Implementing a Six-Element Framework of Safety Culture in the Thai Cosmetics Industry

This study investigates the implementation of safety culture systems in the cosmetics industry using a structured six-element framework: Input, Processing, Output, Working Environment, Ergonomics, and Safety Experience. A mixed-methods approach was employed to capture both quantitative and qualitative insights, highlighting significant improvements in workplace safety, operational efficiency, and regulatory compliance with GMP and ISO 22716 standards. Quantitative findings revealed statistically significant advancements across all six elements, with notable gains in resource allocation and the integration of safety protocols. Qualitative data identified leadership commitment and tailored training programs as critical facilitators of success, whereas technological constraints and cultural resistance were recognized as key implementation challenges. In response to these barriers, the study emphasizes the importance of scalable and adaptive technological solutions. Specifically, it proposes the future integration of Industry 4.0 technologies—such as IoT-enabled monitoring systems and AI-driven analytics—to enhance hazard prediction, streamline safety management, and support real-time decision-making. These findings provide a forward-looking roadmap for advancing safety culture practices not only in the cosmetics sector but also across similarly structured manufacturing industries

Performance Improvement for Germanium-Based Near-Field Thermophotovoltaic Converter

Near-field Thermophotovoltaic (NF-TPV) converter utilizes the tunnelling of an evanescent wave to surpass the blackbody limit, enhancing the radiative heat transfer of the TPV converter by several orders of magnitude. One of key challenges for commercial NF-TPV converter is cost reduction. Germanium is known to be a cost-effective material with bandgap energy compatible with a TPV application. In this study, the germanium-based NF-TPV converter is introduced with an addition of air-bridge gap (ABG) at the interface between a TPV cell substrate and a metal back surface reflector (BSR) as a strategy to improve sub-bandgap photon utilization. Effects of air-bridge gap and germanium substrate thickness on radiative heat fluxes and converter performance are investigated. At the radiator temperature of 1400 K and the optimum air-bridge gap thickness of 1300 nm, system efficiency of NF-TPV converter increases from 17.16% to 24.98% for a 175  thick TPV cell, and from 16.73% to 31.39% for a 40  thick TPV cell under moderate surface passivation. In addition, the converter's performance under varying radiator temperature is analysed. This study demonstrates the potential of the air-bridge gap to optimize NF-TPV converter performance

An Empirical Study of Grid Fins Aerodynamic Performance in Low-Subsonic Flight

Deploying payloads from aerial platforms at low altitudes poses significant challenges, particularly in maintaining an accurate freefall trajectory. Traditional fin designs are often used in dropped payloads at low altitudes. However, these designs frequently fail to preserve the intended trajectory, reducing precision and effectiveness. This challenge is particularly evident in high-wind conditions, where achieving landing precision or maintaining a desired trajectory becomes more difficult. To address these limitations, alternative fin designs, such as grid fins, offer a promising solution due to their unique aerodynamic properties. The current study investigates the aerodynamic performance of grid fins in low-subsonic flight, focusing on their application in drone-dropped payloads. The primary objective is to assess how different grid fin design parameters affect aerodynamic performance. Experimental analyses are conducted through subsonic wind tunnel testing of various grid fin designs. The study highlights the differences in aerodynamic performance resulting from grid count variations and the grid members' horizontal versus diagonal placement. The experimental results indicate that grid fins with a diagonal configuration outperform their horizontal counterparts with the same grid numbers and dimensions. The findings reveal the presence of an optimal configuration, achieving a peak lift-to-drag (L/D) ratio of approximately 1.99, compared to 1.4 for the least optimal design

Alternative Design and Development of Material Handling Platform

In a world where efficiency and sustainability in logistics are increasingly significant, innovative load-carrying solutions are essential. This study investigates the mechanical performance of a conventional plastic pallet compared to a newly proposed P-Sling design using Finite Element Analysis (FEA). FEA serves as a powerful tool to simulate real-world conditions, enabling accurate predictions of stress, deformation, and structural reliability. Initial results show that the original P-Sling design exhibits higher displacement and stress, which aligns with its intended flexibility for dynamic use. In contrast, the plastic pallet, designed as a rigid structure, displays only minor bending but may be prone to failure under prolonged stress. To further enhance the P-Sling’s performance, its base thickness is increased from 1 mm to 10 mm, significantly reducing stress and deformation while maintaining its flexible nature. Although the plastic pallet maintains greater rigidity, the improved P-Sling presents a promising and adaptable alternative, particularly in environments that require both flexibility and strength under dynamic loads. This study addresses an important gap by applying Finite Element Analysis (FEA) to the P-Sling design, offering new insights into optimizing the performance of flexible load-bearing structures in changing conditions

Development and PCA Evaluation of Lunar Mortar Compositions from Lunar Simulant and Potato-Based Materials

Space activities have taken another step, particularly on the Moon, where the establishment of a human exploration base has garnered interest from many space agencies. One of the challenges lies in constructing shelter and accommodation using locally available resources. This study investigates the lunar mortar compositions using Thailand lunar regolith simulants and potato-based materials, including potato starch and fibers, as potential binding agents. Extensive experiments optimized the mortar formulation by varying the ratios of Thailand lunar simulant (TLS-01), potato starch, and fresh/fermented potato fibers. Compressive strength tests evaluated the effects of fiber reinforcement, TLS-01 percentage, potato starch, and heat treatment. Microstructural analysis via SEM revealed the internal structure and cohesion. Principal Component Analysis (PCA) identified major influencing variables on compressive strength and their correlations. The LC-FrF-3 sample, with TLS-01 (39.47%), potato fresh fiber (7.9%), and freezing (-10ºC), exhibited maximum 0.65 MPa compressive strength. SEM showed specimens with dense cohesion and reduced voids, such as LC-FrF-3, had better strength. PCA highlighted 'TLS-01', 'Potato starch', 'Heat', and 'Freeze' as the most significant influencing variables. This research demonstrates the potential of lunar regolith simulants and potato-based materials for developing suitable lunar mortar for construction, contributing to in-situ resource utilization for space exploration

Employment and Human Development for Foreign Civil Engineer in Japanese Construction Industries

This thesis extracts the facts and issues on employment and human development for foreign civil engineer in Japanese construction industries and proposes the solutions to potential future problems. Compared to other industries, aging of workers and engineers in construction industries has been ongoing rapidly. And Japanese construction industries require recruitment foreign skilled workers and civil engineers to make up this shortage of workers and engineers. This thesis focuses on foreign civil engineers and studies issues from the both sides of foreign civil engineers and Japanese employers. Additionally, it proposes sustainable solution for Japanese construction industries to achieve long term employment of foreign civil engineers. Especially, Japan’s qualifications processes and multi-layered subcontractor system composed of prime contractors and lower subcontractors are highlighted as important points of foreign civil engineers. Interviews on foreign civil engineers and their Japanese managers and management personnels were taken place and collected data such as reasons of preference of Japanese contractors, motivations, career development plan from foreign civil engineers, and recruitment criteria, promotion system, training and education programs from their Japanese managers and management personnels. Based on these data and studies, this thesis analyses and concludes key solutions on long term employment and human development for foreign civil engineers in Japanese construction industries

Data-Driven Solutions for Backcalculating Elastic Moduli of Flexible Pavements from FWD Test

Traditional methods for calculating pavement layers elastic moduli from falling weight deflectometer (FWD) tests often rely on computationally intensive iterative processes and lack struggle to capture complex variable relationships. This article highlights the utilization of machine learning (ML) algorithms, which include artificial neural networks (ANN), long-short-term memory (LSTM), and random forests (RF), to predict the elastic moduli of multi-layered flexible pavement based on FWD test. All ML algorithms were developed using synthetic databases derived from the exact stiffness matrix scheme, which was employed for the analysis of multi-layered pavements under axisymmetric surface loading. The development of ML models involves preprocessing of data, hyperparameter optimization, and performance evaluation.  The input variables consist of the FWD surface deflections, the magnitude of applied loading, and the layer thicknesses, while the output variables represent the predicted layered elastic moduli of the pavement structure. The ANN and LSTM models capture complicated relations more effectively than the RF model in the backcalculation of the layered elastic modulus based on the FWD test. Among the two, LSTM achieves higher accuracy, with the average values across all layer moduli of R2 and MAPE being 99.04% and 2.41%, respectively, in the test set. The applicability of LSTM model is further demonstrated by comparing with the backcalculated elastic modulus based on the FWD field experiments performed on the infrastructure of roads in Thailand. Furthermore, a sensitivity analysis reveals that deflections near the center of loading predominantly impact the predictions of upper layer moduli, while the moduli of lower layers are influenced by deflections across all geophones

Comparison of Thai and English Speaking Signals from Brain Using Deep Learning and EEG

This study investigates the decoding and comparison of brain signals associated with spoken Thai and English words using deep learning techniques and EEG equipment. In the field of Brain-Computer Interfaces (BCI), researchers have extensively explored methods to decode brain signals into text. Two primary approaches exist: invasive (e.g., ECoG) and non-invasive (e.g., EEG). Invasive methods require surgery and offer high-quality signals but carry infection risks. Conversely, non-invasive methods employ scalp electrodes, resulting in lower signal quality but greater practicality for daily use. The present research utilizes three datasets each for Thai and English to evaluate the effectiveness of EEG and compare the outcomes for both languages. The Thai word data consists of three sets: single words (หิว, ปวด, เจ็บ, หนาว, ร้อน), two-word phrases (หิวมาก, ปวดท้อง, เจ็บแขน, หนาวมาก, ร้อนมาก), and three-word sentences (ฉันหิวมาก, ฉันปวดท้อง, ฉันเจ็บแขน, ฉันหนาวมาก, ฉันร้อนมาก). The English word datasets correspond semantically to each Thai set. All results are tested and compared using two machine learning approaches: Multi-Layer Perceptron (MLP) with statistical features and Convolutional Neural Network (CNN) with stacked spectrogram features. The MLP achieved an overall accuracy of 98%, while the CNN achieved 64%

Permeability of Saturated Sands and Gravels: Pore Constriction Size Perspective

This study re-analyzes saturated permeability data for sands and gravels to identify the key predictors. The dataset (76 tests) is evaluated against void ratio, specific surface area, mean pore size, and mean constriction size. The strongest correlation is found with mean constriction size. Based on this finding, the validated and refined predictive permeability model is proposed using mean constriction size as a representative parameter. The uncertainty and statistical analysis of the model indicates promising accuracy and reliability in predicting permeability for the dataset. The model is applicable for soils with coefficient of uniformity (CU) up to 76 and effective particle size (D10) up to 6 mm

Reanalysis of Vertical Land Motion at Tide Gauge Stations in Thailand Utilizing GNSS Continuous Operating Reference Stations

Sea level monitoring is critical for coastal management, water resource planning, and climate change studies, particularly in Thailand, where agriculture forms the backbone of the economy. In Thailand, sea level observations primarily rely on tide gauge stations. However, tide gauge measurements are often influenced by vertical land motion (VLM), including land subsidence or uplift. To address this, the Global Navigation Satellite System (GNSS) offers a reliable solution for determining VLM. This study leverages the established network of Continuous Operating Reference Stations (CORS) in Thailand, utilizing co-located GNSS CORS with tide gauge stations in the Gulf of Thailand to quantify VLM at tide gauge stations. The VLM corrections were applied to tide gauge data to refine sea level estimates and provide insights into long-term sea level changes. The findings reveal that sea level changes corrected for VLM demonstrate discrepancies of approximately 4–5 millimeters when compared to sea level changes derived from satellite altimetry. This indicates that GNSS-derived VLM from the CORS network in Thailand is influenced by additional factors that may introduce biases in corrected sea level measurements. These results highlight the importance of addressing these influences to improve the accuracy of sea level monitoring and contribute to more reliable climate and coastal management strategies