Southern Taiwan University of Science and Technology

Given the context of the COVID-19 pandemic disrupting global logistics, coupled with the Russia–Ukraine war causing global energy price changes, examining both the linear and nonlinear associations between shipping cost and oil price is crucial in a global context. This study empirically exhibits the association among Global Commodity Prices Index (GPI), Oil Price (OP), Gold Future Price (GFP), and Baltic Dry Index (BDI) by employing Linear Autoregressive Distributive Lag (ARDL) as well as Nonlinear Autoregressive Distributive Lag (Nonlinear ARDL) from January 2003 to January 2023. The findings indicate that the influence of OP on BDI has a negative impact in the long run and a positive impact in the short run. Furthermore, the OP has an asymmetric effect on BDI in both the long and short terms. Finally, the predictive performance of the NARDL model outperforms the ARDL model in forecasting OP and BDI. The empirical findings derived from the ARDL and NARDL algorithms offer valuable insights for policymakers in designing public policies and for investors in portfolio construction.

This study investigates portfolio optimization within the Mean–Variance framework using alternative estimators for expected returns and risk. Based on daily data of VN30 stocks from March 2021 to December 2025, nine portfolio strategies are constructed by combining three return estimators (historical mean, CAPM, and Exponential Moving Average) with three risk estimators (sample covariance, Ledoit–Wolf shrinkage covariance, and semicovariance). Portfolios are optimized by maximizing the Sharpe ratio under realistic weight constraints and a post-optimization sparsification procedure to improve implementability. Empirical results indicate that portfolios using EMA-based expected returns combined with Ledoit–Wolf or semicovariance estimators deliver more stable and superior out-of-sample performance over 1-, 3-, and 6-month horizons. In contrast, strategies relying on historical mean returns with sample covariance suffer from severe performance degradation, while CAPM-based approaches underperform consistently. These findings underscore the importance of adaptive return estimation and robust risk modeling in emerging equity markets and offer timely implications for portfolio construction in Vietnam as it prepares for a potential upgrade to emerging market status.

Abstract In the era of digital transformation, businesses are forced to realign strategic priorities, innovate their operating models, improve service quality and increase customer-centricity to maintain competitiveness. As customer expectations continue to rise, the extent to which firms adopt customer obsessed behaviours has become a critical topic in contemporary strategic management. However, the limited empirical evidence exists on how customer obsession, innovation and service quality influence firm performance in Asian markets. This study aims to examine the effects of customer obsession, innovation, and service quality on firm performance using accounting-based profitability indicators. Building on the Resource Based View, Dynamic Capabilities and Service-Dominant Logic, the study develops an integrated analytical framework and applies panel data regression techniques to firm level secondary data. The empirical analysis is conducted using data from 50 publicly listed Taiwanese firms over the period from 2020 to 2025. The findings reveal three key insights. First, customer obsession exhibits only a weak association with firm profitability, indicating that the financial returns of customers centric strategies tend to emerge gradually rather than immediately. Second, firm resource variables, particularly innovation related investment and firm scale, demonstrate strong and stable positive effects on profitability outcomes. Third, innovation activities and service quality enhancement show negative short-term effects on financial performance, reflecting the substantial costs associated with innovation investment and service quality improvement. The research contributes additional empirical evidence from the Taiwanese context and proposes an integrated framework linking customer obsession, innovation, and service quality in the analysis of corporate strategy and firm performance.

Budget underperformance remains a persistent problem in Indonesia’s decentralized fiscal system, where many provinces struggle to achieve efficient and accountable spending despite transparency initiatives. Weak digital infrastructure often limits citizens’ ability to access and monitor fiscal information, reducing the effectiveness of open governance efforts. This study investigates the determinants of provincial budget performance by focusing on the interplay between fiscal transparency and digital infrastructure. Using panel data from 34 provinces from 2014 to 2024, a two-way fixed-effects regression model examines how transparency, internet penetration, and their interaction influence budget realization. Control variables include budget adaptability, GDP per capita, population size, and intergovernmental transfer ratios. The findings indicate that both transparency and internet penetration significantly improve budget performance, and their interaction strengthens this effect, suggesting that digital readiness amplifies the benefits of transparency. Conversely, budget adaptability, population, and transfer dependency hinder performance, while higher GDP per capita enhances it. These findings confirm that fiscal reforms are context-dependent and require enabling technological conditions. Future research should employ mixed-methods or experimental designs to examine the causal mechanisms linking digital inclusion and fiscal accountability, particularly in less-connected provinces seeking to improve public financial management outcomes.

This study presents a noncontact vital sign monitoring system based on pulse radar, designed to detect human respiration and heartbeat. The system employs a patch-fed wideband slot antenna to transmit pulsed signals toward the subject, and analyzes the reflected signals to extract physiological parameters such as thoracic respiratory activity, respiratory rate, and breathing depth. The antenna used in the system has dimensions of 100 mm × 150 mm × 0.8 mm (L×W×H) and operates in the 5G C-band (4.4–5 GHz) and WLAN bands at 5.2/5.8 GHz (5.15–5.825 GHz). The antenna exhibits a return loss below 10 dB and demonstrates reliable performance in both respiration and heart rate monitoring.

Deep learning (DL) methods for continuous blood pressure (BP) monitoring utilizing photoplethysmogram (PPG) signals have been confined only to research modeling and not applied. Recently, research on the weighing-scale-based system proved potential DL practical deployment (Edge AI) as existing systems rely on the Pulse Transit Time (PTT) method. The PTT method depends only on manual feature extraction, limiting its performance, and requires inconveniently one additional sensor per method (electrocardiogram, ballistocardiogram, and impedance plethysmography). This study deployed a DL model, cBP-Tnet Edge AI, into a single clip-on BP measuring system mounted on the sensor bar of a body-fat weighing scale, aiming for practical use case. This setup ensures familiar and secure skin contact minimizing motion artifacts. Specifically, we evaluated the performance of integrating a PPG sensor (MAX30102) to single-board computer (Raspberry Pi 5), deployed with cBP-Tnet edge AI for continuous BP estimation. To validate, we recruited 10 volunteer participants and compared cBP-Tnet Edge AI’s performance to established PTTbased and traditional cuff oscillometric BP measurement methods. In comparison to PTT-based, cBP-Tnet Edge AI attained Pearson correlations of $\mathrm{r}=0.788$ for systolic blood pressure (SBP) and $\mathbf{r} \boldsymbol{=} \mathbf{0. 8 4 1}$ for diastolic blood pressure (DBP). When compared to traditional cuff-based BP, measurements were preliminarily feasible (SBP: $\mathrm{p}=0.063$; DBP: $\mathrm{p}=0.791$). The mean absolute errors were 9.7 mmHg for SBP and 6.3 mmHg for DBP, with overall accuracies of $91.7 \%$ and $91.2 \%$, respectively. Thus, cBP-Tnet Edge AI is comparable in performance to both established PTT-based and traditional cuff-based methods, providing potential to include SBP and DBP measurements to commercial body-fat scales.