BAHRA University

Emphasizing the integration of Rice Husk Ash (RHA), steel fibers, and various water-cement ratios to generate sustainable and high-performance construction materials, this study focuses on the optimization of cementitious composites utilizing Response Surface Methodology (RSM). Twenty mix designs were experimentally evaluated to assess their effects on water absorption, flexural strength, and compressive strength. A water–cement ratio of approximately 0.35 combined with a 10–15% RHA replacement yielded compressive strengths ranging from 24.6 MPa to 40.5 MPa, which are comparable to or higher than those reported for similar sustainable concrete systems in existing literature. Flexural strength varied from 6.2 MPa to 7.8 MPa; optimum results were obtained for steel fibers with an aspect ratio of 80–100 and modest degrees of RHA. With the minimal absorption seen at balanced water-cement ratios and 10% RHA content, water absorption ranged between 5.1% and 6.5%. The value of RHA in lowering water absorption (p = .047) and enhancing strength properties was shown statistically. Surface and contour plots help one to fully understand different interactions and underline the need of parameter tuning. The findings verify the capacity of RHA and steel fibers to generate robust, low-cost, ecologically friendly composites, thereby supporting sustainable construction techniques and best use of resources.

This paper presents a hybrid approach incorporating a differential equation model and machine learning for simulating and predicting the dynamics of chemical processes. Differential equation models simulate time-dependent concentrations of reactants, intermediates, and products that provide information on the development of specific patterns: for example, exponential decay, transient peaks, and steady accumulation of the final product. The proper prediction of concentration profiles was done based on machine learning techniques with excellent performance in terms of the RMSE being less than 0.01 and higher than 0.99 for the value of . The proposed framework offers significant advantages and is applicable to various industrial sectors, such as pharmaceuticals, petrochemicals, and food processing for real-time monitoring, process optimization, and decision-making.

This study explores the optimization of Casson fluids, focusing on the role of ternary hybrid nanofluids in enhancing thermal efficiency in industrial and engineering applications. Specifically, the impact of thermophoretic particles and chemical reactions on bioconvective Casson ternary hybrid nanofluid flow through a vertical microchannel embedded with porous media is examined. The governing equations are reduced using similarity transformations, and the resulting nonlinear equations are solved using the Runge–Kutta–Fehlberg 4th and 5th order method. The findings reveal that increasing the thermophoretic constraint leads to a decrease in nanoparticle concentration, highlighting the impact of thermophoretic forces on particle movement and deposition. Additionally, the porous parameter causes a reduction in flow velocity, which is observed to affect the overall fluid dynamics in the system. The presence of variable thermal conductivity enhances the thermal field, suggesting that temperature distribution can be significantly improved. Moreover, increasing the nanoparticle volume fraction enhances temperature distribution, indicating a positive correlation between nanoparticle concentration and thermal efficiency. On the other hand, increasing the thermophoretic constraint results in a decrease in mass transfer rate, emphasizing the trade‐off between enhanced thermal performance and reduced mass transport. These findings are valuable for the design of advanced micro‐cooling devices, micro‐heat exchangers, micro‐pumps, and macro mixing technologies, where both thermal and mass transfer performance are critical.

This current work aims to analyze the fabrication of Cobalt and Dysprosium co-doped BaFe12O19 of chemical composition, Ba1-xCoxDyyFe12-yO19 (x = y = 0.2–0.6) via, the sol-gel auto-combustion (SGAC) technique, with its main objective to improve their structural, surface, microstructural, optical, and magnetic traits. X-ray diffraction reveals the hexagonal structure of prepared co-doped barium hexaferrites with an additional phase of α-Fe2O3 and it is also validated by the Rietveld refinement. The computed crystallite size (D) falls between 22.40 and 41.02 nm. The grain size distribution at the surface of doped barium hexaferrites is reported by the field emission scanning electron microscopy (FESEM) study within in the range of 102.05 to 189.43 nm. The FESEM results confirms a hexagonal morphology for all the samples. The fundamental metal-oxygen (M-O) stretching vibrations at the tetrahedral and octahedral positions are detected via Fourier-transform infrared spectroscopy (FTIR). This approves the existence of distinctive functional groups within the developed hexaferrites. Seven Raman active band positions are found in the Raman spectra of Ba1-xCoxDyyFe12-yO19 hexaferrites. The computed specific surface area is found to be 3.478 and 3.352 m²/g for the CDH1 and CDH3 hexaferrites. The magnetic results show that with the doping, the coercivity and saturation magnetization decreases. Therefore, due to the excellent Brunauer-Emmett-Teller (BET) and Vibrating sample magnetometry (VSM) results, the Ba1-xCoxDyyFe12-yO19 hexaferrites are highly beneficial in the sensors and magnetic recording applications. Graphical Abstract

Catechin is a group of bioactive flavonoids found in various plant sources such as tea, cocoa, and fruits. Recent studies have suggested that catechins has significant potential in preventing and treating cancer. Catechin exhibits a variety of biological activities that may contribute to its anticancer effects, including antioxidant, anti-inflammatory, and pro-apoptotic properties. Studies have demonstrated that catechin can inhibit cancer cell proliferation, induce cell cycle arrest, and promote apoptosis across multiple cancer types, including skin, breast, lung, liver, prostate, and colon cancers. Furthermore, catechin has shown the ability to inhibit angiogenesis, a critical process for tumor growth and metastasis, by restricting new blood vessel formation. Catechin’s impact on cancer extends beyond its direct effects on cancer cells. It modulates various signaling pathways involved in cancer progression, such as those associated with cell survival, inflammation, and metastasis. Despite these promising findings, additional research is needed to clarify the precise mechanisms of catechin’s anticancer action, optimal dosing strategies, and long-term safety in cancer prevention and treatment. This review will explore the current research landscape on tea polyphenols, particularly catechin, and their potential role in cancer prevention and therapy.

Thermoelastic dissipation (TED) is a primary source of energy loss in extremely small structures, making the precise determination of its magnitude vital for the optimal design and performance of such components. The inclusion of two-dimensional (2D) heat conduction alongside size effects in both the structural and thermal domains plays a key role in enhancing TED analysis for small-scale beam resonators. The modified couple stress theory (MCST) and Moore–Gibson–Thompson (MGT) heat equation, within the context of the energy approach, are employed in this paper to create a novel size-dependent framework for TED in small-scale beams subjected to 2D heat conduction. After comparing the developed framework with existing research, numerical simulations are carried out to reveal the differences between 2 and 1D models, as well as the impact of employing size-dependent mechanical and thermal formulations. For beams with large thickness-to-length ratios, especially under clamped–clamped (CC) boundary conditions, the proposed model shows significant differences when compared to 1D model. Based on the findings, the ratio of 2D TED to 1D TED in CC beams with an aspect ratio of 10 can be up to 1.6 times. The integration of size effects and 2D heat transfer in the established framework is expected to provide benchmark results for accurate TED simulations and facilitate the optimal design of ultra-small beam resonators.

This review provides a comprehensive overview of current progress in catalytic technologies for converting CO2 to ethanol, emphasizing the importance of sustainable and environmentally friendly alternatives. A range of methodologies is explored, including thermodynamic analysis, thermocatalytic, electrocatalytic, and photocatalytic approaches, while discussing fundamental reaction mechanisms and catalyst design strategies. Significant advancement has been made in the thermocatalytic hydrogenation of CO2, with mixed metal and metal oxide catalysts achieving selectivities exceeding 90%. However, challenges remain in optimizing catalyst performance for enhanced selectivity and conversion rates. Electrocatalytic reduction offers a promising pathway, focusing on alkaline electrolytes and innovative catalyst designs such as Cu/Au and Al‐Cu/Cu2O. Meanwhile, photocatalytic systems harness solar energy, with various novel photocatalysts showing potential for high efficiency. This review aims to elucidate the current landscape and future perspectives on CO2‐to‐ethanol conversion technologies, highlighting their potential role in sustainable energy solutions.

Knowledge of soil temperature (ST) is important for analysing environmental conditions and climate change. Moreover, ST is a vital element of soil that impacts crop growth as well as the germination of the seeds. In this study, four machine-learning (ML) paradigms including random forest (RF), radial basis neural network (RBNN), multi-layer perceptron neural network (MLPNN), and co-active neuro-fuzzy inference system (CANFIS) were used for estimation of daily ST at different soil depths (i.e. 5 cm: ST5; 15 cm: ST15; and 30 cm: ST30) during 2016–2019 at Bathinda weather station, located in South-western Punjab (India). Five different combinations were formulated using four meteorological data, namely Tmean (mean air temperature), RH (relative humidity), WS (wind speed), and SSH (bright sunshine hours), and the optimal one was nominated by employing the gamma test (GT) for each soil depths, respectively. During the validation period, the outcomes of the RF, RBNN, MLPNN, and CANFIS models were evaluated according to performance metrics such as mean absolute error (MAE), root mean square error (RMSE), scatter index (SI), coefficient of efficiency (COE), Pearson correlation coefficient (PCC), and index of agreement (IOA), as well as through pictorial interpretation (Taylor diagram, box-whisker plots, time-variation, scatter plot, and radar chart). The comparison of the results of ML paradigms revealed the highest accuracy was achieved by the CANFIS model at all depths with MAE (RMSE) = 0.788, 0.636, 0.806 (1.074, 0.854, 1.041) °C, SI = 0.040, 0.033, 0.040, and COE (PCC)/IOA = 0.986, 0.991, 0.985 (0.994, 0.995, 0.993)/0.996, 0.998, 0.996. Thus, the results highlight the capability of the CANFIS model with Tmean, RH, WS, and SSH inputs for daily ST estimation at different soil depths on the study site.

There has been an increasing number of pressures on the construction sector, one of the cornerstones of economic development in emerging countries, to adopt sustainable practices in order to remain competitive. As the need for cost reduction in thermal energy storage (TES) technology grows, energy efficiency becomes increasingly important. Two methods of insulating TES systems are assessed: powders that are evacuated and materials that are placed to the outer of the storage. The goal of this research is to improve building energy efficiency via the development of novel construction materials and techniques. There is a growing trend to replace traditional glass with transparent wood because its optical transparency and thermal insulation qualities are superior to those of glass. Moreover, developments in protective materials, such as rice straw-based thermal insulation and nanomaterials-based solutions, could help to reduce the amount of energy used by reducing the energy consumption of buildings. Energy efficiency can also be enhanced through novel approaches such as 3D printing of nanocomposites and thermal energy storage systems. Sustainable building practices are essential to addressing global energy challenges since they provide economic and environmental benefits.

This study examined the capability of white-box machine learning methods in the intelligent design of concrete technology. Therefore, three data-driven methods, multivariate adaptive regression splines (MARS), gene expression programming (GEP), and group method of data handling (GMDH) approaches, were adopted to model the compressive strength (CS) and environmental impact points (P) of fly ash admixture concrete. The main feature of the proposed methods is that they provide formulas for predicting CS and P. The study's findings indicated the acceptable performance of the suggested methods in concrete technology. In general, the MARS approach for the estimation of CS is more acute than the GMDH and GEP approaches. In addition, MARS had results similar to those of the evolutionary polynomial regression (EPR) model generated in the earlier research to predict CS. Moreover, the MARS model performs slightly better than EPR for predicting P. It is noteworthy that MARS presented more straightforward equations than EPR for predicting CS and P. Sensitivity analysis indicated a more effective parameter on CS and P. The accuracy of the developed models was assessed through statistical parameters and scatter, Taylor, and Violin plots. The presented predictive models can have practical applications in the construction of buildings.

In this paper, quantitative analysis of the pore structure of AgNPs is presented by combined analysis of advanced electron microscopy techniques. The synchrotron-based analysis of silver nanoparticles with 18-30 nm in size confirmed detailed information about the internal structure of their porous nature of the nanoparticles and their surface characteristics. Although the pore volume of AgNPs changed from 28 nm³ to 40 nm³, pore size ranged between 3 nm to 10 nm. Specific pore volume values referring to AgNP mass were within 10–26 nm 2 /g depending on nanoparticle size. Furthermore, the surface area values varied between 25 m²/g and 50 m²/g evidencing the influence of nanoparticle size on internal as well as exterior surface area. Taken together, the findings suggest a direct dependency of size dependent nanoparticle on the pore structure and surface area of the support material: Diameter of AgNP has direct impact on porosity of the samples. These findings are useful for optimizing internal porosity and surface properties of AgNPs for particular uses such as catalysis, drug delivery, and sensing. This vast study provides a framework for synthesising AgNPs with any types of pore structures to improve nanotechnology applications through careful tailoring of materials.