Yazid Chetbani

Purpose: This study aims to investigate the development of eco-friendly self-compacting concrete (SCC) by partial replacement of Portland cement (PC) with crushed dune sand (CDS) and ground granulated blast-furnace slag (GGBFS). The effects of these materials on the fresh and hardened concrete properties are evaluated, and the optimal mixture providing a balanced combination of performance and sustainability is identified. Design/methodology/approach: The experimental design consisted of 15 SCC mixtures based on a three-factor mixture-design methodology. The fresh properties included slump flow, T500 flow time, L-box ratio and segregation resistance, while the hardened properties comprised 7 and 28-day compressive strength and 28-day porosity. The experimental data were analyzed using Design-Expert 13 to develop predictive statistical models describing the relationships between mixture proportions and SCC performance. The developed models were evaluated using analysis of variance (ANOVA) and performance indicators, including R2, RMSE, MAE, SI and MAPE. A multi-objective optimization procedure based on the desirability function was then used to determine the optimum SCC mixture. Unlike recent machine-learning-based approaches proposed for predicting the performance of advanced cementitious materials and SCC, the present study adopts an interpretable mixture-design-based statistical framework for prediction and optimization. Findings: The developed predictive models showed high reliability, with R2 values ranging from 89.36% to 95.42%, and were statistically significant according to ANOVA (p < 0.05). Validation results demonstrated high prediction accuracy, with low RMSE, MAE and SI values and MAPE values mostly below 3%; the close agreement between predicted and measured responses confirmed the applicability and reliability of the models within the investigated mixture-design domain. Increasing PC content improved matrix compactness, thereby increasing compressive strength and reducing porosity, whereas increasing GGBFS and CDS enhanced sustainability while maintaining acceptable SCC performance, despite a slight reduction in early-age strength. SCC14 (5% CDS) was identified as the optimal mixture, while SCC7 (10% GGBFS + 5% CDS) provided an effective alternative with reduced cement consumption. Originality/value: This study provides an assessment of the combined use of GGBFS and CDS as alternative materials for SCC production. Beyond experimental characterization, it demonstrates the effectiveness of predictive statistical modeling and multi-objective optimization for identifying sustainable, economical and high-performance SCC mixtures. The proposed framework provides an interpretable and experimentally efficient approach for predicting SCC performance and optimizing mixture proportions, with experimental validation supporting its applicability within the investigated mixture-design domain and complementing recent machine-learning-based methods reported for advanced cementitious materials.

Purpose: This study aims to focus on the evaluation of the spatiotemporal variability of methane (CH4), carbon monoxide CO, sulfur dioxide (SO2) nitrogen dioxide (NO2) and formaldehyde (HCHO) over Sigus, northeastern Algeria, during 2019–2025 using Sentinel-5P/TROPOMI data processed in Google Earth Engine, with particular emphasis on the environmental influence of emissions associated with the Sigus Cement Plant. Design/methodology/approach: Sentinel-2-derived Normalized Difference Vegetation Index (NDVI), temperature from NASA POWER and wind data from the Copernicus Climate Data Store were integrated to interpret seasonal pollutant variations and assess environmental impacts through a comprehensive satellite-based monitoring framework. Findings: The results showed distinct temporal patterns among the investigated pollutants. CH4 concentrations increased progressively during 2019–2025, while CO, SO2, NO2 and HCHO exhibited moderate interannual fluctuations with strong seasonal variability. NO2 and SO2 levels were higher during colder periods, while HCHO peaked in summer due to enhanced photochemical reactions at elevated temperatures. Wind speeds (3.5–18.1 m s−¹) contributed to pollutant dispersion and transport, particularly for short-lived atmospheric species. Pearson correlation analysis revealed a moderate negative correlation between NDVI and NO2 (r = −0.566), indicating that reduced vegetation cover was generally associated with increased NO2 levels. Conversely, NDVI showed a moderate positive relationship with HCHO (r = 0.510). Weak correlations with CO, SO2, CH4 and O3 suggest that atmospheric processes and meteorological factors mainly controlled their variability. Research limitations/implications: Given the limited seven-year dataset, these correlations represent indicative trends rather than conclusive statistical relationships. Originality/value: This study highlights the integration of Sentinel-5P, Sentinel-2 and Google Earth Engine as a reproducible framework for long-term pollution monitoring, supporting environmental management and sustainable industrial development in data-scarce regions.

Background/Objectives: Chronic inflammation sustained by cyclooxygenase-2 (COX-2) and inducible nitric oxide synthase (iNOS) underlies many human diseases, while the cardiovascular liabilities of coxibs have renewed interest in plant-derived alternatives. This study characterised the volatile composition of Corymbia citriodora essential oil under two distillation regimes and identified constituents able to inhibit human COX-2 and iNOS. Methods: Oils obtained by steam distillation (SD) and microwave-assisted steam distillation (MSD) were profiled by GC–MS/MS. All identified constituents were screened with SASA Vina against human COX-2 (PDB: 5KIR, 3LN1), ovine COX-1 (4COX) and human iNOS (3E7G), with native-ligand re-docking validating each protocol (RMSD 0.39–0.84 Å). The lead compound underwent density functional theory (B3LYP/3-21G/CPCM), 100 ns all-atom molecular dynamics, MM/GBSA and MM/PBSA decomposition, and pkCSM ADMET prediction. Results: Sixty-three constituents were identified (99.85% SD; 99.97% MSD). MSD enriched oxygenated monoterpenes (93.23% versus 88.27%), citronellal rose from 38.24% to 48.41%. (Z,Z,Z)-1,5,9,9-Tetramethyl-1,4,7-cycloundecatriene ranked highest at COX-2 (−8.0 to −8.2 kcal/mol) and also bound COX-1 (−8.8 kcal/mol) and iNOS (−6.8 kcal/mol). DFT indicated high kinetic stability (ΔE = 6.12 eV; η = 3.06 eV) and purely non-covalent hydrophobic binding. The COX-2 complex remained stable over 100 ns (Cα RMSD 0.17 ± 0.02 nm), with MM/GBSA and MM/PBSA binding energies of −23.98 and −21.95 kcal/mol and Val523 as the principal hotspot. ADMET prediction returned 96.61% human intestinal absorption and no mutagenicity, hepatotoxicity or hERG I blockade. Conclusions: MSD offers a faster route to a citronellal-enriched oil, and C. citriodora hydrocarbon sesquiterpenes emerge as chemically stable, multi-target COX-2/iNOS scaffolds. Comparable binding at COX-1 indicates that isoform selectivity remains to be established; in vitro enzymatic and cell-based validation is therefore required.

This study applies mixture design, ANOVA, and desirability function analysis to optimize concrete performance with river (RS), crushed (CS), dune (DS) sands, and their combinations. Experimental validation confirms the reliability, practical applicability, and sustainability of optimized mixes, while highlighting key property relationships. Study optimized concrete mixes and developed predictive models using a multi-objective desirability approach. Twenty-one mixtures with varying RS, CS, and DS were designed via a three-factor, five-level simplex lattice method. Key properties including slump, 7 and 28-day compressive (CS) and flexural (FS) strengths, capillary absorption, and UPV were measured. ANOVA and statistical modeling in Design-Expert13 validated factor significance and interactions, enabling reliable mathematical models to predict concrete performance. ANOVA results showed strong predictive performance (R2 = 0.80–0.91). RS improves workability, while moderate amounts of CS and DS sands enhance strengths and reduce open porosity. Best 28-day CS (36 MPa) was recorded for the mix consisting of 0.4RS+0.6CS. Ternary mix M10 (20%RS+60%CS+20%DS) achieved the highest 28-day CS, though excessive dune sand (>40%) decreases performance. Model predictions deviated less than 9% from experiments, confirming reliability. Significant correlations (R2>0.91) among CS, open porosity, density, UPV, and dynamic elasticity modulus enable non-destructive assessment. Local sands (CS, DS) are viable sustainable alternatives to RS.