Rajamangala University of Technology Isan

We investigated the dynamics of molecular reorientation in freely suspended smectic liquid–crystal films (FSLCFs) under the influence of heat flux. We also examined how external thermal gradients affect molecular alignment in these ultra-thin films. FSLCFs were fabricated in a temperature-controlled chamber in this study. When heat flux was applied perpendicular to the film plane, rotation of line defects, known as 2π walls, was observed. This rotation resulted from thermomechanical torque acting on the molecular director, a phenomenon referred to as the Lehmann effect. By analyzing the changes in defect evolution, how heat flux drives the self-organization of liquid–crystal structures can be understood. In this study, we combined experimental observations and computational simulations to model and interpret the results. The results enhance the understanding of the underlying mechanisms governing molecular reorientation and defect dynamics in FSLCFs, particularly in non-equilibrium conditions, to study this mechanism in the microgravity environment. The results also contribute to the development of advanced liquid–crystal technologies, with potential applications in energy-efficient devices, adaptive materials, and space technology systems.

High-performance composites for the removal of organic methylene blue (MB) dye, specifically bimetallic nanocluster particles CuO@NiO fabricated on geopolymer, have been successfully developed from swollen finger grass extract solution by a simultaneous reduction process incorporated into biomass ash geopolymer materials. Using an ultraviolet-visible (UV-Vis) spectrometer, we observe that the CuO@NiO/geopolymer composites with 10 wt% exhibit superior catalytic absorbent activity in absorbing MB dye compared to pure biomass ash geopolymer and other metal oxides incorporated with geopolymer materials. We attribute this enhanced ability to the simultaneous processes of photodegradation and adsorption. This work demonstrates a new concept in the development of low-cost and eco-friendly materials with high efficiency for removing organic MB dye, applicable in wastewater treatments.

This study presents a novel prototype of an induction heating dryer integrating hysteresis control with phase-shifted pulse width modulation (PWM) for the first time. The system replaces conventional resistance heating, improving energy efficiency and thermal stability. The 2 kW prototype comprises a drying chamber and a hot air unit with controlled airflow of 1.5 m/s. Phase angle adjustment reduces voltage, current, and power consumption while maintaining the power factor within acceptable limits. The temperature control maintains stability within ±1 °C of the setpoint. The results demonstrate fast, energy-efficient, and precise drying, offering potential benefits for food processing and textile industries, and providing a foundation for future development of intelligent, energy-efficient induction dryers.

This study develops a 90-day rainfall forecasting model for rain-fed agricultural systems in areas lacking ground-based weather sensors. To address this limitation, open data from the NASA POWER satellite (2014–2024) covering Nadi Subdistrict, Mueang District, Surin Province, Thailand, is utilized. The proposed hybrid framework combines Long Short-Term Memory (LSTM) networks with Linear Regression (LR) to join the ability to learn nonlinear temporal dynamics with the potential to model long-term trends, complemented by lag and rolling windows techniques. The results indicate that the lag–rolling–augmented LSTM–LR model achieved the lowest Root Mean Squared Error (RMSE) of 0.0413 and Mean Absolute Error (MAE) of 0.0230 among all 10 models. The Friedman test confirmed the significant difference (x² = 364.33, p < 0.001), and the Nemenyi test showed that the proposed model significantly outperformed the traditional BiLSTM, Convolutional Neural Network (CNN), CNN (with lag and rolling features), and LSTM+LR (p < 0.0001). Furthermore, the model maintained high accuracy during both heavy and drought conditions. The novelty of this study lies in the integration of temporal hybrid models with open satellite data to create a low-cost and statistically validated tool for climate risk management and decision support approaches for smallholder agriculture.

Background: In forensic ballistics, identifying ammunition types on physical evidence is critical, particularly when metallic residues are minimal. This study comparatively analyzes the elemental signatures deposited by two common projectiles—Full Metal Jacket (FMJ) (Cu/Zn jacket) and Lead Round Nose (LRN) (exposed Pb core)—on complex targets, including pig bone/tissue and mango wood. Methods: Using a semi-automatic handgun at an intermediate range of 5.0 m, residues were examined through high-resolution benchtop Micro-XRF (M4 Tornado) for micro-spatial analysis and Portable XRF (Elio) for rapid field characterization. Additionally, fresh pork leg samples were subjected to a 3-month environmental degradation period to assess trace persistence. Results: Observations indicated that LRN projectiles exhibit markedly elevated Lead (Pb) concentrations along the wound track in bone, hence confirming Pb as a reliable indicator for unjacketed ammunition; specifically, the median Pb concentrations at bullet wiping were 10.39 wt% for M4 and 7.34 wt% for Elio. Conversely, FMJ traces remain strictly confined to the surface bullet wipe area, with median concentrations of Pb, Cu, and Zn being 2.21 wt%, 0.24 wt%, and 0.59 wt% via M4, respectively. Statistical analysis showed a strong correlation for high-concentration elements on tissue, but significantly greater variance on wooden surfaces where FMJ traces exhibited a very weak negative correlation (r = −0.2774) due to minimal and irregular metal transfer. Taphonomic evaluation revealed that the Pb signature from LRN is exceptionally stable (r ≈ 0.9999) even after decomposition, while FMJ signatures are highly sensitive to environmental exposure. Conclusions: This research underscores the necessity of high-sensitivity Micro-XRF (M4) for definitive ammunition verification, providing a refined analytical framework for shooting incident reconstruction even involving degraded remains or complex environmental scenes.

The hom-thong banana, being a high-value Thai export variety, is facing significant risk from disease outbreaks affecting crop yield and quality. Traditional visual inspection methods in detection of diseases are labor consuming, error-prone. This research addresses these limitations by developing a new artificial intelligence (AI)-based automatic disease detection system for the hom-thong banana industry on top of cutting-edge computer vision technology. The study employed deep learning object detection models, contrasting Roboflow, you only look once (YOLO)v11, and YOLOv12 architectures, which were trained on a large dataset of 2,576 images of Thai banana plantations. With systematic data augmentation techniques, the dataset was augmented to 6,184 images of seven types of disease under varied environmental conditions. The method entailed extensive preprocessing and evaluation of performance through precision, recall, and mean average precision (mAP) metrics. Outcomes indicated that YOLOv12 outperformed with 93.3% accuracy, 83.3% sensitivity, and 86.3% mAP@50 compared to standard inspection schemes. This research is applicable to Thailand's smart agriculture initiative by providing farmers with low-cost, accurate, and effective disease monitoring equipment. The application of this AI system has the ability to enhance the yield of crops, reduce losses, and enhance the competitiveness of Thai banana exports in the global market, in support of sustainable agricultural development.