Bases

This study seeks to explore the potential of the Project-based learning method in enhancing three important soft skills, including critical thinking, social-emotional skill, and self-regulation, which are believed to be beneficial for students’ sustainable development, among non-English-major students in a university in Ho Chi Minh city, Vietnam.

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This research article explores the potential of active learning pedagogies to enhance student engagement, knowledge retention, and essential skill development in higher education. These learner-centered approaches, such as experiential, project-based, and inquiry-based learning, contrast with traditional lecture-based methods that prioritize passive information transfer.

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The findings from the research revealed the students’ troubles in practicing TOEIC through TikTok regarding 3 main themes: technical, psychological and pedagogical issues. Based on the given challenges, some solutions were also suggested by both teachers and students to improve the effectiveness of practicing TOEIC on TikTok.

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The fuzzy time series model has become a research topic attracting attention because of its practical value in the field of time series forecasting, specifically, it is useful for time series with small observations or the one of strong fluctuations. This paper introduces a fuzzy time series model based on hedge algebra with a new formula for calculating forecasting values.

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This study intends to investigate the task-based language teaching (TBLT), known for its effectiveness in English Language Teaching (ELT) to improve students' communicative English skills. The study also aims to get insights into students' perspectives on learning grammar through Task-Based Language Teaching (TBLT).

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The Balanced Scorecard (BSC) is seen as an effective administrative tool for enterprises to achieve their strategic objectives. This study aims to examine the demand and required conditions to build BSC for HVN, based on its mission, vision, and strategic objectives, thereby suggesting BSC conformed to HVN.

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This study seeks to comprehend the influencing factors on the economic growth of the pivotal economic region in the Southwest. The authors conducted research based on the dataset from four provinces and cities in the region during the period 2005-2022, employing panel data estimation methods such as OLS pooled regression, fixed effects (FEM), and random effects (REM).

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The study aims at examining EFL teachers’ perceptions on their roles and how they occupy these roles in project-based learning at a Vietnamese state university. Narrative interviews were employed to collect qualitative data with the participation of five teachers at the Faculty of Foreign Languages at Ho Chi Minh City University of Technology and Education.

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The study was conducted with the hope that the result gained could clarify the effects of peer feedback on students’ writing performance at a high school, Thai Nguyen province. Based on the aims, an action research design was employed.

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This study is designed to validate the impact of entrepreneurial mindset on product innovation outcomes in technology-based enterprises while concurrently examining the mediating influence of business model innovation.

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Silver nanoparticles (Ag NPs) were synthesized and characterized by transmission electron microscopy (TEM), X-ray powder diffraction (XRD), and absorption spectroscopy. Using Debye-Scherrer’s formula and the result of the XRD spectrum, the size of the Ag NPs was estimated at approximately 8 nm.

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Folding plates with wavy shapes made of composite materials have been widely applied, hence designing this type of structure is significant in practice. In this research work, the results of static calculation of the sinusoidal corrugated composite plates will be analyzed.

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Accurate forecasting of the electrical load is a critical element for grid operators to make well-informed decisions concerning electricity generation, transmission, and distribution. In this study, an Extreme Learning Machine (ELM) model was proposed and compared with four other machine learning models including Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU).

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This study presents a novel neural network (NN) framework for developing force fields specific to graphene monolayers, utilizing data obtained from first-principles calculations. The authors analyze three primary force components, force magnitude and the cosines of two angles across different configurations of surrounding carbon atoms.

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The process of neural stem cell (NSC) differentiation into neurons is crucial for the development of potential cell-centered treatments for central nervous system disorders. However, predicting, identifying, and anticipating this differentiation is complex. In this study, we propose the implementation of a convolutional neural network model for the predictable recognition of NSC fate, utilizing single-cell brightfield images.

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In this paper, we propose an effective AMC using deep learning (DL) for flexible and adaptive OFDM-based optical networks. The proposed DL-based AMC is able to classify four typical modulation schemes such as binary phase-shift keying (BPSK), quadrature PSK (QPSK), 8-PSK, and 16- quadrature amplitude modulation (QAM) in dynamic network conditions.

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The rest of this paper is arranged as follows. Section II shows some theoretical bases as PID controller, fuzzy PID controller, DC motor, microcontroller STM32F103C8T6… System design steps are provided in section III. The results of the proposed system are presented and discussed in section IV. Finally, section 5 closed the paper with some relevant conclutions.

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This paper is structured as follows. The following section presents related work. Section 3 summarizes the characteristics of the two datasets utilized in the model and the system’s overall architecture for image-based disease diagnosis. Section 4 provides our experimental results that compare the performance metrics with other studies.

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Sentiment Analysis and Opinion Mining have emerged as highly popular fields for analyzing and extracting valuable information from textual data sourced from diverse platforms like Facebook, Twitter, and Amazon. These techniques hold a crucial role in empowering businesses to actively enhance their strategies by gaining comprehensive insights into customers' feedback regarding their products.

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High quality of service (QoS) requirements in multi-priority wireless sensor networks pose new challenges to the Internet of Things (IoT). In a multi-event wireless sensor network (MWSN), nodes generate different types of data packets with different priority such as urgent (high priority) or normal (low priority), with different traffic proportion.

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