The Azadirachta Indica biodiesel was blended in neat diesel in the ratio of 20% and 40% as a primary fuel while HHO gas was varied to be operated as gaseous fuel in dual fuel diesel engine. The DTBP as an oxygenated fuel additive was included in the primary fuel. The effect of EGR (5%) in combination with biodiesel, DTBP, and HHO has been studied to evaluate the engine performance and exhaust emissions. The study was experimented on a dual fuel diesel engine, having maximum power of 3.5 kW, operated at a constant speed of 1500 rpm. The experimental study revealed that operating with Azadirachta Indica biodiesel, the BTE was improved by 16.8% and 16% for 20% and 40% share at the maximum load. Also, the addition of DTBP in diesel, HHO gas, and the implementation of EGR resulted in an increased BTE. Again, NOx formation was reduced by 15.3% and 9.97% with 40% and 20% share of Azadirachta Indica, while CO emissions was reduced by 25% and 16.6%, respectively. The addition of DTBP and HHO gas, and the implementation of EGR, resulted in improved BTE and reduced CO and CO2, while HC and smoke increased, compared to neat diesel.

Motors are commonly used in industrial applications, where bearing problems frequently occur, posing a significant safety risk to industrial production. Traditional fault diagnostic methods, which often rely solely on signal processing techniques, have proven to be ineffective. To address this issue, deep learning (DL) has rapidly developed and achieved remarkable results in fault diagnosis. This paper proposes an intelligent fault diagnosis and classification method for rolling bearing faults based on Ensemble Empirical Mode Decomposition (EEMD) and a stacked Gated Recurrent Unit (GRU) neural network. The vibration signal is decomposed into several Intrinsic Mode Functions (IMFs) using EEMD to eliminate random noise interference from the original vibration signal. Selecting sensitive features from both the time and frequency domains of IMF components is crucial, and this is achieved by using the correlation coefficient value. Finally, a GRU model is developed to classify faults based on the extracted features. The proposed model accurately classifies different types of faults under real operating conditions and is compared with existing techniques. The method demonstrates superior diagnostic performance, achieving an overall model accuracy of 100

Reliability assessment of Nuclear Power Plants (NPPs) is important for ensuring operational safety and efficiency. This study presents a novel integration of Fuzzy Bayesian Network (FBN) with Common Cause Failure (CCF) modelling for multistate NPP reliability assessment under uncertainty. This study uses a Fuzzy Bayesian Network (FBN) approach to assess the reliability and sensitivity of a multistate Nuclear Power Plant (NPP). It considers Common Cause Failures (CCF) by combining Bayesian Networks with fuzzy probability. This helps to model uncertainties and the relationships between system components. The reliability analysis examines three operational states: fully operational, degraded performance, and failure. It also compares system performance in cases with and without CCF. The posterior probability distribution of root nodes is computed to determine the influence of individual components on system states, while sensitivity analysis identifies critical components affecting system reliability. The results show that the probability of the fully operational state increases, while the probabilities of degraded and failure states decrease when CCF is considered. The main contribution of this work is that it develops a simple and effective framework that considers multiple system states, uncertainty, and component dependencies together by including CCF within a single FBN model. The findings indicate that incorporating CCF improves overall system reliability by reducing the likelihood of failure and degraded performance. Sensitivity analysis indicates that the Steam Generator, Coolant System, and Condenser have the highest impact on system failure, emphasizing their importance in maintenance prioritization. The proposed approach provides a systematic framework for analyzing multistate NPP system under uncertainties.

Reliability and ranking estimation of complex systems such as Wind Turbine plant (WTP) is essential for their efficient operation and vulnerability to the operational issues and conditions. Thus, this paper addresses the importance of the reliability assessment of a wind turbine plant incorporating both the routine as well as preventive maintenance strategies using Fuzzy logic. Wind turbines, which are used for the power generation through renewable sources of energy, require effective reliability analysis for the optimal level of performance. For this, generalized trapezoidal fuzzy numbers (GTFNs) are introduced with certain level of confidence. Lambda-Tau methodology together with GTFNs and associated mathematical operations have been employed for calculating the different performance measures e.g. Reliability, Availability, Maintainability, Mean Time to Failure, Mean Time to Repair, Mean Time Between Failure, Expected Number of Failure (ENOF) of the wind turbine plant. Authors also performed ranking analysis for the components of WTP to determine the key components of the same. The outcome of the study can be considered as a valuable reference to the maintenance team to plan strategy in a better way. Based on the results, it can be concluded that this methodology is highly effective for the performance analysis of WTP system. Additionally, the authors have outlined the future scope of the research work at the end of the conclusion section.

Automated slaughterhouse system is crucial in the modern food processing industry, where a system’s high reliability indicate high operational efficiency, safety and sustainability. However, to understand the behaviour of the different reliability measure of a complex system one must have precise data related to the different component failure and repair which is, most often, not easily available due to fluctuating operating and environment conditions. The aim of this manuscript is to analyse the performance of a smart slaughterhouse system. Smart slaughter-houses is revolutionising meat processing through automation, cleanliness and smart monitoring. In this paper, the workability of such a system is modelled in terms of a mathematical model through Trapezoidal Fuzzy Numbers (TFNs) to cater to the uncertainty in the failure and repair rates. Automated conveyor, smart stunning unit, robotic cutter, hygiene monitoring, and the quality control unit based on AI are the major components of the considered smart slaughter-house system. Authors used the TFNs-based reliability centric approach to estimate the reliability, availability, and mean time to failure (MTTF) of the considered slaughterhouse system, which take into consideration data uncertainty. The results of the presented work can be considered as a good reference for the planning, minimizes waste, optimize energy consumption (which is aligned to SDG-12), and operation of intelligent slaughterhouses which significantly contribute as decision support tool for designing sustainable and fault tolerant smart slaughter-house systems. Furthermore, the presented mathematical model emphasize to safer and reliable working conditions which is aligning with SDG.

As the global need for sustainability and reduced carbon emissions has amplified, the automotive industry has witnessed a major push in the manufacturing of electric vehicles. The rapid increase in EVs has made it indispensable to use standardized protocols, as they play a vital role in coherent communication, safety, and efficiency. This paper examines the primary EV protocols that align with national and global standards. By analyzing standards such as ISO 15118, CHAdeMO, and OCPP, this study provides insights into the evolution of EV protocols aimed at interoperability and safety.

Natural convection, driven by buoyancy, is utilized for the heat transport in various applications including thermal exchangers, cooling of heat sources, solar collectors, geothermal power systems, electronic devices, microelectronics, and nuclear industries. This research focuses on the free convection of a hybrid nanoliquid containing Ag–MgO nanoparticles in an enclosure having partially active borders. The hybrid nanosuspension utilized is a mixture of MgO and Ag nanoparticles in equal proportions, suspended in water as the base liquid. The square enclosure is subject to the Lorentz force impact. The study examines two cases. In Case 1, the left wall experiences heat dissipation via a heat sink at a fixed temperature T c , whilst the right wall is partly affected by the active chamber borders with a heater at temperature T h (where T h > T c ). The rest sections of vertical borders are adiabatic. In addition, the cavity is thermally insulated on both the upper and lower surfaces. In Case 2, the chamber's vertical sides are heated to a certain extent ( T h ), whereas the bottom wall is somewhat cold ( T c ) and has some level of activity. The remaining inactive sections of the cavity are adiabatic. The control flow equations were resolved with the help of COMSOL Multiphysics, which is complex modelling software for computational fluid dynamics (CFD). The computational study has been performed with the following parameters, Rayleigh number ( Ra ) = 10 3 –10 6 , Hartmann number ( Ha ) = 0–80, and nanoparticles volume fraction (ϕ) = 0.01, 0.02. The effect of important variables, such as Hartmann and Rayleigh numbers, in conjunction with the concentration of nano additives has been examined by analyzing streamlines and isotherms to understand their effect on thermal convection. It is found from the isotherms within the cavity in Case 2, that increment in Ha leads to slight rise the temperature within the cavity. Further, in Case 1, Nu avg is decreasing function of Ha and Q . While in Case 2, the average Nu is decreasing function of Q and increasing function of Ra and ϕ.

In this work, we extend the modified homotopy analysis transform method (MHATM) for studying the three different coupled time-fractional physical problems. The first one is coupled time-fractional Whitham-Broer-Kaup (W-B-K) equations, and others are coupled modified Boussinesq equations and coupled approximate long wave equations as the special cases of W-B-K equations. The fractional W-B-K model is a coupled structure that describes the nonlinear evolution of shallow-water waves. The novelty of the proposed algorithm, the fractional derivative, is taken in the Caputo-Fabrizio (CF) sense, which consists of an exponential form of the non-singular kernel. With the aid of Banach's fixed point theory, the uniqueness and convergence analysis for the coupled W-B-K equations is presented through the theorems. With the help of Picard's stable approach, the stability analysis of the proposed technique is shown. We study the comparison for the solutions of MHATM for CF time-fractional derivative with the solutions derived with the aid of other techniques. The main advantage of the MHATM with the assistance of CF derivative is that, it offers solutions to problems in a rapidly convergent series leading to ideal solutions. The accuracy and efficiency of the present method have been shown through different graphical as well as tabulated analyses. However, the results indicate that MHATM with CF fractional derivative is a good organization and applicable to solve highly nonlinear various fractional physical problems like W-B-K equations.

The present work aims to analyze the hydrothermal behavior of multi-nanoparticle nanofluid flow (Cu–Al2O3/H2O) within an octagonal cavity containing a cylinder with rectangular fins. Cylinders with rectangular fins are found in a large number of applications, viz. heat exchangers, automotive radiators, semiconductors, engines for cooling air, and hydrogen fuel cells. To enhance the heat transfer mechanism and thermal efficiency, the hybrid nanofluid is more efficient than the base fluid due to the introduction of various types of nanoadditives. The lower and top surfaces of an octagonal cavity are heated, whereas the vertical borders remain cooled with temperature, while the inclined borders are adiabatic. The internal heated cylinder with a radius r = 0.15L. It is presumed that the heated cylinder within the octagonal enclosure is surrounded by solid fins of variable sizes l = 0.07L, l = 0.12L, and l = 0.17L. The governing systems of equations are non-dimensionalized by incorporating suitable transformation. The resultant system is solved by applying the Galerkin finite element method in the computational COMSOL Multiphysics software. The study is focused on the influence of the radiation parameter (R), nanoadditives concentration (ϕ), angle of applied magnetic field (α) and size of fins (H) on the profiles of velocity (horizontal vertical), velocity magnitude, temperature, Nusselt number, Bejan number, and entropy generation via streamlines and isotherms are simulated. The findings reveal that the height of the fins and nanoadditives concentration play an important role in establishing and maintaining the temperature and heat transfer within the octagonal cavity. It is found that the angle of applied magnetic field significantly controls the entropy production and Bejan number.

In a recently published paper, two methods were proposed to solve interval-valued Fermatean fuzzy multi-criteria decision-making problems (those in which the rating value of each alternative over each criterion is represented by an interval-valued Fermatean fuzzy number). In this paper, some numerical examples are considered to show that these existing methods fail to find the correct ranking of the alternatives. Also, the reasons for the failure of these existing methods are pointed out. Furthermore, new methods are proposed to solve the interval-valued Fermatean fuzzy multi-criteria decision-making problems by modifying existing methods. Moreover, the proposed modified methods are illustrated with the help of numerical examples. Finally, the ranking of the alternatives of the two existing real-life interval-valued Fermatean fuzzy multi-criteria decision-making problems is obtained by the proposed methods.

The Computerization of vehicles rapidly accelerated, and as vehicle networks link to external networks, automotive security becomes a critical issue. The Controller Area Network (CAN) bus is the most widely used internal control network in automobiles, facing growing vulnerabilities. With the development of networked and self-driving technology, the sealing nature of automotive internal control networks is eroding, leaving the security standards inadequate by modern benchmarks. This paper aims to provide a brief overview of the defence strategies against CAN Bus infiltrations, identify current limitations to these existing strategies, and discuss the challenges of enhancing the security in automotive embedded systems.

Background: Chickpea is a second most important pulse crop grown in 56 countries and India rank first in production which shares 61.4% of the total world chickpea production however, productivity is very low as compared to other countries. Therefore, varietal development with inherent tolerance to biotic and abiotic stress is the prime objective to improve component productivity traits to get better yield in rainfed agro-climatic conditions.Methods: Ninety germplasm accessions of chickpea along with four check viz., JG 14, JG 16, JAKI 9218 and Radhey were evaluated in augmented block design at experimental research farm of Banda University of Agriculture and Technology, Banda, Uttar Pradesh, India. Phenotypic data were subjected to study the genetic parameters and association analysis of yield and its component traits using SPAR 2.0 Package and Windostat Version 9.2.Result: The significant variation was observed for all the traits except number of secondary branches, number of pod per plant, number of seeds per pod among the genotypes. The maximum GCV and PCV was observed for height of first pod (35.28 and 39.29), followed by seed yield per plant (29.77 and 40.32) and number of primary branches (25.63 and 31.44). The high magnitude of heritability with genetic advance was estimated for seed index (96.61%), while the high genetic advance as per cent of mean was recorded for first pod height (65.27%). The positive and significant association of seed yield with number of pods per plant, seed index, number of seeds per pod, number of secondary branches, number of primary branches and height of first pod indicating the importance of these traits in selection criteria. Path analysis identified that number of seeds per pod, number of pods per plants, seed index and number of secondary branches per plant as highly desirable component for direct effect on seed yield per plant. The genotypes ICVT-181106 had highest selection indices for seed yield followed by ICVT-181107, PUSA-1053, JG-218, GNG-1999, ICVT-181102 and HC-5. Therefore, high GCV and PCV, significant positive direct and indirect correlation and high estimate of selection indices for grain yield can be directly and indirectly used for chickpea breeding program.

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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The diesel engine process including fuel injection, fuel spray, air and fuel mixing, ignition and combustion which influences its performance and emission characteristics. The fuel injection and fuel spray are important process as they are directly related to the quality of fuel which results into fluctuating the ignition and combustion characteristics. Ignition in a diesel engine occurs at the edge of the spray depending on the in-cylinder pressure and temperature. Experiments were conducted on a modified dual-fuel diesel engine (3.5 kW power) with fuel additives, diesel and hydrogen (gaseous fuel) at a stable speed of 1500 rpm. Spray behavior and ignition delay were analyzed to determine the impact of fuel additives in neat diesel combined with hydrogen. Additionally, for dual-fuel Cases, the ignition delay correlations provided for diesel engines have been modified as per the experimental results. The injection velocity was maximized with a 15-25% hydrogen substitution range because the fuel density was reduced to a minimum, but it was decreased with the addition of fuel additives. The SMD for hydrogen with neat diesel and diesel with fuel additives both increased up to 20% hydrogen substitution, whereas for higher hydrogen substitution, the Sauter mean diameter decreased. It was found that the addition of fuel additives in diesel resulted into an increased fuel injection velocity (3.3%) with reduced sauter mean diameter of 3.4% while with the lower substitution of H2 (15%), a maximum increment of 32% in the injection velocity was seen as compared with neat diesel operation. With 15% hydrogen substitution, the change in pressure and density of diesel along with fuel additives accommodated the optimum spray penetration equivalent to neat diesel. In all Cases of hydrogen substitution, the experimental and theoretical values obtained for the chemical ignition delay were found to be consistent with one another except for 35% H2 showing a maximum deviation of 0.2%.

Graphene is synthesized on a lab scale by a reduction approach and used to create a graphene-based heat flux sensor.An oil bath approach is used for the calibration of the sensor to find its sensitivity and temperature coefficient of resistance.The graphene sensor proved useful in measuring thermal changes as it performed well in detecting both temperature and heat flow.Graphene measured temperature and heat flow with effectiveness.Resistance and temperature show a strong linear connection in the graphene sensor.The temperature coefficient of resistance is found to be 0.0013/ ⁰C.Fabricated graphene sensor is sensitive to 0.715 Ω/ ⁰C.The sensor is appropriate for use in heat flux detecting applications since it demonstrated consistent resistance variations under heat load.

Jiskani et al. (Resources Policy 76 (2022) 102591) claimed that to the best of their knowledge, there is no study for investigating undesired events and their specified primary causes in the surface mines and quarries. To fill this gap, they proposed a Z-number based fuzzy fault tree approach (ZNBFFTA). Jiskani et al. applied their proposed approach to the surface quarries to analyze the mine health and safety (MHS) risk in the surface mines and quarries in Pakistan. Mine managers of other developing countries may be attracted towards ZNBFFTA to analyze MHS risk in the surface mines and quarries. ZNBFFTA can be used in a variety of sectors and is highly effective. It is pertinent to mention that although ZNBFFTA towards the problem of MHS is valid. However, in Step 4 of ZNBFFTA, Kang et al.’s method (Journal of Information & Computational Science 9(3) (2012) 703–709) is used to transform Z-number (ZN) into fuzzy number (FN) as no other method was available at that time. While, an improved version of Kang et al.’s method is proposed in 2021 by Cheng et al. (33rd Chinese Control and Decision Conference (CCDC), Kunming, China, (2021) 3823–3828). So, the aim of this paper is to make the researchers aware that ZNBFFTA will be more efficient if in its Step 4, Cheng et al.’s method is used instead of Kang et al.’s method.

The charge carrier formation and transport in the pristine polymers as well as in the polymer–fullerene blend is still a hot topic of discussion for the scientific community. In the present work, the carrier generation in some prominent organic molecules has been studied through ultrafast transient absorption spectroscopy. The identification of the exciton and polaron lifetimes of these polymers has led to device performance-related understanding. In the Energy Gap Law, the slope of the linear fit gradient (γ) of lifetimes vs. bandgap are subjected to the geometrical rearrangements experienced by the polymers during the non-radiative decay from the excited state to the ground state. The value of gradient (γ) for excitons and polarons is found to be −1.1 eV−1 and 1.14 eV−1, respectively. It suggests that the exciton decay to the ground state is likely to involve a high distortion in polymer equilibrium geometry. This observation supports the basis of Stokes shift found in the conjugated polymers due to the high disorder. It provides the possible reasons for the substantial variation in the exciton lifetime. As the bandgap becomes larger, exciton decay rate tends to reduce due to the weak attraction between the holes in the HUMO and electron in the LUMO. The precise inverse action is observed for the polymer–fullerene blend, as the decay of polaron tends to increase as the bandgap of polymer increases.

The depleted state of fossil fuels has led to the need to find an alternate solution for the utilization of transportation vehicles. The use of hydrogen, fuel additives, and nanoparticles has been part of the research in the recent past. Additionally, the use of artificial neural network was applied to anticipate the results for the optimization of the work and output, which has also been a study of interest recently. In the current work, different blends of diesel with hydrogen, TGME as a fuel additive, and Al2O3 nanoparticles were utilized to check for the best performance and the least emissions. The best performance was seen with the inclusion of TGME in diesel along with hydrogen, while the use of nanoparticles increased CO formation. An improvement in the BTE was also found with the blends of TGME and nanoparticles in diesel along with hydrogen. The usage of TGME along with H2 performed the best in terms of BTE (15.8%) and while the least emission of NOx, HC, and CO was found with the combination of diesel and H2. The artificial neural network was applied afterwards to predict the results using the obtained data from the experimental results. It was found that the values of regression coefficients for BTE and ITE were close to 1 (0.99 for BTE and 0.98 for ITE). In the above cases, the value of the mean square error was also observed to be least. Furthermore, the results for the regression coefficient of emissions were close to 1 for NOx (0.94), HC (0.89), and CO (0.93) with minimum possible mean square errors in all the cases. The results showed that they were in line with the predicted data as well as the available literature for the anticipation of the obtained results.