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Publication

Test N3N

(n3n, 2026-01-01) N3N

Item

Silanol-Rich-Nanosized Cu-MOR Zeolite for Nonoxidative Dehydrogenation of Bioethanol to Acetaldehyde

(ACS Applied Engineering Materials, 2026-5-1) Anittha Prasertsab; Chomphunuch Wansa; Watinee Nunthakitgoson; Wachira Jeevapong; Ploychanok ladrat; Tawan Sooknoi; Chularat Wattanakit

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Continuously Tunable Frequency and Phase Biquad Oscillator

(Electronics, 2026-5-2) Sorawat Chivapreecha; Piyapan Suwannawach

This paper presents an improvement to the performance of a biquad oscillator, which is a recursive oscillator known for its excellent long-term stability. However, a significant limitation is that the oscillation frequency cannot be changed while the system is operating. Directly changing the frequency during operation causes the amplitude of the generated signal to vary significantly, either increasing or decreasing. A zero-input response analysis is used to understand the cause of this problem and to develop a solution that allows the amplitude of the generated signal to remain constant even when the oscillation frequency is changed during operation. In addition, this paper presents a method for controlling the phase of the generated signal by using a 1st-order IIR phase shifter structure. The proposed structure is specifically designed to allow the phase of the signal at the oscillation frequency to be adjusted continuously and independently of the frequency parameter. This integrated oscillator enables real-time, independent control of both signal frequency and phase without amplitude drift, making it suitable for applications requiring precise and dynamic signal synthesis.

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Machine Learning-Driven Portfolio Optimization Using Money Flow Index-Based Sentiment Signals

(International Journal of Financial Studies, 2026-5-2) Prapassara Singsiri; Jiraphat Yokrattanasak

Market indices serve as a benchmark for performance comparison, guide asset allocation decisions, and reflect overall market sentiment and economic conditions, thereby influencing investment strategies by representing a segment of the market. Unquestionably, investor sentiment impacts price movement. In this paper, the objectives were to study the effectiveness of the Money Flow Index (MFI) in enhancing the performance of predictive analysis by capturing market psychology, developing an investment strategy, and analyzing the performance of the method mentioned. This study applies machine learning algorithms with technical indicators and optimizes portfolio allocation based on three notable market indices in Southeast Asia (SEA): SET50 in Thailand, STI in Singapore, and VN30 in Vietnam. Firstly, we combined technical indicators with machine learning—Support Vector Classifier (SVC), Random Forest (RF), and Extreme Gradient Boosting (XGBoost)—by comparing datasets with and without MFI over the period from 2013 to 2023. The results showed that XGBoost with MFI delivered the best predictive performance across three indices. These findings indicate that MFI significantly enhances prediction accuracy, even during volatile market conditions (COVID-19). Additionally, the predictions were integrated into the Markowitz Mean-Variance (MV) model to construct an optimal portfolio, which was then benchmarked against an equal-weight portfolio (1/N). Ultimately, the findings demonstrate that incorporating the machine learning predictions into the MV framework efficiently generates wealth.

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A Comparative Study of NLP-Based Models: Popularity Trends in Pop Mart Comments

(2025-12-20) Pattama Charoenporn; Kititinun Poonsawat; Anupong Banjongkan