King Mongkut's Institute of Technology Ladkrabang

A sensor capable of estimating the dielectric properties of the outer and inner materials of a concentric dielectric sphere is essential for assessing the quality of thick rind fruits. This paper presents a sensor design that operates at a single frequency and is straightforward. The underlying principle of the sensor involves transmitting a microwave signal to a concentric dielectric sphere with two power levels. The low-power signal yields backscattered wave from the outer sphere, while the high-power signal provides information about backscattered wave from both the outer and inner spheres. The sensor, which operates at a frequency of 10 GHz using cost-effective components, effectively detects defects in spherical-shape fruits, e.g. mangosteens. The accuracy in classifying translucent mangosteens from normal mangosteens is 92.5 % enables effective classification of defected fruits.

Knowing who follows whom and what patterns they are following are crucial steps to understand collective behaviors (e.g. a group of human, a school of fish, or a stock market). Time series is one of resources that can be used to get insight regarding following relations. However, the concept of following patterns or motifs and the solution to find them in time series are not obvious. In this work, we formalize a concept of following motifs between two time series and present a framework to infer following patterns between two time series. The framework utilizes one of efficient and scalable methods to retrieve motifs from time series called the Matrix Profile Inference Method. We compare our proposed framework with several baselines. The framework performs better than baselines in the simulation datasets. In the dataset of sound recording, the framework is able to retrieve the following motifs within a pair of time series that two singers sing following each other. In the cryptocurrency dataset, the framework is capable of capturing the following motifs within a pair of time series from two digital currencies, which implies that the values of one currency follow the values of another currency patterns. Our framework can be utilized in any field of time series to get insight regarding following patterns between time series.

Organic piezoelectric materials offer sustainable alternatives for mechanical energy harvesting (MEH), yet their potential remains underexplored compared to inorganic counterparts. This study pioneers the use of triglycine sulfate (TGS), a rarely studied organic piezoelectric, within a flexible three‐phase composite with bacterial cellulose (BC) and chitosan (CS) for piezoelectric (PENG) and triboelectric (TENG) nanogenerators. Unlike widely researched systems, TGS's unique hybrid organic–inorganic nature is leveraged here for the first time in MEH. Optimized at a 50:50 BC:CS ratio with 40 wt.% TGS, achieves a TENG output of 141.2 V and 93.3 µA post‐poling—1.8 and 2.4 fold higher than unpoled samples—driven by TGS's dipole alignment. Separately, the configuration utilizing a 5 wt.% TGS loading yields 13.7 V and 0.19 µA. Advanced characterization (ATR‐FTIR, SR‐XTM) and simulations (COMSOL, DFT) reveal TGS's synergy with BC/CS roughness, enhancing charge generation. Delivering 118.65 µW cm⁻², the TENG (from the 40 wt.% TGS poled sample) powers a digital watch, showcasing practical promise. This work not only introduces TGS as a novel MEH candidate but also provides mechanistic insights into its polarization, advancing bio‐hybrid nanogenerator design.

Equatorial plasma bubbles (EPBs) disrupt satellite‐based communication and navigation systems, particularly in equatorial regions. Reliable detection and classification of EPBs from all‐sky imager (ASI) images are essential for accurate space weather monitoring and forecasting. This study presents a novel bootstrapping convolutional neural network (CNN) approach to optimize automated EPB detection on ASI images for operational space weather monitoring applications, and overcoming challenges related to image variability and imbalanced data sets. Data used for CNN training were obtained from the optical mesosphere thermosphere imagers ASI installed at the Space Environment Research Laboratory, National Space Research and Development Agency, Abuja during the period from 2015 to 2020. Our method involved training three sub‐models, and aggregating their predictions. The CNN trainings were conducted on three sub‐datasets of 3,000 images each, categorized as “EPB,” “Noisy/Cloudy” or “No EPB.” Three corresponding sub‐models were developed from the CNN trainings. The three sub‐model classifications independently gave prediction accuracies of 98.67%, 98.33%, and 95.83% on a reserved test data set of 600 images. Ensemble models further improved the model prediction accuracies to 99.17% and 99.33% for methods based on the mean of sub‐model probabilities and the mode of sub‐model classifications respectively. Our results indicate that the bootstrapping CNN technique enhanced the EPB detection accuracy, providing a powerful tool for real‐time space weather monitoring applications, and implications for improving operational reliability of satellite‐based navigation and communication in the equatorial region.

This study proposes a simple method for tailoring the morphology and activity of cerium oxide (CeO2) catalysts in converting carbon dioxide (CO2) and methanol to green organic carbonate, dimethyl carbonate (DMC), to utilize and reduce CO2 emissions. CeO2 was prepared by urea precipitation at 85, 105, and 125 °C for 2 h, then calcining at 600 °C for 2 h. The phase structure and morphology of CeO2 correlated with the urea hydrolysis rate. A low degree of supersaturation at 85 °C led to heterogeneous precipitation of cerium oxycarbonate (Ce2O(CO3)2.H2O) and CeO2 with spherical morphology, while a higher degree of supersaturation at 105 °C and 125 °C resulted in homogeneous precipitation of single-phase Ce2O(CO3)2.H2O with spindle and elongated octahedral morphology, respectively. The spindle-shaped CeO2 prepared at 105 °C with a predominant surface (111) facet showed the highest catalytic activity, with a DMC yield of 18.81 mmol.gcat⁻¹. The enhanced catalytic efficiency of spindle-shaped CeO2 was due to the high concentration of surface-active defect sites of exposed cerium cations and oxygen vacancies, which optimized the number of acid–base sites in adsorbing and activating CO2 and methanol to produce DMC. Graphical abstract

Iron phosphide (FeP) has emerged as an efficient catalyst for converting palm oil, a biomass-derived feedstock, into bio-jet fuel through the hydrocracking process. The catalytic performance of FeP is strongly influenced by the choice of support material. In this study, microporous MWW-type zeolites (MCM-22 and MCM-36) and mesoporous materials (MCM-41 and MCM-48) were successfully synthesized from entirely natural precursors, silica derived from rice husk and aluminosilicate gel extracted from kaolin clay, via a hydrothermal method, and employed as supports for FeP catalysts. Among these materials, MCM-22 zeolite exhibited the highest microporosity, followed by zeolite MCM-36, resulting in superior acidity compared to the mesoporous materials, MCM-41 and MCM-48. FeP supported on MCM-22 (FeP/MCM-22) demonstrated the best catalytic performance, liquid hydrocarbon yield (∼33%), and bio-jet selectivity (∼78%) were obtained, outperforming FeP/MCM-36, FeP/MCM-41, and FeP/MCM-48. This is due to its high surface area of micropores (∼187 m² g⁻¹) and the excellent acidity of this zeolite, which helped prevent FeP overloading and promote uniform metal distribution. Furthermore, it exhibited remarkable stability and reusability, with performance improving over three consecutive reaction cycles, LHCs yield increasing to 50% and bio-jet selectivity stabilizing at about 83%, attributed to enhanced acidity accessibility and progressive formation of the FeP active phase.

The adsorption of methylene blue on calcium oxide synthesized from chicken eggshells was investigated as a method for wastewater purification. Calcium oxide adsorbent was prepared by calcining ground eggshells in a furnace at 900°C for 3 hours, resulting in a material with approximately 97% purity. Adsorption experiments were conducted in batch mode, using a 0.25g: 50 ml ratio of adsorbent to solution and maintaining a temperature of 35°C. The contact time and initial methylene blue concentration were varied between 0-480 minutes and 10-200 mg/L, respectively. Equilibrium was reached within 240 minutes, the adsorption isotherm can be modelled by Langmuir model with a maximum specific amount adsorbed of 27.03 mg/g.

This study optimizes spray-coating parameters for cellulose nanocrystals extracted from young coconut husks onto paper substrates using response surface methodology. CNCs were produced through acid hydrolysis and mechanical grinding, yielding nanocrystals with an average size of 116 nm and a crystallinity index increase from 28.89 % to 86.13 %. XRD and FTIR analyses confirmed high purity, while UV-vis revealed significant optical absorption in the UV range. Spray-coating parameters, including CNC concentration, volume, heating temperature, and heating duration, were optimized using a central composite design. The 2FI model revealed that CNC concentration and heating duration significantly affected film thickness, where higher CNC levels and longer heating durations produced thicker coatings. However, excessive CNC content led to agglomeration, compromising film quality. The quadratic model highlighted a significant relationship between coating parameters and tensile strength. Heat treatment notably enhanced mechanical properties, with optimal tensile strength reaching 26.15 ± 0.61 MPa-15 % higher than uncoated paper-under conditions of 4 % w/v CNC concentration, 1.5 ml volume, 75 °C heating temperature, and 35 min heating duration. This research highlights the potential of CNCs from young coconut husks as a sustainable reinforcement material, promoting agricultural waste valorization and enhancing paper properties.

This study proposes a derivative-based, carrierless pulse position modulation (PPM) scheme utilizing a voltage-controlled oscillator (VCO) and a monostable multivibrator. In contrast to conventional PPM systems that rely on reference carriers or complex demodulation methods, the proposed architecture simplifies signal generation by directly modulating the time derivative of the message signal. The modulated signal, when processed through standard analog demodulators, inherently yields the derivative of the original message. This behavior is first established through theoretical derivations and then confirmed by simulations and circuit-level experiments. The proposed method includes a differentiator feeding into a VCO, followed by a monostable multivibrator to generate a carrierless PPM waveform. Experimental validation confirms that, under all tested demodulation approaches—integrator-based, PLL-based, and quasi-FM—the recovered output aligns with the differentiated message signal. The integration of this output to retrieve the original message was not performed to maintain focus on verifying the modulation principle. Additionally, the study aimed to ensure the consistency of derivative recovery. Signal-to-noise ratio (SNR) expressions for each demodulator type are presented and discussed in the context of their relevance to the proposed system. Limitations and directions for further study are also identified.

Introduction Gepants, a calcitonin gene-related peptide (CGRP) receptor antagonist, is a class of migraine therapeutic options with extensive evidence supporting a favorable efficacy and safety profile. However, as a novel class of medication in Thailand, specific guidelines or recommendations regarding rational drug use are currently unavailable. This could hinder physicians from utilizing the medications for eligible patients and prevent pharmacists from providing information to physicians and patients. Main body In order to develop consensus-based statement recommendations, a modified Delphi approach was employed, which included two rounds of surveys, discussions, and voting. General recommendations were made, as well as specific recommendations of gepants in both acute and preventive treatment roles. Additionally, clinical settings where gepants could be suitable options were identified, along with the recommendations for their use in special populations and relevant precautions. Conclusion Gepants can serve as both acute and preventive therapy for migraines. They provide an alternative to first-line therapies for patients with limitations to conventional agents, including contraindications or intolerance. Gepants can be utilized as monotherapy or in combination with other treatment approaches. Optimal prescribing practices for eligible patients could ensure that patients receive maximum benefit with minimal risk.

Colorectal cancer, arising from adenomatous polyps, is a leading cause of cancer-related mortality, making early detection and removal crucial for preventing cancer progression. Machine learning is increasingly used to enhance polyp detection during colonoscopy, the gold standard for colorectal cancer screening, despite its operator-dependent miss rates. This study explores the impact of RGB color adjustment on Convolutional Neural Network (CNN) models for improving polyp detection and localization in colonoscopic images. Using datasets from Harvard Dataverse for training and internal validation, and LDPolypVideo-Benchmark for external validation, RGB color adjustments were applied, and YOLOv8s was used to develop models. Bayesian optimization identified the best RGB adjustments, with performance assessed using mean average precision (mAP) and F1-scores. Results showed that RGB adjustment with 1.0 R-1.0 G-0.8 B improved polyp detection, achieving an mAP of 0.777 and an F1-score of 0.720 on internal test sets, and localization performance with an F1-score of 0.883 on adjusted images. External validation showed improvement but with a lower F1-score of 0.556. While RGB adjustments improved performance in our study, their generalizability to diverse datasets and clinical settings has yet to be validated. Thus, although RGB color adjustment enhances CNN model performance for detecting and localizing colorectal polyps, further research is needed to verify these improvements across diverse datasets and clinical settings.•RGB Color Adjustment: Applied RGB color adjustments to colonoscopic images to enhance the performance of Convolutional Neural Network (CNN) models. •Model Development: Used YOLOv8s for polyp detection and localization, with Bayesian optimization to identify the best RGB adjustments. •Performance Evaluation: Assessed model performance using mAP and F1-scores on both internal and external validation datasets.

Dengue fever poses ongoing public health challenges due to its complex reinfection dynamics and antibody-dependent enhancement (ADE). To address limitations in classical models, this study proposes a novel fractional-order model utilizing the Atangana–Baleanu–Caputo derivative to capture memory and non-local effects inherent in dengue transmission. The model explicitly incorporates reinfection mechanisms and stages of infection, offering a more accurate depiction of disease progression. The existence and uniqueness of solutions are established using fixed-point theory, and the global stability of equilibria is analyzed via Lyapunov methods. Model fitting with real-world data from Thailand in 2023 confirms predictive accuracy, while sensitivity analysis identifies the biting and mosquito mortality rates as critical parameters influencing the basic reproduction number. This framework enhances the realism of epidemic models and provides actionable insights for designing targeted public health interventions in dengue-endemic regions.

This work focuses on developing a new flow-circulation system for simultaneous detection and degradation of oxytetracycline (OTC) in fish farm wastewater to address a need for antibiotic abatement in wastewater treatment. Polyvinyl pyrrolidone capped bismuth oxybromide assembled with a reduced graphene oxide (PVP-BiOBr@rGO) photocatalyst was solvothermally synthesized and characterized. The prepared photocatalyst exhibited a morphological flower-like structure with a high surface area, 47.59 m² g⁻¹. Its band gap energy was 2.93 eV. A ternary PVP-BiOBr@rGO composite showed lower charge recombination than its pure form. PVP-BiOBr@rGO was filled inside a catalyst column of a flow system, with a spectrophotometer at the column end. Wastewater was continuously transported through the column and OTC spectrophotometrically examined during its degradation. The wastewater was recirculated until the OTC concentration was minimized. This system achieved 90.3% degradation of OTC within 180 min. The catalyst column could be regenerated for 2 cycles. The proposed flow system offers the advantages of ease of use, inline operation, and real-time sensing. This highlights a potential for real-world sustainable wastewater treatment applications.

A thorough investigation of perovskite structures formed through doping is essential for advancing the efficiency and stability of perovskite solar cells. In this study, Bi-doped FAPbI3 perovskite films with varying Bi concentrations (0.5–2%) were fabricated using a spin-coating technique on ITO glass substrates. Then the films’ phase structure, local structure, and optical characteristics were analyzed. X-ray diffraction (XRD) analysis revealed that the pristine FAPbI3 film exhibited both hexagonal and cubic phases, indicating structural instability. In contrast, Bi-doped FAPbI3 films predominantly displayed a cubic perovskite structure, with a notable reduction in the XRD peak intensity corresponding to the hexagonal phase. UV–Vis spectroscopy showed that the undoped FAPbI3 film had an absorption edge in the visible-near infrared range, while Bi-doping caused a redshift, indicating a reduction in the optical band gap. The calculated results show that optical band gaps decrease with increasing Bi, from a value of 1.49 (pure) to 1.43 (2% Bi) eV. X-ray absorption near edge structure (XANES) analysis confirmed the oxidation states of Pb²⁺ and Bi³⁺ ions across all samples, with Bi ions replacing Pb in the local structure. Photoluminescence (PL) measurements revealed an increased PL intensity with 1% Bi doping (7 $\times$ 10⁵) compared with pristine FAPbI3 (4.7 $\times$ 10⁵), suggesting a reduction in carrier recombination. These findings demonstrate the potential of Bi-doping to stabilize perovskite structures with improved optoelectronic properties.

This study investigated the impact of electric vehicle (EV) chargers on residential electrical systems through a real-world case study in a condominium located in Bangkok, Thailand. A two-week field measurement was conducted to analyze load profiles, current and voltage behavior, phase symmetry, and harmonic distortion during EV charger operation. The results show that single-phase charging dominated usage patterns, leading to phase imbalance and significant neutral current flow. Voltage unbalance was quantified using the maximum deviation method, with an average value of 0.535 percent and a peak of 2.18 percent observed during charging activity. A harmonic distortion analysis revealed a substantial increase in current total harmonic distortion (THD) during active charging, with values rising to between 15 and 20 percent. These findings highlight nonlinear loading effects that may reduce power quality and pose risks to electrical equipment and system stability. In retrofitted electrical infrastructures, these effects are often exacerbated by design limitations and the absence of coordinated load management. This study’s findings offer practical insights for engineers, facility managers, and policymakers in designing EV-ready residential systems that are both efficient and resilient.

Cricket (Gryllus bimaculatus) is a high-protein insect species with a favorable amino acid and fatty acid profle, widely recognized as an alternative to soybean meal in nonruminant diets. However, research on its use in ruminant nutrition remains limited, particularly regarding its efects on feed efciency and performance. Tis study evaluated the impact of completely replacing soybean meal with cricket meal on feed intake, nutrient digestibility, rumen fermentation, microbial populations, and growth performance in Tai native beef cattle. Eight male Tai native beef cattle (150 ± 15 kg; ∼2 years old) were used in a completely randomized design with two dietary treatments (n � 4 per group). One group received a conventional soybean meal-based diet, while the other received a diet in which 100% of the soybean meal was replaced with cricket meal at an inclusion level of 12% of dry matter. Both diets were formulated to be isonitrogenous and isocaloric. Feed intake was similar between treatments. Crude protein digestibility was higher in the cricket meal group (67.5%) compared to the soybean meal group (63.7%; p � 0.04), while other digestibility parameters showed no diferences. Blood metabolites, rumen fermentation characteristics, and microbial populations were unafected by dietary treatment. Cattle fed the cricket meal-based diet showed greater average daily gain (+55.7%; p � 0.02) and a 32.9% improvement in feed conversion ratio (p � 0.02) compared to cattle fed the soybean meal-based diet. Tese results suggest that cricket meal can Wiley Veterinary Medicine International Volume 2025, Article ID 6428834, 9 pages https://doi.org/10.1155/vmi/6428834 serve as a complete replacement for soybean meal in beef cattle diets, enhancing protein digestibility and growth performance without compromising rumen function.

The structural, energetic, and electronic properties of Mn2S monolayers and their functionalized derivatives were systematically investigated using first-principles calculations. The pristine Mn2S monolayers, existing in the 1T and 2H phases, exhibited metallic characteristics with intrinsic magnetism. However, phonon dispersion analysis reveals dynamic instability in both phases, indicating that pristine Mn2S is not a stable monolayer material. To enhance stability, we examined the effects of oxygen (O), fluorine (F), and chlorine (Cl) functionalization on Mn2S, constructing twelve distinct Mn2ST2 configurations for each phase. The findings demonstrate that functionalization significantly alters the energetic hierarchy, with different configurations emerging as the most stable phase across all functional terminations. Phonon dispersion analysis confirms the dynamical stabilities of the 1T-Mn2SO2, 2H-Mn2SO2, 2H-Mn2SF2, and 1T-Mn2SCl2. Mulliken charge analysis further highlights significant charge redistribution upon functionalization, enhancing charge localization and stability. Among the various Mn2ST2 structures, functionalization plays a crucial role in stabilizing the monolayers. The 1T and 2H phases of Mn2SO2 are identified as the most energetically and dynamically stable configurations, characterized by strong Mn-O bonding and a ferromagnetic half-metallic ground state. In contrast, the most stable form of Mn2SF2 adopts a 2H phase, exhibiting antiferromagnetic ordering and a band gap of 1.617 eV. Additionally, a stabilized 1T-Mn2SCl2 demonstrates ferromagnetic behavior and functions as a ferromagnetic semiconductor with a narrow band gap of 0.196 eV. These findings highlight the critical role of surface functionalization in stabilizing Mn2S monolayers and tailoring their electronic and magnetic properties, paving the way for potential applications in spintronics and nanoelectronics.

Accurate financial time series forecasting is essential for informed investment and risk management decisions. Traditional methods, including statistical techniques such as SMA, ARIMA, VAR, and LASSO, and deep learning models like LSTM and Transformer, often fall short of capturing the complex seasonal and cyclical dynamics inherent in financial data. To overcome the noted shortcomings, this study introduces a transfer learning approach utilizing an enhanced Transformer model with correlation-based attention mechanisms. This proposed model significantly improves its capacity to capture long-term dependencies and perform robust cross-market predictions. Initially trained on the Dow Jones Index, it demonstrates superior transferability to diverse asset classes, including stock indices, commodities, and cryptocurrencies. Experimental evaluations across multiple metrics, including MSE, MAE, MHD, and R2, reveal that the proposed model consistently outperforms benchmarks. Notably, it achieves outstanding predictive accuracy in BTC (MSE: 0.0352) and SET (MSE: 0.1701), establishing a strong foundation for advanced transfer learning applications in financial forecasting across varied markets.