Amid the COVID-19 pandemic, the imperative for alternative biometric attendance systems has arisen. Traditionally, fingerprint and facial recognition have been employed; however, these methods posed challenges in adherence to Standard Operational Procedures (SOPs) set during the pandemic. In response to these limitations, iris detection has been advanced as a superior alternative. This research introduces a novel machine learning approach to iris detection, tailored specifically for educational environments. Addressing the restrictions posed by COVID-19 SOPs, which permitted only 50% of student occupancy, an automated e-attendance mechanism has been proposed. The methodology comprises four distinct phases: initial registration of the student's iris, subsequent identity verification upon institutional entry, evaluation of individual attendance during examinations to assess exam eligibility, and the maintenance of a defaulter list. To validate the efficiency and accuracy of the proposed system, a series of experiments were conducted. Results indicate that the proposed system exhibits remarkable accuracy in comparison to conventional methods. Furthermore, a desktop application was developed to facilitate real-time iris detection.
The transition from manufacturing activities to an economy dominated by industrial production signifies the evolution of the Industrial Revolution. Presently, the global landscape is undergoing the Fourth Industrial Revolution, a natural progression from its digital predecessor, Industry 3.0. This era is distinguished by an influx of innovations across global business sectors. Solutions, particularly aiming to enhance industrial production and address volatile consumer demands, have gained paramount importance. These global shifts lead to profound structural transformations in industrial production. With the rapid integration of contemporary technologies in Industry 4.0, coupled with significant technical advancements, the notion of “smart factories” has emerged as a focal point in research, engineering, and practical applications. In the realm of sustainable business operations, the concept of the smart factory is frequently debated. While its intent is the diminution of manual tasks and the enhancement of customer service, it inherently demands the incorporation of various technologies inherent to Industry 4.0. Recognizing the profound impact of Industry 4.0 on production methodologies and the workforce's perspective, emphasis is placed on understanding the pivotal role of advanced technologies within the smart factory paradigm. This abstract seeks to elucidate the core processes in the smart factory concept with a spotlight on refining intralogistics activities through the lens of Industry 4.0 technologies.
Microelectromechanical systems (MEMS) have instigated transformative advancements, notably in controlled microdroplet generation, offering applications across diverse industrial sectors. Precise control of fluid quantities at microscales has emerged as pivotal for myriad fields, from microfluidics to biomedical engineering. In this investigation, the impacts of fluid viscosity and surface tension on microdroplet formation were meticulously studied. For this purpose, a microdispenser, actuated piezoelectrically and fitted with an 18-micrometer diameter nozzle, was employed. This setup facilitated precise fluid manipulation, enabling a systematic study of fluid behavior during droplet creation. Three fluids, specifically water, ink, and ethanol, were examined to decipher the influences of their inherent properties on microdroplet generation. Emphasis was laid on both primary and satellite droplets due to their direct implications in industrial applications. Observations revealed that fluids with elevated surface tension and diminished viscosity yielded larger microdroplets. Conversely, fluids manifesting greater surface tension underwent rapid breakup upon ejection, culminating in the genesis of several diminutive droplets. Such findings underscore the intricate relationship between fluid properties and droplet formation dynamics. This newly acquired understanding holds the potential to guide MEMS device design, ensuring the desired droplet size and distribution. Furthermore, these insights are poised to facilitate optimal microdispenser design and judicious fluid selection for applications spanning inkjet printing, microreactors, and drug delivery mechanisms.
The pivotal role of suction cup handling systems within various industrial and commercial applications, notably in the lifting and manoeuvring of glass window panels and the secure retention of specimens, is underscored by myriad practical implementations. The present research endeavours to meticulously design and rigorously assess the efficacy of suction cup holding systems, employing Catia design software for the creation of the CAD design and utilising the ANSYS simulation package for structural analysis. Particular attention is accorded to the investigation of the suitability of disparate materials for the suction cup, specifically emphasising Nitrile Butadiene Rubber (NBR) and polyurethane, whilst the plate material undergoes examination utilising a carbon fibre composite. Contrastive assessments, grounded in parameters such as stress, deformation, and equivalent elastic strain, are elucidated for these varied material applications. Preliminary findings indicate that, amid numerous suction cup diameters explored, a 141 mm diameter manifests the lowest equivalent stress (ES), whilst a diameter of 118 mm reveals the maximal ES. A 141 mm diameter emerges as optimal in suction cup design and, to minimise deformation, polyurethane rubber (PR) is identified as the most propitious material. Pertaining to the suction cup body, carbon composite material (CCM) is delineated as the pre-eminent selection, offering an enhancement in the strength-to-weight ratio that is notably superior when compared with a carbon steel suction cup apparatus.
The paramountcy of English in the contemporary global landscape necessitates the enhancement of English language proficiency, especially in academic settings. This study addresses the disparate levels of English proficiency among college students by proposing a novel approach to evaluate English text readability, tailored for the higher education context. Employing a deep learning (DL) framework, the research focuses on developing a model based on convolutional neural networks (CNNs) to assess the readability of English texts. This model diverges from traditional methods by evaluating the difficulty of individual sentences and extending its capability to ascertain the readability of entire texts through adaptive weight learning. The methodology's effectiveness is underscored by an impressive 72% accuracy rate in readability assessment, demonstrating its potential as a transformative tool in English language education. The application of this DL-based text readability evaluation model in college English training is explored, highlighting its potential to facilitate a more nuanced understanding of text complexity. Furthermore, the study contributes to the broader discourse on enhancing English language instruction in higher education, proposing a method that not only evaluates text comprehensibility but also aligns with diverse educational needs. The findings suggest that this approach could significantly support the enhancement of English teaching methodologies, thereby promoting a deeper, more accessible learning experience for students with varying levels of proficiency.
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