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This study investigates the effect of TiO2 nano additives in conjunction with 1-decanol on the performance and emission characteristics of biodiesel. The analysis involves four different blends: 90D7TO3DO+25 ppm TiO2 (90 % Diesel+7 % Tire oil + 3 % decanol and 25 parts per minute Titanium dioxide), 80D14TO6DO+50 ppm TiO2 (80 % Diesel+14 % Tire oil + 6 % decanol and 50 parts per minute Titanium dioxide), 70D22TO8DO+75 ppm TiO2, (70 % Diesel+22 % Tire oil + 8 % decanol and 75 parts per minute Titanium dioxide), and 100TO+100 ppm TiO2.(100 % Tire oil + 100 parts per minute Titanium dioxide), (DOE), design of experiments approach is used in this analysis. BTE is high for 90D7TO3DO+25 ppm TiO2 (36.12 %), compared to Diesel (30 %), i.e. 90D7TO3DO+25 ppm TiO2 possesses 20.67 % higher than diesel. The blend 90D7TO3DO+25 ppm TiO2 exhibited the lowest fuel consumption (0.25 kg/kWh), compared to diesel (0.28 kg/kWh), i.e. it was 12 % less than diesel. Inline cylinder pressures were optimal at 70 bar for the blend 90D7TO3DO+25 ppm TiO2, indicating favorable combustion properties. In terms of emissions, diesel emits NOx (1850 ppm), and the blend 100TO+100 ppm TiO2 achieved the lowest NOX emission of 1550 ppm, representing a 16.3 % reduction compared to diesel. The RSM model's coefficient of determination (R-squared) for all the parameters considered was remarkably high (nearly 0.98), meaning that it accounts for 98.00 % of the variability in the parameters. The corrected R-squared value validates the model's robustness. The results of the coefficient of determination (R2) for Bayesian Ridge regression clearly illustrate that the method is capable of making accurate predictions across the majority of the measurements as compared to Random Forest (RF).

The low thermal efficiency of parabolic trough solar collectors (PTSCs) is a major drawback that has hindered their development as a viable renewable energy resource. Among the available methods to enhance the thermal performance of PTSCs, installing internal fins within the collector tube is one of the most reliable, economical, and straightforward passive techniques. However, while internal fins can significantly improve thermal performance in turbulent flows, they also lead to a substantial increase in pumping work. Here, we demonstrate that with an optimal design of helical fins, the thermal efficiency of PTSCs can be improved without causing significant pressure losses through the receiver tube. The results proved that higher thermal efficiency and lower pressure losses were achieved when annular fins are replaced by an axial helical insert. Installation of a helical fin with 4 turns and a height of 4 mm led to a 4.5 % increase in thermal efficiency (η) while raising the friction factor (f) by 30 %. Optimal performance was observed with helical fins having 10 turns. For instance, at Re = 105, switching from 16 to 4 turns of 8 mm helical fins resulted in η and Nu enhancements, and f reduction of 1.6 %, 33 % and 64 %, respectively. While these changes were 2.0 %, 55 % and 54 % when n reduced from 16 to 10.

For analyzing hydrogenation process for treatment of petroleum-based fuels, solubility of hydrogen in the feed should be well correlated. The main correlation factors are temperature and pressure which have a great effect on hydrogen solubility. This research paper presents the development of three models for predicting the solubility of hydrogen gas (H2) in diesel. Pressure and Temperature are the input parameters and solubility is single output. The models were fine-tuned using the Bat Algorithm (BA). The three models include Orthogonal Matching Pursuit Regression (OMP), K Nearest Neighbors Regression (KNN), and Tweedie Regression (TDR). The results of the study revealed that the OMP model achieved the highest level of accuracy, with an R2 score of 0.98, and the least RMSE and MAE error rates of 0.24 and 0.19, respectively. The KNN model also performed well with an R2 score of 0.92, an RMSE of 0.42, and an MAE of 0.37. The TDR model had the lowest accuracy compared to the other two models. These results imply that the OMP model is the most suitable one for predicting H2 solubility. The models can be used to enhance the efficiency of fuel production by providing accurate predictions of H2 solubility.

Background: Medications, including chemotherapeutic drugs, contribute to male infertility as external factors by inducing oxidative stress in testicular cells. Shilajit is a naturally occurring bioactive antioxidant used in Ayurvedic medicine to treat a variety of ailments. Objective: This study examines the potential of Shilajit to counteract the negative effects of the chemotherapeutic drug cyclophosphamide (CPA) on testicular germ cell dynamics. Material and methods: Male Parkes mice received single intraperitoneal CPA injection (200 mg/kg BW) on day one, followed by daily supplementation of Shilajit (100 and 200 mg/kg BW) for one spermatogenic cycle. Results: CPA adversely affected testicular germ cell dynamics by inhibiting the conversion of spermatogonia-to-spermatids, altering testicular histoarchitecture, impairing Sertoli cell function and testicular steroidogenesis, and disturbing the testicular oxido-apoptotic balance. Shilajit supplementation restores testicular germ cell dynamics in CPA-exposed mice, as evidenced by improved histoarchitecture of the testis. Shilajit improves testicular daily production and sperm quality by promoting the conversion of spermatogonia (2C) into spermatids (1C), stimulating germ cell proliferation (PCNA), improving Sertoli cell function (N-Cadherin and β-Catenin), and maintaining the Bax/Bcl2 ratio. Additionally, Shilajit enhances testosterone biosynthesis by activating enzymes like 3β-HSD, and 17β-HSD. Shilajit also reduces testicular oxidative stress by increasing antioxidant enzyme activity (SOD) and decreasing lipid peroxidation (LPO). These effects are mediated by upregulation of the antioxidant protein Nrf-2 and downregulation of Keap-1. Conclusion: The findings underscore the potent androgenic and antioxidant characteristics of Shilajit, as well as its ability to enhance fertility in cases of testicular damage caused by chemotherapeutic drugs.

The most widely used equation to calculate water saturation or suitable shaly water saturation in clean or shaly formation, respectively, is the modified Archie formula. The quality of Archie parameters including saturation exponent affects the preciseness of water saturation, and thus estimated oil and gas in place. Therefore, estimating the saturation exponent by the soft computation methods deems to be necessary. In this study, intelligent models such as multilayer perceptron neural network, least squares support vector machine, radial basis function neural network, and adaptive neuro-fuzzy inference system are developed to predict saturation exponent in terms of petrophysical data including porosity, absolute permeability, water saturation, true resistivity, and resistivity index by utilizing a databank for middle east oil and gas reservoirs. The introduced models are optimized using particle swarm optimization, genetic algorithm, and levenberg marquardt techniques. Graphical and statistical methods are used to demonstrate the capability of the constructed models. Based on the statistical indexes obtained for each model, it is found that radial basis function neural network, multilayer perceptron neural network, and least squares support vector machine are the most robust models as they possess the smallest mean squared error, root mean squared error and average absolute relative error as well as highest coefficient of determination. Moreover, the sensitivity analysis indicates that water saturation has the most effect and porosity has the least effect on the saturation exponent. The developed models are simple-to-use and time-consuming tools to predict saturation exponent without needing laboratory methods which are tedious and arduous.

Thermoplastic-based polymers are gathering importance in several engineering fields like electrical, electronic, automotive, aerospace, and structural. The additions of secondary phase reinforcements such as natural and synthetic fibre improve thermoplastic-based polymer’s properties. The thermoplastic and natural fibre combinations are found to have low mechanical strength and incompatibility and need special treatment for synthesizing the natural fibre. The present experimental investigation deals with the enhancement of polypropylene hybrid composite by using the combinations of glass (synthetic)/hemp (natural) fibre for the ratio of 0 : 35, 5 : 30, 10 : 25, and 15 : 20 reinforced with 5 wt% compatibilizer through injection moulding. The revealed test results of polypropylene hybrid composite showed improved mechanical impact and flexural and tensile strength of 37.5%, 14.2%, and 21.1%, respectively. The thermal adsorption characteristics were evaluated by thermogravimetric analysis apparatus. It showed the decomposition of composite limited by hemp fibre at 27°C to 700°C.

Neural Network Algorithm (NNA) is a recently proposed Metaheuristic that is inspired by the idea of artificial neural networks. The performance of NNA on single-objective optimization problems is very promising and effective. In this article, a maiden attempt is made to restructure NNA for its possible use to address multi-objective optimization problems. To make NNA suitable for MOPs several fundamental changes in the original NNA are proposed. A novel concept is proposed to initialize the candidate solution, position update, and selection of target solution. To examine the optimization ability of the proposed scheme, it is tested on several benchmark problems and the results are compared with eight state-of-the-art multi-objective optimization algorithms. Inverse generational distance(IGD) and hypervolume (HV) metrics are also calculated to understand the optimization ability of the proposed scheme. The results are statistically validated using Wilcoxon signed rank test. It is observed that the overall optimization ability of the proposed scheme to solve MOPs is very good. • This paper proposes a method to solve multi-objective optimization problems. • A multi-objective Neural Network Algorithm method is proposed. • The proposed method solves difficult multi-objective optimization problems.

We quantitatively study the percentage of fake events present in CCQE and CCRes interactions and the impact of final state interactions on the neutrino oscillation parameters at DUNE. The effect of final-state interactions for DUNE oscillation physics is analyzed in an ideal and realistic detector scenario. Resonance interaction will be the most dominant interaction in the oscillation sensitive region of DUNE. The νμ-disappearance oscillation channel is studied using LAr detector. We find that nuclear effects and detector threshold play a significant role in CCQE and CCRes interactions and these nuclear effects induce a significant bias in the determination of atmospheric neutrino oscillation parameters. The impression of nuclear effects on the determination of θ23 is quantified in this work, which will help in reducing the systematic error at DUNE.

Nanostructures of titanium nitride (TiN) have recently been considered as a new class of plasmonic materials that have been utilized in many solar energy applications. This work presents the synthesis of a novel nanostructure of TiN that has a nanodonut shape from natural ilmenite ore using a low-cost and bulk method. The TiN nanodonuts exhibit strong and spectrally broad localized surface plasmon resonance absorption in the visible region centered at 560 nm, which is well suited for thermoplasmonic applications as a nanoscale heat source. The heat generation is investigated by water evaporation experiments under simulated solar light, demonstrating excellent solar light harvesting performance of the nanodonut structure.

In this study, we applied cluster beam deposition (CBD) as a new approach for fabricating efficient plasmon-based photocatalytic materials. Au nanoclusters (AuNCs) produced in the gas phase were deposited on TiO2 P25-coated silicon wafers with coverage ranging from 2 to 8 atomic monolayer (ML) equivalents. Scanning Electron Microscopy (SEM) images of the AuNCs modified TiO2 P25 films show that the surface is uniformly covered by the AuNCs that remain isolated at low coverage (2 ML, 4 ML) and aggregate at higher coverage (8 ML). A clear relationship between AuNCs coverage and photocatalytic activity towards stearic acid photo-oxidation was measured, both under ultraviolet and green light illumination. TiO2 P25 covered with 4 ML AuNCs showed the best stearic acid photo-oxidation performance under green light illumination (Formal Quantum Efficiency 1.6 × 10−6 over a period of 93 h). These results demonstrate the large potential of gas-phase AuNCs beam deposition technology for the fabrication of visible light active plasmonic photocatalysts.