Deep learning (DL) has significantly advanced various industries, including semiconductors, by providing sophisticated methods for analyzing emerging device data. Transfer learning (TL), a prominent DL topology, lever...
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In the Philippines road accidents were prevalent among motorcycle riders. The motorcyclist was required to wear their helmet when on the road. Some motorcyclists didn't follow the rules on wearing a helmet as safe...
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ABSTRACTBackground and PurposeMetavisualization plays a key role in science and technological learning in which visualization is practiced. We conducted two sequential studies with the purposes to first develop an ins...
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ABSTRACTBackground and PurposeMetavisualization plays a key role in science and technological learning in which visualization is practiced. We conducted two sequential studies with the purposes to first develop an instrument that allows individuals to report or reflect on their metavisualization, and then validate the instrument and investigate the relationships among the dimensions of *** total of 320 university students participated in this *** exploratory factor analysis (EFA) in the first study and partial least squares – structural equation modeling (PLS-SEM) in the second study, the validity and reliability of the instrument measuring the four dimensions of metavisualization were reported, and a theoretical model delineating the relationships among the four variables was *** was found that metacognitive skills, rather than metacognitive knowledge, significantly contributed to the demonstration of epistemic knowledge and judgment criteria during visualization. Metacognitive knowledge may also play an indirect role in learners’ epistemic performance through its relation with metacognitive *** results provide insights to advance understanding of metavisualization and its relation to metacognition and epistemic practice. The validated instruments may be used for future quantitative or mixed research to advance understanding of the role metavisualization plays in various contexts of learning in science and technological education.
This paper tackles the short-term hydro-power unit commitment problem in a multi-reservoir system - a cascade-based operation scenario. For this, we propose a new mathematical modelling in which the goal is to maximiz...
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Various deep learning applications on smartphones have been rapidly rising, but training deep neural networks (DNNs) has too large computational burden to be executed on a single smartphone. A portable cluster, which ...
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The study uses machine learning to determine the accuracy of different algorithms for predicting MetS. Early prediction of MetS through routine health screening is an important goal for preventive medicine. The subjec...
The study uses machine learning to determine the accuracy of different algorithms for predicting MetS. Early prediction of MetS through routine health screening is an important goal for preventive medicine. The subjects in this study were employees aged 20 to 65 years old who signed a consent form for in-hospital health screening data were collected from 2015 to 2020. Total of 3538 individuals who underwent health screening were included in this study. Baseline characteristics of the 315 participants diagnosed with metabolic syndrome and 3222 participants without metabolic syndrome. This is a machine learning approach to identify metabolic syndrome using biochemical values and lifestyle habits. We found that the F1 score for detection of metabolic syndrome by neural network is the most accurate and higher compared to other models. Our study also identified some new indicators, including heart rhythm, RBC, WBC, Hb, and uric acid, as candidates for better detection of metabolic syndrome.
We explored THz emission from $\mathrm{Si}^{2} / \mathrm{SiO}_{2} / / \mathrm{Ta} / \mathrm{Fe} / \mathrm{Ru} /$ $\mathrm{Ni} / \mathrm{Al}_{2} \mathrm{O}_{3}$ spintronic emitters. We tuned magnetization alignment of ...
We explored THz emission from $\mathrm{Si}^{2} / \mathrm{SiO}_{2} / / \mathrm{Ta} / \mathrm{Fe} / \mathrm{Ru} /$ $\mathrm{Ni} / \mathrm{Al}_{2} \mathrm{O}_{3}$ spintronic emitters. We tuned magnetization alignment of Fe and Ni layers by varying the interlayer exchange coupling (IEC) strength using a range of Ru layer thickness t. Depending on IEC strength, magnetization hysteresis shows either ferromagnetic $(t=1.1 \mathrm{~nm}, 1.5 \mathrm{~nm})$, antiferromagnetic $(t=1.3 \mathrm{~nm})$ or canted $(t=1.7 \mathrm{~nm}, 1.9 \mathrm{~nm})$ relative alignment. Competition between IEC and an external magnetic field results in a dramatic difference in THz emission from the ferromagnetically (FM) and anti-ferromagnetically (AFM) coupled structures. The resulting THz emission from IEC structures is a result of an interference of THz transiens generated by the individual $\mathrm{Fe} / \mathrm{Ru}$ and $\mathrm{Ru} / \mathrm{Ni}$ emitters.
In this work, our study comprises of design and investigation on negative capacitance (NC), metal-oxide-semiconductor (MOS) field effects transistors (MOSFETs) with spacer and source/drain (S/D) overlap engineering. T...
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ISBN:
(数字)9781728142326
ISBN:
(纸本)9781728142333
In this work, our study comprises of design and investigation on negative capacitance (NC), metal-oxide-semiconductor (MOS) field effects transistors (MOSFETs) with spacer and source/drain (S/D) overlap engineering. The scope of the work is to boost the performance and high-energy efficiency of the studied NC-MOSFETs by using the ferro electric material (FE). The NC-MOSFETs with the spacer technology can achieve the admirable I on /I off ratio and subthreshold swing (SS), compared with planar MOSFETs. It makes device scaling possible by eliminating the short channel effect (SCE). We further estimated the effect of FE thickness and spacer, which are another critical parameter of obtaining better electrical characteristics and reducing SS.
With increasing dependence on precision electronic equipment, self-protection is becoming essential. The protection circuits commonly used before were fuses and circuit breakers which, however, they are not enough for...
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A knowledge-grounded chat model based on a sequence-to-sequence neural network is proposed to perform more natural question answering. The previous chat model suffered from the same words being repeated during decodin...
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A knowledge-grounded chat model based on a sequence-to-sequence neural network is proposed to perform more natural question answering. The previous chat model suffered from the same words being repeated during decoding. To reduce this word repetition, we propose a new decoding mechanism that considers the generation distribution of previously generated words. In experiments with question-answering sentences that are semi-automatically constructed, the proposed model outperformed a representative knowledge-grounded chat model, with a better accuracy of 4.35% p in finding answer phases. In addition, it demonstrated 1.63-2.05%p better results in all evaluation measures such as BLEU and ROUGE for evaluating the quality of responses and 2.83%p better results in evaluation measures such as distinct-1 for evaluating the diversity of responses.
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