University of Jijel

This study presents the synthesis, characterization, and theoretical investigation of 5-benzoyl-2-(2,5-dimethylphenyl)-4-hydroxy-6-phenylpyridazin-3(2H)-one (BDP) to confirm its structural identity, properties, and potential applications in various fields. Experimental characterization techniques included FT-IR, Raman, UV-visible,1H, and 13C NMR spectroscopy. Theoretical studies were conducted using the Gaussian 09 software with Density Functional Theory (DFT), employing the B3LYP functional and 6–31G (d, p) basis sets. Computational analyses encompassed optimized geometrical structures, UV-Vis spectra, Natural Bond Orbital (NBO) analysis, and harmonic vibrational frequencies, with vibrational spectra interpretation assisted by the Vibrational Energy Distribution Analysis (VEDA4) program. NBO analysis confirmed the presence of intramolecular interactions such as electron delocalization and conjugative effects, while HOMO-LUMO energy calculations and molecular electrostatic potential (MEP) demonstrated charge transfer within BDP. Experimental and theoretical results were found to be in excellent agreement. The antibacterial activity of BDP was evaluated using the well diffusion method against Gram-positive and Gram-negative bacteria, with results indicating that BDP exhibited higher antibacterial activity than standard reference drugs. Furthermore, molecular docking studies assessed BDP’s binding affinity to active sites of Staphylococcus aureus (PDB ID: 2W9S) and Escherichia coli (PDB ID: 1WXH). The docking results revealed strong interactions via hydrogen bonding and hydrophobic interactions, highlighting BDP’s potential as a broad-spectrum antibacterial agent. Overall, this study demonstrates the promising structural, chemical, and biological properties of BDP, supporting its future application in antibacterial therapies.

This study investigates the potential of cork biomass, an underutilized lignocellulosic residue, to produce second-generation bioethanol. The process involved biomass preparation, Thermochemical pretreatment, concentrated acid hydrolysis, and alcoholic fermentation to convert the carbohydrate fraction of cork into fermentable sugars. The resulting bioethanol, obtained through distillation, reached a purity of 30 % (v/v). Physicochemical and thermodynamic caracteristics of the produced bioethanol included measurements of density, viscosity, flash point, calorific value, and vapor pressure. Spectroscopic analyses (FT-IR and Raman) were also conducted to confirm ethanol composition and purity. Despite its moderate purity, the bioethanol exhibited properties in line with initial fermentation - stage fuels, confirming the feasibility of cork as a raw material for biofuel production. These findings demonstrate a promising valorization route for cork waste within the framework of circular bioeconomy and sustainable energy development.

• Optimized concrete mix design using recycled coarse aggregates through DNN and IGWO. • Multi-objective optimization framework reveals trade-offs between density and ultrasonic pulse velocity. • Innovative use of machine learning for modeling water content, density, and UPV in recycled aggregate concrete. • Hybrid approach of Grey Wolf Optimizer and Deep Neural Network achieves superior predictive accuracy. • Validated models offer a sustainable framework for concrete mix design in eco-friendly construction. This study presents a thorough methodology for forecasting and enhancing the essential performance characteristics of concrete with Recycled Coarse Aggregates (RCA), including water content (Wt), density, and Ultrasonic Pulse Velocity (UPV). The attributes are represented as functions of cement content (300–400 kg/m³), RCA percentage (0–100 %), and slump (5–12 ± 1 cm). An experimental database was created using a three-factor, three-level design, followed by exploratory analysis using correlation matrices and three-dimensional response surface plots to identify significant input-output associations. Various modeling techniques, such as Response Surface Methodology (RSM), Decision Tree (DT), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and a Deep Neural Network (DNN) enhanced by the Improved Grey Wolf Optimizer (IGWO), were assessed. The accuracy of the model was meticulously evaluated by 4-fold cross-validation, Taylor diagrams, and radar plots, demonstrating that the DNN-IGWO hybrid surpassed all other models, achieving the minimal prediction errors and the maximal correlation coefficient (R 2 ) values for all target responses. The validated models were later employed as surrogates inside a multi-objective optimization framework employing the Multi-Objective Grey Wolf Optimizer (MOGWO). The optimization produced well-distributed Pareto fronts, emphasizing the intrinsic trade-off between density and UPV. The resultant "knee" solutions identify specific concrete compositions that optimize UPV (3.6–3.7) while ensuring a high density (≥2400 kg/m³) and moderate water content. This study provides a scientifically robust, data-driven paradigm for the sustainable design of concrete mixtures, facilitating the appropriate incorporation of recycled aggregates while enhancing material qualities.

In the industrial sector, ensuring reliability and robustness is of utmost importance. The research goes beyond conventional non-destructive testing by exploring advanced methods for comprehensive defect detection and imaging. It employs state-of-the-art eddy current testing, enhanced by sensor arrays composed of multiple elements arranged in an updated sequential matrix. This innovative configuration overcomes the challenges of magnetic repulsion between sensor elements, significantly speeding up the testing process while ensuring accuracy through detailed imaging of failure paths. Additionally, this study introduces a novel approach for characterizing random defects in aluminum sheets by utilizing machine learning techniques within the Radial Basis Function (RBF) network. This tool, trained on initial data, enables precise predictions of defect shapes and paths, allowing for accurate identification and dimension estimation later. By integrating defect imaging with predictive modeling, our approach not only advances technology in data security but also reduces effort, time, and costs. The research presents a pioneering methodology for defect identification and prediction, as detailed in the following sections.

This study examines the interplay between national interests, domestic politics, and international climate cooperation, focusing on the Paris Climate Agreement as a landmark in global environmental policy. The agreement marked a potential shift toward enhanced multilateral cooperation. However, nearly a decade since its adoption, persistent compliance challenges have raised concerns about its effectiveness in reducing global greenhouse gas emissions. Employing an analytical-descriptive approach combined with a chronological analysis to evaluate states' compliance with the agreement's commitments. The analysis is grounded in a dual theoretical framework: institutional neoliberalism to assess the agreement's institutional achievements and realism to interpret how national interests and domestic pressures impede compliance. Findings indicate that domestic priorities significantly shape adherence, revealing a disparity between ambitious Nationally Determined Contributions (NDCs) and their practical implementation.

This study aimed to evaluate the performance of Islamic investment funds worldwide, through a case study of the Al Rajhi Saudi Equity Fund. Using a descriptive-analytical approach, the study examined the global landscape of Islamic investment funds, analyzed their performance, and assessed the reality and performance of the Al Rajhi Saudi Equity Fund from 2020 to 2025. The study concluded that the size and assets of Islamic investment funds worldwide are significant and show notable growth. It also found that Saudi Arabia is the leading country in adopting Islamic investment funds, hosting a large number of such funds, most notably the Al Rajhi Saudi Equity Fund, which has shown consistent asset growth over the years. Furthermore, the study concluded that the Al Rajhi Saudi Equity Fund achieved good performance during the study period and recommended that Algeria pay greater attention to Islamic investment funds and benefit from the experience of the Al Rajhi Saudi Equity Fund.