167 research outputs found

    Can Educational Robots Improve Student Creativity: A Meta-analysis based on 48 Experimental and Quasi-experimental Studies

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    Cultivating innovative talents has become a critical strategy for building China into a strong country in science and technology. Catering to the trend of educational reform in the intelligent era, the use of robotics in developing student creativity proves to be of greater practical value. The findings of this study are that: first, the overall effect of educational robotics on student creativity reaches above-moderate level; second, educational robotics has more significant effects on creativity of primary and junior secondary students; third, in terms of subjects, robotics courses can most effectively promote student creativity; fourth, among various teaching topics, prototype creation has the most substantial impact on student creativity; fifth, in terms of instruction methods, inquiry-driven teaching can best stimulate student creativity; sixth, compared with ordinary classrooms, the laboratory environment is more favorable for the development of student creativity. The paper also offers recommendations for popularizing robotics curriculum at different education levels

    Coupling system of silver carbonate nanoparticles and bismuth oxyiodide nanosheets with enhanced photocatalytic properties

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    274-284In this work, silver carbonate nanoparticles and BiOI nanosheets have been prepared separately and binary Ag2CO3/BiOI composite are synthesized via a facile solvo thermal method. The as-prepared materials have been well characterized using techniques covering X-ray diffraction (XRD), energy dispersive X-ray spectroscopy (EDX), transmission electron microscopy (TEM), High-resolution transmission electron microscopy (HRTEM), ultraviolet-visible (UV-vis) diffuse reflectance spectroscopy, X-ray photoelectron spectroscopy (XPS), Photoluminescence (PL) emission spectroscopy. The photocatalytic activity of the as-synthesized materials has been evaluated for degrading various model pollutants (methyl orange, phenol and p-nitroaniline). It has been found that the incorporation of co-catalyst BiOI could promote the catalytic activity of Ag2CO3 and suppress the serious photocorrosion of Ag2CO3. Thus the Ag2CO3/BiOI composite showed excellent catalytic recycling stability. Moreover, the underlying mechanism has been investigated through radical trapping experiments. The results demonstrate that photoinduced holes are the main active species. The improvement in activity of Ag2CO3/BiOI could be attributed to the charge transfer between the heterojunction. Also, its good stability and reusability establish its promising potential for catalytic applications

    Fit evaluation of virtual garment try-on by learning from digital pressure data

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    Presently, garment fit evaluation mainly focuses on real try-on, and rarely deals with virtual try-on. With the rapid development of E-commerce, there is a profound growth of garment purchases through the internet. In this context, fit evaluation of virtual garment try-on is vital in the clothing industry. In this paper, we propose a Naive Bayes-based model to evaluate garment fit. The inputs of the proposed model are digital clothing pressures of different body parts, generated from a 3D garment CAD software; while the output is the predicted result of garment fit (fit or unfit). To construct and train the proposed model, data on digital clothing pressures and garment real fit was collected for input and output learning data respectively. By learning from these data, our proposed model can predict garment fit rapidly and automatically without any real try-on; therefore, it can be applied to remote garment fit evaluation in the context of e-shopping. Finally, the effectiveness of our proposed method was validated using a set of test samples. Test results showed that digital clothing pressure is a better index than ease allowance to evaluate garment fit, and machine learning-based garment fit evaluation methods have higher prediction accuracies

    Study on Leading Vehicle Detection at Night Based on Multisensor and Image Enhancement Method

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    Low visibility is one of the reasons for rear accident at night. In this paper, we propose a method to detect the leading vehicle based on multisensor to decrease rear accidents at night. Then, we use image enhancement algorithm to improve the human vision. First, by millimeter wave radar to get the world coordinate of the preceding vehicles and establish the transformation of the relationship between the world coordinate and image pixels coordinate, we can convert the world coordinates of the radar target to image coordinate in order to form the region of interesting image. And then, by using the image processing method, we can reduce interference from the outside environment. Depending on D-S evidence theory, we can achieve a general value of reliability to test vehicles of interest. The experimental results show that the method can effectively eliminate the influence of illumination condition at night, accurately detect leading vehicles, and determine their location and accurate positioning. In order to improve nighttime driving, the driver shortage vision, reduce rear-end accident. Enhancing nighttime color image by three algorithms, a comparative study and evaluation by three algorithms are presented. The evaluation demonstrates that results after image enhancement satisfy the human visual habits

    CBCRS: An open case-based color recommendation system

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    In this paper, a case-based color recommendation system (CBCRS) is proposed for online color ranges (CRs) recommendation. This system can help designers and consumers to obtain the most appropriate CR of consumer-products (e.g., garments, cars, architecture, furniture 
) based on the color image perceptual data of each specific user. The proposed system is an open system, permitting to dynamically integrate new CRs by progressively learning from users’ and designers’ perceptual data. For this purpose, a Color Image Space (CIS) is initially established by using Basic Color Sensory Attributes (BCSAs) to obtain the color image perceptual data of both designers and consumers. Emotional Color Image Words (CIWs) representing CRs are measured in the proposed CIS through a knowledge-based Kansei evaluation process performed by designers using fuzzy aggregation operators and fuzzy similarity measurement tools. Using this method, new CIWs and related CRs from open resources (such as new color trends) can be integrated into the system. In a new recommendation, user's color image perceptual data measured in the proposed CIS regarding different BCSAs will be compared with those of CIWs previously defined in the system in order to recommend new CRs. CBCRS is an adaptive system, i.e. satisfied CRs will be further retained in a Successful Cases Database (SCD) so as to adapt recommended CRs to new consumers, who have similar user profiles. The general working process of the proposed system is based on case-based learning. Through repeated interactions with the proposed system by performing the cycle of Recommendation – Display - Evaluation – SCD adjustment, users (consumer or designer) will obtain satisfied CRs. Meanwhile, the quality of the SCD can be improved by integrating new recommendation cases. The proposed recommendation system is capable of dynamically generating new CIWs, CRs and new cases based on open resources.SMDTex Project funded by the European Erasmus Mundus Progra

    Construction of Garment Pattern Design Knowledge Base Using Sensory Analysis, Ontology and Support Vector Regression Modeling

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    Garment pattern design is an extremely significant factor for the success of fashion company in mass customization and industry 4.0. In this paper, we proposed a new approach for constructing a garment pattern design knowledge base (GPDKB) using sensory analysis, ontology and support vector regression (SVR) modeling, aiming at systematically formalizing the complete knowledge on garment pattern design and realizing garment pattern associated adaptation. This approach has been described and validated in the scenario of personalized men's shirt design. The GPDKB consists of three components: conceptual knowledge base, relationship knowledge base and adaptation rules knowledge base. After selecting the optimal garment patterns using data twins-driven technique, the GPDKB has been built by learning from quantitative relationships between garment structure lines, controlling points and garment patterns and then simulated for pattern parameters prediction and pattern associate adaptation. Finally, the performance of the presented approach was compared with other classical data learning techniques, i.e., multiple linear regression and backpropagation-artificial neural network. The experimental results show that SVR-based approach outperform another two techniques with the lowest average of mean squared errors (0.1279) and average of standard deviation (0.1651). And the adaptation effect of GPDKB is equivalent to existing grading method. The general principle of the proposed approach can be adapted to creation of design knowledge bases for other type garments such as compression leggings. In fashion industry, the proposed GPDKB can effectively support designers by rapidly, accurately and automatically predicting relevant pattern adaptation parameters during garment pattern design

    Knowledge-Based Open Performance Measurement System (KBO-PMS) for a Garment Product Development Process in Big Data Environment

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    Globally, customers are getting increasingly demanding in terms of personalization of products and are asking for shorter product development periods with more predictable product performance, especially in fashion industry. Current market pressures drive firms to adapt new design process in product development (PD) processes. Nevertheless, choosing the effective PD process is a challenging, complex decision. There is a critical need to develop a performance measurements system (PMS) for choosing appropriate product development (PD) processes in garment design to support product mangers to effectively respond to market. This paper presents a knowledge-based open performance measurement system (KBO-PMS) in big data environment, in order to support complex industrial decision-making for new product development. Its dynamic and flexible structure enables the whole system to be more adapted to knowledge sharing of product managers and processing of various time-varying data. The proposed KBO-PMS is composed of an interactive structure, capable of both integrating new KPIs from the open resource and tracking the evolution of the KBO-PMS components with time. The proposed KBO-PMS has been validated by realizing the performance evaluation of product development (PD) in fashion industry. It can be regarded as an application of open-resource based dynamic group decision-making in fashion big data environment

    Enhanced Continental Weathering as a Trigger for the End‐Devonian Hangenberg Crisis

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    The Hangenberg Crisis coincided with a large decline of biodiversity and widespread anoxia in the end-Devonian ocean. Previous research attributed marine anoxia to the spread of deeply-rooted plants and/or increased volcanism on the continents, but crucial links have not been thoroughly explored. Herein, we propose enhanced weathering as a key trigger, as evidenced by a negative shift (∌8‰) in lithium isotopes and a coupled response in carbon isotopes of marine carbonates in South China. Our findings imply that rapid weathering of crustal rocks increased nutrient delivery to the ocean, as indicated by an increase in the carbonate-associated phosphate levels, contributing to oceanic eutrophication. In the absence of massive volcanic emissions and intense orogeny, the cause of enhanced continental weathering was likely the expansion of the terrestrial rhizosphere, highlighting the potential for land plant evolution to initiate weathering changes of sufficient severity to trigger a major bio/environmental crisis in the Earth system

    Clinical presentation of acute primary angle closure during the COVID-19 epidemic lockdown

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    PurposeThis study aimed to investigate the clinical presentation of acute primary angle closure (APAC) during the COVID-19 epidemic lockdown in Wuhan.MethodsConsecutive patients seeking APAC treatment at the Wuhan Aier Eye Hospital during the 76 days (January 23–April 8, 2020) when the lockdown policy was implemented due to the COVID-19 pandemic were compared to those during the same period the following year (January 23–April 8, 2021), when the lockdown policy was not implemented. The cohorts were compared to assess demographic variables and clinical presentations.ResultsA total of 54 patients (64 eyes) were included in the 2020, compared with 46 patients (51 eyes) in the 2021. Demographic factors were similar between the groups. Significantly more patients developed blindness in the 2020 cohort (21.87%) than in the 2021 cohort (7.84%). Patients in the 2020 showed a longer time from symptom to treatment (241.84 ± 211.95 h in 2020 vs. 121.53 ± 96.12 h in 2021; P = 0.001), higher intraocular pressure at presentation (52.63 ± 12.45 mmHg in 2020 vs. 45.16 ± 9.79 mmHg in 2021; P = 0.001), larger pupil diameter (5.47 ± 1.62 mm in 2020 vs. 4.33 ± 1.27 mm in 2021; P = 0.001), and more glaucomatous optic neuropathy diagnoses [20/64 eyes (31.25%) in 2020 vs. 7/51 eyes (13.73%) in 2021; P = 0.03].ConclusionThe time between the onset of APAC symptoms and its treatment during the COVID-19 epidemic lockdown was significantly prolonged, which increased the blindness rate of APAC patients