Customers’ requirements for product quality are increasing and more and more manufacturing companies are improving the quality of processing conditions at each production process by applying advances in Internet Technologies, Internet of Things, Big Data, Industry 4.0, Industrial Engineering Tools, Multi-objective optimization algorithms, Digital Numerical Control (DNC) method, and Six Sigma tools to improve productivity and quality of product. Continuous improvement of the manufacturing process presents a huge opportunity for the transformation of the normal production model to smart manufacturing. Manufacturing condition data, production data, quality of product data, and power quality data are measured and collected in real-time forming big data fed into operation analysis of the development strategy of the manufacturing company. In this study, the implementation of the Hybrid DMAIC (Define-Measure-Analyze-Improve-Control) method in six sigma is presented in detail based on the statistical hypothesis testing techniques for analyzing production data applied at the measurement phase, Multi-objective decision Fuzzy TOPSIS techniques applied to choose the best improvement solution applied at the analysis phase, Multi-objective optimization technique optimizes production conditions, and the DNC method performs automatically program call by RFID system applied at improvement phase, measurement technique, real-time measurement result collection and analysis by Industry 4.0 system and the method of phase shift is used to keep Total Harmonic Distortion (THD) and Total Demand Distortion (TDD) less than 5% within the international standard limits defined by IEEE 519:2022 and IEC 61000 applied at control phase. A common data system linking all activities at a particular manufacturing process and hybrid DMAIC in Six Sigma continuous improvement model is proposed in this study. This research’s results, the rate of occurrence of smaller-than-standard outer diameter defects decreased from 31.20% to 4.5% per month, the grinding process productivity achieved 610 pcs per day while the number of operators decreased from 4 people to 2 people per day, and productivity per person per hour increased from 15 PCS to 30 PCS. The profit is achieved at 11,184 USD per year. The Hybrid DMAIC method in Six Sigma is considered a model of continuous improvement that can be applied to production processes in manufacturing companies. The Hybrid DMAIC method in Six Sigma is simple to implement, easy to deploy into the real environment at production processes, and uses commercial software such as Minitab, LABVIEW, and SPSS. Manufacturing companies apply this research model to help improve competitiveness, improve business performance, and improve customer satisfaction.

The reaction time, temperature, ratio of precursors, and concentration of sodium citrate are known as the main factors that affect the direct synthesis process of SiO₂@Au based on the chemical reaction of HAuCl4 and sodium citrate. Hence, we investigated, in detail, and observed that these factors played a crucial role in determining the shape and size of synthesized nanoparticles. The significant enhancement of the SERS signal corresponding to the fabrication conditions is an existing challenge. Our study results show that the optimal reaction conditions for the fabrication of SiO₂@Au are a 1:21 ratio of HAuCl₄ to sodium citrate, with an initial concentration of sodium citrate of 4.2 mM, and a reaction time lasting longer than 6 h at a temperature of 80 °C. Under optimal conditions, our synthesis process result is SiO₂@Au nanoparticles with a diameter of approximately 350 nm. In particular, the considerable enhancement of Raman intensities of SiO₂@Au compared to SiO₂ particles was examined.

Timber price forecasting plays a crucial role in the strategic planning of the forestry sector, particularly in countries like Vietnam, where the market is highly volatile and influenced by complex factors such as supply-demand imbalances, environmental policies, and globalization. However, traditional forecasting models such as ARIMA, SARIMA, and VAR often fail to provide accurate predictions in non-linear and multi-dimensional market conditions. This study aims to develop a more effective forecasting approach by integrating Graph Neural Networks (GNNs) and Long Short-Term Memory (LSTM) models. The GNN component captures spatial dependencies among timber-producing regions, while the LSTM component models long-term temporal trends. Together, they address the limitations of conventional models and provide a comprehensive framework for price forecasting. Using data from 2000 to 2024 – including timber prices, harvesting volumes, climate data, and export records – the proposed GNN-LSTM model demonstrated superior predictive performance. It achieved a Mean Squared Error (MSE) of 0.0023, Root Mean Squared Error (RMSE) of 0.0480, and Mean Absolute Error (MAE) of 0.0340, along with an R ² score of 0.94. These results confirm the model’s capability to capture complex spatial-temporal relationships and improve forecasting accuracy. The findings suggest that the GNN-LSTM hybrid model provides a valuable decision-support tool for stakeholders in Vietnam’s forestry industry, including forest managers, investors, and policy makers. It enables more informed decision-making, risk mitigation, and resource optimization. Additionally, the study highlights the potential of applying advanced machine learning techniques to data-constrained markets. Future research should focus on enhancing data availability and incorporating mechanisms to better capture the effects of external disruptions, such as global supply chain shocks.

Accurately calculating the dose for patients in cancer treatment is a crucial task. Currently, there are many methods as well as simulation software for dose calculation, most of which have certain limitations in terms of flexibility, ease of use, and error in organ’s borders when calculating the dose at voxel level using the traditional Monte Carlo method. However, GAMOS developers have implemented an application specifically for nuclear medicine dosimetry - Gamos NMGUI. This software simplifies the dose calculation process with just a few buttons. The addition of the “parallel geometry” utility eliminates error at the boundaries, resulting in more optimal dosesfor patients. The results of the S values for the ICRP phantoms with Gamos NMGUI show a good agreement with the results calculated by Stephanie Lamart et al. The doses for the boundaries of some organs (Gallbladder Wall, Stomach Wall, Heart Wall, Urinary Bladder Wall) calculated by the “parallel geometry” method has deviations of up to 26% (heart wall) when compared with the traditional MC method. This shows the need to improve the dose calculation by using the parallel geometry technique. In summary, Gamos NMGUI is a convenient, simple but highly effective dose software.

Through employing the solid-state process under significant temperature, we produced a sequence of single-element radiation-adjustable phosphors Ca8MgR(PO4)7:Eu2+,Mn2+ or CaMg:EM (R = La, Y), and thoroughly analyzed the crystalline arrangement, photoluminescence (PL), PL excitation (PLE), as well as the degrading period. The findings suggest that Ca8MgLa(PO4)7:Eu2+,Mn2+ could be a green contender to be used within close ultraviolet-chip white light-emitted diode (WLED) devices. It is possible to change the emitting green hue from Ca8MgLa1-xYx(PO4)7 treated with Eu2+ to cyan by adjusting the La3+/Y3+ proportion for the purpose of lowering the crystal field potency. Furthermore, power shift among Eu2+ and Mn2+ would be used to create a conventional one-stage white CaMg:EM having a CIE hue coordinate shown as (0.330, 0.328). The power transmission process was shown in the form of resonant dipole-quadrupole nature. In addition, we also computed the critical range. The white-emitting single-composition CaMg:EM was effectively coated above one close ultraviolet chip under 375 nm, indicating that it may become a strong choice of phosphor within close ultraviolet WLED devices.