Communications in Science and Technology
Communications in Science and Technology [p-ISSN 2502-9258 | e-ISSN 2502-9266] is an international open access journal devoted to various subjects including natural science, medicine, technology, and engineering. CST publishes research articles, reviews, and letters in all areas of the aforementioned disciplines. The journal aims to provide a comprehensive source of information on recent developments in the field. The emphasis will be on publishing quality articles rapidly and making them freely available to researchers worldwide. All accepted manuscripts will be indexed in Scopus, DOAJ, EBSCO, Google Scholar, SINTA. To provide the maximum exposure to the articles, the indexation in WoS will be provided in the future. The journal will be important reading for scientists and researchers who wish to keep up with the latest developments in the field.
Komunitas Ilmuwan dan Profesional Muslim Indonesia
Copyright Open Access authors retain the copyrights of their papers, and all open access articles are distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution and reproduction in any medium, provided that the original work is properly cited. The use of general descriptive names, trade names, trademarks, and so forth in this publication, even if not specifically identified, does not imply that these names are not protected by the relevant laws and regulations. While the advice and information in this journal are believed to be true and accurate on the date of its going to press, neither the authors, the editors, nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein. This work is licensed under a Creative Commons Attribution 4.0 International License.
In-vitro, In-vivo, and In-silico Studies of Insulinotropic Activity and Cytotoxicity of Mahagony Seed Extracts and Its Nanophytosome
This present study set out with the objective of evaluating the effect of ethanol–water solvent composition on extraction yield, insulinotropic activity (IA), cytotoxicity, and phytochemical profile (PP) of mahogany (Swietenia mahagoni) seed (MS) extracts. It also sought to study in silico and encapsulate the best extract, and to assay the antidiabetic of the extract and its nanophytosome. The extraction of MSs was achieved through the utilization of ethanol, ethanol–water (1:1), and water through ultrasound–assisted extraction. The extracts of IA and cytotoxicity were assayed in vitro using BRIN–BD11 cell lines and analyzed using LC-MS. The most effective extract was identified for its active compounds using molecular docking, encapsulation, characterization, and testing for its antidiabetic properties in vivo. The highest yield was observed in the water extract (MW), followed by the ethanol–water extract (MEW) and the ethanol extract (ME). MEW and MW demonstrated low level of cytotoxicity, with MEW exhibiting the highest level of IA. An in silico study identified four key antidiabetic compounds through the IA mechanism. The study demonstrated that MEW and its nanophytosome (250 mg/kg mouse weight) effectively reduced blood glucose level, and enhanced both erythrocyte function, and insulin secretion. This finding provides a new perspective in the development of natural-based antidiabetic agents through the nanophytosome formulation of mahogany seed extract.
Copyright (c) 2025 Communications in Science and Technology http://creativecommons.org/licenses/by/4.0
Microwave Absorption Performance of La0.7Sr0.3MnO3/AC Composite Material Based on Activated Carbon from Gnetum gnemon Seed Shell
The 5G internet network has been proven to facilitate daily life for people and render electronic devices such as smartphones as an integral component of people's daily routine. However, in conjunction with the ease of use, there is an issue of electromagnetic radiation. To cope with this issue, magnetic and dielectric composite microwave absorber materials have been undertaken. To address this, we investigated the limitations of activated carbon composite material from Gnetum gnemon seed shells (AC) on the microwave absorption ability of (La0.7Sr0.3MnO3)1-y/(AC)y. The composite material (La0.7Sr0.3MnO3)1-y/(AC)y (y = 0; 0.3; 0.5; 0.7) was synthesized through a stirring process with a 96% ethanol catalyst using La0.7Sr0.3MnO3 synthesized by sol-gel method and activated carbon material from Gnetum gnemon seed shell (AC) synthesized by chemical activation method. The XRD and SEM characterizations indicated a single-phase structure, with smaller crystals and particles that were uniformly distributed throughout the composite sample. The presence of activated carbon grains from Gnetum gnemon seed shells (AC) were observed between the La0.7Sr0.3MnO3 grains in the composite sample. The EDS results confirmed the material’s purity. VNA characterization demonstrated that (La0.7Sr0.3MnO3)1-y/(AC)y was capable of producing two reflection loss troughs with the largest absorption percentages recorded at 82.99% and 85.82% respectively within the frequency range of 8 – 12 GHz. This research highlights the significance of controlled composite composition in enhancing microwave absorption capability, particularly in perovskite-based composites with biomass-activated carbon, which holds a considerable promise for applications in electromagnetic wave attenuation and absorption technologies.
High-performance eco-friendly Ni Cu/bamboo activated carbon catalysts for oxidative desulfurization of high-concentration DBT
This study investigated how the metal impregnation method affects the oxidative desulfurization (ODS) of dibenzothiophene (DBT) using H2O2 over Ni–Cu catalysts supported on bamboo-derived activated carbon. Catalysts with 1% and 2% Ni–Cu were prepared via simultaneous impregnation, while the effect of sequence was evaluated by comparing simultaneous and sequential impregnation (2%Ni-2%Cu/AC and 2%Cu-2%Ni/AC). The 2%Ni-2%Cu/AC catalyst was identified as the best catalyst, with a surface area of 802.36 m2/g, average pore diameter of 2.4761 nm, and total acidity of 3.1239 mmol/g. This catalyst achieved the highest DBT reduction of 90.81% under optimal conditions (0.2 g catalyst weight, 60 minutes, 40 °C, and 0.66 mL H2O2), confirming that the sequential impregnation route significantly enhances catalytic performance. In conclusion, the impregnation sequence in designing highly efficient desulfurization catalysts is important due to spray impregnation resulting in higher surface area, acidity, and catalytic activity compared to the simultaneous impregnation method.
The Effect of Ultrasonication on the Quality of Keratin Extraction Based on Ionic Liquid from Duck Feather
This study investigates the effect of ultrasonic-assisted extraction (UAE) and solvent extraction (SE) on keratin recovery from duck feathers using sodium sulfide-based ionic liquids under different pH conditions. The results showed that SE at acidic pH (pH=3) achieved the highest yield (92%), whereas UAE showed lower recovery (28%) under mildly acidic conditions (pH=5). Spectroscopic and electrophoretic analyses using FTIR confirmed the β-sheet structure with characteristic peaks at 3400 cm−1 (O–H and N–H stretching) and 1660 cm−1 (C=O stretching). Sodium Dodecyl Sulfate Polyacrylamide Gel Electrophoresis analysis (SDS-PAGE) revealed protein bands in the 15–25 kDa range, typical of β-keratin, with higher intensity in SE. Morphological analysis using SEM revealed finer and more homogeneous particles for UAE, while SE produced denser aggregates. Thermal analysis revealed two main degradation stages, occurring at 0–100 °C and 250–500 °C, with UAE samples exhibiting lower residual mass (5.46%) than SE (8.65%). Particle size analysis showed UAE samples had larger but more uniformly distributed particles. XRD results confirmed semi-crystalline structures, with UAE increasing amorphous content and SE maintaining crystallinity. These findings highlight the complementary advantages for tailoring keratin properties toward diverse applications.
Surface Chemistry and Adsorption Behavior of Methylene Blue on Functionalized Carbon Materials: A Comprehensive Study
The increasing release of synthetic dyes, particularly methylene blue (MB), from textile effluents has become a major environmental and health concern, highlighting the urgent need for efficient remediation strategies. Adsorption remains one of the most effective techniques for dye removal due to its simplicity, low cost, and high efficiency. This review discusses the surface chemistry and adsorption behavior of MB on functionalized carbon-based materials, emphasizing how physicochemical characteristics, surface modifications, and functional groups influence adsorption capacity and selectivity. Recent progress in developing engineered carbonaceous adsorbents—such as activated carbon (AC), graphene derivatives, carbon nanotubes, and hybrid carbon composites—has significantly improved the removal performance of MB through enhanced structural and chemical interactions. The ACHC-KOM-1 carbon composite, for instance, exhibits remarkable photocatalytic-assisted adsorption, achieving complete MB degradation under optimized conditions. Surface functional groups and pore architecture are decisive factors governing adsorption efficiency. Oxygen-containing moieties, including carboxyl (–COOH) and hydroxyl (–OH), create active sites that facilitate electrostatic attraction and hydrogen bonding with cationic MB molecules. Nitrogen functionalities (–N), introduced via heteroatom doping, enhance electron-donating properties and π–π interactions between MB aromatic rings and the conjugated carbon framework, thereby strengthening molecular affinity. Pore dimensions further regulate accessibility and diffusion, with micropores (<2 nm) providing strong confinement and high adsorption energy, while mesopores (2–50 nm) promote rapid diffusion and prevent pore blockage. The synergistic combination of abundant surface functionalities and hierarchical porosity governs the overall adsorption capacity, kinetics, stability, and regeneration potential of carbon-based adsorbents for dye removal. The mechanistic framework presented here distinguishes biomass-derived and non-biomass carbon adsorbents, enabling rational design of high-performance materials. These findings offer practical optimization guidelines for industrial-scale methylene blue removal while supporting sustainable, circular-economy-aligned water purification technologies.
Material Properties Extraction of Mango (Mangifera indica) Leaves at Ka-Band Using a Waveguide Measurement System
This study investigates the material properties (permittivity, dissipation factor, and conductivity) of mango leaves (Mangifera indica) over the 26–40 GHz Ka-band frequency based on a waveguide measurement system with a vector network analyzer instrument to capture the data. The data analysis employs the Nicolson-Ross-Weir method to extract material properties. The result reveals that the real part of permittivity decreases from about 11.0 to 5.0 with increasing frequency. Meanwhile, the imaginary part of permittivity remains low and stable, suggesting minimal absorption losses. The dissipation factor is consistently below 0.05 along the band. Effective conductivity ranges from 0.2 to 0.6 S/m, with a slight increase at higher frequencies. These findings suggest that at Ka‐band frequency, signal degradation through mango foliage is primarily driven by dispersion and scattering rather than strong dielectric absorption. The results provide essential information for improving foliage attenuation models and designing 5G and 6G communication systems in tropical regions. This study provides a reliable Ka-band dielectric dataset for mango leaves that improves the accuracy of tropical foliage-attenuation models and supports more robust 5G/6G link design and deployment in vegetation-dense environments.
Fabrication and Photocatalytic Performance of ZnO-Biochar Composites for Eliminating Dye Waste
The synthesis of the ZnO-biochar composite from palm kernel shell biomass waste has been accomplished through the utilization of the solvothermal method, yielding a satisfactory outcome. The resulting composite, a combination of ZnO and biochar, has been utilized in the degradation of methylene blue waste compounds. The objective of this research is to synthesize ZnO-biochar composites from palm shells, and to determine the optimal solvothermal temperature and duration. This research was initiated with the preparation of palm shells into biochar. Subsequently, the ZnO-biochar composite was synthesized with variable solvothermal temperatures and solvothermal times. The ZnO-biochar composite was characterized using analytical techniques including SEM-EDX, FT-IR, XRD, BET and UV-vis DRS. The most effective degradation of methylene blue was exhibited by the ZnO-biochar composite sample synthesized at a solvothermal temperature of 180 ̊C and a solvothermal time of 10 hours, achieving a degradation of 88.29%. The enhanced photodegradation performance of this composite sample is attributed to its high surface area, capacity for visible light absorption, and the dimensions of the active crystals, which can account for the high performance of the ZnO-biochar composite for photocatalytic degradation.
Active-Reflective Learning Style Detection Using EEG and Abrupt Change Detection
Recognizing the varying learning styles of students is vital to creating customized educational approaches and maximizing academic success. While commonly used, conventional evaluation methods such as self-report surveys are frequently characterized by subjective biases and inconsistent accuracy. To address this limitation, this present study proposes an EEG-driven approach for learning style classification, specifically targeting the Active and Reflective dimensions of the Felder-Silverman Learning Style Model (FSLSM). Data was acquired from 14 participants using an 8-channel OpenBCI headset, with cognitive engagement stimulated through Raven’s Advanced Progressive Matrices (RAPM). Initially, the raw EEG data underwent bandpass filtering process purposely to remove noise. Subsequently, the data was divided into consecutive 1-second segments. For feature extraction, the CUSUM algorithm was employed, with an aim to effectively capture significant signal variations. These features were then fed into an LDA classifier for style discrimination. The performance evaluation revealed impressive results—98.26% accuracy in standard Train-Test validation, and an even higher 99.29% under LOOCV testing. Notably, our approach consistently outperformed existing techniques including 1-DCNN and TSMG across all metrics. Notably, computational efficiency and reliability were improved, with the "Odd-only" subset yielding peak accuracy (99.24%). These findings demonstrate that integrating EEG signals with conventional machine learning enables real-time, high-precision learning style detection. Additionally, this work addresses the computational constraints and dataset limitations observed in recent studies, providing a robust foundation for adaptive learning systems. It is recommended that future research explore larger, more diverse datasets and additional FSLSM dimensions to enhance generalizability and practical implementation of the research.
Exploring Fluidization Dynamics and Chemical Performance in Silicon Tetrachloride (SiCl4) Hydrochlorination Processes within a Fluidized Bed Reactor: Development and Analysis of an Eulerian-Granular Model
The present work examines the complex fluidization pattern and reactive interactions of silicon tetrachloride (SiCl4) during hydrochlorination in a fluidized-bed reactor (FBR), a system that remains difficult to model accurately. To address this gap, we develop a new Eulerian–granular CFD framework that for the first time couples the Eulerian–Eulerian fluid model with KTGF specifically for SiCl4 hydrochlorination, enabling prediction capabilities that are unavailable in previous FBR studies. The validity of the model was confirmed through comparisons with empirical bed-expansion correlations and Hsu’s gas-temperature data, that demonstrated strong agreement and the ability of the model to capture the coupled thermal–hydrodynamic behavior of the system. In addition to the conventional observations documented in previous studies, this study identifies distinct flow-regime transitions and bed-voidage evolution that are unique to SiCl4. These findings demonstrated the impact of SiCl4’s reactive transport behavior on fluidization stability. Under bubbling conditions, the model uncovered a characteristic SiCl4 distribution pattern that more significantly enhanced gas–solid mixing in comparison to previous report. Additionally, it predicts rapid heat equilibration within ~10 mm of bed height - a behavior not documented in earlier hydrochlorination studies. Chemically, the model predicted a maximum SiHCl3 concentration of 13.08% and an SiCl4 conversion of 28.97%, thereby offering new mechanistic insight into how fluidization dynamics directly govern reaction performance. Overall, this work provides the first specialized CFD framework for SiCl4 hydrochlorination, thus establishing a novel mechanistic understanding of its fluidization–reaction coupling. Furthermore, it offers a more accurate predictive basis for optimizing industrial FBR systems employed in silicon-based chemical manufacturing.
Green Polyols from Tamanu Seed Oil: Reaction Kinetics and Process Optimization
Using methanol, this study examined the hydroxylation process of epoxidized tamanu seed oil (ETSO), with an oxirane number of 3.92 to 4.04 mmol/g, under the catalyzation of sulfuric acid (H2SO4). The objectives of this study were, first, to synthesize polyol from ETSO, and, second, determine how temperature and catalyst concentration play a role in the hydroxylation process. During the experiment, a second-order reaction kinetic model was used for analysis. The hydroxylation process was conducted in a batch reactor for 4 hours under constant temperatures and stirring speed. During the experiment, the samples were taken every 30 minutes. The oxirane number of ETSO and the concentration of polyols were used to the reaction rates. The optimal conditions were found at a temperature of 65°C, with a methanol-to-epoxide mole ratio of 4:1 and a catalyst concentration of 3%. The pre-exponential factor (A) and the calculated activation energy (Ea) were found to be 59,041.74 g.mmol-1.min-1 and 44.69 kJ/mol, respectively. This research, therefore, has successfully identified the optimal conditions for the synthesis of bio-based polyols from tamanu oil.
Artificial Diet for the Cultivation of Eri Silkworm (Samia ricini Drury 1773) (Lepidoptera: Saturniidae)
The objective of this present study was to identify a suitable artificial diet formulation to support the development of eri silkworms, with castor leaves (Ricinus communis) constituting the primary ingredient. The quality of the artificial diet was evaluated using neonate larvae, comparing it to fresh castor leaves. The nutritional value was assessed by analyzing the protein content in the hemolymph of fifth-instar larvae using the Folin-Ciocalteu method and proximate analysis. The findings demonstrated that an artificial diet containing castor leaf powder during early instars, with fresh leaves being incorporated into the diet later instars, resulted in higher larval protein content. The weight of cocoon, empty cocoon, and pupa was 1.59 ± 0.05 g, 0.23 ± 0.02 g, and 1.37 ± 0.05 g, respectively. The shell ratio, female wingspan, and egg fertility were found to be 15.31 ± 0.11%, 2.42 ± 0.20 cm, and 79.2 ± 5.83 eggs, respectively. Formulation P2 exhibited the lowest larval mortality (4.23 ± 0.58%) and hemolymph protein content of 27.51 μg/mL These findings are of imperative for the cultivation of eri silk worm using artificial diet to avoid pathogen contamination and controllable nutrient content considering the early larval instar that is highly sensitive to microbes and nutrient deficiencies.
Optimization of an Automatic Water Disposal System Based on Internet of Things to Support the Smart Energy Concept
The management of an Internet of Things (IoT)-based water system powered by solar energy faces significant challenges in achieving high power efficiency and long-term thermal reliability. The objective of this study is to identify the most optimal hardware configuration that balances specific energy efficiency (ηspecific) with minimizing thermal energy dissipation in a DC water pumping system. The methodology employed is a comparative experiment that tests six different scenarios, varying the topology of the voltage regulator (Linear Regulator and Buck Converter) and the pump actuator, with a total of 30 pumping cycles at a fixed water volume of 30 litres. The processing of data was undertaken through the utilization of descriptive statistical analysis and Standard Deviation (SD) on the parameters of Power, Temperature, and Water Discharge to measure stability. The findings indicated a critical trade-off between peak energy efficiency and operational stability. Scenario 4 (12V Relay and Mini 560 Buck Converter) emerged as the most optimal configuration, recording the highest Specific Energy Efficiency (1.94 Ws/L). This superiority is evidenced by its excellent thermal stability (36.88°C), which is comparable to that of low-power configurations. In contrast, although Scenario 6 (Synchronous Buck, 51 mm Pump) demonstrated the highest operational stability (Power SD=0.02111), it compromises pumping speed. It is concluded that the implementation of the Mini 560 Buck Converter is imperative in achieving a balance between energy savings and minimizing thermal dissipation, thereby rendering it an ideal selection for solar-powered Smart Energy systems.
Optimization of Electromyography (EMG) Signal Parameters for Assistive Device Control Using a Convolutional Neural Network (CNN)
Facial Electromyography (EMG) signals offer a promising modality for intuitive human-machine interfaces (HMIs). The development of robust control systems, however, remains challenging in view of the inherent complexity, noise susceptibility, and significant inter-subject variability of EMG signals in the facial region. This study addresses these technical challenges by developing and validating an optimized Deep Learning framework for facial gesture recognition. The primary objective of this study is to create a reliable classification model for five essential facial movements: 'Rest', 'Smile', 'Eyebrow Raise', 'Right Lip Movement', and 'Left Lip Movement'. The model will serve as precise control inputs for assistive devices. The proposed methodology employs a systematic workflow comprising signal preprocessing (filtering, normalization, and segmentation) followed by the automated hyperparameter optimization of a one-dimensional (1D) Convolutional Neural Network (CNN). The experimental results demonstrate that the optimized model achieved a classification accuracy of 90% on internal test data, with the learning rate identified as the most critical hyperparameter influencing performance. Furthermore, validation of the model on entirely new participants yielded an accuracy of 71%. While this result underscores the persistent challenge of generalizing across different users, it establishes a reliable baseline. Ultimately, this work provides a validated, optimization-based framework that utilizes low-cost instrumentation, thereby offering a substantial pathway towards more accessible and personalized hands-free assistive technologies to restore autonomy for individuals with severe motor impairments.
Classification of Heart Disorders Using Deep Learning and Machine Learning Approaches
Heart disorders persist a primary cause of mortality worldwide, underscoring the necessity for precise and effective diagnostic support systems. The objective of this study is to classify heart disorders employing a combination of deep learning and machine learning approaches based upon electrocardiogram (ECG) image data., The model’s performance was evaluated through 5-fold cross-validation per patient to ensure robust generalizability. The dataset comprised 486 ECG images from 284 patients. A total of six models were subjected to comparative analysis, including Support Vector Machine (SVM), VGG16, ResNet50, Custom CNN, Xception, and Inception-V3, by utilizing key evaluation metrics including accuracy, precision, recall, specificity, F1-score, and AUC-ROC. The experimental results demonstrated that Inception-V3 achieved the optimal overall performance, demonstrating a balance between sensitivity and precision. Furthermore, deep learning models generally outperformed traditional methods such as support vector machines (SVM). The mean performance across all models yielded an accuracy of approximately 78.6% and an AUC-ROC of 0.83, demonstrating reliable discrimination in cardiac disorder classification. Deep learning-based architectures, particularly Inception-V3 and Xception, demonstrated considerable potential in the development of automated and accurate diagnostic systems for the early detection of cardiac disorders. Future research could explore hybrid approaches and larger and more diverse datasets to enhance clinical applicability. This study provides improved accuracy and reliability in cardiac disorder classification by leveraging and comparing machine learning and deep learning approaches. The proposed model has been demonstrated to effectively capture complex patterns in medical data, thereby supporting early diagnosis and improving clinical decision-making.
Oxygen-functionalized carbon nanofibers from kulim wood for high- performance supercapacitors via an integrated chemical–physical catalyst approach
This study highlights the critical role of oxygen-functionalized hierarchical nanofiber structures derived from kulim wood waste in improving the electrochemical performance of symmetric supercapacitors. A one-step catalyst-assisted method was developed to synthesize these carbon nanofibers. The synthesized material exhibited well-defined oxygen functionalities (9.34% oxygen content) and exceptional porosity (1070 m2/g), demonstrating a micro-mesoporosity ratio of nearly 4:1. This structural design led to enhanced capacitive properties, achieving a capacity of 172 F/g in acid media and a rate capability of 81.2% at 10 A/g. The oxidation reactions indicated a mixed energy storage mechanism, with 83% EDL-controlled and 22% redox-controlled processes. Additionally, the material showed an energy output of 19.91 Wh/kg and a power density of 1.02 kW/kg, showcasing its potential for sustainable energy storage applications. Importantly, this work offers a scalable, low-cost, and biomass-driven strategy for producing high-performance carbon electrodes, advancing sustainable supercapacitor technologies.
Analytical Modeling of LEO Satellite Mutual Visibility under Perturbations
This present study develops an analytical model for mutual visibility in perturbed Low Earth Orbit (LEO), explicitly accounting for Earth’s oblateness (J2 and J3) and atmospheric drag. The framework extends classical geometric visibility calculations to include long-term perturbation effects, thus enabling rapid and reliable prediction of visibility intervals. A series of numerical simulations were conducted on three LEO satellite configurations and revealed that perturbations have the capacity to shift rise–set times, alter event frequency and duration, and generate new visibility intervals. These effects are of particular significance for satellites operating at lower altitudes and those characterized with moderate semi-major axis differences and large inclination disparities. The results of the study underscore the importance of considering secular and long-period perturbations into the planning of reliable communication relay, the management of constellation, and the scheduling of autonomous mission. Importantly, the closed-form formulation enables fast visibility and rise–set prediction without full numerical orbit propagation, supporting preliminary constellation design, inter-satellite link scheduling, and onboard visibility assessment in large LEO networks.
Hybrid-Intelligent P&O-InC Algorithm with Deterministic Adaptive Weighting for Maximum Power Point Tracking under Dynamic Conditions
Electricity generation remains dominated by fossil fuel–based sources, underscoring the necessity to optimize the utilization of solar energy through photovoltaic (PV) systems in support of the Sustainable Development Goals (SDGs). Variations in solar irradiance and temperature significantly influence PV performance, necessitating effective Maximum Power Point Tracking (MPPT) methods. This present study proposes HI-POnIC as an adaptive development of conventional MPPT algorithms using a deterministic, feature-based decision mechanism. The method employed dynamic weighting and adaptive step adjustment to modify the control response to changes in PV operating characteristics, without any reliance on learning processes. Performance of the system was evaluated through convergence analysis, energy and power tracking efficiency, and spatial accuracy assessment. The findings from the simulation demonstrated that HI-POnIC achieved faster convergence and enhanced stability around the maximum power point when compared with conventional methods. Its lightweight and easily implementable adaptive structure has rendered HI-POnIC suitable for PV systems operating under dynamically varying environmental conditions.
Enhanced Soil Fertility and Baby Maize Yield Through Bacillus megaterium CM2 under Reduced Nitrogen Input
This present study evaluated the effectiveness of Bacillus megaterium CM2 (strain CM2) as a biofertilizer for the enhancement of soil fertility and baby maize (Zea mays L.) productivity under reduced nitrogen (N) input in the Mekong Delta, Vietnam. A field experiment was conducted using a randomized complete block design (RCBD) featuring five treatments combining strain CM2 inoculation and varying nitrogen fertilizer rates. Each treatment was replicated four times. The treatments included a non-inoculated control receiving the full recommended N rate (350 kg urea ha−1) and four strain CM2-inoculated treatments supplied with 100%, 75%, 50%, and 25% of the recommended N dose (350, 262.5, 175, and 87.5 kg urea ha−1, respectively). Laboratory characterization demonstrated that strain CM2 possessed high nitrogenase activity, strong thermotolerance, and broad adaptability, confirming its suitability for field application. In comparison with the non-inoculated control, strain CM2 inoculation under reduced N regimes significantly improved soil chemical properties, including soil pH, cation exchange capacity, soil organic matter, total nitrogen, available phosphorus, and exchangeable potassium. This present study revealed overall soil fertility indicators exhibited an increase by approximately 10–95%, with the lowest responses as observed in under treatments subjected to 25% and 57% reductions in nitrogen fertilizer application. Whilst the most pronounced enhancement were recorded at the 50% N reduction level, particularly for pH (6.99), CEC (14.8 cmol+ kg−1), and SOM (2.99%). Thirty days after sowing, CM2-inoculated plants exhibited substantial increases in leaf number (32%), chlorophyll content (17%), and plant height (19%) relative to uninoculated control. Furthermore, yield performance was also substantially enhanced, with total edible cob yield reaching 2.98 t ha−1 and the proportion of grade-1 cobs increasing to 65.8%, corresponding to yield gains of 12.9% and 27.6%, respectively. The enhancement observed were attributed to enhanced biological nitrogen fixation, phytohormone-mediated growth promotion, and improved nutrient acquisition efficiency. The integration of strain CM2 with 25–50% reduced N fertilization-maintained yield and improved soil fertility, highlighting its potential as an eco-friendly microbial inoculant for sustainable baby maize cultivation.
Synthesis of Cu Nanoparticles using Anredera cordifolia Extract and their Potential as Antidiabetic with Alpha Amylase Enzyme Inhibition
This study reports a green synthesis of copper nanoparticle using binahong (Anredera cordifolia) leaf extract as both a bioreductant and a capping agent. The synthesis was optimized by varying the extract-to-precursor ratio (1:3, v/v) and the reaction pH (6–11) with pH 10 selected as the optimal condition for nanoparticle preparation. Nanoparticle formation was confirmed using UV–Vis spectroscopy, showing a strong absorption band at 325 nm, indicative of oxide-based copper nanostructures and/or surface oxidation during green synthesis. XRD patterns revealed Cu2O as the dominant crystalline phase, characterized by reflections at 2θ ≈ 29.6°, 36.4°, 42.3°, 61.3°, 73.5°, and 77.3°, with a possible minor contribution from metallic Cu due to peak overlap. FTIR spectra confirmed the presence of biomolecules (O–H and C–H bands, along with carbonyl/COO−-related bands) involved in nanoparticle stabilization, as well as Cu–O vibrations around 600–620 cm−1 consistent with Cu2O. PSA showed a dominant hydrodynamic size in the 60–70 nm range (average 65 nm), whereas SEM indicated aggregation into micrometer-scale clusters upon drying. In vitro α-amylase inhibition assays demonstrated concentration-dependent inhibition, with CuNPs (IC50 6.18 μg/mL) and the extract + CuNPs mixture (IC50 6.83 μg/mL) approaching that of acarbose (IC50 5.04 μg/mL) and exhibiting stronger activity than the extract alone (IC50 8.89 μg/mL). The key contribution of this work is the development of a simple, aqueous, environmentally friendly route that leverages local biological resources while producing Cu2O-rich nanoparticles with α-amylase inhibitory activity approaching that of acarbose. These findings highlight the promise of a low-cost antidiabetic candidate for postprandial glucose control and provide a basis for further investigations into nanoformulation and preclinical evaluation.
Evaluation of the Impact of PGRs in Bean Sprouts Extract on Vegetative Growth, Total Phenolics, and Antioxidant Activity of Chili Peppers, Soybeans, and Potatoes
This present study aims to evaluate the effect of administering natural growth regulators (PGRs) based on bean sprout extract (Vigna radiate) on vegetative growth, total phenolics, and antioxidant activity of chili pepper (Capsicum frutescens L.), soybean (Glycine max (L.) merrill), and potato (Solanum tuberosum L.) plants. Bean sprout extract was synthesized through maceration, employing 96% ethanol solvent with a ratio of solvent and simplicia at 1:5. The resulting extract was administered to the plant by means of watering starting at 7 days after planting (DAP) and continuing once every 7 days until the 13th week. Vegetative growth parameters, including plant height and number of leaves, were measured on a weekly basis. At the thirteenth week, the wet biomass, total phenolic content, and antioxidant activity were analyzed under various PGR treatments. All data were subjected to normality testing, followed by ANOVA or Kruskal–Wallis analysis. This was followed by DMRT or the relevant post-hoc tests. The results demonstrated that the application of bean sprout extract PGRs significantly enhanced plant growth, number of leaves, wet biomass, and total phenolic content in soybean plants. In addition, the application of bean sprout extract PGRs significantly enhanced the antioxidant activity of chili pepper and soybean plants. These findings highlight the advantages of bean sprout extract as a low-cost, renewable, and eco-friendly natural PGR, offering a sustainable alternative to synthetic growth regulators while enhancing plant growth and functional quality. The utilization of this natural PGR also supports eco-friendly agricultural practices and has the potential to increase horticultural crop productivity with minimal environmental impact.
HKUST-1-Catalyzed Chan–Evans–Lam C–O Coupling Under Mild Conditions with Catalyst Reusability
The Chan–Evans–Lam coupling refers to a valuable method for constructing C–O and C–N bonds under mild conditions. Nevertheless, the development of efficient and reusable heterogeneous catalysts remains limited. In this study, we investigated copper-based metal–organic frameworks as catalysts for C–O bond formation between 4-methoxyphenylboronic acid and phenol. The study revealed that HKUST-1 exhibited a significant enhancement in performance when compared to Cu-BDC, yielding up to 86% at room temperature and demonstrating superior accessibility of Cu2+ active sites. A systematic optimization of reaction parameters identified NEt3 as the most effective base, DCM as the optimal solvent, and a mild temperature increase to 40 °C as the key factor enabling a maximum yield of 94%. Studies on recyclability demonstrated that HKUST-1 exhibited high catalytic performance over multiple cycles without significant structural degradation, as confirmed by PXRD analysis. Overall, this work highlights HKUST-1 as an efficient, robust, and reusable heterogeneous catalyst for Chan–Evans–Lam C–O coupling, thus demonstrating its potential for sustainable synthetic applications.
Insight into Aluminum Leaching with Microwave from Peat Clay: A Comparative Kinetic Study of SC and BIC Models
The depletion of bauxite reserves has prompted the research of various types of soil as alternative sources of aluminum, such as the peat clay used in this study. The complexity of the minerals requires a more efficient leaching methods, while microwave-based leaching offers a potential approach through rapid and uniform heating. This study examines the effect of microwave power, HCl concentration, operating temperature, and particle size on the leaching efficiency of aluminum from peat clay soil. The leaching process was modeled using two approaches, namely the shrinking core (SC) model and the broken-intact cell (BIC) model under pseudo-steady state conditions. The results showed that increasing HCl concentration, microwave power, and temperature accelerated leaching, while increasing particle size decreased leaching efficiency. Optimum conditions were achieved at 4 M HCl concentration, 100 W power, 40 °C temperature, and 0.0074 cm particle size. The shrinking core (SC) model showed better fit under most conditions, while the intact-broken cell (BIC) model was more accurate at lower temperatures and particle sizes. The simulation results showed that the most suitable parameter values in the SC model were De = 0.0049 cm2/s, k = 10.5 cm/s, and kc = 2.49 cm/s, while in the BIC model De = 0.04808 cm2/s and K = 0.02689 g/cm3 were obtained. These results confirm the superiority of the SC model in representing microwave-based leaching mechanisms in general, while the BIC model provides additional insights under diffusion-limited conditions. Process Performance Index (PPI) analysis showed that optimum conditions were achieved at 4 M HCl and 40 °C, but lower acid concentrations also yielded competitive PPI. This confirms that leaching effectiveness is determined by a combination of alumina recovery and reagent consumption efficiency. These findings contribute to the development of leaching kinetics models and the optimization of more efficient and energy-saving aluminum extraction processes.
Interpretability Evaluation of Rule-Based Classifier in Myocardial Infarction Classification Based on Syntactical Features of ECG Signal
Cardiovascular diseases remain the leading cause of mortality on a global scale, with myocardial infarction (MI) representing a critical and life-threatening condition. Electrocardiography (ECG) is a widely utilized method for the detection of myocardial infarction (MI), and artificial intelligence (AI) has demonstrated a promising performance in the automated ECG-based diagnosis. However, most existing studies emphasizepredictive accuracy while failing to provide substantial evidence that model decision logic aligns with clinical reasoning, thereby limiting clinical adoption. This present study evaluates the interpretability of three rule-based machine learning classifiers—Decision Tree, RIPPER, and Rough Set—for MI detection from ECG signals, including a comparison between models with and without feature selection. Interpretability of the system is assessed through rule complexity analysis and a standardized qualitative clinical validation protocol involving three cardiologists, based on contemporary AHA/ESC ECG diagnostic guidelines. The findings indicate that the Rough Set classifier attains the optimal overall performance, with 80% of its generated rules demonstrating clinically aligned, thereby outperforming the other models regarding interpretability. The findings demonstrate the benefit of guideline-based clinical validation for advancing trustworthy ECG-based MI diagnostic systems.
YOLOv8-Based Detection of Convective Storm Clouds for Cumulonimbus Classification
Cumulonimbus (CB) clouds are vertically developed convective systems that are capable of producing severe weather phenomena, including turbulence, heavy rainfall, and lightning. These phenomena pose a significant threat to aviation safety. This paper considers an automated CB cloud detection approach using the deep learning algorithm You Only Look Once version 8 on NOAA-19 satellite imagery. The images of 640 × 640 pixels each were labeled into two classes: CB and non-CB. In general, rotation, flip, and random brightening are performed to develop a more robust model. After 100 training epochs, the proposed model produced reliable detection performance, as evidenced by 1,694 TP (true positives), 438 FP (false positives), and 304 FN (false negatives) cases, with a precision of 0.79, recall of 0.84, and an F1-score of 0.81. Validation using METAR reports from the Indonesian Meteorological, Climatological, and Geophysical Agency (BMKG) confirmed the consistency of the model with observed weather conditions. The results demonstrated that YOLOv8 could provide a rapid and reliable framework for real-time detection and classification of CB clouds, thereby enhancing situational awareness for aviation operations and facilitating the effectiveness of satellite-based early warning systems in convectively active tropical regions.
A High-Capacity Reversible Watermarking Technique Using Bit-Level Expansion and Pixel Shifting
This paper proposes a high-capacity reversible watermarking method using adaptive bit-level expansion and pixel-class-guided shifting. Pixels are classified into expandable (P0) and non-expandable (P1) according to their 2-bit LSB patterns, and a lightweight reversible transformation converts P1 into P0 with minimal distortion. A shifting map enables exact recovery and avoids overflow/underflow. Secret data are embedded through a 2-bit LSB expansion rule that ensures full reversibility. Experiments on common and medical images demonstrate a consistent embedding capacity of 1.0 bpp, achieving PSNR values above 46 dB and SSIM above 0.97. In addition, the scheme exhibits low computational overhead (<0.7s per image, >380 kbps) while preserving the original histogram distribution. These results demonstrate that the proposed scheme provides an effective balance between embedding capacity, visual quality, and computational efficiency for secure medical imaging and authenticated reversible data embedding.