The conversion of CO₂ into gasoline-range hydrocarbons (C₅–C₁₂) using Fischer–Tropsch synthesis (FTS) represents a compelling pathway toward sustainable fuel production. In this study, we compiled and statistically analyzed a dataset of over 100 experimental records from the published literature focused on CO₂-FTS performance, predominantly featuring Co‑ and Fe‑based catalysts, which are the most frequently reported in gasoline-range studies. We evaluated four machine learning models—XGBoost, CatBoost, Random Forest, and Neural Networks—to predict CO₂ conversion and gasoline-range selectivity. CatBoost achieved the highest predictive accuracy with a test R² score of approximately 0.8, and was selected for further interpretation using SHAP-based post hoc analysis. The model revealed that the optimal operational conditions for maximizing gasoline-range hydrocarbon yield are aligned with ranges commonly reported: a temperature of 280–320 °C, pressure of around 2 MPa, and space velocity (GHSV) between 900 and 120,000 mL  h⁻¹ g⁻¹ (most studies cluster in the 1,000–5,000 range). Conditions were associated with enhanced chain growth probability and suppressed methane formation, especially in Co-based systems. The SHAP analysis also highlighted the principal role of catalysts containing cobalt (often supported on γ-Al₂O₃ with Re promoter) in increasing C₅⁺ chain growth and gasoline-range selectivity. Additionally, Co-based catalysts demonstrated clear benefits: increased chain-growth probability, reduced methane selectivity, and higher selectivity toward gasoline fractions under the identified optimal conditions. Our ML-driven framework not only predicts performance but also provides mechanistic insights into the influence of catalyst composition and reaction parameters. This integrated approach accelerates rational catalyst and process design for CO₂‑to‑fuel technology.

Brazil is recognized for its vast biodiversity, home to biomes such as the Cerrado, where the mangaba (Hancornia speciosa) is found, a fruit with high nutritional and medicinal potential. This study aimed to evaluate the mineral content and antioxidant activity of mangaba fruits collected in the Cerrado Goiano. The fruits were processed and analyzed for their mineral content (N, P, K, Ca, Mg, S, Fe, Mn, Cu, Zn, B), carotenoids (β-carotene and lycopene), phenolic compounds, and antioxidant capacity using the DPPH method. The results showed a high concentration of nitrogen, sulfur, and potassium, essential for the development of the fruit and beneficial for human health. In addition, relevant amounts of iron and magnesium were identified, which reinforces their nutritional value. Regarding bioactive compounds, 34.00 mg/100 g of β-carotene, 3.00mg/100 g of lycopene, and 40.00 mg/g of DPPH were found, indicating a significant antioxidant capacity, although lower than other fruits of the Cerrado. The phenolic content was also higher than that reported in other studies, which can be attributed to environmental and methodological factors. The results show that mangaba is a rich source of minerals and antioxidant compounds, with potential for applications in the food and pharmaceutical industry. Its use can contribute to the development of natural functional products, promoting the valorization of native species of the Cerrado.

Abstract Short Message Service (SMS) is still a vital communication tool in our daily life activities. Despite the growing popularity of internet-based messaging platforms, Short Message Service (SMS) remains a widely used means of communication. However, this continued reliance has given rise to SMS phishing commonly known as smishing, which poses a significant cybersecurity threats. Traditional detection methods, including heuristic analysis, rule-based systems, and blacklists, often struggle to identify evolving smishing tactics. Similarly, conventional machine learning models such as Random Forest, SVM, RNN, CNN, and LSTM face limitations when handling long text sequences due to the vanishing gradient problem. To address these challenges, this study proposes SmishNet, an enhanced hybrid model that combines Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, and an Attention Mechanism for improved smishing detection. The model is trained on a combined dataset comprising the Kaggle SMS Smishing Collection and locally sourced phishing messages from Nigeria, ensuring contextual and linguistic relevance. The model’s performance was evaluated using standard metrics, including accuracy, precision, recall, and F1-score. Experimental results show that SmishNet achieves a high accuracy of 99.3%, outperforming CNN with 98.6%, and LSTM with 71.9%. These findings demonstrate the effectiveness of attention mechanism in handling vanishing gradient problem, and the efficacy of hybrid approach in smishing detection.

Injera Baking Stove (Mitad) Type Determines Injera Quality

Oli Legassa and Workneh Abebe

Bishoftu Agricultural Research Center, Ethiopian Institute of Agricultural Research

Corresponding author’s: e-mail: workinehabebe.zeleke@uva.es

Abstract

Injera is the national staple of Ethiopians, mainly prepared from tef grain and other cereals or their blends. Injera quality is known to vary from process to process, depending on factors like the cereal type and other ingredients used, and baking techniques. This study evaluated the texture, sensory attributes, and shelf stability (at 25°C, 60% storage relative humidity) of injera baked using three types of injera baking stoves: biomass-fired Lakech (LS), common electric (CES), and pizza-baking-type (PBTS) stoves. PBTS exhibited the fastest reheating rate (0.22°C/sec) but produced injera with higher moisture (7.13% d.b.), lower sensory scores, accelerated texture degradation, and the shortest shelf life (56 h). LS-baked injera had superior sensory acceptance (4.9/5). slower firming and longer shelf life (80 h), while CES injera showed relatively closer performance in most parameters to LS. The findings highlight the critical role of injera baking stoves, indicating the superior performance of LS despite its energy inefficiency and inconvenience, and the need to optimize heating rate and distribution, together with moisture management in the electric-powered stoves, mainly in the PBTS.

The optoelectronic and transport properties of lead-free ytterbium (CsYbCl₃) perovskites have been characterized. The purpose of this study is to predict the electronic structure of the CsYbCl₃ perovskite crystal and propose guidelines for material design to improve its photovoltaic performance. The CsYbCl₃ crystal was prepared based on the cesium lead chloride crystal structure, structurally optimized, and the band structures and absorption characteristics predicted using first-principles calculations. The electronic structure consists of an occupied 4f orbital of the Yb²⁺ ion in the valence band state and a 5d orbital of the Yb²⁺ ion in the conduction band state, and it exhibits a direct transition band gap of 0.55 eV. Real (Re) and imaginary (Im) dielectric functions were found to be Re = 26.5 at 1.7 eV and Im = 32.2 at 2.9 eV. The absorption coefficient was widely distributed in the range of 163 – 1768 nm. From the intercept of photon energy with the slope of the Tauc plot, the band gap was found to be 0.55 eV. The acoustic phonon as lattice vibrations exhibited dynamic instability derived from the tilt of the octahedral structure. The temperature behavior of electrical conductivity decreased with increasing thermal conductivity. The hole conductivity was based on the combination of carrier diffusion with lattice vibration as acoustic phonons in the high-temperature region. As a novelty, CsYbCl₃ crystals, due to theoretical predictions, are expected to be applicable as photoactive materials operating at high temperatures for optoelectronic applications such as solar cells and fluorescent devices.

This study investigated the synthesis and characterization of carboxymethyl cellulose (CMC) derived from wheat husk cellulose and evaluated its antimicrobial efficacy. Cellulose was extracted using acidified sodium chlorite and subsequently converted to CMC through etherification involving sodium hydroxide and monochloroacetic acid. The process resulted in a cellulose yield of 15.6% and a CMC yield of 60.8%, with a degree of substitution (DS) of 0.2. Fourier-transform infrared (FT-IR) spectroscopy confirmed successful chemical modification, indicated by the appearance of a carbonyl absorption band at 1640 cm⁻¹. X-ray diffraction (XRD) analysis showed a decrease in crystallinity in the CMC compared with the native cellulose, while scanning electron microscopy (SEM) revealed a shift from a slightly cracked to a smoother surface morphology in the CMC. The antimicrobial properties of the synthesized CMC and zinc oxide-incorporated CMC (ZnO-CMC) biofilms were tested against Escherichia coli, Staphylococcus aureus, Salmonella typhi, and the fungus Cladosporium spherospermum. ZnO-CMC biofilms exhibited enhanced antimicrobial activity and greater resistance to degradation than CMC biofilms alone. These findings suggest that CMC synthesized from agricultural biomass such as wheat husk is a promising biodegradable material for food and pharmaceutical packaging. Additionally, the incorporation of ZnO nanoparticles further enhances its potential as an antimicrobial material for use in pharmaceutical applications.

Climate change greatly affects farming by reducing crop yields, thereby challenging sustainable farming. Real-time data and analytics help identify key factors to boost crop yield and support smart farming through better decision making. To study how different factors affect crop yield, this paper considers various parameters, namely, temperature, precipitation, CO₂ emissions, extreme weather, use of fertilizers/pesticides, irrigation, soil health, economic factors, and location, and their influence on the crop yield is estimated. The Pearson Correlation Coefficient (PCC) is commonly used to find relationships between variables, but it only detects linear connections. Since climate factors often change in complex, nonlinear ways, this paper suggests a hybrid approach that combines the PCC with the XGBoost machine learning method for better analysis. In the proposed hybrid model, XGBoost identifies which factors are most important, while the PCC measures their impact on crop yield, thereby performing an effective correlation analysis. Simulations show that 'economic impact' has the strongest direct influence on crop yield, with a correlation coefficient of 0.73. Further, it is also noticed that the combination of 'average temperature' and 'economic impact' has the highest indirect influence, with a correlation of 0.2. The proposed hybrid model performs better than the standard PCC method, achieving an R-squared of 0.57, RMSE of 0.62, and MAPE of 29.14%, compared to PCC’s R-squared of 0.55, RMSE of 0.71, and MAPE of 31.48%. This study uses the 'climate change impact on agriculture' dataset from Kaggle and supports UN Sustainable Development Goals (UN SDGs 13 and 15) for promoting sustainable farming.

Missing values significantly impede data analysis and machine learning, especially in healthcare where complete data is vital. They can reduce predictive model performance, making robust imputation essential. Traditional methods like mean and median substitution often perform poorly with high missingness. This study compares traditional statistical imputers with machine learning models for handling missing data. Seven machine learning algorithms were tested on four datasets with substantial missing values, revealing performance declines in both statistical and ML-based imputation methods when missingness was high. To overcome this, the study proposes a stacking ensemble combining Random Forest, Linear Regression, and Ridge Regression to boost predictive accuracy and reduce error.The proposed model was evaluated using standard metrics, such as Accuracy and Root Mean Squared Error (RMSE), and was compared against individual models and traditional imputation methods. Results show that the ensemble technique achieved accuracy of 98.2% and RMSE 0.2093 outperforming all seven individual machine learning models and statistical methods on the breast cancer dataset. RF with 97.08% and XGBoost with 95.9% accuracy also consistently outperformed statistical imputers across all datasets. Notably, the Decision Tree model exhibited poor performance across all datasets, with high RMSE and low accuracy. These findings highlight the importance of selecting appropriate imputation strategies and algorithms to enhance predictive accuracy in the presence of missing data. This work contributes to the growing body of research on machine learning-based imputation and predictive modeling in healthcare and other domains.

In this study, a novel organic compound—naphthalen-2-yl 2-thiocyanatoacetate—was synthesized via a nucleophilic substitution reaction (SN2 mechanism) in dimethylformamide (DMF) as the solvent. The molecular structure of the synthesized compound was confirmed by infrared (IR) and nuclear magnetic resonance (NMR) spectroscopy. The compound was evaluated for its analytical properties, particularly its ability to form a colored complex with Ni(II) ions, which was investigated using a photometric detection method. A comprehensive optimization of the detection conditions was performed. Factors such as the acidity of the reaction medium (pH), the order of reagent addition, the amount of reagent, the selection of optical filter, maximum absorption wavelength (λmax), Beer’s law linearity range, molar absorptivity, Sandell sensitivity, limit of detection (LOD), equilibrium constant, and the molar ratio of the complex components were studied in detail. The results demonstrated that the synthesized compound exhibits high sensitivity, selectivity, and stability in complex formation with Ni(II) ions. The developed photometric method is simple, cost-effective, and rapid, and can be implemented using basic instrumentation. Therefore, the method shows great potential for application in environmental monitoring, industrial wastewater analysis, and trace metal detection. This work contributes to the development of novel thiocyanate-based analytical reagents and expands the toolbox for photometric determination of transition metal ions, particularly Ni(II).

Phosphating is one of many methods of obtaining protective films on steel products at low temperatures. Phosphate coatings are obtained from solutions based on Mazev Salt containing Mn(H₂PO₄)₂∙2H₂O and iron phosphates (the proportion of phosphoric acid, in terms of P₂O₅, 46–52%; the mass fraction of manganese is not less than 14%; the mass fraction of iron is not more than 0.5%). To accelerate phosphating and carry out the process without heating, the solution contains metal nitrates and nitrites. To obtain blue and green colored phosphate coatings, it is proposed procyon olive green and methylene blue dyes are introduced in an amount of 8 g/l into phosphating solutions. Unlike conventional gray phosphate films, coatings from solutions with dyes are deposited unevenly on the steel surface. The thickness of the phosphate coatings was estimated from micrographs of the cross-sections; its value was 3–4 microns. When assessing the heat resistance of phosphate films, it was found that they continue to exhibit protective properties when heated to 100 °C, and when heated to 200 °C, the protective ability is low. Colored phosphate films have a low coefficient of friction, which does not allow them to be recommended as wear-resistant coatings. In colored phosphate coatings, the breakdown voltage is 180–200 V, which characterizes a low electrical insulation ability.