Rajamangala University of Technology Srivijaya

In this work, we aim to study and develop a low-cost multi-stage biogas system for household applications using local materials. The system consisted of three sequential stages: hydrogen sulfide removal using ferric chloride and sodium hydroxide solution-coated clay pellets, carbon dioxide scrubbing with lime water, and moisture control with mangrove charcoal. From the experiments, H2S can be reduced from over 100 ppm-1.67 ppm to achieve the non-corrosive level for domestic appliances by coated clay pellets. 10 g/L of lime water yielded the highest efficiency (7.84 %) for CO2 removal. An increase in lime water concentration over 10 g/L diminishes the CO2 removal due to calcium carbonate precipitation and mass-transfer limitations. In the moisture control stage, the adsorption of mangrove and compressed briquette charcoal is compared; mangrove charcoal exhibited higher capacity and maintained adsorption for similar to 110 min, while compressed briquettes reached saturation earlier at similar to 55 min. The methane concentration has remained stable in every stage while doing the experiment, with 0.5-1 % of total loss, which is a negligible penalty. The proposed integrated system effectively eliminated H2S, reduced moisture, and partially removed CO2, which increased the safety and usability of biogas. While commercial technology (PSA or membrane separation) provides high methane purity and high capital and operation costs. The proposed system offers a more affordable and accessible solution for small-scale rural households. The findings highlight both the feasibility and limitations of using simple, low-cost materials, while future research should emphasize kinetic modeling, CO2 scrubbing optimization, and long-term adsorbent regeneration to strengthen scalability and reliability.

This paper presents a novel extra-X second-generation current-controlled conveyor (EX-CCCII) with controllable current gain. Unlike the conventional EX-CCCII, the proposed EX-CCCII provides a controllable current gain between the x- and z-terminals. To demonstrate the advantages of the EX-CCCII with the controllable current gain, the proposed EX-CCCII is employed to realize a universal current-mode filter and a three-phase current-mode oscillator. The universal filter can realize five standard filtering responses (low-pass, high-pass, band-pass, band-stop, and all-pass) using the same topology. The current gains of these filters can be controlled through the current gain of the EX-CCCII, while the natural frequency of the universal filter can be electronically tuned via the intrinsic resistance at the x-terminal. When the proposed EX-CCCII is used to implement the three-phase oscillator, the condition of oscillation can be adjusted through the current gain of the EX-CCCII, whereas the oscillation frequency can be tuned using the parasitic resistance of the x-terminals. The proposed EX-CCCII and its applications were verified through SPICE simulations using the transistor model parameters NR100N (NPN) and PR100N (PNP) of the bipolar array ALA400-CBIC-R from AT&T to confirm the functionality and feasibility of the proposed topologies. Furthermore, experimental verification of the EX-CCCII and its integration into a three-phase oscillator further substantiates the proposed concept and demonstrates its practical viability.

This study examines the short-run effect of carbon taxation on the growth rate of GDP per capita, the annual first difference in log GDP per capita, using a panel of 16 carbon-pricing economies spanning Europe, the Americas, Asia and Africa over 2020–2024. Country fixed-effects estimation with country-clustered robust standard errors follows formal model selection (F-test, Hausman test), checked for cross-sectional dependence. Three baseline specifications are estimated, Model 3 excluding the COVID-19 dummy as a robustness check; a fourth adds carbon tax interaction terms with inflation, investment, energy intensity and political stability to test whether these factors condition the relationship. A higher carbon tax rate has a small but statistically significant negative effect on growth across all three baseline models (a USD 10 increase implies roughly a 1.2-percentage-point reduction in annual growth, preferred specification); none of the interaction terms is significant, indicating no detectable conditioning effect. Investment shows a robust positive association with growth; inflation, a robust negative one. Energy intensity and the COVID-19 dummy enter with signs contrary to expectations once year fixed effects are excluded, and the carbon tax coefficient loses significance under a lagged specification, cautioning against a strictly causal reading. Findings support pairing carbon tax design with investment and price-stability policies.

Rice is a major economic crop in Thailand, with a high export rate within the agricultural product group. Rice leaf disease is a significant factor affecting agrarian productivity. This paper proposes a model for classifying rice leaf diseases to support accurate, timely disease diagnosis. It uses the Xception model as its core and replaces the classification layer with ArcMargin, enabling adaptive margin adjustment for each class. And use Class Activation Mapping guided regularization to identify important disease lesion areas on rice leaves, helping the model classify each disease class more accurately. To increase both the accuracy and the ability to explain the decision results of the model. Experiments were conducted using the rice leaf dataset from www.kaggle.com, comprising four classes and 5,932 images. The evaluation methods were stratified, and the Group 5-Fold methods were used to reduce information leakage and to verify the ability to explain the results. Gradient-weighted Class Activation Mapping experimental results show that the proposed model achieves accuracy of 99.29%, macro-F1 of 99.29%, Cohen's kappa of 99.05%, and Matthews correlation coefficient of 99.06%, which are higher than the baseline Xception model that achieves accuracy of 98.16%, macro-F1 of 98.21%, kappa of 97.63%, and MCC of 97.67%. In addition, the proposed method is applied to other core models, including DenseNet121, MobileNetV2, VGG16, and ResNet50, achieving accuracies of 100%, 96.57%, 99.84%, and 99.54%, respectively. The results demonstrate that the proposed approach effectively enhances both the classification performance and the visual explainability of deep learning models for rice leaf disease diagnosis.

This research investigated the temperature-tuned acid extraction of cellulose from a single luffa sponge source to produce four distinct cellulose forms: amorphous cellulose (AC), microcrystalline cellulose (MCC), cellulose nanofibrils (CNF) and cellulose nanocrystals (CNC). Reaction temperatures ranging from 0 to 60 degrees C were systematically varied to control cellulose structural transformation, while sulfuric acid concentration and reaction time were optimized to improve production yield and dispersion stability. The obtained cellulose samples were extensively characterized using zeta potential analysis, FTIR, XRD, TGA and TEM techniques. Temperature was identified as the dominant factor governing cellulose structure, yielding AC at 0 degrees C, MCC at 30 degrees C, CNF at 45 degrees C and CNC at 60 degrees C, whereas acid concentration and reaction time mainly influenced synthesis yield and colloidal stability. All cellulose types were incorporated into natural rubber foams to evaluate oil removal performance. The resulting cellulose-rubber composite foams exhibited open-cell structures, enhanced mechanical strength and improved oil absorption capacity with stable performance over 30 sorption-desorption cycles, demonstrating superior durability compared to neat natural rubber foam. These results highlight temperature-controlled extraction as an effective strategy for tailoring cellulose structure and enhancing the oil absorption efficiency of bio-based rubber composite foams.