In this research, a solution is offered for the uniform deposition of AgNWs on patterned surfaces, paving the way for the next generation of optical devices. [ABSTRACT FROM AUTHOR]

Consumer demand for high resolution and high refresh-rate displays has naturally led to the fabrication of liquid crystal displays with ever smaller pixels. As a consequence, fringing fields between adjacent pixels grow in magnitude, leading to abrupt changes in orientation. Electric field strengths above some threshold can lead to order melting and, in turn, disclinations. This paper presents accurate modeling of disclinations induced by fringing fields due to interdigitated electrodes in a nematic liquid crystal calculated by means of the Landau–de Gennes theory. Disclination paths are determined while taking into account the flow of the liquid crystal. Making use of interdigitated electrodes, precise electrical control over the creation and positioning of defects is demonstrated for homeotropic, planar, hybrid, and in-plane surface alignments. [ABSTRACT FROM AUTHOR]

Machine learning (ML) and artificial intelligence (AI) methods are emerging as promising technologies for enhancing the performance of low‐cost photovoltaic (PV) cells in miniaturized electronic devices. Indeed, ML is set to significantly contribute to the development of more efficient and cost‐effective solar cells. This systematic review offers an extensive analysis of recent ML techniques in designing novel solar cell materials and structures, highlighting their potential to transform the low‐cost solar cell research and development landscape. The review encompasses a variety of ML approaches, such as Gaussian process regression (GPR), Bayesian optimization (BO), and deep neural networks (DNNs), which have proven effective in boosting the efficiency, stability, and affordability of solar cells. The findings of this review indicate that GPR combined with BO is the most promising method for developing low‐cost solar cells. These techniques can significantly speed up the discovery of new PV materials and structures while enhancing the efficiency and stability of low‐cost solar cells. The review concludes with insights on the challenges, prospects, and future directions of ML in low‐cost solar cell research and development. [ABSTRACT FROM AUTHOR]