Engineering reports : open access最新文献

In this study, a broadband polarization and angle-independent metamaterial absorber (MA) is investigated in the microwave range. It is made up of a periodic array of multi-layered metal-dielectric stepped cones. Since the dimensions of the layers forming the unit cell are different, each layer resonates at different frequencies with overlapping bands. The overall response of the structure, with its extremely wide bandwidth, can be obtained by summing all the overlapping frequency responses corresponding to each layer. In numerical simulation, it is observed that the absorption at normal incidence is above 90% in the frequency range between 9.68 and 17.45 GHz and 95% in the frequency range between 9.91 and 14.86 GHz. The energy harvesting ratios of the structure are also evaluated in a wide spectral band. A power ratio of around 90% is obtained in the same frequency range in accordance with absorption response. A noticeable harvesting efficiency of up to 82% is observed, which represents the energy level converted into electrical energy on resistors.

Discarded medical face masks endanger the environment worldwide. In this study, experiments were conducted to investigate the effect of a shredded face mask (SFM) at 0% (control mix), 0.5%, 1.0%, and 2.0% by volume of concrete in the form of pieces that were 1 cm wide and 2 cm long on the properties of fresh and hardened concrete. After performing experimental testing on the materials, finite element masonry prisms with dimensions of 400 × 200 × 560 mm³ were modeled on the ANSYS platform. Four prisms with different fabric contents were numerically examined to study the compressive behavior, and 12 prisms with three different mortar joints were analyzed under an incremental horizontal load in the presence of four vertical displacements of 0.5, 1.0, 3.0, and 4.5 mm. The results revealed that increasing the SFM content in concrete led to a decrease in fresh and hardened concrete properties, including density, slump, split-tensile strength, and compressive strength, by 9.5%, 20%, 24%, and 34%, respectively, compared with the control concrete at 0.5%. Moreover, the addition of 0.5% SFMs to the prism bricks reduced the maximum compressive load, deflection, and strain energy by 24%, 10%, and 39%, respectively. Altering the mortar type and vertical load affected the lateral cyclic behavior of the prisms. Compared with the M3 prism subjected to the same axial displacement, the M2 prism had 21.36%, 11%, 27.2%, and 10.48% higher lateral peak load, lateral peak displacement, equivalent stress, and strain energy, respectively. Furthermore, the lateral stiffness of the prism increases as the axial pressure increases. The lateral peak load of the M3 prism measured at 1.0, 3.0, and 4.5 mm axial displacement was raised by 60%, 142%, and 182%, respectively, as compared with the same prism at 0.5 mm axial displacement. The outcome provides a feasible concept for reusing masks in concrete construction with controllable strength deterioration on the masonry prism at 0.5% recycled SFM, resulting in attractive responses of these composites at the nonstructural scale

Large fingerprint databases can make the automated search process tedious and time-consuming. Fingerprint pattern classification is a significant step in the identification system's complexity in terms of time and speed. Although several fingerprint algorithms have been developed for classification tasks, further improvements in performance and efficiency are still required. Most of the fingerprint algorithms use deep learning techniques. However, some deep learning techniques can be resource-intensive and computationally expensive, while others can disregard the spatial relationships between the features used in classifying fingerprint patterns. This study proposes using lightweight deep learning models (i.e., MobileNet and EfficientNet-B0) integrated with attention modules to classify fingerprint patterns. The two lightweight models are modified, yielding MobileNet+ and EfficientNet-B0+ models. The lightweight deep learning models can help achieve optimal performance and reduce computational complexity. The attention modules focus on distinctive features for classification. Our proposed approach integrates four attention modules for fingerprint pattern classification into two lightweight deep learning models, that is, MobileNet+ and EfficientNet-B0+. To evaluate our approach, we use two publicly available fingerprint datasets, that is, the NIST special database 301 dataset and the LivDet dataset. The evaluation results show that the EfficientNet-B0+ model achieves the highest classification accuracy of 97% with only 854,086 training parameters. As a conclusion, we consider the training parameters small enough for the EfficientNet-B0+ model to be deployed on low-resource devices.

To enhance the teaching quality (TQ) of English translation (ET) courses, multivariate evaluation methods have garnered significant attention for their ability to identify deficiencies in the teaching process and subsequently improve instructional standards. Traditional evaluation techniques have shown limited effectiveness. Therefore, this paper proposes an ET in multiple teaching quality (MTQ) evaluation method with improved deep learning in internet of things (IoT). First, the paper establishes evaluation indicators for ET in MTQ, which mainly encompass ET teaching effectiveness, pedagogic competency in ET, teaching methods, content, as well as attitudes towards ET teaching. It then utilizes IoT technology to preliminarily collect data on these indicators, and using clustering algorithm based on weighted attributes and density (CABWAD) algorithm to mine data on English teaching MTQ evaluation indicators for ET. The extracted evaluation indicator data is then denoised using a probabilistic undirected graph model. Ultimately, the multilayer perceptron in deep learning is improved through Wolfe line search optimization, and this enhanced multilayer perceptron is employed to construct an ET in MTQ evaluation model. The denoised indicator data is inputted into the model, which then outputs precise MTQ evaluation results. The results show that the absolute value of the average Pearson correlation coefficient of this method is the highest, the Spearman correlation coefficient is the lowest, the mean average precision value is 0.965, and the positive category imbalance degree and the negative category imbalance degree are the lowest, indicating that the proposed method has outstanding performance in all aspects, and has certain application value in the field of TQ evaluation.

Typical data available to engineering educators about the state of degrees awarded is disseminated via various groups. The most salient limitations to each of these sources are a lack of disaggregating data by multiple personal identities and an annualized reporting schedule hindering interpretations over time. This analysis ascertains how female degrees earned in engineering changed for bachelor's, master's, and doctoral degree levels from 2005 to 2021. We sought to understand trends by disaggregating ASEE records by gender, race, and engineering discipline. Data gathered from EDMS were cleaned, analyzed, and visualized, following principles for data sense making and human factors. Results highlight women gravitating towards Biological, Environmental, and Computational engineering disciplines. The total number of all degrees awarded is increasing for all genders in all disciplines, but these trends are not evenly distributed across disciplines. While it is true that the overall proportion of women in engineering wavered near 20%, this statistic does not tell the whole story of what has been occurring in engineering. By disaggregating infographics, we tracked percentage growth in certain fields as well as overall increases in number of degrees awarded at all levels of higher education. Future research is needed to determine causes for women's choices in engineering.

Designing circulator as an antenna interface device becomes a daunting task, particularly active-quasi circulator. This article focuses on demonstrating the basic operation principle, design methods, technical parameters, and performance metrics of active quasi-circulator. In addition, the study provides an analogy of the circuits and structures proposed by the researchers to enhance certain performance metrics. Active signal cancellation and passive signal cancellation are identified as the major design approaches. Tunable, wideband, and wideband-tunable are the major types of circulators found in existing literature. Moreover, this article provides a performance comparison of the active-quasi circulators available in existing literature. Several active quasi-circulators were able to operate in high frequencies such as 60 GHz with acceptable isolation levels. On the other hand, several designs have over 30 dB isolation, which is a highly desired parameter. At last, the future design challenges associated with active-quasi circulators have been discussed to provide insight into future research.

Interactions between individuals and digital material have completely changed with the advent of the Metaverse. Due to this, there is an immediate need to construct cutting-edge technology that can recognize the emotions of users and continuously provide material that is relevant to their psychological states, improving their overall experience. An inventive method that combines natural language processing adaptive content generation algorithms and neuro-fuzzy-based support vector machines natural language processing (SVM-NLP) is proposed by researchers to meet this demand. With this merging, the Metaverse will be able to offer highly tailored and engaging experiences. Initially, a neuro-fuzzy algorithm was developed to identify people's emotional moods from their physiological reactions and other biometric information. Fuzzy Logic and Support Vector Machine work together to manage the inherent ambiguity and unpredictability, which results in a more exact and accurate categorization of emotions. A key component of the ACGA is NLP technology, which uses real-time emotional data to dynamically modify and personalize characters, stories, and interactive features in the Metaverse. The novelty of the proposed approach lies in the innovative integration of neuro-fuzzy-based SVM-NLP algorithms to accurately recognize and adapt to users' emotional states, enhancing the Metaverse experience across various applications. The proposed method is implemented using Python software. This adaptive approach significantly enhances users' immersion, emotional involvement, and overall satisfaction within the augmented reality environment by tailoring information to their responses. The findings show that the SVM-NLP emotion identification algorithm based on neuro-fuzzy, has a high degree of accuracy in recognizing emotional states, which holds promise for creating a Metaverse that is more emotionally compelling and immersive. Stronger human–computer interactions and a wider range of applications, including virtual therapy, educational resources, entertainment, and social media networking, might be made possible by integrating SVM-NLP. These sophisticated systems are around 92% accurate in interpreting the emotions.

MnS which has good plasticity is a non-metallic inclusion commonly found in steel. For most steel types, the size, shape, and distribution of MnS have a significant influence on the properties of steel. The large-sized MnS inclusions disrupt the continuity of the steel and cause the anisotropy in steel. The result is a decline of steel's overall performance. In contrast, the small-sized MnS inclusions which in the shape of spherical or spindle in steel can diminish the incidence of thermal embrittlement and improve the machinability of steel. The morphology of MnS in steel is mainly affected by the ingredients of steel and heat treatment manner. MnS inclusions in steel are present in spherical, polyhedral, dendritic, and irregular shapes. The precipitation behavior is mainly affected by the steel ingredients, heat treatment system, and other factors. This paper summarizes the latest research results about the factors affecting MnS inclusions and controlling measures in high-quality steel in recent years.

The rapid progress in conversational AI has given rise to advanced language models capable of generating human-like texts. Among these models, ChatGPT and Bard, developed by OpenAI and Google AI respectively, have gained significant attention. With their wide range of functionalities, such as human-like response generation, proficiency in professional exams, complex problem solving, and more, these models have captured interest. This study presents a comprehensive survey exploring and comparing the capabilities and features of ChatGPT and Bard. We delve into their architectures, training methodologies, performance evaluations, and limitations across various domains. Ethical considerations such as biases and potential misconduct are also examined. Our findings highlight ChatGPT's exceptional performance, positioning it as a leading model. This survey is a vital resource for scholars, innovators, and interested parties operating within the domain of conversational artificial intelligence, offering valuable insights for the advancement of cutting-edge language models.

Tricubic interpolation, originally introduced by Lekien and Marsden (Int J Numer Methods Eng. 2005; 63(3): 455–471), has been a cornerstone in the field of interpolation, providing $��{�C�}^{�1�}�$ continuous interpolations within three-dimensional spaces. However, real-world applications often demand higher levels of smoothness within $��M�$-dimensional spaces. This paper introduces LocalPoly interpolation, a novel generalization of tricubic interpolation that extends to $��{�C�}^{�n�}�$ continuity and $��M�$ dimensions. A key property is the use of solely local data for interpolation, allowing for on-demand computation of interpolation polynomials, which is particularly advantageous in scenarios where a minor subset of the space is of interest. We rigorously prove the $��{�C�}^{�n�}�$ continuity achieved by the LocalPoly interpolation method; the proof features a numerically exact method for computing polynomial coefficients. The enhanced continuity is of great relevance in optimization algorithms, where efficient convergence often relies on the availability of $��{�C�}^{}$