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This paper aims to present an analytical investigation of energy and exergy performance on a solar flat plate collector (SFPC) with Cu-CuO/water hybrid nanofluid, Cu/water and CuO/water nanofluids as collector running fluids.

Heat transfer characteristics, pressure drop and energy and exergy efficiencies of SFPC working on these nanofluids are investigated and compared. In this study, a comparison is made by varying the mass flow rates and nanoparticle volume concentration. Thermophysical properties of hybrid nanofluids are estimated using distinctive correlations available in the open literature. Then, the influence of these properties on energy and exergy efficiencies of SFPC is discussed in detail.

Energy analysis reveals that by introducing the hybrid nanoparticles in water, the thermal conductivity of the working fluid is enhanced by 17.52 per cent and that of the individual constituents is enhanced by 15.72 and 15.35 per cent for Cu/water and CuO/water nanofluids, respectively. This resulted in 2.16 per cent improvement in useful heat gain for hybrid nanofluid and 1.03 and 0.91 per cent improvement in heat gain for Cu/water and CuO/water nanofluids, respectively. In line with the above, the collector efficiency increased by 2.175 per cent for the hybrid nanofluid and 0.93 and 1.05 per cent enhancement for Cu/water and CuO/water nanofluids, respectively. Exergy analysis elucidates that by using the hybrid nanofluid, exergy efficiency is increased by 2.59 per cent, whereas it is 2.32 and 2.18 per cent enhancement for Cu/water and CuO/water nanofluids, respectively. Entropy generation is reduced by 3.31, 2.35 and 2.96 per cent for Cu-CuO/water, Cu/water and CuO/water nanofluids, respectively, as compared to water.

However, this is associated with a penalty of increment in pressure drop of 2.92, 3.09 and 2.74 per cent for Cu-CuO/water, Cu/water and CuO/water nanofluids, respectively, compared with water.

It is clear from the analysis that Cu-CuO/water hybrid nanofluids possess notable increment in both energy and exergy efficiencies to use them in SFPCs.

This study aims to present the numerical analysis of exergy transfer and irreversibility through the discrete filling of high-porosity aluminum metal foams inside the horizontal pipe.

In this study, the heater is embedded on the pipe’s circumference and is assigned with known heat input. To enhance the heat transfer, metal foam of 10 pores per inch with porosity 0.95 is filled into the pipe. In filling, two kinds of arrangements are made, in the first arrangement, the metal foam is filled adjacent to the inner wall of the pipe [Model (1)–(3)], and in the second arrangement, the foam is located at the center of the pipe [Models (4)–(6)]. So, six different models are examined in this research for a fluid velocity ranging from 0.7 to7 m/s under turbulent flow conditions. Darcy Extended Forchheimer is combined with local thermal non-equilibrium models for forecasting the flow and heat transfer features via metal foams.

The numerical methodology implemented in this study is confirmed by comparing the outcomes with the experimental outcomes accessible in the literature and found a fairly good agreement between them. The application of the second law of thermodynamics via metal foams is the novelty of current investigation. The evaluation of thermodynamic performance includes the parameters such as mean exergy-based Nusselt number (Nu_e), rate of irreversibility, irreversibility distribution ratio (I_DR), merit function (MF) and non-dimensional exergy destruction (I^*). In all the phases, Models (1)–(3) exhibit better performance than Models (4)–(6).

The present study helps to enhance the heat transfer performance with the introduction of metal foams and reveals the importance of available energy (exergy) in the system which helps in arriving at optimum design criteria for the thermal system.

The uniqueness of this study is to analyze the impact of discrete metal foam filling on exergy and irreversibility in a pipe under turbulent flow conditions.

Pervasive health-care computing applications in medical field provide better diagnosis of various organs such as brain, spinal card, heart, lungs and so on. The purpose of this study is to find brain tumor diagnosis using Machine learning (ML) and Deep Learning(DL) techniques. The brain diagnosis process is an important task to medical research which is the most prominent step for providing the treatment to patient. Therefore, it is important to have high accuracy of diagnosis rate so that patients easily get treatment from medical consult. There are many earlier investigations on this research work to diagnose brain diseases. Moreover, it is necessary to improve the performance measures using deep and ML approaches.

In this paper, various brain disorders diagnosis applications are differentiated through following implemented techniques. These techniques are computed through segment and classify the brain magnetic resonance imaging or computerized tomography images clearly. The adaptive median, convolution neural network, gradient boosting machine learning (GBML) and improved support vector machine health-care applications are the advance methods used to extract the hidden features and providing the medical information for diagnosis. The proposed design is implemented on Python 3.7.8 software for simulation analysis.

This research is getting more help for investigators, diagnosis centers and doctors. In each and every model, performance measures are to be taken for estimating the application performance. The measures such as accuracy, sensitivity, recall, F1 score, peak-to-signal noise ratio and correlation coefficient have been estimated using proposed methodology. moreover these metrics are providing high improvement compared to earlier models.

The implemented deep and ML designs get outperformance the methodologies and proving good application successive score.

The present study examines the mediating role of teleworking and the moderating role of resilience in explaining the relationship between perceived stress and psychological well-being of working mothers in India. Conservation of resource theory (COR) is taken to support the present study.

The data of 326 respondents has been collected from working mothers in various sectors of Delhi NCR region of India. Confirmatory factor analysis was used for construct validity, and SPSS Macro Process (Hayes) was used for testing the hypotheses.

The results of the study found an inverse association between perceived stress and psychological well-being. Teleworking acted as a partial mediator and resilience proved to be a significant moderator for teleworking-well-being relationship.

The study is based at Delhi NCR of India, and future studies may be based on a diverse population within the country to generalize the findings in different cultural and industrial contexts. The present work is based only on the psychological well-being of the working mothers, it can be extended to study the organizational stress for both the genders and other demographic variables.

The study extends the research on perceived stress and teleworking by empirically testing the association between perceived stress and psychological well-being in the presence of teleworking as a mediating variable. The findings suggest some practical implications for HR managers and OD Practitioners. The organizations must develop a plan to support working mothers by providing flexible working hours and arranging online stress management programs for them.

Although teleworking is studied previously, there is a scarcity of research examining the impact of teleworking on psychological well-being of working mothers in Asian context. It would help in understanding the process that how teleworking has been stressful for working mothers and also deliberate the role of resilience in the relationship between teleworking and psychological well-being due to perceived stress, as it seems a ray of hope in new normal work situations.

The purpose of this paper is to understand the effect of basin water depth towards the cumulative distillate yield of the traditional and developed single basin double slope solar still (DSSS).

Modified single basin DSSS integrated with solar operated vacuum fan and external water cooled condenser was fabricated using aluminium material. During sunny season, experimental investigations have been performed in both conventional and modified DSSS at a basin water depth of 3, 6, 9 and 12 cm. Production rate and cumulative distillate yield obtained in traditional and developed DSSS at different water depths were compared and best water depth to attain the maximum productivity and cumulative distillate yield was found out.

Results indicated that both traditional and modified double SS produced maximum yield at the minimum water depth of 3 cm. Cumulative distillate yield of the developed SS was 16.39%, 18.86%, 15.22% and 17.07% higher than traditional at water depths of 3, 6, 9 and 12 cm, respectively. Cumulative distillate yield of the developed SS at 3 cm water depth was 73.17% higher than that of the traditional SS at 12 cm depth.

Performance evaluation of DSSS at various water depths by integrating the combined solar operated Vacuum fan and external Condenser.

In this way, the aim of this study is to expand and evelop the application of this technique in FMEA to rank failure modes of ESQ of academic libraries in an intuitionistic fuzzy environment. Assessment of electronic service quality (ESQ) of libraries is significantly important according to their major roles. It should be noted that the ESQ has a significant impact on customer satisfaction, which improves organizational performance. Accordingly, low ESQ means waste of organizational resources and poor user satisfaction. So, there is a dire need to reflect reasons inducing failure modes in academic library ESQ. Thus, investigation of failure modes affecting academic library ESQ is highly important. One solution in this area is utilization of the intuitionistic fuzzy (IF) failure mode and effects analysis (FMEA) as one of the widely used methods for prediction and identification of failure modes.

The present study in terms of objective is applied and in terms of the type of method is descriptive-analytical. The research sample included four experts of Yazd academic Libraries (Iran). To collect data, three types of questionnaires were distributed among experts. The purpose of the first questionnaire was to identify and reach an agreement on e-library failure modes. Type II questionnaire was used to determine the importance of identified risk factors and Type III questionnaire was used to prioritize the factors.

Results indicate that the difficulty of using websites, lack of provided information feedback to users and lack of links on the website to users' are the main priorities for improving ESQ in the studied academic libraries.

In this approach, the Intuitionistic fuzzy Elimination Et Choix Traduisant la REalité and technique for order of preference by similarity to ideal solution method were used to rank failure modes in academic library ESQ within the FMEA framework.]

Bearings and gears are major components in any rotatory machines and, thus, gained interest for condition monitoring. The failure of such critical components may cause an increase in down time and maintenance cost. Condition monitoring using the machine learning approach is a conceivable solution for the problem raised during the operation of the machinery system. The paper aims to discuss these issues.

This paper aims engine gearbox fault diagnosis based on a decision tree and artificial neural network algorithm.

The experimental result (classification accuracy 85.55 percent) validates that the proposed approach is an effective method for engine gearbox fault diagnosis.

This paper attempts to diagnose the faults in engine gearbox based on the machine learning approach with the combination of statistical features of vibration signals, decision tree and multi-layer perceptron neural network techniques.

Introduction: Artificial intelligence (AI), the engineering of brilliant machinery, performs intelligent human intelligence tasks, such as learning and problem-solving. Insurance is a financial protection policy either for individuals or entities to reimburse losses from the insured company. The role of AI in insurance always helps enhance customer services and understand their behaviour.

Purpose: This chapter aims to determine the role of AI in the insurance industry in India. The insurance industry is expanding very fast, and to further increase its horizons, the part of the technology of AI is essential. However, this sector has initiated using AI technology and is expanding its scope to benefit the customers.

Methodology: The authors selected research papers of the last five years to review and determine how the technology changed during the period and how an increase in AI benefits the industry and facilitates delivering the best services, and understanding the customer’s needs and behaviour.

Findings: It has been found that the industry is moving very fast and adopting the AI technology methods to enhance customer services, betterment for growing India, and serve insurance services to the nation efficiently.